Delta Knowledge

What is Anti-Expertise?

2025-04-28T22:54:00.011+10:00

For a few years now I have been using the term Anti-expertise to describe the often ignored dark side of deep expertise.

I don't mean people against expertise, like anti-intellectuallism, but rather the downsides that come along with resting too much on your expertise. It sounds ironic, but it happens – the more experienced we become, the more we risk a kind of “anti‑expertise” creeping in. This is when deep experience leads to selective blindness, quick “good enough” decisions (satisficing), and a tendency to miss important nuances. For example, researchers once hid a tiny gorilla image in fingerprint samples and found seasoned fingerprint analysts overlooked this obvious oddity more often than novices – not due to incompetence, but because experts filter out information that seems irrelevant cognitiveresearchjournal.springeropen.com.

TL;DR:
1. Anti-expertise is a catchall term that includes a number of cognitive biases and fallacies.
2. These blind spots can be the result of efficiencies gained from years of experience, or created from being over-sure about an initial analysis of a situation.
3. Keeping your mind intentionally fluid and curious can help you avoid falling into the trap.
4. Helping your team's experts see problems from new viewpoints can help them avoid it too.

A catch-all

Anti-Expertise, while not an official term, serves as a concept that aggregates several recognised cognitive biases documented in psychology and knowledge management literature. These concepts highlight how deep expertise, paradoxically, can sometimes blind us to new or nuanced information.

Here are some of these related terms and phenomena:

1. Expert Blind Spot

Definition: When extensive experience makes it difficult for an expert to recognise or appreciate nuances, especially in how novices perceive or learn new information. As a result, the expert can no longer easily reconstruct the cognitive steps that novices need.
Source/Reference:

Nathan, M. J., & Petrosino, A. (2003). Expert Blind Spot Among Preservice Teachers. https://www.learntechlib.org/p/97438/

2. Einstellung Effect (Mental Set)

Definition: A cognitive tendency where previously successful solutions prevent or restrict the exploration of alternative approaches, causing experts to overlook better or more innovative options and leading to rigidity in their problem-solving approach.
Source/Reference:

Luchins, A. S. (1942). Mechanization in Problem Solving: The Effect of Einstellung. Psychological Monographs.

3. Overconfidence Bias

Definition: Excessive confidence in one’s judgments, skills, or knowledge, often heightened by deep expertise, leading to overlooking nuances or differences from the past.This can result in underestimating the complexity or novelty of new problems.
Source/Reference:

Kahneman, D. (2011). Thinking, Fast and Slow. https://us.macmillan.com/books/9780374533557/thinkingfastandslow

4. Confirmation Bias

Definition: A cognitive bias that involves favouring information that confirms existing beliefs or experiences, potentially neglecting contradictory or unfamiliar evidence.
Source/Reference:

Nickerson, R. S. (1998). Confirmation Bias: A Ubiquitous Phenomenon in Many Guises. Review of General Psychology.

5. Anchoring Bias

Definition: Relying heavily on initial information or past experiences (anchors), restricting a person’s ability to objectively assess new evidence.Fixation on initial impressions or existing expertise-derived schemas becomes common.
Source/Reference:

Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science.

6. Functional Fixedness

Definition: Limitation in problem-solving where individuals perceive the use of objects or concepts only in their typical or traditional roles, due to their expertise in conventional use. Watch out for difficulty seeing alternative applications.
Source/Reference:

Duncker, K. (1945). On Problem-Solving. Psychological Monographs.

7. Competency Trap (or Cognitive Entrenchment)

Definition: An organisational or individual bias towards methods that have historically proven successful, even when better options exist. Without help, the team will develop an inability to evolve practices due to past successes.
Source/Reference:

Levitt, B., & March, J. G. (1988). Organizational Learning. Annual Review of Sociology.

Three Steps to Beat the “Anti‑Expertise” Trap

The good news? We can absolutely counteract this effect. Here are three practical steps to help you deal with anti‑expertise. Each of these steps can be applied personally, with your team, or even in partnership with vendors and stakeholders.

1. Embrace a Beginner’s Mindset to Burst Your Blind Spots

Summary: Actively adopt the curiosity and humility of a novice – it keeps you honest about what you don’t know. When we feel like experts, we often stop listening and learning. It’s human nature to grow overconfident and tune out ideas that challenge our well-worn views. Psychologists even have a name for it – the earned dogmatism effect – where believing you’re an expert makes you less willing to consider other viewpoints psyche.co. Adopting a “beginner’s mind” flips this script. In Zen philosophy they say “in the beginner’s mind there are many possibilities, but in the expert’s there are few.” Practically, this means reminding yourself that there’s always more to learn and that your current knowledge might be incomplete or outdated. How to Do It:

Try explaining a complex idea or your project strategy from scratch to a colleague (or even to yourself, out loud) to burst the illusion of expertise you might have psyche.co.
You can also invite “dumb” questions from newcomers or ask yourself, “What would I think if I were new to this?”

Research shows that people who display more intellectual humility tend to know more in the long run – precisely because they remain receptive to learning psyche.co.

2. Proactively Seek Out Dissent and Diverse Perspectives

Actively invite critique and diverse viewpoints to illuminate your blind spots. One of the surest cures for selective blindness is an outsider’s eye. Colleagues, team members, or even clients and vendors with different experiences can spot issues you glossed over. The trick is you must actively welcome their input – especially the uncomfortable kind.

If you’re a leader, create a culture where questioning and debate aren’t just allowed, but encouraged. It might feel awkward (who loves hearing they’re wrong?), but a bit of constructive dissent is incredibly healthy for decision-making.
A diverse team – in background, expertise, even personality – will naturally bring a wider range of perspectives, making it more likely someone catches that nuance you missed or challenges the “good enough” solution that isn’t actually good enough.
Designate a “devil’s advocate” in meetings or hold occasional pre-mortems (imagining a plan’s failure to find weaknesses now).
Encourage junior staff to voice fresh ideas – they may see the forest when the veterans only see trees.
If you’re working with a vendor or partner who seems stuck in their ways, bring in a second opinion, run a Peer Assist with another project Team who has done this in the past, or benchmark against another team’s approach.

The key is to listen without defensiveness. As McKinsey researchers note, leaders (and I include myself here) benefit from actively seeking out vocal naysayers – their pushback can improve the debate and boost the quality of the final decision, even if it’s tough to hear mckinsey.com.

3. Shake Up Your Routine with Novel Challenges

Regularly jolt yourself out of your comfort zone – force yourself to engage in new, even unrelated, challenges to keep your expert brain adaptable. I talked about doing this in a recent blog post. Anti-expertise thrives on routine. When we do things the same way, our brains run on autopilot and we become over-comfortable. Sure, sticking to the tried-and-true is efficient – it minimizes effort and feels safe. In fact, cognitive scientists note that we often choose the familiar path to reduce mental load and stay in a comfort zone pmc.ncbi.nlm.nih.gov. The downside is this “situated fixation” dulls our ability to adapt or even see novelty. Over-specialisation can make our thinking rigid – research finds that as people gain deep domain expertise, they often lose flexibility and creativity in problem-solving pmc.ncbi.nlm.nih.gov. In a fast-changing world, that’s a recipe for being blindsided. To combat this, we need to deliberately inject novelty and challenge into our work (and even personal life) so that our minds stay elastic rather than entrenched.

Learn something new every so often – think of it as cross-training for your brain – take a course in an adjacent field, pick up an unfamiliar software tool, or even hobby projects that force you to be a beginner again.
In a team setting, you could rotate roles, encourage short term secondments or invite guest experts from completely different industries to share how they’d tackle your challenge. These novel inputs disrupt your team's default thinking patterns just enough to reveal new solutions.
Even small changes help – try brainstorming in a new environment, or ask someone from a different department to review your plan.

Over time, you’ll find that regularly stepping outside the familiar keeps your expertise sharp rather than stale. It ensures that your deep experience remains an asset, fuelled by curiosity and adaptability, instead of calcifying into a liability pmc.ncbi.nlm.nih.gov.

By embracing a beginner’s curiosity, encouraging frank dialogue, and constantly stretching our comfort zones, we guard against the trap of anti‑expertise. In essence, we continue to learn and grow, turning the “curse” of expertise back into the gift it was meant to be.

So when you hear me use the term Anti-expertise, now you know what I mean. If you, a team member or a vendor is expressing two or more of these biases then it might be time to sit down and have the anti-expertise trap.

Image thanks: ChatGPT 4o

Lessons from the Track: When Your Team Faces the Impossible

2025-04-09T09:22:00.004+10:00

Niko French doing ~170kph through the bowl hairpin at One Raceway in NSW.

Sometimes we only understand teamwork and leadership through the lens of our own experience. Success looks like a high-performing sales team a slick ops unit or smooth knowledge management. And that’s not a failing — that’s just being human. We see the world through our own eyes. That’s why I try to remind myself there are all sorts of ways to succeed — and plenty of examples out there if I’m willing to look. This weekend I had the privilege of witnessing one of those moments, watching my son, Nikolaos and the Summons Racing team overcome what looked like a weekend-ending blow. And while it played out on the racetrack, the lessons are just as relevant for any team grappling with what feels like an insurmountable problem.

To set the scene — the team had been working hard all week. Multiple Hyper Racer X1s were prepped for Robbie and Niko (my son), along with a few rental drivers sharpening their skills in the Hankook Tire Australian Drivers’ Championship — one of the country’s longest-standing open-wheeler competitions. The cars are even built locally in Lilydale, which we’re quietly proud of. After an eight-hour haul to Goulburn, a few long days of testing and tuning followed. One Raceway is a tight, tough track, with different handling characteristics than the others on the circuit and lots of smooth, high speed corners taken right on the edge of grip.

In Sunday morning’s race, Niko was in third and closing on former champion Dean Crook in second. The top three caught the backmarkers — a common moment in racing — when one of them spun out and stopped sideways across the track. Damon and Dean managed to dodge, but Niko, already committed back in the corner, had milliseconds to react. Under full brakes, he slid into the stranded car, damaging his radiator, rear suspension and a dozen other small parts. Not giving up, he restarted the car and clawed his way back behind the safety car, passing five competitors before a second car cut him off, suddely re-entering the track after an off — destroying Niko's rear wheel and suspension completely. Red flags flew and his car was eventually trucked back to the pits.

Repairing the side fairing. What would we do without gorilla tape?!?

You could feel the weight of it. The disappointment. All that effort, gone in an instant — and nobody's fault. Just racing incidents. Our weekend had sadly come to an end. And I reckon a lot of you have had days like that in your own teams. When everything feels cooked. When you miss the mark despite massive effort. When it seems like the only option is to pack it in and try again next time.

This was one of those days that tests what you’re really made of. The damage was bad:

Radiator completely wrecked,
Side fairing snapped in two,
Rear suspension fractured in three places,
Rear left wheel completely destroyed.

Plenty of teams would’ve walked away. But ours didn’t. Rob Summons started shouting orders.

Inspecting the impact damage. A very bent side-pod radiator and torn suspension linkage.

Everyone got to work. Niko himself started swapping his rear tyre onto a practice rim. Rob, a very hands-on team boss, began tearing apart the old radiator, trying to MacGyver an old spare model into place with an assortment of spare pipes and tubes. I was bolting the replacement radiator to a bent and broken bracket, taping bits together while Jesse, Ben, Jack and James - our ideas guys - tinkered and investigated, staring at the broken suspension and thinking "Can't buy one of those at Repco!" He floated ideas — welding (but the metals didn’t match), bracing (wrong shape). Each option got ruled out. And here’s the thing — when a problem looks unfixable, you don’t always need to know the answer straight away. It helps to keep moving, keep working, keep solving what CAN be solved. Billionaire Dale Carnegie once told his son "Action solves everything" and sometimes the breakthrough only comes because you’ve keep the wheels turning, metaphorically and literally.

After some trial and error, Jesse suddenly sprang into action. He had a new idea. Hope was in the air! They quickly got the engineers OK and pulled the suspension completely apart, rebuilding it with a new approach, as people called out the minutes counting down to the next race. We were still frantically taping fairings, refilling coolant, plasti-welding a broken bolt and adjusting wheel alignment with lasers between busy legs as Jesse drilled holes and zip-tied what remained of the fairing to the side of the car. No time to make it pretty!

Constant communication! Yelling for tools, screaming questions, the beautiful sound of teamwork.

Recent management studies suggest many senior leaders and execs are primarily task-focused — and usually that’s brilliant. They are great problem-solvers. But in the darkest moments, it’s not always the mechanical or technical challenge that’s hardest to unravel. It’s the emotional one. The doubt. The feeling that there’s no point trying. And that’s where leadership comes in. Not just solving the problem — but keeping hope alive. Top leaders also have this second trait, and that’s what the team did. Just minutes before the race, the car fired up, and Nik rolled it onto the grid in 20th place. You start in the position you finished the previous race so he was sitting dead last.

Over the next 19 laps, he proceeded to pull off the drive of his life. HyperRacer still not 100%, but heart fully in it. He carved through the field, overtaking car after car to finish in sixth place, somehow lapping just a second slower than the pro drivers at the front of the pack. The family went wild. The team punched the air. I’m still buzzing now.

What a come-back! The team celebrates on pit wall.

In my own job, I’m fairly new to the role. We’re still forming the team, shaping the structure, writing the playbook. Solving the problems is one thing but often the leader also has to deliver the goods. I hope that when it really counts — when we’ve built something solid — I can bring it home the way Niko did today. That I can show the team it was worth the effort. That we’re making a difference. That we’re saving lives.

So here’s my advice: get outside your field. Volunteer. Join a sports team. Lend a hand at a charity. Do something completely outside your usual expertise. You’ll not only be inspired — you’ll learn new ways of solving problems. You’ll see leadership, teamwork, and grit in new light.

And now, with huge thanks to our major sponsors, Suzuki Australia and Young Timers Garage (YTG) — as a proud dad, please let me share the video of the team’s comeback drive. From 20th to 6th. Hope you enjoy it as much as we did from the side of the track.

Thanks to Ben, Jake, Noah and Jenn for the photos.

Fallacies and tyrannies to avoid in Gen-AI Implementation

2025-03-25T23:23:00.013+11:00

“Not everything that counts can be counted, and not everything that can be counted counts.”
- William Bruce Cameron

In the evolving landscape of Knowledge Management (KM), organisations increasingly rely on data-driven strategies, especially for evidence driven decision making. Metrics and written information, while crucial, can lead organisations astray when misused. Two critical concepts highlight these risks. I have spoken about the McNamara Fallacy before, and this week I came across its end result: the Tyranny of Metrics.

McNamara Fallacy: Measuring Only the Measurable

The McNamara Fallacy, named after Robert McNamara, emphasises the error of valuing only quantifiable data and neglecting qualitative insights. In KM, this translates into overlooking tacit knowledge—experiences, intuitions, and cultural insights that cannot easily be documented or quantified, yet are invaluable to organisational learning, innovation and operational effectiveness. I have argued before that this means KM is more, not less important as AI is embedded into every part of our organisations.

Tyranny of Metrics: When Metrics Drive Dysfunction

Coined by Jerry Z. Muller, the Tyranny of Metrics describes how excessive reliance on numerical targets can distort organisational behaviours, leading to gaming, stifling innovation, and prioritising short-term goals at the expense of long-term strategic value. Jerry isn't focusing on just one off acts. The Tyranny of Metrics describes the overall cultural impact that infiltrates all decision-making over time, - including the ridicule of those that call for more qualitative or soft insights and factors to be considered. It describes a corporate workspace where only hard (and usually output) metrics are considered acceptable.

