This article and my own experimentation with AI reflect the challenge those of us who are not coders, researchers or AI experts face as we learn and use it.  We represent the majority of new users.  It seems that whatever I learn on Tuesday is outdated by Friday either because I’ve obtained new insights from someone with greater expertise or AI itself has changed.  We’re all trying to jump on a fast-moving train.  

That said, we’re in the early innings.  Large language models (LLM) such as ChatGPT and Claude astound us, but their predictions are limited by the universe of words—they can’t interpret real-world physics, emotions, or abstract reasoning in the way humans do^[1].  That’s coming fast and will make today’s LLMs seem as quaint as green screens in the early PC days.

AI usage is following a familiar pattern of technology adoption.  Early adopters use AI to replace tasks and processes with which they’re familiar.  Like any new technology, AI makes mistakes but when replacing existing methods, we know what good output should be and therefore can catch errors.  Replacement results also are easily compared to current results and improvements drop quickly to the bottom line. 

Since much of daily work is repetitive processes, replacement opportunities abound.  Banks use AI to analyze loan applications and assess risk.  Radiologists use it for first level screening of images.  AI helps customer service agents route calls, prioritize support tickets and with the latest AI agents, often solve problems without further human intervention.  

AI and Innovation

However, as valuable as replacement is, I’ve always been more intrigued by creating the next new thing than fixing today’s hiccups.  The challenge of innovation is that the outcome is not known at the start, nor is the market acceptance, cost, etc.  The more stretch and boldness there is in the quest, the less data, precision, and facts there are to help you get there.   

On the one hand, innovation might appear to fly in the face of AI since at its core, LLMs predict the next word based on ingesting trillions of pages of data from the past.  On the other, while AI’s predictions are based on past data, its ability to cross-pollinate ideas across disciplines can generate unexpected creative insights.  How to use AI as a tool to accelerate new product development starts with leadership.

In my past innovation writing, I’ve described the importance for leaders to modulate between loose, curious exploration and tightening via critical design, technology and business choices^[2].  If one stays too tight, innovation tends toward incrementalism.  If too loose, projects never end or reach the market.  Modulating between these two dimensions is artful and I’ve found it’s one area where experience really counts. 

This leads to my main point:  AI is a rapidly improving tool that will dramatically change work and society yet as is often said about cars, it’s the nut behind the wheel that matters.  How people use, control, and evolve AI’s use will spell the difference. 

Take the notion of loose-tight modulation in new product development.  AI’s ability to rapidly simulate using alternative constraints makes it possible to modulate within each of the five phases of new product development (NPD) faster and more frequently.  But, each iteration requires a person to frame a problem and provide clean data sources for it to do its magic.

In broad terms, AI quickens the cycles of learning that are the heart of innovation.  Prior to AI, loose leadership was biased towards early development phases whereas tight became more important as one shifted to operations and product launch.  Guided and used with discretion, AI enables simulation within all phases while concurrently expanding horizons through pattern recognition across seemingly unrelated data sets.

Here are some examples of where AI is currently used.  

1. Idea Generation: AI can facilitate brainstorming, find patterns in customer feedback, suggest variations and combinations of existing ideas, and generate insights from diverse data sources from industry experts to social media.  
2. Concept Feasibility and Market Research: AI-powered tools can simulate market reactions to concepts, prioritize ideas based on data, generate initial concept designs, conduct sentiment and competitive analysis to uncover insights on customer behavior that differentiate offerings.
3. Design and Development: AI can create multiple design iterations based on constraints, optimize designs for manufacturability/service using virtual prototyping, monitor competitors’ activities, identify gaps, and provide recommendations for differentiation.
4. Testing and Validation:  AI can automate test case generation, identify issues using predictive analytics and enhance A/B testing efficiency.  
5. Product Launch:  AI driven advertising platforms can draft, adapt, and personalize launch efforts, simulate alternative pricing strategies and accelerate marketing communications.