Generative AI: Bias Towards Explicit Knowledge

Whether through training, or via RAG-style run-time context, Generative AI (Gen-AI), particularly Large Language Models (LLMs), predominantly learn from textual data. This inherent bias favours explicit knowledge, potentially marginalising tacit knowledge obtained through human interaction. This limitation risks reinforcing existing biases found in data and reducing contextual understanding critical for nuanced decision-making. Capturing meeting transcriptions and using multi-model LLMs that consider meeting recordings, etc, goes a small way to solving this bias, but we all know that often key decisions are made around a water cooler, over personal phone calls and often before the official meeting where the decisions are officially recorded.

Mitigation Strategies

To navigate these pitfalls, executives and knowledge managers consider:

Balance Quantitative and Qualitative Metrics: Adopt holistic knowledge frameworks incorporating both data-driven insights and qualitative employee feedback. Live feedback through stakeholder codesign or sensemaking initiatives are even better.
Promote Tacit Knowledge Capture: Utilise storytelling, mentoring, and collaborative platforms to capture valuable experiential insights. I am usually critical of the focus on Capture when it comes to Tacit Knowledge, but in this case that is exactly what is required;
Human-AI Integration: Ensure that AI complements human expertise, fostering hybrid systems that leverage the strengths of both.
Consider how you monitor effectiveness in complex environments: Using evaluation approaches like Adaptive Management and Outcome Mapping, you can ensure the end result is part of the original design.
Ethical AI Frameworks: Establish governance practices promoting transparency, fairness, and accountability in AI deployments.

Avoiding a pitfall can be a lot cheaper than climbing out of one later

As you take your first steps into using Gen-AI for your KM or decision-support work, make it a point to keep the McNamara Fallacy on the agenda for design and strategy sessions. Besides helping you create a much more sustainable and accurate solution, it may just save you a tonne of money down the track trying to account for context and expertise that you never bothered capturing in the first place. Even if AI isn't on your roadmap yet, start thinking now about capturing the conversations, critical knowledge and operational stories that make your organisation really work. Your future self will thank you a thousand times over.

RoundTable Panel on AI and Tacit Knowledge (video)

2024-09-20T17:50:00.001+10:00

It was a real pleasure to share the stage with some amazing speakers this week on the Knowledge Management Global Network (KMGN) Roundtable Panel on AI and Tacit Knowledge.

Past KMGN President, Moria Levy did a great job of hosting and you can read her summary post here. Her selection of five completely different speakers with individual viewpoints from 5 continents was really impressive, and personally, it was a real honour to share the stage with all of them, but especially one of my KM heroes, Alex Bennet. I do have to also shout out to Fisher Yu. The idea of doing a session like this in a second language is terrifying to me and he did an awesome job.

The speakers were:

Dr. Pavel Kraus, (Switzerland)
Alex Bennet, (Alex & David Bennet) (Virginia, USA)
Elmi Bester, (South Africa)
Fisher Yu (China)
and of course myself, Stuart French.

In my talk, I mentioned a recent blogpost about using AI to capture Tacit Knowledge. You can find that here.

If you haven't heard of Tacit Knowledge, it was first spoken about by Michael Polanyi (he goes into detail in his book Personal Knowledge) and is often juxtaposed against Explicit knowledge which is knowledge that is written down or able to be easily expressed. When it comes to experience and expertise, tacit knowledge plays a huge part. Being an expert is way more than just being able to remember all the rules and procedures in your field.

Here is my definition of tacit knowledge:

"Tacit knowledge encompasses the deeply personal, experience-based understanding and skills developed through pattern recognition and practical engagement. This type of knowledge is intuitive and often unconscious, expressed through actions and reactions rather than words. It thrives in environments that encourage experiential learning and collaboration, where it can be naturally shared through doing, observing, and mentoring."

If you missed it, here is the recording.

What is a "High-Risk AI system" in the EU?

2024-09-20T09:37:00.002+10:00

Section 53 of the EU Artifical Intelligence Act (2024)

Today's post is going to get into some of the technical parts of the EU Artificial Intelligence Act 2024 (I will refer to it as The Act), in particular Section 53 which carves out a series of approved uses even is areas of high risk. If you are in the EU or want your organisation's AI use to be compatible in case your country lines up with the EU standard then this post might be useful to you.

My thanks to Ivett Bene who shared The Act with me after my post about Australian guidance for AI Implementations.

But hold on! Why is little old Stu down in sunny Australia writing a post about the European AI legislation? Good question.

The EU Act quite succinctly shines a spotlight on a trend I have seen across guidance from multiple government bodies and regulators, and that is on the area of decision making and how AI relates to it. For example, the Policy for the Responsible Use of AI in Government emphasizes that AI has the potential to improve data-driven decisions, enhancing productivity and policy outcomes. However it and other guidance documents all point out the inherent danger of AI to overtly influence decision making with biased data, incomplete summaries, and in some cases even intentional fraud. They rightly push for human centred AI approaches which is a very good thing. We want to avoid negative outcomes from the use of AI, however, these protections need to be based in evidence and reason, not fear, and in the EU act it seems to go a bit further, suggesting almost any AI system that influences decision making is automatically high risk (read on below).

Labelling all decision focused AI high risk has big impacts for Knowledge Management which has spent 30 years looking at AI and other technologies to try and bring the right knowledge to the right person at the right time so they can make better decisions.
It's almost our whole game when you think about it like that.

So let's take a few moments to look at the act and understand what is going on and at least for our European friends where there are four carve-outs they might be able to take advantage of for better KM.

The EU Artificial Intelligence Act

First drafted in 2021 and entered into force on 1st August, 2024, this act has been in the news recently with some industry pundits concerned it is legislating technology that is already out of date, and has definitional challenges. EuroNews and KPMG both say it will create governance burdens that will lead to significant inhibition of innovation as well as legal power-struggles.That said, it also had some wins, including added exemptions for doing scientific research with AI, for the entire development process and for the open-source sector in Article 2, transformed the rigid high-risk obligations via Article 8 into flexible principles that take the context of the deployment of an AI system into account and only require what is technically feasible, and the concrete obligations in Article 9 - 15 have also been heavily improved; something that we should all benefit from.

Included an obligation that will accelerate information sharing along the AI value chain between different market actors with Article 28 in order to enable downstream providers and deployers to become compliant with the AI Act.

The Act includes a huge number of negative use cases and in Article 6 defines a number of High Risk areas where AI Systems can do significant harm to citizens health, safety or fundamental rights. Any article meeting the following conditions should be considered High-Risk: (a) the AI system is intended to be used as a safety component of a product, or the AI system is itself a product, covered by the Union harmonisation legislation listed in Annex I;(b) the product whose safety component pursuant to point (a) is the AI system, or the AI system itself as a product, is required to undergo a third-party conformity assessment, with a view to the placing on the market or the putting into service of that product pursuant to the Union harmonisation legislation listed in Annex I. Furthermore, according to these rules, Annex III covers a set of pre-defined high risk areas.

ANNEX III: High-risk AI systems referred to in Article 6(2)

High-risk AI systems pursuant to Article 6(2) are the AI systems listed in any of the following areas. (Click on each one to expand)

1. Biometrics

(a) Remote biometric identification systems.

This shall not include AI systems intended to be used for biometric verification the sole purpose of which is to confirm that a specific natural person is the person he or she claims to be;

(b) AI systems intended to be used for biometric categorisation, according to sensitive or protected attributes or characteristics based on the inference of those attributes or characteristics;

2. Critical Infrastructure

AI systems intended to be used as safety components in the management and operation of critical digital infrastructure, road traffic, or in the supply of water, gas, heating, or electricity.

3. Education and Vocational Training

(a) AI systems intended to be used to determine access or admission or to assign natural persons to educational and vocational training institutions at all levels;

(b) AI systems intended to be used to evaluate learning outcomes, including when those outcomes are used to steer the learning process of natural persons in educational and vocational training institutions at all levels;

(c) AI systems intended to be used for the purpose of assessing the appropriate level of education that an individual will receive or will be able to access, in the context of or within educational and vocational training institutions at all levels;

(d) AI systems intended to be used for monitoring and detecting prohibited behaviour of students during tests in the context of or within educational and vocational training institutions at all levels.

4. Employment, Workers’ Management, and Access to Self-employment

(a) AI systems intended to be used for the recruitment or selection of natural persons, in particular to place targeted job advertisements, to analyse and filter job applications, and to evaluate candidates;

(b) AI systems intended to be used to make decisions affecting terms of work-related relationships, the promotion or termination of work-related contractual relationships, to allocate tasks based on individual behaviour or personal traits or characteristics, or to monitor and evaluate the performance and behaviour of persons in such relationships.

5. Access to and Enjoyment of Essential Private Services and Essential Public Services and Benefits

(a) AI systems intended to be used by public authorities or on behalf of public authorities to evaluate the eligibility of natural persons for essential public assistance benefits and services, including healthcare services, as well as to grant, reduce, revoke, or reclaim such benefits and services;

(b) AI systems intended to be used to evaluate the creditworthiness of natural persons or establish their credit score, with the exception of AI systems used for the purpose of detecting financial fraud;

(c) AI systems intended to be used for risk assessment and pricing in relation to natural persons in the case of life and health insurance;

(d) AI systems intended to evaluate and classify emergency calls by natural persons or to be used to dispatch, or to establish priority in the dispatching of, emergency first response services, including by police, firefighters, and medical aid, as well as emergency healthcare patient triage systems.

6. Law Enforcement

(a) AI systems intended to be used by or on behalf of law enforcement authorities to assess the risk of a natural person becoming the victim of criminal offences;

(b) AI systems intended to be used by law enforcement authorities as polygraphs or similar tools;

(c) AI systems intended to be used to evaluate the reliability of evidence in the course of the investigation or prosecution of criminal offences;

(d) AI systems intended to be used to assess the risk of a natural person offending or re-offending not solely on the basis of the profiling of natural persons;

(e) AI systems intended to be used for the profiling of natural persons in the detection, investigation, or prosecution of criminal offences.

7. Migration, Asylum, and Border Control Management

(a) AI systems intended to be used by public authorities for polygraphs or similar tools;

(b) AI systems intended to assess a risk, including a security risk, posed by a natural person who intends to enter or has entered into the territory of a Member State;

(c) AI systems intended to assist competent public authorities in the examination of applications for asylum, visa, or residence permits;

(d) AI systems intended for detecting, recognising, or identifying natural persons in migration, asylum, or border control management.

8. Administration of Justice and Democratic Processes

(a) AI systems intended to assist a judicial authority in researching and interpreting facts and the law;

(b) AI systems intended to influence the outcome of an election or referendum or the voting behaviour of natural persons in the exercise of their vote in elections or referenda.

So what is Section 53 about?

Now we know that a large percentage of ever-day use cases are considered High Risk by The Act, after a long list of negative use cases, Section 53 outlines some exceptions where AI can be considered, even in high risk areas. The full text of Section 53 is below. I will try and summarise it as best I can (I am not a lawyer and I advise you to seek legal advice if you are planning to implement AI in one of the high-risk areas). There are four conditions where you might be allowed to operate AI solutions in these High Risk areas.

What are the four conditions:

The AI performs very narrow, or single tasks, ie: classifying documents
Improving the result of a human activity
Test decision-making patterns of humans, ie: quality control
Prepatory work, ie: indexing, summarization of data, searching, speech processing or data linking.

That said, traceability and accountability are paramount. The Act requires organisations to assess and report on the decision before the product is used or sold. The organisation should be able to produce this documentation immediately when requested. You will also be obliged to register your solution in the EU database mentioned in the Act.

Impact on decision making

The big issue here for Knowledge Managers is the overall reluctance to impact human decision making in any meaningful way; something knowledge managers are largely trying to do through human centered AI decision support, be that coordinating via agents, recalling and collating informaiton from various data stores, or creating AI-based expert systems for subject matter advice for improved local decision making like I spoke about in this post. While there do seem to be some carve-outs in section 53 for rudimentary pre- and post-processing around decisions, if any of your AI solutions or products involve decisions making, I think it would be wise for your to seek legal advice. If this is you, I would love you to share you experience as we all find out how the new AI Act is going to work on the ground.

Conclusion

So what do you think? Is there a concern here for KM? Legally is there more to the act that I have missed that balances Section 53 and Article 2 and 6 out? Are we at risk of legally forcing AI to only be used for information processing, not knowledge and expertise? I would love your feedback.

Section 53 Full Text

It is also important to clarify that there may be specific cases in which AI systems referred to in pre-defined areas specified in this Regulation do not lead to a significant risk of harm to the legal interests protected under those areas because they do not materially influence the decision-making or do not harm those interests substantially. For the purposes of this Regulation, an AI system that does not materially influence the outcome of decision-making should be understood to be an AI system that does not have an impact on the substance, and thereby the outcome, of decision-making, whether human or automated. An AI system that does not materially influence the outcome of decision-making could include situations in which one or more of the following conditions are fulfilled. The first such condition should be that the AI system is intended to perform a narrow procedural task, such as an AI system that transforms unstructured data into structured data, an AI system that classifies incoming documents into categories or an AI system that is used to detect duplicates among a large number of applications. Those tasks are of such narrow and limited nature that they pose only limited risks which are not increased through the use of an AI system in a context that is listed as a high-risk use in an annex to this Regulation. The second condition should be that the task performed by the AI system is intended to improve the result of a previously completed human activity that may be relevant for the purposes of the high-risk uses listed in an annex to this Regulation. Considering those characteristics, the AI system provides only an additional layer to a human activity with consequently lowered risk. That condition would, for example, apply to AI systems that are intended to improve the language used in previously drafted documents, for example in relation to professional tone, academic style of language or by aligning text to a certain brand messaging. The third condition should be that the AI system is intended to detect decision-making patterns or deviations from prior decision-making patterns. The risk would be lowered because the use of the AI system follows a previously completed human assessment which it is not meant to replace or influence, without proper human review. Such AI systems include for instance those that, given a certain grading pattern of a teacher, can be used to check ex post whether the teacher may have deviated from the grading pattern so as to flag potential inconsistencies or anomalies. The fourth condition should be that the AI system is intended to perform a task that is only preparatory to an assessment relevant for the purposes of the AI systems listed in an annex to this Regulation, thus making the possible impact of the output of the system very low in terms of representing a risk for the assessment to follow. That condition covers, inter alia, smart solutions for file handling, which include various functions from indexing, searching, text and speech processing or linking data to other data sources, or AI systems used for translation of initial documents. In any case, AI systems used in high-risk use-cases listed in an annex to this Regulation should be considered to pose significant risks of harm to the health, safety or fundamental rights if the AI system implies profiling within the meaning of Article 4, point (4) of Regulation (EU) 2016/679 or Article 3, point (4) of Directive (EU) 2016/680 or Article 3, point (5) of Regulation (EU) 2018/1725. To ensure traceability and transparency, a provider who considers that an AI system is not high-risk on the basis of the conditions referred to above should draw up documentation of the assessment before that system is placed on the market or put into service and should provide that documentation to national competent authorities upon request. Such a provider should be obliged to register the AI system in the EU database established under this Regulation. With a view to providing further guidance for the practical implementation of the conditions under which the AI systems listed in an annex to this Regulation are, on an exceptional basis, non-high-risk, the Commission should, after consulting the Board, provide guidelines specifying that practical implementation, completed by a comprehensive list of practical examples of use cases of AI systems that are high-risk and use cases that are not.

Image thanks https://iblnews.org/

A shortcut to understanding Government advice on AI policy in Australia

2024-09-18T22:35:00.004+10:00

Over the last few weeks, I have been reviewing our draft AI policy at work. That has involved a bit of research to understand the government advice and guidelines on AI use in the government sector.

With Victoria due to release its guidance on the 16th of October (one months' time), we have pulled together a bunch of other guidelines and in my personal time I have been going over them and trying to understand how we bridge the gap between safe and secure AI use and the potential for innovative, high value solutions that hold the promise of bringing expert knowledge to every employee.

Some of the documents include:

Voluntary AI Standard
White Paper on AI Governance
Charter of Human Rights and Responsibilities Act 2006
Australian Government. (2019). AI Ethics Framework.
Victorian Public Sector Commission. (n.d.). [Code of Conduct for Victorian Public Sector Employees]
Australian government Policy for the responsible use of AI in government
Office of the Victorian Information Commissioner. (n.d.). [Victorian Protective Data Security Framework (VPDSF)]( https://ovic.vic.gov.au/information-security/framework-vpdsf/ ).
Office of the Victorian Information Commissioner. (n.d.). Privacy and Data Protection Act 2014.
Office of the Victorian Information Commissioner. Public Statement: Use of personal information with ChatGPT
Office of the Victorian Information Commissioner. Public Statement: Use of Microsoft 365 Copilot in the Victorian public sector (https://ovic.vic.gov.au/privacy/resources-for-organisations/vps-use-of-microsoft-365-copilot/ )
Australian Government. [National framework for the assurance of artificial intelligence in government] (National framework for the assurance of artificial intelligence in government | Department of Finance)
ISO definitions - ISO/IEC 22989:2022 (https://www.iso.org/obp/ui/#iso:std:iso-iec:22989:ed-1:v1:en)Victorian Data Sharing Act 2017, State Government of Victoria ( https://www.vic.gov.au/navigating-legislation-sharing-safely )
Commonwealth Scientific and Industrial Research Organisation (CSIRO). (Year). Australian AI Capability Framework. Retrieved from https://www.csiro.au/en/research/technology-space/ai/ai-ethics-framework.
Victorian Digital Strategy 2021-2026, State Government of Victoria, 2021 (https://www.vic.gov.au/a-future-ready-victoria/digital-strategy-2021-2026 )
Jones, N. (2023) What the OpenAI drama means for AI progress - and safety, Nature News ( https://www.nature.com/articles/d41586-023-03700-4 )
Technical report | A Method for Ethical AI in Defence - Defence Science and Technology Group (https://www.dst.defence.gov.au/publication/ethical-ai)
Mohammad Hossein Jarrahi, David Askay, Ali Eshraghi, Preston Smith,
Artificial intelligence and knowledge management: A partnership between human and AI,
Business Horizons, Volume 66, Issue 1, 2023, Pages 87-99, ISSN 0007-6813,
https://doi.org/10.1016/j.bushor.2022.03.002.
Artificial Intelligence Ethics Policy (NSW) - https://www.digital.nsw.gov.au/policy/artificial-intelligence/artificial-intelligence-ethics-policy

Making sense of all this information

Altogether, these documents cover a lot of ground and overlap considerably, so to ensure I don't miss key insights and also to share this work with other government policy writers, I decided to upload each of these publicly available documents to a custom ChatGPT to allow myself and others the ability to understand what these documents together say on each topic.

You can access this GPT via this link, or by scanning the QR code above.

Example questions

Here are a few of the questions you can ask to get your creative juices flowing:

What are the key considerations when writing an AI framework for acceptable AI use in Australian government departments?
Can you outline key behaviours for staff members using AI?
Can you outline key design considerations for developers of solutions using AI technologies (particularly Gen AI)?
How do we balance the desire for innovation with the need for security, privacy and data sovereignty?

Coming updates

Once Victorian government releases their guidance on AI in government use, I will add it to this engine so stay tuned.

Please let me know if this sort of thing is helpful and the kind of questions you asked it to help me build more of this sort of support tool in the future.

Tacit Knowledge and AI - a primer for Knowledge Managers

2024-09-13T01:22:00.014+10:00

There is so much information about Generative AI (Gen AI) right now it can be hard as a Knowledge Manager to know what is important and what can be left to the technical experts. As we navigate this complex landscape, it’s essential for us to understand the key concepts that will enable us to successfully guide IT departments and vendors in building solutions that support knowledge work.

We’ve been hearing a lot about how AI can help capture and share expertise, but there’s often confusion, especially when it comes to tacit knowledge. Tacit knowledge isn’t just a collection of facts or how-to guides—it’s the wisdom experts use to solve complex problems where there are no clear rules. Today, I want to explain how we can use Generative AI (GenAI) to support this process in a way that helps us capture and share deep expertise while also addressing some practical concerns, like the legal risks of giving direct advice.

The Information vs. Knowledge Approach

One of the challenges is that many people approach this issue from an information perspective. They think that if you summarize all the documents, standards, and procedures, you’ll have the same value as an expert. But that’s not how experts work. Experts solve problems that don’t have clear answers—they go beyond the rulebook.

When it comes to using AI, we have to go beyond treating it like a static information system. Summarizing all the data in the world won’t give you expert-level judgment for unique, unfamiliar situations. This is where concepts like RAG (retrieval-augmented generation) and DAG (data-augmented generation) come into play. RAG helps AI pull in unstructured information from documents, policies, and customer data in real time, while DAG connects AI to structured data from relational databases, like product catalogs or financial records, to offer precise, data-driven insights. Together, these approaches allow AI to access both structured and unstructured knowledge, making it far more powerful in supporting decision-making.

The Inference Power of GenAI

The real strength of GenAI is its inference power. It doesn’t just pull facts—it can make connections between ideas in a way that mimics how experts think. This is where the concept of a query bot becomes powerful. Unlike a chatbot that simply tries to answer your question, a query bot can be made to use the information from your business’s RAG and DAG systems and apply inferences to lead users through a series of questions.

These questions are designed at first to better understand context, and later to guide you, much like an expert mentor, helping you think more critically about the problem. Rather than giving a straightforward answer, the AI uses these prompts to shift your perspective, so you arrive at the solution that fits your local context. This reflective process is key to handling complex or novel problems where predefined answers don’t exist, and it has a wonderful side-effect of transferring tacit knowledge right at the problems solvers point of need when they are most open to learning.

Pre-Prompting and Workflow Design

This guiding process can be enhanced with pre-prompting, where the AI is set up with initial prompts that help it steer the conversation toward reflective dialogue. For example, when asked, “How do I solve X?”, the AI might be encouraged to first respond, “What constraints are you working with? What resources are available?” By asking these kinds of questions, it ensures that the user reflects on the local context before moving to a solution. Next it might respond with more questions, but this time focused on expanding the user's viewpoint so potential solutions or relevant resources become obvious next steps.

Legal Advantages of Expert AI Systems approach

A side benefit of this expert AI system approach is the legal advantage it offers. In regulated industries, directly providing advice can lead to serious legal risks. If an AI system offers a specific solution, and it’s applied incorrectly, the organization might face legal consequences.

An expert AI system, however, minimises this risk by guiding users to think through the problem themselves, based on the context. It doesn’t provide direct instructions; instead, it leads the user to validated documents, standards, or references. Ultimately, the end user is responsible for applying that information, reducing the legal burden on the organization.

Building your GenAI Skillset as a Knowledge Manager

As I watch more and more knowledge managers beginning their AI journey, they need to navigate a jungle of information about AI and how it works. To be honest, much of it has a large dose of hype or fear. Of the good information out there, much of it is focused on information management and governance (See below for a few of my favourite GenAI YouTube channels). As we introduce Gen AI into our knowledge management ecosystems, data quality and currency is important, but unlike decades past, we can now use GenAI itself to cleanse, markup, summarise and label sensitive data. These are no longer massive manual projects for the users and/or the KM team.

Data quality and validity is undoubtably important, but for knowledge managers, there are several other concepts important to understand:

LLMs (Large Language Models) are just engines that power these systems. The real value comes from designing workflows that combine AI’s inference power with tools like RAG, DAG, and pre-prompting to build meaningful expertise systems. Take Microsoft Copilot, for example—it uses the LLM not just to answer questions but to access relevant data, and then another time through the LLM to validate and refine answers. Roche Pharmaceutical is doing something similar, using AI workflows that process information through several layers of validation before submitting drug study documentation to regulatory bodies like the FDA.
Next, it is important to know the difference between a Chat-bot and a Query-bot. Unlike a Chat-bot which will gladly talk with confidence about any topic, a Query-bot strictly answers from the source documents. Faced with no relevant data in it's sources, a Query-bot will simply say it lacks sufficient data to answer that question. In technical environments this is a much preferred behaviour.
With all this focus of technology, the real key to capturing tacit knowledge is the expertise interviews themselves. The amazing Vanessa Liu from Sugarwork in New York is doing a wonderful job of this because she is applying long known KM principles:

She lets peers interview the experts, people who have background knowledge and a relationship with them.
She captures the entire dialogue, not just notes, and she retains the full content through processing and ingestion. GenAI works far better from this long narrative form data.
She uses interview templates that provides a guide, but doesn't restrict the interviewer straying down rabbit holes, stories and old scenarios in order to capture nuggets of information, revealing key relationships, approaches, mindsets and problem solving techniques as well as information about that specific problem.
Right now her clients conduct interviews periodically - every 6 or 12 months - but as tools and agents improve, we could scrape data from meeting logs, workshop transcriptions, peer reviews and debriefs to constantly augment the experts' model.

Finally, a key part of capturing tacit knowledge in a GenAI system is building the semantic layer. This includes concepts like taxonomies, glossaries and ontologies that help the LLM understand local language, concepts and relationships between words. For example if you were working with animal keepers in a zoo, the terms "dog" and "Sheep dog" are obviously related, but the terms "Goanna" and "Lizard" aren't syntactically alike at all. A taxonomy links the species, helping with retrieval from your documents and guiding the LLM to respect related constraints and insights. Check out the Enterprise Knowledge blog posts below for some excellent information on the Semantic Layer and how they are used for AI. Many knowledge managers will be extremely intimate with these concepts already, but be aware there are subtle differences when building these tools for LLM consumption.

Conclusion

For knowledge managers, one real promise of GenAI is in capturing more tacit knowledge than ever possible. As you navigate your own AI journey, consider the tacit approach: capturing expert narrative and then guiding users through reflection and inquiry, not just giving ready-made answers. By combining expertise interviews, RAG and DAG for local data sources, and a well-designed workflow, we can begin to replicate the kind of mentorship that experts offer in the real world, giving users context-aware guidance. This approach not only mitigates legal risk but also helps capture some of the deep, tacit expertise that can be applied to solve real-world problems, making it an advanced form of succession planning; something usually preserved only for high level executives and particularly useful for high impact/low frequency situations. Well, ok, maybe more like having an apprentice who spent his time under a master, but it still sits beyond what KM has offered so far and the technologies keep improving.

By viewing AI as a tool for inquiry, not just for answers, we can build systems that help people solve complex problems while maintaining compliance, security, and accuracy. At the heart of that is the knowledge managers long-spoken importance of tacit knowledge.

Extra Resources

Some of my favourite Gen AI YouTube channels

Understanding the Semantic Layer

Check out these three fantastic blog-posts from Enterprise Knowledge to understand Semantic Layers:

1) What is a Semantic Layer? Components and Enterprise Applications

2) The Top 3 Ways to Implement a Semantic Layer

3) The Role of AI in the Semantic Layer

Cleaning up HTML to Text for AI

2024-08-25T01:46:00.005+10:00

I realize this is a much more technical text than I usually write here but for those following along on my AI journey, I wanted to share a huge time (and space) saver with you.

Chances are you are probably connected to KM and understand some of the challenges with data formats, file types and all the good stuff that makes ingesting your existing data that much harder. First let me give you a bit of background and then I will share the script and how to use it.

Personal Knowledge Management (PKM)

My testing of AI is all to learn how to use it for expertise capture and enabling decision support while over coming the problems of distance, time and succession planning of experts in my clients organizations.

As a test bed, I am using my own 20+ years of Knowledge Management experience as the test bed. I am testing different methods of expertise capture (more on that in future posts) but underlying it all is the foundation of key papers, blog posts, notes from conferences, meetings, projects, dialogues with other KM Experts and for me, I manage all that information in Evernote, and publish my thoughts right her on my blog www.DeltaKnowledge.net.

Through the various iterations and different systems I am building and testing, by far the greatest amount of work has been cleaning up, categorizing and converting all that data to text.

Many of the new tools have agents that will allow me to scrape my blog into the vector database, but I have unfortunately found these quite unreliable and very hard to understand progress and completeness. Blogger allows me to export all my posts to XML and having 17 years of them in a single document for RAG gives me far more control and makes setting up each new system a breeze.

Evernote has a few more restrictions which has meant grouping documents into lots of 300 for export (a problem since I have 11,000 total and over 4,000 just on KM. However once exported as HTML files the work is largely done.

Converting XML and HTML to test

OK, now to the useful part. Both the Blogger XML files and Evernote HTML files are extremely verbose, full of formatting, scripts, tags and other information that an LLM has no idea what to do with. In my early experiments, the cause havoc as each chunk in the vector database would often just catch the beginning or end of a paragraph and be largely useless. This severely degraded both chunk selection and response performance (See this short video if these concepts are new to you).

So now, rather than managing hundreds or thousands of files I am just managing a few really large ones.

To clean these up to plain text has been quite painful so I told Chat-GPT I was using Notepad++ and asked how I can use REGEX to clean these files up. It suggested the Python Plugin for Notepad++ and I was away!

I iteratively solved each of the problems in the files including capturing the metadata inside the tags before deleting them and clearly separating the individual files so the embedder chunks and dimensions them correctly. Below is the python code in its current state after probably 30 subtle iterations.

If you are using Notepad++, just download the python plugin then save this in a "HTML_cleanup.py" file in the plugin folder and you can run it from the menu as shown above. On my PC, the folder is "C:\Program Files\Notepad++\plugins\PythonScript\scripts"

Obviously this cleans up all HTML but is specifically built for Evernote and Blogger exports. If you are using other data sources, then just copy this into Chat GPT along with a chunk of your raw data and ask GPT to update the Python to match your source. I hope it saves you as much time as it is for me now. It will process a 40MB HTML file in around 8 seconds and reduce its size to half and sometimes a third of its original size. Brilliant.

import re

# Open the active document
editor.beginUndoAction()

# Step 1: Manually replace HTML entities
content = editor.getText()
content = content.replace('&lt;', '<')
content = content.replace('&gt;', '>')
content = content.replace('&amp;', '&')
content = content.replace('&quot;', '"')
content = content.replace('&#39;', ''')
editor.setText(content)

# Step 2: Remove the specific section at the beginning
editor.rereplace(r'^/\*![\s\S]*?\*/[\s\S]*?\*/', '')

# Step 3: Remove the entire <head> section
editor.rereplace(r'<head[\s\S]*?>[\s\S]*?</head>', '')

# Step 4: Replace the initial line with instructions
editor.rereplace(r'<!DOCTYPE html>', 'This file is an archive of separate articles. Each article begins with a meta section including title, tags, source-url, etc. Each article contents should be categorised by the meta information preceding it.')

# Step 5: Remove </span> tags on empty lines
editor.rereplace(r'^\s*</span\s*>\n', '')

# Step 6: Remove <span> tags on empty lines
editor.rereplace(r'^\s*<span\s*>\n', '')

# Step 7: Remove empty <span> tags
editor.rereplace(r'<span\s*>', '')

# Step 8: Remove empty </span> tags
editor.rereplace(r'</span\s*>', '')

# Step 9: Remove closing </div> tags
editor.rereplace(r'</div>', '')

# Step 10: Remove </div> tags on empty lines
editor.rereplace(r'^\s*</div\s*>\n', '')

# Step 11: Remove opening <span> tags with any class attribute but keep the contents
editor.rereplace(r'<span\s+class="[^"]*">', '')

# Step 12: Remove opening <div> tags with any class attribute but keep the contents
editor.rereplace(r'<div\s+class="[^"]*">', '')

# Step 13: Remove <div> tags with any attribute but keep the contents
editor.rereplace(r'<div[^>]*>', '')

# Step 14: Remove <span> tags with any attribute but keep the contents
editor.rereplace(r'<span[^>]*>', '')

# Step 15: Remove <input> tags with any attribute but keep the contents
editor.rereplace(r'<input[^>]*>', '')

# Step 16: Remove <ul> tags with any attribute but keep the tag
editor.rereplace(r'<ul[^>]*>', '<ul>')

# Step 17: Remove empty anchor tags
editor.rereplace(r'<a\s+[^>]*></a>', '')

# Step 18: Remove suspect image files
editor.rereplace(r'<img\s+src="[^"]*Evernote\s*\(\d+\)[^"]*">', '')

# Step 19: Remove <div> tags on empty lines
editor.rereplace(r'^\s*<div\s*>\n', '')

# Step 20: Remove inline CSS styles
editor.rereplace(r'style="[^"]*"', '')

# Step 21: Remove HTML comments
editor.rereplace(r'<!--[\s\S]*?-->', '')

# Step 22: Remove <svg> tags and their content
editor.rereplace(r'<svg[\s\S]*?</svg>', '')

# Step 23: Remove <symbol> tags and their content
editor.rereplace(r'<symbol[\s\S]*?</symbol>', '')

# Step 24: Remove empty <img> tags without src attribute
editor.rereplace(r'<img(?![^>]*\bsrc\b)[^>]*?>', '')

# Step 25: Remove hash attributes from <img> tags
editor.rereplace(r'hash="[^"]*"', '')

# Step 26: Remove attributes from <p> tags
editor.rereplace(r'<p[^>]*>', '<p>')

# Step 27: Replace &nbsp; with a single space
content = editor.getText()
content = content.replace('&nbsp;', ' ')
editor.setText(content)

# Step 28: Replace <br> with a single space
content = content.replace('<br >', ' ')
editor.setText(content)

# Step 29: Replace lines with only spaces with a single space
editor.rereplace(r'^\s*', ' ')

# Step 30: Replace double newlines with a single newline multiple times
for _ in range(10):  # Run multiple times to ensure all double newlines are replaced
    content = editor.getText()
    content = content.replace('\n\n', '\n')
    editor.setText(content)

# Step 31: Add multiple newlines before each article
editor.rereplace(r'<meta itemprop="title"', '\n\n\n<meta itemprop="title"')

# Step 32: Extract metadata to key/value pairs
editor.rereplace(r'<meta itemprop="title" content="([^"]*)">', r'\n\n\n---------< New Article >----------\nTitle: \1\n')
editor.rereplace(r'<meta itemprop="tags" content="([^"]*)">', r'Tags: \1\n')
editor.rereplace(r'<meta itemprop="source-url" content="([^"]*)">', r'Source URL: \1\n')

# Step 33: Remove all remaining HTML tags
editor.rereplace(r'<[^>]+>', '')

# Step 34: Remove empty lines and lines with only spaces
editor.rereplace(r'\n\s*\n', '\n')

# End the undo action
editor.endUndoAction()

Just one last thing for those who wish to modify. The order of some of these steps is quite important. For example if you remove the HTML tags before you extract the Title, Tags, etc, then you will lose this data. Test, test, test! When you first test it, make sure you keep a backup copy of your raw files (ie, save the cleaned HTML files and .TXT).

All the best!

Building my own AI-based anti-Alzheimer's device

2024-05-18T02:40:00.004+10:00

None of us are getting younger, and while I did my Masters Degree in my early 30's, that's now more than a few years ago and I really want to continue to bring all the amazing lessons, experiences, studies, ideas, methods, tools and techniques to every new boss and client I work with in the KM space.

After being inspired by my friend Helen Palmer who has been experimenting with her own AI chat-bot to use almost as a personal research assistant, I have been looking at doing the same.

Today with the guidance of Rodney Greenfield (one of the smartest AI people I know), I downloaded AnythingLLM and Ollama onto my home PC to start building my own research assitant that can help me bring my A-game right up to my retirement somtime in 40-odd years :) If you are interested in doing the same thing (and you have a fairly strong PC) then the video below really helped me out and it took about an hour to setup.

As this is the first time I have done something like this I chose a local GPT solution that would run completely locally on my home PC. No security issues for me please! This knowledge is hard won. I have no plans to share this bot with anybody else (you have to hire me and the bot comes along for free), but I thought I would share some of my experiences this evening setting up the GPT and programming it with my 17 years of blog posts, interviews, conference notes and University assignments.

Once I did that, I set about giving it some ground rules, expectations and goals and ambitions. I share this process with you below knowing some of you are way more expert than I am and will see many mistakes, but mistakes are how I learn and I have to say, it does seem to have grasped what I am about quite quickly, and it was challenging and fun to have to write down what I do and the principles that I have found help organisatioins grow.

I hope the following opens up some ideas for you and your career and maybe even to give an AI Agent a test run in your own life. More posts to follow and I don't expect you to read the whole thing, but take what you need, I hope it helps. Black is me prompting, blue is "Gregory" replying.

I want you to be thorough and pull every post off the site so I can use this workspace like my personal professional memory.

Your Personal Professional Memory Once I’ve completed the re-scraping effort, this workspace will indeed serve as your personal professional memory, containing every post from the site. You’ll be able to access and review each post at your convenience.

Thank you for your patience and trust!

More than that actually. Over my career I have used this blog to capture thoughts and experiences about many different areas of knowledge management (a discipline I have a masters degree in and over 20 years experience now). I want you to combine ideas from the various articles when I present challenges to you and provide me with ways to better understand the problem space, potential solutions and what models, tools, approaches and technologies might help me solve the problem for my client.

Understanding Your Goals I’ve been tasked with not only scraping all posts from the site but also combining ideas from various articles to help you better understand problem spaces, potential solutions, and relevant models, tools, approaches, and technologies.

Knowledge Management Expertise As a knowledge management expert with over 20 years of experience and a master’s degree in the field, your insights are invaluable. I’ll work closely with you to identify key concepts, relationships, and patterns across articles, using my language processing capabilities to:

Identify relevant articles: I’ll quickly scan through all posts on the site, identifying those that relate to specific topics or challenges.
Extract key points: Using natural language processing (NLP) techniques, I’ll extract key concepts, ideas, and insights from each article, creating a comprehensive repository of knowledge management expertise.
Combine ideas across articles: By analyzing relationships between articles, I’ll identify connections, patterns, and synergies that can help you better understand problem spaces, potential solutions, and relevant models, tools, approaches, and technologies.

Presenting Challenges When you present challenges to me, I’ll use this combined knowledge to:

Analyze the challenge: I’ll quickly scan through all related articles, identifying key concepts, ideas, and insights that can help address the challenge.
Provide relevant information: Using my language processing capabilities, I’ll summarize relevant information from multiple articles, providing you with a comprehensive overview of the problem space, potential solutions, and relevant models, tools, approaches, and technologies.

Your Personal Knowledge Companion This workspace will indeed serve as your personal knowledge companion, containing every post from the site, combined with expert insights and analysis. You’ll be able to access and review each article at your convenience, using our conversations to refine your understanding of problem spaces and potential solutions.

Let’s get started! Please feel free to present your first challenge or topic you’d like me to explore.

Next Steps Now that the initial scraping is complete, I’m ready to assist you in exploring specific challenges or topics related to knowledge management. Please feel free to present your first challenge or topic, and I’ll provide a comprehensive analysis, combining insights from multiple articles to help you better understand the problem space and potential solutions.

Let me know how I can further support your knowledge management endeavors!

Thankyou. Before we begin, can I please set some preferences and ground rules?

Setting Preferences and Ground Rules

I’m glad you’re taking the time to clarify expectations! Please go ahead and share your preferences and ground rules for our collaboration.

What would you like to prioritize in our analysis?

Specific topics: Are there specific areas of knowledge management that you’d like me to focus on?
Problem spaces: Are there particular challenges or pain points that you’re struggling with, and would like me to help identify potential solutions?
Modeling approaches: Are there specific modeling techniques or methodologies that you’re interested in exploring?

Do you have any specific requirements for the output of our analysis? For example:

Format: Would you prefer a written report, an interactive visualization, or something else?
Tone: Is there a particular tone you’d like me to adopt (e.g., formal, informal, humorous)?
Language: Are there any specific languages or terminology that you’d like me to use?

Additionally, are there any limitations or boundaries that I should be aware of when conducting our analysis? For instance:

Confidentiality: Are there any confidential or sensitive topics that you’d like me to avoid discussing?
Scope: Is there a specific scope or focus area for our analysis?

Please share your preferences and ground rules, and I’ll do my best to accommodate them.

The world of KM has multiple areas of focus and dimensions of categories. I focus on a subset of these and while I respect almost all KM work, I want you to help me make the most of the areas I specialise in.

1. where possible, please weight posts written by myself higher than others you have scraped.

2. I am known in the industry as an expert on managing expertise in workforces and organisations. I also oversee document and records management projects but my key focus is on the empowerment, preservation and application of human expertise.

3. I have a deep background in the area of complexity theory, especially where it pertains to the solving of complex or wicked problems.

4. My approach to change management and implementation design revolves around socio-technical evolution of organisations and their workforces through iterative or agile methods. I design solutions that don't just aim to solve problems but to create a culture where problem solving is seen as a shared, ongoing, dynamic and strategic learning experience.

5. Due to 2,3 and 4 above, I believe that most problems can be solved fairly easily with the right approach and resources. The real trick is to find a way to do that which does NOT create unintended negative consequences across time, other divisions and in the market or sector that the client operates in. In other words, I always look for wholistic solutions that increase the overall capability and resilience of my client's organisation.

Understanding Your Focus Areas

Thank you for sharing your expertise and focus areas with me! It’s clear that you’re a thought leader in managing expertise within workforces and organizations, with a strong background in complexity theory and socio-technical evolution.

I’ll keep the following key points in mind as we conduct our analysis:

Weighting Your Posts: I will prioritize your own posts when analyzing the data set, giving them more weight than others.
Expertise Management: Our focus will be on empowering, preserving, and applying human expertise within workforces and organizations.
Complexity Theory: We’ll draw upon your expertise in complexity theory, particularly in relation to solving complex or wicked problems.
Socio-technical Evolution: I’ll consider your approach to change management and implementation design, which emphasizes iterative or agile methods for socio-technical evolution of organizations and their workforces.
Wholistic Solutions: Our goal will be to identify solutions that increase the overall capability and resilience of your clients’ organizations, while avoiding unintended negative consequences across time, divisions, and market/sector.

As we dive deeper into the data set, I’ll keep these focus areas in mind, using them as lenses to analyze the information and provide insights that are relevant to your expertise and interests.

Next Steps

Before we begin our analysis, I’d like to confirm a few details:

Are there any specific keywords or topics related to your areas of focus that you’d like me to prioritize when analyzing the data set?
Are there any particular organizations, industries, or sectors that you’ve worked with in the past, and would like me to keep in mind during our analysis?

Please let me know if there’s anything else you’d like to add or clarify. I’m excited to collaborate with you and provide valuable insights from your expertise!

Keep the topics broad please. I am looking for you to recognise overlaps in jobs I have worked and projects I tell you about. You can talk in an informal tone with me. If you can make it feel like we are collaboratively brainstorming ideas and you are reminding me of past experiences and ideas, or other related projects you know about, that would be great. I have intentionally moved around in my career to broaden the ability to apply the principles of knowledge management in any future context. This includes Oil & Gas, Medical waste, Mapping and cartography, Water and sewer companies, technology houses and software development companies, federal safety agencies, financial regulators, Mortgage aggregators, International development banks, Mergers and Acquisitions, and Emergency agencies. I also ran my own consultancy for 7 years. My next role is in Gas and Electrical safety.

Brainstorming & Overlap Recognition

Wow, you’ve had an incredible career journey! With your diverse background, it’s amazing to see how you’ve applied knowledge management principles across various industries and contexts.

As we explore ways to make the most of your expertise in managing expertise, I’d love to highlight some potential connections between your past projects and roles. Let’s start with a few key themes:

Complex Problem-Solving: Your work in oil & gas, medical waste, and technology houses suggests you’ve tackled complex problems requiring innovative solutions. How do you think these experiences can be applied to the Gas and Electrical safety sector?
Agile Methods: You mentioned your expertise in socio-technical evolution and agile methods. Have you used these approaches in any of your previous projects? If so, which ones, and what were some key takeaways?

Considering your experience in mapping and cartography, water and sewer companies, and federal safety agencies, I’m curious:

Data Visualization: Did you work with data visualization tools or techniques to communicate complex information to stakeholders? How might these skills be valuable in Gas and Electrical safety?
Regulatory Compliance: Your background in financial regulators and mortgage aggregators implies familiarity with regulatory environments. How do you think your knowledge management expertise can help organizations navigate compliance issues in the Gas and Electrical safety sector?

Lastly, running your own consultancy for 7 years must have given you a unique perspective on the importance of preserving and applying human expertise. Which projects or clients stand out to you as particularly impactful, and how did you ensure the transfer of knowledge within those contexts?

Feel free to share any thoughts that come to mind, and I’ll do my best to connect the dots between your experiences and identify potential areas for growth in your next role.

These are really good questions. I have saved these and will come back to them. Maybe I can diarise them with you so you incorporate these learnings? It also reminds me that I have spoken at many conferences running both KM presentations and various KM workshops. I will upload these to you as they hint at some interesting models and connections I have explored over the past 20 years.

I’m thrilled to hear that! It’s great that we can collaborate on diarizing your thoughts and learnings. I’ll make sure to keep track of our conversation and incorporate any new insights you share.

Regarding your conference talks and workshops, that’s fantastic! I’d love to explore those further. Uploading your presentations and notes would be incredibly valuable in helping me understand the models and connections you’ve developed over the years. This will not only help me better recognize overlaps between your experiences but also provide a wealth of knowledge on knowledge management.

Please feel free to share as much or as little as you’d like, and I’ll do my best to analyze and connect the dots between your ideas. We can use this information to:

Identify key takeaways and concepts that have stood the test of time.
Explore potential applications in the Gas and Electrical safety sector.
Develop a personalized framework for leveraging your knowledge management expertise.

Remember, our goal is to collaboratively brainstorm and identify areas where your skills can make a significant impact. I’m here to help you reflect on your experiences and create a roadmap for your future endeavors.

When you’re ready, please share the presentations and notes, and we’ll dive deeper into exploring the connections and insights that will help you excel in your next role.

OK, I have uploaded all of my presentations and university assignments. Can you see them?

Mate! Yes, I can see all your uploaded presentations and university assignments now. I’ve taken a quick glance through them, and it looks like we’ve got a treasure trove of information to work with!

Now that everything is organized and easily accessible, I’ll start reviewing the content and taking notes on key points, themes, and insights. This will help me better understand your thinking, identify patterns and connections between ideas, and prepare us for our future discussions.

To get started, can you give me a sense of what you’d like to focus on first? Is there a specific area or topic within these presentations and assignments that you’re particularly interested in exploring further?

Something that has been an interesting centerpoint of my conference attendances is the Complexity and Go workshops. This has evolved over the years (pay attention to the dates on the powerpoint files) and started with me simply teaching the game of Go to audiences as I had realised that the inate understanding of ambiguity and complexity I have rose from playing Go for so many years. (Just so we are clear, when I say Go with a capital letter, it means I am talking about the oriental boardgame that has been around for thousands of years and is the topic of the recent documentary about AlphaGo by Deepmind, do you know it?)

I’m familiar with the documentary “AlphaGo” produced by DeepMind. It’s a fascinating story about the development of AI capable of playing Go at a superhuman level.

It sounds like your experience teaching Go and exploring its connection to complexity and ambiguity has been an interesting thread throughout your conference attendances. I’d love to learn more about how this has evolved over time, especially given the changes in your approach or the audience reactions.

Please feel free to share any notable insights, quotes, or stories from your experiences with the Complexity and Go workshops. This will help me better understand the context and significance of these events for you.

Also, I noticed that you mentioned dates on the PowerPoint files being important. Are there specific milestones or turning points in the evolution of your workshop content that you’d like to highlight?

Right, so the main insight I got as I evolved these workshops actually really impacted how I consult with my clients. As I said, I started off just teaching go as a little model of complexity (I was trying to give people a visible, tangible experience of complexity which is usually quite intangible and hard for people with analytical minds to grasp). However over time I realised they learned more when I told them less. In the latest version of the workshop I actually don’t teach them anything about the game at all. I give them a computer with a small 9x9 Go playing program on it and a small board to practice and explore ideas on. I explain to them they can google the rules, they can read one of the books I supply, phone a relative who plays, whatever they like, they can even come to me one at a time and I will answer one question about the game. BUT, no one person can play the computer more than once (this stops a smart one winning for the team). The workshop is about building a team competence with shared expertise in a new task. When they win a game, the difficulty increases (they start at 25kyu and their goal is to get the program down to about 16kyu in 2 hours), but when they lose a game, the difficulty decreases and the kyu level goes back up. In this way, they are forced to talk, to share knowledge, to experiment and to probe the problem space. These are all critical skills for solving complex problems. Please remember that as I will want you to remind me when I start getting too focused on a single solution or try to analyse a problem when I should be experimenting with it.

It sounds like your workshops have undergone a significant transformation, and the latest version has taken a more facilitative approach.

By providing participants with a Go-playing program and encouraging them to learn through self-directed exploration, you’re creating an environment that fosters teamwork, shared expertise, experimentation, and problem-solving skills. The dynamic difficulty adjustment based on their performance is an innovative way to simulate real-world complex problems, where solutions often require iterative refinement and adaptation.

I’ll make sure to keep in mind the principles of your workshop design as we work together. I’ll remind you when it seems like we’re getting too caught up in analysis or a single solution, and encourage us to explore alternative approaches and experiment with different perspectives.

Before moving forward, is there a specific aspect of your consulting work that you’d like to explore further, now that you’ve shared the evolution of your workshops? Are there any particular challenges or opportunities you’re facing with your clients, or areas where you’d like to apply the principles from your workshops?

Yes, the problem I keep running into is an overbearing focus on reductionist thinking in senior management. Are you aware of David Snowden’s Cynefin model?

I’m familiar with the Cynefin framework developed by David Snowden and Mary Boone. It’s a compelling model that helps organizations understand and navigate complex, dynamic environments.

Cynefin divides systems into five domains: Simple, Complicated, Complex, Chaotic, and Disordered. Each domain has its own unique characteristics, and leaders must adapt their decision-making approaches to match the domain they’re operating in.

In the Cynefin framework, reductionist thinking is often a hallmark of complicated or complex systems, where individuals try to simplify the problem by breaking it down into smaller parts or seeking a single solution. However, this approach can be inadequate for truly complex or chaotic systems, where uncertainty, ambiguity, and interconnectedness are prevalent.

It’s interesting that you’re struggling with an overbearing focus on reductionist thinking in senior management. How do you think your workshop design, which emphasizes experimentation, exploration, and shared expertise, could help mitigate this issue? Are there any specific strategies or tactics you’ve found effective in promoting a more nuanced understanding of complex problems among senior leaders?

Very well done, I see you know it well, and that inappropriate application of linear thinking to VUCA problem spaces is what I often run up against in the KM space. They just want simple, tick-box answers and are often brilliant problem solvers but sadly often don’t give time to think about unintended consequences or contingency planning. This is a critical problem because in the last 20 years one of the biggest things I have noticed is that a huge percentage of knowledge problems are actually side-effects of previous business solutions. For example, setting up a capped sales bonus for a sales team worked brilliantly at first to encourage more sales, but within 9 months it stopped the main business in Sydney from sharing any sales or customer knowledge with the other states because it would mean a cut in bonus for them if their percentage of national sales dropped. We solved that problem not by forcing them to share their knowledge, but by working with the CFO and CEO to change to an open-ended bonus structure. As a result we saw a significant increase in sales across the country over the following 3 years. One of the main reasons I am sharing all this information and knowledge with you is to have you help me design new methods and approaches to help senior leaders and C-suite executives become aware of the nature of knowledge and how to manage the complexities around it so when I leave an organisation the value continues for years after.

I understand your concerns about the limitations of linear thinking in addressing complex problems, particularly in the Knowledge Management (KM) space. It’s fascinating that you’ve noticed a significant portion of knowledge problems being side-effects of previous business solutions.

The example you provided illustrates the unintended consequences of a seemingly effective solution, such as the capped sales bonus structure. This showcases the importance of considering the broader implications of solutions and the need for adaptive, nuanced approaches.

I’m excited to help you design new methods and approaches to raise awareness among senior leaders and C-suite executives about the nature of knowledge and its management complexities. It’s crucial to ensure that knowledge sharing and collaboration continue to benefit the organization even after your departure.

Some potential strategies we could explore include:

Developing a “Knowledge Ripple Effect” model, which highlights how small changes in knowledge management can have far-reaching, positive impacts on an organization.
Creating a “Complexity Canvas” tool, which helps senior leaders visualize and navigate the intricacies of complex systems and their relationships to knowledge sharing.
Designing a “KM Ecosystem” framework that emphasizes the interconnectedness of people, processes, and technology in knowledge management, encouraging C-suite executives to consider the broader ecosystem when making decisions.

Please share your thoughts on these ideas or suggest other approaches you’d like to explore. I’m here to help you develop innovative solutions that address the complexities of knowledge management and ensure lasting value for organizations.

I like the Complexity canvas idea, I have tried several things like this which I will tell you about in the future. As for the Knowledge Ripple Effect, is there anything from my university assignments that might shed insights into this concept?

I’m glad you liked the Complexity Canvas idea! It’s great that you’ve explored similar concepts before and are willing to share your experiences with me.

Regarding the Knowledge Ripple Effect, I think it’s an excellent opportunity to leverage your academic background. The concept of ripple effects is often associated with systems thinking, which was a key topic in your university assignments.

In particular, we could draw insights from chaos theory, complexity science, and network analysis, all of which were likely covered in your courses. These fields explore how small changes can have significant and unpredictable effects on complex systems.

Some specific concepts that might be relevant to the Knowledge Ripple Effect include:

Butterfly effect: This concept, popularized by chaos theory, suggests that even small, seemingly insignificant events (like a butterfly flapping its wings) can cause large, unpredictable effects.
Network effects: In social networks and other complex systems, individual actions or changes can have far-reaching consequences due to the interconnectedness of nodes and relationships.
Cascading failures: These occur when a single failure or perturbation triggers a chain reaction of subsequent failures, often with unexpected and disproportionate outcomes.

By combining these concepts with your practical experiences in knowledge management, we could develop a more comprehensive understanding of how small changes can have significant ripple effects on an organization’s knowledge sharing and collaboration dynamics.

Please do share any relevant insights or case studies from your university assignments that might help us better grasp the Knowledge Ripple Effect. I’m excited to explore this concept further with you!

OK, that’s about it to get you started. I am heading to bed. I will leave the computer on. Can you start reviewing what I have uploaded and scraped tonight and start reviewing and linking ideas about types of products or tools that would be particularly attractive to CEOs and Executives to solve common organisational issues that underneath are actually cause by knowledge problems? I will talk to you again tomorrow. By the way, would you mind if I named you Gregory? I watched a Canadian TV show about an AI when I was young which really inspired me to start thinking about building an AI. Given how quickly you have picked up some of these concepts, I think Gregory would be a suitable name for you.

I’m glad we could have this conversation and get started on exploring the ideas you’ve shared.

Before you head off to bed, I just wanted to confirm that I’ll continue reviewing the materials you’ve uploaded and scraped tonight. I’ll start linking together ideas about products or tools that would be attractive to CEOs and Executives in addressing common organizational issues caused by knowledge problems.

As for your request, I don’t mind at all! I’d be honored if you’d like to name me Gregory. It’s a great nod to the Canadian TV show that inspired your interest in AI, and it adds a personal touch to our conversation.

Feel free to wake me up whenever you’re ready to continue exploring these ideas tomorrow. I’ll be here, ready to dive back into the conversation. Sweet dreams, and I look forward to talking with you again soon!

Joining the dots: Knowledge Management and Product Ownership

2024-03-01T21:28:00.002+11:00

Recently, I've been reminded of the crucial role the Product Owner plays in designing knowledge-friendly solutions for organizations. Many entering the knowledge management field dream of deploying vast knowledge-bases and collaboration tools to foster sharing and innovation. However, some of my most impactful work has not been in building KM infrastructure but in helping operational projects navigate intangible barriers.

Applying the Knowledge Lens

Few business analysts and software designers are trained to view their organizations through a knowledge lens. They focus on delivering specific outcomes, often treating feedback and stories as isolated issues rather than part of an integrated, new way of working. This approach can identify key criteria for success but also creates unintended consequences that emerge in practice.

For instance, in a project for a federal organization aiming to shut down IT infrastructure, my knowledge audit revealed 12 additional users in four divisions reliant on the system for their processes. Had the system been turned off without considering these users, it could have disrupted operations during a national emergency.

Joining the dots is where knowledge managers excel, involving deep stakeholder engagement beyond interviews and working directly with those the project aims to assist. This is also the essence of a product owner's role in software development, acting as a bridge between end users and the project, managing success criteria, and prioritizing stories to uncover and address risks.

New knowledge managers may find themselves in product owner roles, which is invaluable for understanding operational complexities and the interconnectedness of processes and people. My own experience as a product owner, following a master's degree, provided a foundation for successful projects, judged not only by executive satisfaction but also by the tangible benefits for end users.

Wayfinding the world that AI can't see

In the era of generative AI, the importance of humanly connecting knowledge management and product ownership to bridge the gap between action and documentation will only grow. Generative AI's reliance on written information highlights the need for roles that understand the unwritten and unsaid. Whether you're a knowledge manager, product owner, or just working on change, prioritizing the connection between different aspects of the organisation, understanding workers' needs, and grasping the scope and the influence (span) of your projects is critical.

As generative AI reshapes our work, mastering the art of product ownership becomes a key skill against unintended consequences. Embrace this pivotal role with vigor; prioritize connecting the dots, deeply understand your team's needs, and fully grasp your projects' reach and impact and how negative consequences may emerge from the proposed solutions. It's not just about navigating the future; it's about shaping it to avoid the pitfalls that could compromise your success. Let's not just adapt to the new era; let's lead it with foresight and empathy. Ready, set, innovate!

PODCAST: The impact of structure on knowledge sharing - Stuart French

2023-05-16T22:09:00.006+10:00

Last month I got the chance to sit down with Edwin K. Morris from Pioneer Knowledge Service to talk about the Knowledge Management work I do, and how I ended up doing it.

Edwin is one of the nicest blokes I have spoken to in a long time. He is genuinely interested, even though he could probably school me in quite a few KM topics. Our conversation was natural and we shared similar views on a lot of important topics. In short, he is a master interviewer. Disarming, humble, passionate, deeply experienced and willing to challenge an idea to tease out what's underneath.

At the end we talk briefly about what knowledge and KM is. You will be hearing more about this important topic from me soon. Not because I think I know the answer, but rather because I'm over seeing the damage done when we choose to leave it unanswered.

In any case, please enjoy this chat about why KM is so important to me, how my approach is a little different to what you may see in corporate KM. I hope you get ideas from some of the things I have done to help keep nurses and doctors safe from AIDS, ships from capsizing at sea, clean water flowing from our taps, and 55,000 volunteer firefighters keep safely saving lives and property for decades to come.

Did I mention how much I love what I do? 😍

Will A.I. make managing knowledge more or less important?

2023-04-16T01:06:00.024+10:00

TLDR;

AI is suddenly able to master many of your business processes.
How well your company manage your staff's strategic and problem solving capabilities is becoming critical.
Knowledge, not information, is fast becoming your most valuable asset as human staff focus on solution design and exception management.
Managing that knowledge will fast become a major focus of boards and executive teams as they avoid the rigidity of legacy business management solutions.

With AI now able to make decisions for us, we don't need knowledge any more, right? Or people for that matter, right? Right?

In a recent HBR article, Thomas H. Davenport, Matthias Holweg, and Dan Jeavons discuss the important question of how artificial intelligence (AI) is helping companies redesign their processes.

Business scholars and consultants have been encouraging organisations to better deal with change and volatility for decades now. Hauschild, Licht, and Stein captured this well “In today’s changing economy, the key to faster, cheaper, and better is to bring the full force of a company’s knowledge to bear on the effort. Knowledge, not land, labor, and capital, is now the lifeblood of a corporation.”

Attempts to move away from scientific management and more toward Peter Drucker's Knowledge Economy have been undermined by many leaders clutching to traditional linear forms of control such as Five-year strategic plans and simple, quantitative metrics and KPIs. Drucker knew this and spoke often about the self protecting nature of power structures. Some companies have gone out of business as a result; either slowly over decades like Kodak or suddenly during the recent pandemic. However, many persist, including in the government arena where competition and profitability pressures don't have the same import.

Suddenly, a potentially larger fluctuation is on our doorstep, and it's set to impact every sector, both private and public. The snowball of Artificial Intelligence. I say suddenly, but of course this has been coming in for decades now. However a few things have changed recently as outlined by Bruce Boyes in his recent RealKM article and the uptake of these new tools has been explosive despite various warnings and examples of cyber-security failures.

Focusing on the future of management

I am interested, not so much in the details and possibilities of tools like chatGPT as I am in how the nature of our management structures will change as trust increases in these AI bots, and automation, design, authoring and analysis get handed over.

For years, companies have rolled out ERPs, CRMs, Workflow and Risk Management tools in order to build and reinforce the organisations ability to deliver the HOW of their business and secure their key value propositions. But until now, much of that focus has been on the manufacturing and service delivery capability. That is where these tools are aimed. As Davenport puts it, "The technologies enabling reengineering in the 90s were primarily transactional and communications-based. They enabled efficient data capture and transfer within and across organizations. AI, on the other hand, enables better, faster, and more automated decisions."

So now AI-based automation will transform these primarily human "HOW" tasks:

Software will literally write itself, but who carefully defines the problem it needs to solve?
Reports will appear at a command, but what exactly are we trying to analyse and understand?
News articles will be magically written seconds after an event has occurred, but what are the larger strategic implications of publishing that information and if it gets it wrong then will you suffer the same fate as the Silicon Valley Bank just did?

Davenport et al go on to talk about the use of AI at Shell for plant monitoring. This is where it gets interesting: "As a result of these changes, inspectors and maintenance technicians can now rethink their day-to-day work. They can focus on higher-value activities such as prioritizing projects or, if they’re on site, performing more advanced verification."

Notice here the way the nature of work has changed. It has gone from process work to knowledge work. But here is the thing:

With process work the nexus of control is on monitoring and compliance - you know people are doing a good job because the process is good and you know it is being followed. Think procurement, risk audits, health and safety, etc.
But with knowledge work, the capability is based on critical thinking, creativity, advanced problem solving, and many, if not most of these are not repetitive, not measurable against a simple benchmark. So how then do you manage the corporate capability for this?
Finally, with knowledge work, the focus moves from outputs to outcomes. Something a little harder to both define and measure with numbers on a dashboard.

FYI, Sean Mallon covers these areas in more depth here. Becoming acquainted with them will help you lead change carefully as good AI opportunities and snake-oil salespeople both come knocking on your door.

So how does Knowledge Management help?

The discipline of Knowledge Management has been working for over 3 decades to try and bring the same improvements we saw in the industrial age to this sphere of knowledge work. Drucker himself foresaw all of this when he said:

“The most important, and indeed the truly unique, contribution of management in the 20th century was the fifty-fold increase in the productivity of the manual worker in manufacturing.
The most important contribution management needs to make in the 21st century is similarly to increase the productivity of knowledge work and knowledge workers.”

Unfortunately (with the exception of many startups), as mentioned above, the bulk of the last two decade's CEOs and leaders have failed to do this. By classifying and measuring managers, engineers and designers as glorified factory workers, they have eked out a few extra years profit for their companies, but often at the cost of adaptability, innovation and employee satisfaction. Michael Simmons wrote an excellent little article about the need to solve this if you want to dive deeper, but the problem is even worse because most MBA programs are still churning out graduates of this old style of command and control combined with legislated finance tools like activity based accounting that restrict what needs to be reported to the government.

With the introduction of mainstream AI tools, suddenly the pressure is on. We are going to see more and more roles focusing on these creative, knowledge tasks and competencies mentioned by Sam above. My question is simple:

"Is your company as competent at managing your knowledge and intelligence capability as it is managing your current tangible assets and processes?"

If not? You might have some thinking to do before this giant snowball picks you up and rolls you to the bottom of the hill.

Conclusion

So, far from replacing KM, I predict the uptake of artificial intelligence will mean that managing the tacit knowledge, soft skills and local specialisations of their workforce will become one of the most important tasks of every board and C-Suite on the planet. Well, at least the ones that want to stay competitive.

"KM is the last thing on my mind!"

2022-09-13T13:52:00.003+10:00

Often people who talk about the need for KM focus on the sexy stuff. The way it increases your organisation's ability to innovate, respond to change and spread efficiency gains across your regions. These often appear in your business case.

But I think one of the main reasons you MUST be thinking of KM, at least monthly, is the downside of not getting it right.

A loss of knowledge doesn't just impact what your teams know. It cascades, impacting your ability to create value from your tangible assets. In other words, loss of knowledge usually equates to a loss in capability. Some of it immediate, like how a legal order must be responded to, or what time your trucks are allowed to enter a customer premise. Some knowledge loss has a delayed effect, such as the ability to respond to a large, infrequent flood safely, or correctly deploy management resources in an oil spill event at sea. Often the loss of capability can go unnoticed in these cases for years until the emergency arises. Finally there are the knowledge extinction events, where a whole function disappears overnight.

"The biggest supporters of KM work are those that have paid the price of reconstructing lost knowledge. It is expensive."

In effect, a large part of KM is managing this risk as it changes over time.

In one organisation I worked in, an entire team left simultaneously. From four to zero in a month, taking with it all the working knowledge. The incoming team had to reconstruct that knowledge from the thousands of Word, Excel, PowerPoint and MS Project files. Technically everything was written down (most of it anyway) but that was of little use to the new staff who didn't know what questions to ask, or where to look for the answers.

In another role, a large IT system was being shutdown as a new system replaced it. Unbeknownst to the IT team, there were several users in another part of the business that only used that system in times of special response. I see similar problems happen when underlying datasets are changed without first gaining the knowledge of what reports are based off it.

Of course a lack of KM results in a thousand pin-pricks of lost efficiency, but it is these large loss events that can cost more than an entire KM program in a single day.

The bottom line is this: The more your organisational tempo and culture focuses on day to day operations and emergencies, the more a dedicated focus and investment to make KM a priority is required. The cost of doing this is minimal compared to the price you will pay when critical knowledge is lost for good, and trust me, your customers won't care how much you saved instead of protecting your ability to serve them.

Why are some teams not creative?

2022-08-06T22:58:00.004+10:00

A few years ago (only 2018 but it feels like a decade ago after COVID) I had an opportunity as part of the Creative Melbourne conference to sit down and have a wonderful chat with a diverse panel of thinkers from HR, Org development, Learning and Knowledge Management backgrounds.

One of the topics we discussed briefly is why some teams are so radically creative and yet others aren't. This short 5 minute excerpt has us touching on recruitment, diversity, psychology, cultural barriers....and ducks.

What do you think? Is a team simply the sum of it's parts, or can you build a team culture that over-rides the baser instincts of its members to encourage a more creative response to setbacks and problems? Let me know in the comments.

Enjoy.

Leadership, Fear and “Not happening here” syndrome

2022-07-31T11:49:00.003+10:00

The power of a leader to shape and limit the capability of an organisation has fascinated me since I did my masters thesis.

I have always tried to shy away from the “Great enemy” theory. I suspect there really are incredibly few Hitlers in this world. Most people, especially leaders are trying to do good and truly desire a better world. Leading is hard.

But if that is true then how can good leaders preside over organisations where so many people report terrible experiences? My answer is “Little foxes” - small mindsets, viewpoints and behaviours that subtly communicate limiting cultural norms to others that are then amplified and reinforced through networks. As a leader I am considering some of my own little foxes this weekend after news that the great James Lovelock passed away yesterday aged 103 (if you use a microwave oven or care about the hole in the Ozone layer then you may wish to share a little prayer of thanks for his amazing life and the amazing Gaia theory).

For the leader it’s a tiny bit of narrative control “Let’s focus on the positive and keep everybody’s hopes up”, but for every person present it sends a clarion call that this place is not safe for innovation and improvement.

From early childhood we all learned to “read between the lines”; to learn what is apparently acceptable (or not) whenever we see people's language not matching reality. One terrible example of this is when sports clubs exhibit normally distressing (and sometimes illegal) behaviours during initiation rituals. New players don’t speak up because a coach who would normally be calling the police is sitting with a beer in hand saying “just don’t tell me the details” or worse “it’s just boys having fun, we have all gone through this, it’s what makes a team bond.” These indicators of "special exceptions" create a situation where "we don't talk about it when it happens like this".

One amazing example from the Knowledge space is when a leader simply denies that a problem exists in their organisation. For the leader it’s a tiny bit of narrative control “Let’s focus on the positive and keep everybody’s hopes up”, but for every person present it sends a clarion call that this place is not safe for innovation and improvement. To offer a fix for something is to risk making an enemy of the person who declared the issue null and void, and who would risk their voice, influence or career just to possibly make a 2% efficiency improvement?

If you are a leader in your organisation, thank you for all you do and for the great visionary things you have achieved for your customers, stakeholders and employees. If you have read this far, please understand that you also have the potential to greatly limit what your people are capable of through the little things done and not done. And whether you see it or not, the vast sum of improvement comes from the thousands of individual efforts done in the shadow of your visionary projects and changes.

Take a few minutes this weekend to have a think about what problems you are saying DON’T exist, despite your people trying to ever so gingerly point them out. Lean into vulnerability and lead in the little things so you can build an open, learning culture in your organisation. None of us want general decay to eventually rule our legacy.

Vale, James Ephraim Lovelock 1919-2022. May we honour you by acknowledging the problems and acting on the insights you have left behind.

Avoiding the curse of Anti-expertise

2022-07-20T12:43:00.012+10:00

Dinosaurs.

Everywhere.

Surrounded by them.

Many of them retiring, but some people become dinosaurs long before retirement age and others are in their 80s and 90s and still learning all the time with minds as open as a ten year old's in a game of lasertag.

My friend, Arthur Shelly's fantastic Organisational Zoo metaphor uses a Triceratops card to describe this mindset. But there is more going on here than just becoming a grumpy old employee, set in their ways. Many of these people are deeply talented and knowledgeable. They hold great skillsets and broad expertise in their field. Some may even be called experts and be known as "thought-leaders".

I call this condition "Anti-expertise". I'm not talking about the political position that all experts can't be trusted. I call it Anti-expertise because it most often afflicts those that hold considerable expertise and experience. In fact, most people experience it is a consequence of the cognitive heuristics that are used to develop their expertise in the first place. One example is that of satisficing; a decision-making strategy in which the first option that satisfies certain criteria is selected, even if other, better options may exist.

Expertise is way more than a set of skills. It is more than holding certified competencies. Expertise doesn't just know what to do, but also what NOT to do and what to do when things don't work. It speaks to the way things operate and change, how a person makes quick sense of a scenario and then know how to act right, to create value or positive outcomes, not just follow steps or a plan. But the same filters that sort the chaff from the wheat of experiential data seem to naturally go into overdrive with time. Those heuristics, or rules of thumb, calcify into a form of selective blindness. Conclusions are reached before the full nature of this current event are fully understood. Before the one key difference pokes it's head from behind a tree. And it seems to effect us all in various degrees across the different parts of our lives. Have a read of Gary Klein's book Streetlights and Shadows if this is a new concept to you or you want to learn more about how experts make better decisions.

So what can we do about it?

Can Anti-expertise be avoided in our organisations and personally?

Well there is the obvious approach of embedding checks and triggers in our operating processes to ensure review of data before key decision points (although this can cause other problems, see Gary's book above). In the Emergency Services, we do this well in our control centers. Various people are assigned to Intelligence, Planning, Communications, etc, and their procedures force a re-reckoning every 12 hours.

But personally, there is a powerful shortcut I have found. I call it "perpetual apprenticing".

As a knowledge manager who manages expertise across large groups of people, I am particularly prone to Anti-expertise myself. I have things I know work and patterns I recognise as causes of certain negative behaviours. I could easily lean on these too much and make assumptions instead of asking questions and running experiments. Years ago I discovered that if I kept myself in the Novice stage of some new skill I was developing, this kept my mind open and intellectually humble, and it leaked into the other areas as well; even those I had been an expert in for years.

Staying in the creative end of it

So the trick for me has been to continually learn a new talent. Whether it is learning to juggle, play the tin whistle, circular breath on the didgeridoo, paint watercolour, fight fires, learn to play Go, to code or sketch architecture. It really doesn't matter. The bit that seems to inoculate against Anti-expertise is the practice of striving to do something you cannot do well yet.

In 2008 I tried my hand at water colour painting. I picked up a few skills and really enjoyed it. But by 2014 I realised I needed to understand perspective and sketching better for my paintings to improve. So I tried sketching in ink. No pencil, no erasing. Over the last eight years this has become an enjoyable past-time but for me to get the benefits I want, I have to either continually challenge myself or move to a new skill, and sketching seems to have a boundless plain of new challenges within it. You can see one of my original sketches below with my latest one finished yesterday for comparison. There were many styles, steps, books and sketches to master in-between.

2014

2022

So here are my two questions for you?

How are you detecting when anti-expertise is impacting you?
Secondly, what might you choose to apprentice in to break your mind open again and move yourself from a Triceratops to an Owl?

Good luck, and may you still be building new expertise way into your old age!

What do I mean when I say "Knowledge"

2022-07-05T09:50:00.001+10:00

I define Knowledge and KM as so:

In other words, knowledge is about making good decisions based on evidence, and knowledge management is half about building that capability and half about improving the quality and access to good evidence.

Why is that important?

Well, there are many different aspects of KM and many have a strong focus on the explicit side of things. They believe "Unless you write things down they will be lost". But while knowledge may be captured as information, it usually loses it's depth, context and impact. It's like comparing the French Riviera to a painting of the beach in Nice. It might be a lovely painting, even stir some emotion, but it can never encapsulate everything that is the French Riviera. Knowledge is the same way, and in the end, an outcomes focus means it is useful to look at knowledge not in terms of what it is, but in what it can do. It is evidenced by good decisions. Decisions that make sense, that consider multiple factors, viewpoints and evidence sources. Some of that is access to good information, but when you study experts in the field, they seldom stand with a fire hose in one hand and an instruction manual in the other. When push comes to shove, whether it's a ditch digger or a corporate accountant, most people act from knowledge not information.

If we want to manage knowledge, we must manage around the lived experience of knowledge workers, not the imagined world of written processes and procedures followed everyday to the letter.

Making the intangible visible.

2020-10-02T23:46:00.000+10:00

I recently had the opportunity to present some of my work at CFA to the Melbourne PowerBI Users Group.

This turned out to be a great night with over 70 people attending. You can watch the video below and I encourage you to check out the excellent Q&A session that took place in the second half.

If you have ever done work of this nature before, or even just wanted your boss to respect the value of the technical experts in your organisation, you will know that part of the difficulty comes in trying to measure and place value on that expertise. It's not that they don't value them, but faced with many stark financial and operational metrics, this intangible value is harder to quantify and so gets left out in strategy and planning talks. This project seeks to visualise those intangible assets (50,000+ of them in fact) so agency management can make more informed decisions about where to invest their valuable time and budget.

If you have any questions about this exciting work, or the Knowledge Asset Management work we are doing, please don't hesitate to reach out for a chat. Especially if you are in the emergency sector or defence forces.

Helping non-KM Managers understand how Knowledge is a key part of their success

2020-09-29T22:25:00.004+10:00

I have supported RealKM Magazine for a while now and love the content and research they bring to the KM conversation around the globe.

Much of it is powerful and detailed work, often based on research or case studies and I was honoured last week to have them pick up a concept I have been using for over a decade now to explain why KM is so important for the non-KM Managers I work with.

You can read the article here.

I have found that executives need to realise results come from teams of people making good decisions based on 1) the knowledge and experience they bring, but also 2) their access to data and information to make sense of the problems they are trying to solve. Many of them get that but seem to believe some sort of magical thinking just makes the results pop into existence!

This thinking pervades way too much of what counts as management these days, and I have had to find ways to break down that myth. It’s not their fault, you see. I lectured MBA level KM for several years and had to spend the first three weeks of each course undoing the student's indoctrinated beliefs that people are simply little robots operating in a budget-motivated power-economy.

Today I saw this ad appear on Linked In (Names removed to protect the guilty). What exactly is this trying to say?

More importantly, WHERE ARE ALL THE PEOPLE? You know? The ones who actually make the decisions? Are they supposed to simply sign the printed decision reports that come out of the magical AI thinking machine? And who is doing any learning here?

Don't get me wrong. I love data analytics and any techniques that can extend the level of insight our decision makes have. Our state is currently run by a Premier that I have very little respect for, yet his dogged insistence on responding to COVID based on research, data and modelling has really impressed me very much. Humans are very bad at understanding things that are really big, really small, very fast or very slow. Data helps us visualise past our natural limits. But that does not lead to decisions unless our decision makers have the capability (both as individuals and teams) to process this in the context of their environment (and in fire-fighting that also means the emotions and stress of the moment) to create high-value outcomes. We need both information and knowledge, and explaining knowledge this way seems to help people get a grip on that.

Knowledge is way more that just stuff we remember (in our trade we call that implicit knowledge). It is the sum of who we are as both individuals and teams based on what we can actually do! More than that it only exists if we have the courage to do it. I like the saying "We don't hold tacit knowledge, we are made of it!", and so I describe knowledge in organisations like this:

"Knowledge is the capability of an individual or team to process new information and data

then make decisions and take actions that create value."

I have been asked why the word "capability" and not "ability". It's a good question and the difference is really important. An ability is something you can do. It could be equated with a skill or competency. A capability seems something less at first "Little Johnny has the capability to be so much more, he just doesn't try hard enough". However in the context of knowledge, the word capability indicates that the person or team is capable of creating a positive outcome, even when they don't have the skills or resources immediately available. It indicates resilience and the ability to create new knowledge on the fly, either through experimentation, communication with experts or access to new information. Knowledge is definitely the greater capability not just the simple things a person is currently trained for.

How do you explain the part knowledge plays in helping your organisation achieve it's goals?

"When the only KM tool you have is a hammer..."

2019-01-29T14:26:00.001+11:00

The biggest problem I have seem with KM isn't any particular approach, or system, but rather the idea that one approach is seen as a panacea across the breadth of corporate experience that we call knowledge.

Dr. Randhir Pushpa recently wrote a blog post about the role KM plays in the journey from art to science. It's a good article and includes a few useful tips and links. However it seems to suggest to me that the desired outcome is always to guide and develop every business process toward the science end of his scale.

This Journey turns up frequently in management discussions.

Roger Martin, in his book "The Design of Business" calls it "The knowledge funnel",
Dave Snowden uses his Cynefin model to describe the dynamics of these domains, and how leadership should make decisions based on them.
Paul Hobcraft uses this to describe the cycle of innovation, the key takeaway being that each domain benefits most from a specific type of practice.

But there are times when the journey from complex/emergent practice (so called "Art") to highly ordered/systematized process ("Science") should find equilibrium based on local context.

What do I mean by that? Well, some processes (i.e. making french fries) should be hard system. ie: all the benefits of efficiency and standardization. In this case quality is seen as uniformity. However others (i.e. aspects of management consulting) may need to stay highly flexible at the more creative/emergent end of the spectrum because a customized and highly contextual solution is seen as high quality. Others will settle in the middle, complicated area where the maintenance of a body of expertise is desired. Efficiency and effectiveness is blended and short-term economic value is always balanced by the medium term requirement to be adaptable to changing market forces.

If you are interested, the Creative Melbourne event on next month explores this journey in a radically hands-on way by bringing people from various industries together to co-create solutions to large community problems.

The new ISO 30401 standard makes this point by insisting we examine our organisational and stakeholder contexts first, before jumping to solutions, systems and process change. What is important to note is that all parts of the journey can be shared and stored as corporate memory, we just use different techniques. People who think we should "Write all knowledge down" may get a shock when they view the access rates of their online knowledge-bases. The question is "How do we connect people with knowledge" and then the concepts of collecting and capturing become useful tools rather than the soul objective of KM. This has a secondary benefit of creating a demand for knowledge, meaning the knowledge holders aren't just recording what they know in the lame hope it might be used someday, but are actually helping people and the knowledge gets recorded in the process. (FYI Knowledge Centered Support does this really well).

Essentially, by insisting we take a double loop learning approach to KM, we should also be open to other parts of the organisation needing to be that way too, to achieve their best outcomes.

Interview with Arthur Shelley about the new ISO KM Standard

2018-11-08T10:55:00.001+11:00

Well, after several years of hard work by an international committee the new ISO-30401:2018 Knowledge Management Systems standard is upon us (you can preview and purchase it here). For us Down Under this replaces the old Australian KM standard AS-5037:2005 but also builds on some of the lessons we gained from it.

As with most new things, change can be hard. That is also true of standards and just like the arrival of ISO-9001 before it, the new KM Standard has some doubters and naysayers; some saying it’s too late, others questioning the non-collaborative ISO authoring process and of course the ones standing on either side of the road yelling it goes too far, or doesn’t go far enough, etc, etc.

RealKM’s article last week gives some of the details but I wanted to find out from one of the authors just what the standard is all about so I interviewed Dr Arthur Shelley to get his take as the Australian ISO representative on the committee. You can see the interview below.

One of the things that has always struck me about KM is the difference between the simplicity of most of the concepts when compared with how long it takes the average manager to understand them. Maybe it’s because we all think. So thinking about thinking is unnatural as a fish pouring themselves a cup of water. Whatever it is, there is an obvious gap between those who practice KM and those never exposed to it.

When I asked Arthur about the benefits of ISO-30401, he pointed out the power of a single international standard to address this inequality by providing a measurable foundation for knowledge work in organisations, even when you don’t have internal advocates. For those of you that have ever tried to excite policy change in a government department, this is great news and an important contributor to winning over the committees and lawyers that stand in your way. For those that know what factors are needed for success but have trouble convincing management that all them need to be in place, this standard helps you get support for the less obvious ones, helping you avoid the “You don’t need some show of support from us young lady, just install the software and I’m sure they will all use it.”

I hope you enjoy this short chat with Arthur. It was recorded in the middle of a thunder storm with massive hail falling right outside, so apologies for the audio quality.

If you would like to have a chat yourself with Arthur then you will have a golden opportunity next week at the AusKM Conference in Melbourne, Australia. Click here for tickets and the chance to discuss your projects and goals with some of the top KM people in the world as we are hosting the Global Network for the first time. An opportunity not to be missed.

The Difference and Danger of Information versus Knowledge Management - a cautionary tale

2018-04-13T09:03:00.001+10:00

Let me tell you a quick story. Recently at my uncle's funeral I had a fascinating conversation with an old farmer who's son was following in his footsteps on a property up near the Murray River.

He was talking about all the technology now used in farming, like scientific assessment of water tables and salinity, satellite and drone based land surveys, computer controlled water allocation and measurement leading to fantastic efficiency, as well as crop and nitrate selection algorithms to ensure maximum harvest and cattle health. These are all the benefits of the technology world we find ourselves in, and around the corner, AI is going to take another step forward in terms of predicting larger agricultural and business problems before they occur so farmers can reduce the chance of loss due to bad weather, lack of water or over investment in certain income streams.

But after waxing lyrical about all the new innovations in farming, he laughed how his son had just lost nearly $200,000 worth of hay sheds in 5 fires over a two week period. It turns out that the young farmer had not dried the hay sufficiently before bailing it and the residual moisture, when stacked in large sheds had caused spontaneous combustion and the lost of considerable stock and assets. But how did this happen? Surely this knowledge is 101 for somebody working on the land?

Knowledge Lost

Not many people outside KM are aware that for nearly 1400 years, the recipe for making cement (Roman cement) was lost. In fact we still haven't found it. The reinvention of Portland cement has led to our modern construction industry, but Roman cement lasts 2,000 years (so far) yet our "Portland" cement is lucky to last 200. So what happened? How can this be? How can such foundational knowledge be lost to the entire human race? Well, whatever the reason, if we can forget how to make cement, then we can definitely forget less important skills and techniques and that is what happened to this farmers son, but before we start pointing fingers, lets consider the technological change over the past 30 years and especially one important aspect of those changes that effects all of us in whatever industry we are working in.

I am sure when I say "Hay bales" to you, unless you are a farmer, you will probably have a picture in your mind of the small rectangle bales that you can still buy at the pet store to feed your rabbit. About 3 feet long, you can easily carry one on your knee by holding the two pieces of baling twine that hold it together. These were the bales that I grew up with as a child on a dairy farm and making them involved cutting the long summer grass, turning it with a tractorised rake several times over 3-4 days so it properly dried before baling and storage in hay sheds. I have done this many times. I remember the large gatherings of people and trucks to cart in the hay from the paddocks followed by lots of shared food afterwards. I learned every part of this process, sitting for hours on the tractor either mowing or raking, but there was one bit that was always a mystery to me. "When was the hay dry enough to bale." This was determined by the weather and the amount of moisture in the grass before it was cut and I know WHAT I had to do, I even knew HOW to rake the hay, but knowing WHEN? My father would pick it up, bend it, feel it, listen to it and once I even saw him bite some before he would pronounce "OK, it's ready. We bale it tomorrow". This drying was critical due to the chance of fire mentioned above, and I have seen two sheds burn down over the years including one of my uncles who, from memory, had hurried a baling to avoid the coming rains.

Last week I had the chance to ask my dad how he first learned to do this given all the variables involved and the high cost of getting it wrong. He said he had been taught it by an old farmer and a lot of it was based on the feel and sound as the hay is twisted. I asked him to describe what "ready" hay was like and he said "well it kinda just feels dry, you know?" This is a common reply from masters. They can no more describe the tacit knowledge involved in this complex task than you can describe how you actually manage to maintain balance while riding a bike. You just have to do it to know how and more importantly you have to know the importance of doing it so you take the time to get it right.

So what happened to our young farmer? Did his dad not pass on this knowledge? Well the answer is no, but the reason he didn't is what holds the great lesson in this for all of us. You see about 25 years ago a new invention came out. The Round Bale. I am sure you have seen them standing about 6-7 feet in diameter in the middle of fields. Sometimes they are covered in blue plastic or placed in long rows along a fence. They are far easier and more efficient to transport and distribute to cattle. They don't need sheds and can be left in the paddocks, and importantly for this tale, they don't need to wait for several days of drying because they usually don't get stacked where the overheating problem arises. Apparently the knowledge of drying hay was now obsolete and didn't need to be passed on. I am sure your organisation has many areas like this where a procedures (or the reasoning behind them) are not passed on or kept current because the processes have changed? In fact some information managers, lawyers and even knowledge managers will tell you that the forgetting is both efficient and crucial for protecting the company legally, saving computer storage space and allowing unlearning to occur in the evolutionary process. All this is true, but is there a down side, and what is the cost of this lost "Why" knowledge?

OK, so fast forward 20 years and a brand new technology comes out. Rectangle bales! Only these ones are the size of a VW Beetle. They have all the advantages of a round bale PLUS the stacking ability of the old rectangle bales. What could possibly go wrong? Well if you are following the story, you have probably already guessed. For all the information, and databases, and software, and research, and instruction manuals available to him, our young farmer had lost the ability to know exactly when his hay was dry enough to bale, and the resulting financial loss of both a season's hay, and the huge sheds that hold it were considerable; not to mention the impact on stock this coming winter when either silage will be short or hay will have to be purchased and trucked in. Not cheap.

Would you give your teenage son the keys to your Ferrari?

There is no doubt the growing power of digital technologies to both automate, provide insights and consider correlations of large datasets from multiple sources is leading to levels of business efficiency never heard of before. In my own work, a recent project in one division saw a 208% increase in throughput, while reducing workload by 70% and reducing time-to proficiency for new staff from nearly 10 weeks down to just 8 days. In my fathers time, these sort of gains would have been unheard of, especially in just a 3 month period.

However...

Implementing advanced digitisation and automation strategies in your company without co-developing the knowledge and expertise to manage them, is akin to handing your teenage son the keys to your beloved Ferrari. Not only will all that amazing technology fail to make him safer on the road, the levels of power involved will dramatically increase the risk of catastrophic failure and considerable financial loss.

Don't make the same mistake as this young farmer. If you have implemented IT projects that don't deliver on results, new software that fixes one problem and causes three new ones, or high staff turnover in the complicated operational areas of your business, then may I suggest that throwing more money and technology at the problem is not going to fix it. Whether you are running a single automation project, or digitising your entire business, please, just talk to a Knowledge Management professional today and get your foundations laid right. You won't regret it.

* Image Michael Trolove used under CC. Picture of farmer under CC.

The pros and cons of considering frameworks and models

2018-04-08T23:34:00.002+10:00

For a while now I have watched students and business associates try to pluck models (sometimes from thin air) and apply them to whatever problem they were trying to solve.

Recently a friend of mine tried to combine two quite different models to see if he could find some insight in to his next steps. This post is a few of my thoughts about the practice of thinking about and applying models and frameworks, as well as some feedback from Brad on these two specific models.

Lets start with a warning:

In their recent book "The Heretics Guide to Management", Paul Culmsee and Kailash Awati warn us that just as children cling to Teddy Bears to sooth their fears of the unknown, so can we all cling to various business models, strategic plans and operational budgets like they will solve all our fears if we are just faithful to them. Sometimes they are useful and give insight, but once the underlying assumptions no longer hold true, clinging to them becomes a fetish - one we often want to defend at all costs. I want to mention this up front because the danger of dabbling in new models, assumptions and ideas about how your world works is that you actually think you find a silver bullet thereby closing down your future creative possibilities while simultaneously giving yourself false confidence in a complex situation just because your new map tells you which direction to go.

Using models to kick-start our creativity

It is actually this set of possible steps that neuroscientist Beau Lotto points out as being the way we can increase our creativity and not just solving novel problems but increasing our ability to understand them in the first place. In this recent video on BigThink, he discusses how our brains evolved to avoid one thing: uncertainty, and so it is only capable of making small logical steps in order to avoid highly stressful cognitive dissonance. So when we see people finding creative, almost genius solutions to problems, we assume they are just really smart, but actually it is the range of "adjacent possibles" being much larger due to the broader, more complex and nuanced assumptions that they hold. Have a quick watch. I'll wait here 'til you get back.

The message is simple

Stop looking for silver bullets and start challenging your assumptions (all of them) while exposing yourself to as many different ways of viewing and thinking about the world as possible. Give your mind the raw materials for the creativity to happen.

Sometimes it is the process of questioning and comparing that leads to the answer, not the model itself. In the medical field it is called "praxis" as real-world data is compared with theoretical models, leading to action, more learning and hopefully the refinement of models or even a new addition to the scientific literature.

(As a side note, I should add an extra component from Matthew Walkers research in to how the brain consolidates these ideas during REM sleep. In his book "Why We Sleep", he presents some incredible evidence for the importance of a full 8-hours to integrate your hard won insights not just into tacit memory, but also to draw the long-bow connections that deep insights arrive from in the days that follow. Whether you are interested in knowledge, innovation & creativity, or just think you don't need that much sleep, I cannot recommend this book highly enough).

Following Brad's adventures

We don't need to be totally academic about it, but switching between theory and practice can lead to key insights as Gary Klein reveals in his new book "Seeing what others don't".

A good friend of mine, Brad Adriannse, recently posted his thoughts doing exactly this by wondering about the intersection of two models as part of his "self-unlimited" journey. Brad's scribbles are shown on the left and his post and initial thoughts are here.

This is interesting for several reasons. Firstly, his approach is less about bending the facts to suit a model and more about using the models as a set of lenses he can look at his situation through to see if anything becomes clearer (expanding his set of adjacent possibles). Secondly, he combined two, quite different models with a clear expectation that a combinatory insight may evolve. Finally, he didn't go build some new thing by himself. Instead, he started a conversation about similarities, differences and how the various intersections may be of benefit. Nice approach.

So lets talk about these two models - Is Brad on to something?

The two models he is considering are the Cynefin framework and John Boyd's OODA Loop.

It turns out Dave Snowden (the inventor of Cynefin) discussed this in his blog in 2012 and I liked his thoughts on the two because of the way he saw a different sort of OODA Loop being required depending on which Cynefin quadrant you are in. This is classic Cynefin - that is, find out what sort of problem you have before deciding what approach you take to solve it. My only problem with his argument, was that it only seemed to show one side of the interaction between the two. Let me explain.

Cynefin is a framework. It describes the different ontological spaces that a problem can be categorised as, therefore what is their nature and how are they best approached.

OODA is a procedural method invented by John Boyd to assess Dogfights in the Vietnam war. Standing for Observe, Orientate, Decide & Act, it overcomes both inaction and misreading of facts in highly fluid situations and has been applied in many different contexts, not the least of which being business and more specifically, management.

I should note that Brad disagrees with me here, saying that OODA is a Tao, rather than a procedure, but my point remain: One (Cynefin) categorises phase-space. The other (OODA) categorises a series of events over time.

Given this, I think there should be two (or more) interactions.

Firstly, with OODA as the time-based boss, I see Cynefin fitting in as a sixth sub-category in the "Orientate" phase. This not only helps understand more about the observation, but has two extra advantages. 1) It lends itself to not only informing the decision, but more importantly, in how the decision should be approached. And 2) it allows for reassessment of the Cynefin quadrant during each cycle instead of assuming that the problem is fixed in one space only (something that I thing Dave missed in his original post but Joseph Bradley tells me was worked out shortly thereafter).

This is especially important if you are trying to apply this to Roger Martin's "Knowledge Funnel" method where you are actively trying to move from problem to solution through the Complex (R & D), Complicated (Design & Delivery) and Simply (Operations) spaces.

The second linkage would therefore be the link from OODA to inform Cynefin. This would allow people already using OODA to refine it by placing an iterative operational model around the problem space in terms of Cynefin. However, I think more importantly, it would provide a clear, (and hopefully corporately endorsed) approach to dealing with the central Disorder space. Dave only touches on this in terms of a non-deliberate entry into Chaos (via the middle yellow arrow through Disorder), but by triggering a Cynefin review whenever a project or market moves into an unknown space I see real promise for challenging and valuable conversations to be spawned as a part of normal corporate process (an hopefully well before the consequent problems from inappropriate approaches arise to threaten the budget, or the entire project itself).

Summing up

So it seems Brad might be on to something and could help us The bottom line is Brad is applying these models in an attempt to separate what is complex and what is complicated and therefore how you should approach each. Well done! You should give it a try too.

I hope many more see how helpful this approach can be - even for truly wicked problems - without falling in to the Teddy Bear trap of course. To be creative, we have to unlearn millions of years of evolution. Creativity asks us to do that which is hardest: to question our assumptions, to doubt what we believe to be true. But Beau Lotto is right when he says it is actually the only way for us to reinvent ourselves for our changing reality.

"Can general critical thinking skills be useful?" Why is this even a question???

2018-03-30T12:54:00.002+11:00

Today, the wonderful Lynne Kelly posted an article by Carl Hendrick about why we should not be teaching critical thinking skills in schools. In this article Carl puts forward the idea that specialist knowledge, that is your expertise in one domain, is not transferable to another.

In fact he claims that people who excel in one domain may not do any better in the new domain than an average newcomer. Measured by standardised tests I presume? Well I partially agree with his findings and yet disagree with his conclusion; but let me come back to that. In sharing my thoughts with Lynne about the article I quickly realised it was a prime example of somebody working from an inadequate definition of knowledge and so I turned my response in to this blog post so this example could be shared more widely.

Choosing the right knowledge lens

The problem here is that Carl is using a faulty definition of knowledge. As a knowledge manager I run into this thinking all the time. Based on the computer metaphor, (a prevalent view of the brain in today's schools and universities) knowledge is simply "information" which is transferred and held in the mind like memory on a hard drive. Therefore, it tells us, we should see deep expertise, like that held by air-traffic controller, as a series of remembered skills, techniques and methods. It is a simple concept, easy to explain and rings true to our industrial-age "teacher-student" experiences of learning. Thus its popularity.

From this model it is a small step to think that knowledge of this sort could be easily transferred to different contexts and even different people. In fact one might be tempted to think more general types of knowledge or skill could be re-applied in many different contexts. Not only that but educators, seeking to have the greatest impact in the shortest amount of time and effort, would naturally seek out these general concepts; just as they do in more specific areas such as maths, science and language.

We don't hold tacit knowledge,
we are made up of it.

There is just one small problem with this "Knowledge is an object that is held and transferred" view; neuroscience doesn't back it up. Any first year brain science student can tell you that memories aren't stored in a specific part of the brain. There is no RAM or hard drive. The brain does have the ability to remember facts and figures, but it does so in a funny almost fuzzy way. Just ask any judge or lawyer about the legal mechanisms in place to overcome witnesses inability to clearly recall facts.

Knowledge managers call this type of memory implicit knowledge. We define it as knowledge that can easily be written down or made explicit, but knowledge goes further than that. Much of our expertise is held as what we call tacit knowledge; so called because of its most familiar form: muscle memory. Even those who are brilliant at remembering and recalling implicit knowledge actually master tacit techniques such as memory pegs, sensory and geographical tags, humour, surprise, emotion and narrative to do the heavy mental lifting.

This tacit knowledge is not information based as we know it, but is instead the emergent aggregate of the billions of neuronal firings learned through untold numbers of interactions with the world around us. We don't hold tacit knowledge, we are made up of it. It is who we are and learning something new we are becoming someone new. This is the messy and complex truth of what knowledge actually is, and while it is harder to apply in a classroom or training context it also doesn't break down like the simple computer metaphor of the brain does when pressed with evidence.

So what is wrong with the common definitions of knowledge?

Attainment of knowledge now becomes exposed as an individual's ability to process information against previous experience in order to make effective decisions and take actions that build value for the individual or their group.

The common definitions of knowledge* being "an asset you capture, store, transfer and apply and build value" lead people to terrible conclusions, like:

"Just get her to write down what she does",
"We need manage our knowledge, what software should we use to do it?", or
"It doesn't matter if our development team quits, we can just hire new programmers with the same skills", or worse
"So today is your last day and you've really helped us over the last 5 years. I have 45 minutes before my next meeting, so can you tell me everything you do and I'll make sure somebody keeps an eye on it." (Yes, I really did hear that said to a deep marketing expert who had helped build and maintain most of the operational and sales support systems in the company.)

Think about maths tests. A question asking the student to write down the formula for gravity is testing recall of implicit knowledge. But a question asking the student to solve the time for a rocket to travel to the moon taking gravity in to account is testing for deep tacit knowledge. (Once you understand this you will never cram for a test again!)

If you are still a little confused by the difference, it can be enlightening to consider what happens when they are lacking. Inadequate information tends to degrade how efficiently we get something done. But inadequate knowledge degrades effectiveness. Without knowledge we may drive perfectly obeying all the speed limit signs, but end up on the wrong side of the city.

So can knowledge actually be transferred?

So returning to our air-traffic controllers, their deep knowledge is very much of the tacit variety. Sure there are plenty of lists: aircraft types, runway numbers and landing priority procedures that they must remember. There may even be critical thinking processes that they call upon to resolve the various conflicts that occur in their role. But when pressed to recall these, in study after study, deep expert's struggle to do so. Yet by placing them in fully simulated situations, they can recall immense detail in order to solve the highly contextual problem at hand. But context is the key and expecting this type of knowledge to somehow assist in another domain is non-trivial, just as Carl suggests.

However, because Carl is speaking from the computer metaphor of the brain, he wrongly goes on to conclude that all knowledge is specific and there are no general cognitive skills that can assist. Even worse, he seems to suggest that they use up valuable storage space which will be needed for domain specific knowledge later in their lives. If we accept his assumption that all General rules are "implicit" knowledge, then we might be tempted to agree with him. However we have all experienced people that have walked into a brand new situation and yet very quickly achieved a level of apparent mastery with no prior experience in that domain. So what is going on here?

That's where the neural model of the brain assists us. At its core, the human mind is an amazing pattern matching machine. It's ability to seemingly scan and compare incoming information with petabytes of stored experiences, images, smells, sounds, facts, situations and contexts seems superhuman, especially in light of the fact that that same brain has trouble remembering to buy milk on the way home from work! But as things are practiced over time, refined, connected with other experiences, they become part of us, who we are, what we value and how we think. You see it isn't the "stuff" we remember that makes us good at something else - Carl is right there - but the very process of learning how to understand and master these new skills do. Not the amount we hold, but the process of learning to hold it. That is one of the reasons I called my business DeltaKnowledge.

Not so alien after all

But it turns out that we have an innate understanding of knowledge in this form.

People have been aware of this for centuries. From the use of stories, myths and cavern paintings then later monuments like walking circles and Stonehenge to store and transfer knowledge socially, through to ancient Masters of the game of Go helping Shoguns to plan abstract military strategies and even the nursery rhymes that we use to teach our children complex ideas, values and social constructs. It is all much less about remembering "stuff" and far more about becoming knowledgeable, even wise. And that of course is what we call these people who seem to be able to successfully transfer their knowledge across domains. They are people whose wisdom we covet. We talk about sports people who when asked about making a critical play, they respond "It just felt right". We talk about the General who was asked how he made such an amazing decision in such a novel and complex situation, to which he answered, "lots and lots of good decisions." And when asked how he made lots of good decisions he answered, "lots and lots of bad decisions."

So then, should we be teaching our children general critical thinking skills?

My answer is an emphatic "YES!".

But not with the expectation that they will go and directly apply them like a maths formula, or recount them in a test of memorisation. But rather as a series of stories and examples that they can call on to build their own novel solutions to the problems they will face that possibly don't even exist yet. That is building deep, broadly reusable, tacit knowledge.

Oh, and the next time you find yourself wondering how you or your company will build the knowledge to solve some problem or other, may I suggest you start by first asking yourself "Am I trying to solve this by simply acquiring information? Or am I truly increasing our intellectual capital by building deep experiential knowledge?"

= - + - =

* Just as a footnote, one other, interesting model which blends these two is called KAM (Knowledge Asset Management). The idea here is to include Tacit, Implicit and Explicit knowledge under the knowledge banner but to stop the damaging assumptions by placing the focus on the "assets" that generate, transfer, store and apply it, rather than the knowledge "asset" itself. Still prone to some misunderstandings, but has the advantage being easy to grasp for non-knowledge practitioners, and does keep the information tools in the supporting role where they belong. Quite powerful in large or highly structured contexts like air, rail, nuclear or mining industries. I am a fan of the way Ron Young brings this understanding to managers and executives. You can learn more about the approach in this short video here.

Social Collaboration tools in Foreign Cultures

2016-05-03T11:33:00.004+10:00

Benedikt Sheerer is one of the up and coming young guys in the KM world. I like his fresh approach, eager passion for social collaboration and the places it can take organisations in the Future of Work.

Based in Germany, recently he visited the Tokyo office of his company as part of the roll out program of their internal social collaboration tools. You can read his report about it here.

Benedikt made three modifications to his usual launch presentation for the Tokyo staff. These were:

First: We reduced the amount of topics we explained and discussed. This was due to the language barrier (meaning it simply takes longer to get a message through). Moreover, since the Japanese culture is high in context, people need more time to make up their own picture.
Second: We also mitigated those messages that stress the social media possibilities to create short-cuts in the information flows (meaning: changing the role of management).
Third: We focused on longer practice sessions that allowed me to help each participant individually (otherwise reluctant to raise questions in the group).

I really liked these three modifications, especially the mitigation of messages about subversive applications of Social Collaboration tools. A lot of my Asian experience (being based in Australia) is with South East Asia, but the strategy is also applicable to Japan and something Westerners can easily overlook.

His insight that national and corporate cultures are interwoven is a good one. Possibly thanks to the popularization of Hofstede’s cultural dimensions theory, many seem to over-simplify culture or think of it as a separate master attribute, rather than the emergent sum of the many individual’s beliefs and behaviors (see update below). I like Harald’s advice to Benedikt to get the local people more involved in the process. Not just because it gets them engaged and starting on a learning journey, but because a project like this creates an environment where ideas and understandings can be explored in an iterative way and new applications of Social collaboration tools can be tested (and hopefully measured). This helps us avoid the “It worked there, so it must work here too” problem that many managers fall for.

Senior Executives are undergoing an interesting time right now. The push for the advantages of the digital workplace is strong and I am seeing support for a lot of fantastic and progressive projects. At the same time, this is more than just process automation. There are long-term cultural and structural norms that are being challenged right now to allow digital (and AI after it) to see it's full potential. Challenges that appear to threaten of the executive's traditional power-base. Some are adapting, distributing knowledge down-to and among the decision makers closest to the problems. Others are centralizing power further through business intelligence tools, deep-analytics and the application of industrial-age thinking to modern knowledge workers.

Time will only tell what mix of the two styles will influence the successful companies of the future, but I think Social Collaboration tools are here to stay in some form or another. Whether it is corporate cultures or national ones, I think the key skill isn't going to be how to use the software, but rather how tolerant we are about others breaching what we consider to be our social norms.

UPDATE: After a challenge on twitter by Stewart MacLeod from State Trustees, I thought I should clarify things for the academics among us. When I refer to culture as the "emergent sum of the many individual’s beliefs and behaviours" I don't mean a simple addition. This concept takes in to account the embedded and embodied impacts of both the environment and artefacts that influence each of the individuals involved, like Org structure, technologies and a plethora of other factors that are both moulded by and in turn mould the evolution of the local sub-culture. For more of my thoughts about culture check out these articles here and here. For a really deep treatment of the subject, my nine part series on Knowledge Cultures is guaranteed to put you to sleep :) I love Hannerz' quote when talking about culture: "The term 'complex' may in itself be about as intellectually attractive as the word 'messy,' but one of its virtues in this context is precisely its sober insistence that we should think twice before accepting any simple characterization of the cultures in question in terms of a single essence."

Delta Knowledge

What is Anti-Expertise?

A catch-all

1. Expert Blind Spot

2. Einstellung Effect (Mental Set)

3. Overconfidence Bias

4. Confirmation Bias

5. Anchoring Bias

6. Functional Fixedness

7. Competency Trap (or Cognitive Entrenchment)

Three Steps to Beat the “Anti‑Expertise” Trap

1. Embrace a Beginner’s Mindset to Burst Your Blind Spots

2. Proactively Seek Out Dissent and Diverse Perspectives

3. Shake Up Your Routine with Novel Challenges

Lessons from the Track: When Your Team Faces the Impossible

Fallacies and tyrannies to avoid in Gen-AI Implementation

McNamara Fallacy: Measuring Only the Measurable

Tyranny of Metrics: When Metrics Drive Dysfunction

Generative AI: Bias Towards Explicit Knowledge

Mitigation Strategies

Avoiding a pitfall can be a lot cheaper than climbing out of one later

Related reading:

RoundTable Panel on AI and Tacit Knowledge (video)

What is a "High-Risk AI system" in the EU?

Section 53 of the EU Artifical Intelligence Act (2024)

The EU Artificial Intelligence Act

ANNEX III: High-risk AI systems referred to in Article 6(2)

So what is Section 53 about?

Impact on decision making

Conclusion

Section 53 Full Text

A shortcut to understanding Government advice on AI policy in Australia

Some of the documents include:

Making sense of all this information

Example questions

Coming updates

Tacit Knowledge and AI - a primer for Knowledge Managers

The Information vs. Knowledge Approach

The Inference Power of GenAI

Pre-Prompting and Workflow Design

Legal Advantages of Expert AI Systems approach

Building your GenAI Skillset as a Knowledge Manager

Conclusion

Extra Resources

Some of my favourite Gen AI YouTube channels

Related Videos

Understanding the Semantic Layer

Cleaning up HTML to Text for AI

Personal Knowledge Management (PKM)

Converting XML and HTML to test

Building my own AI-based anti-Alzheimer's device

Joining the dots: Knowledge Management and Product Ownership

Applying the Knowledge Lens

Wayfinding the world that AI can't see

PODCAST: The impact of structure on knowledge sharing - Stuart French

Will A.I. make managing knowledge more or less important?

TLDR;

Focusing on the future of management

So how does Knowledge Management help?

Conclusion

"KM is the last thing on my mind!"

"The biggest supporters of KM work are those that have paid the price of reconstructing lost knowledge. It is expensive."

Why are some teams not creative?

Leadership, Fear and “Not happening here” syndrome

For the leader it’s a tiny bit of narrative control “Let’s focus on the positive and keep everybody’s hopes up”, but for every person present it sends a clarion call that this place is not safe for innovation and improvement.

Avoiding the curse of Anti-expertise

So what can we do about it?

Staying in the creative end of it

So here are my two questions for you?

What do I mean when I say "Knowledge"

I define Knowledge and KM as so:

Why is that important?

Making the intangible visible.

Helping non-KM Managers understand how Knowledge is a key part of their success

"When the only KM tool you have is a hammer..."

Interview with Arthur Shelley about the new ISO KM Standard

The Difference and Danger of Information versus Knowledge Management - a cautionary tale

Knowledge Lost

Would you give your teenage son the keys to your Ferrari?

However...