Steering AI

All the above requires human guidance.  For example, an exciting new potential with AI is to use “synthetic data.” Synthetic data mimics the characteristics, patterns, and distribution of actual data.  It was originally developed to address privacy concerns such as protecting personal health records in clinical trials. 

The term ‘synthetic data’ might seem paradoxical—after all, how can artificially created data produce real-world insights?  Yet, when carefully designed, it can simulate real-world complexity with surprising accuracy.

Its use has expanded to exploring “what if” situations^[3] that might be too costly, difficult or dangerous to try.  Autonomous driving companies such as Waymo^[4] train and validate their algorithms using synthetic data to simulate various driving scenarios, weather conditions, and edge cases without risk to people and cars.

Creating and using synthetic data requires skilled resources to define the problem space, set constraints, and establish success criteria to ensure it accurately reflects reality.  This also applies to AI in general.  While it can ingest massive amounts of data, that data must be cleaned, sorted and structured before AI can process it effectively.

Today’s AI makes mistakes, has embedded biases and isn’t capable enough to be trusted with providing a single “right” answer.  Frankly, it’s often convincing but sloppy and misleading.  Just as a hammer doesn’t automatically strike the nail each time, our expectations for AI need to be measured by its liabilities as well as its capabilities.  However, its today’s limitations which provide pointers for next generation AI.

What Can We Expect from the Next Generation of AI

What fast-moving AI capabilities will do in a year is beyond our sight.  Here are some realistic expectations that I anticipate.

• Incorporate Customer Feedback Directly into Design:  AI has the potential to integrate customer feedback loops directly into design processes.  Imagine if today’s CAD tools not only detected design flaws in real-time but could also integrate structured customer feedback—such as analyzing patterns in product returns or online reviews—to suggest refinements during design.
• Development and Engineering:  Today’s AI already writes code. A reasonable “what-if” is extending that capability across the entire development process to include full-stack development, autonomous hardware design, and self-correcting engineering systems.
• Marketing and Business Evaluation:  As we gain experience using AI, the breadth, accuracy and ability to use broader datasets should lead to more holistic financial modeling and risk assessments as well as predictive models.
• Launch and Post Launch Management:  As AI agents evolve, we can expect not just better prediction for market risks but also seamless monitoring and updating based on individual use cases.

Just as AI’s initial adoption has been skewed to replacement of existing methods, I suspect the application of AI to new product development will initially skew towards the later phases of NPD and slowly creep upstream.  As AI continues its rapid evolution, the winners in innovation won’t be those who simply adopt it, but those who learn to steer it wisely.

If you have a use case or personal experience with AI in new product development, I invite you to share your comments and experience with me.

^[1] Oxford English Dictionary estimates around 171,000 words currently in use.

^[2] Meyer, Christopher, Relentless Growth, Free Press, 1997

^[3] Lucini, Fernando, The Real Deal About Synthetic Data, MIT Sloan Management Review, Winter, 2022

^[4] https://waymo.com/blog/2018/12/learning-to-drive-beyond-pure-imitation_26

“It all started with a tweet on the afternoon of September 3rd, 2018.

‘Believe in something.  Even if it means sacrificing everything.’

Kaboom. Not long after, the full commercial—timed to mark the start of the 2018/2019 NFL season and celebrating the 30th anniversary of the tagline “Just Do It”—lit up the cultural discourse like no ad had done in recent memory.

People loved it. People hated it. People bought Nikes. People burned Nikes. People talked about it at home, at work, on the news. Everywhere.

It was divisive because it jumped on America’s biggest fault lines—race, patriotism, sports, and business.

“It doesn’t matter how many people hate your brand as long as enough people love it,” Knight told Fast Company last year. “And as long as you have that attitude, you can’t be afraid of offending people. You can’t try and go down the middle of the road. You have to take a stand on something, which is ultimately I think why the Kaepernick ad worked.”
                                                                             
Bold, yet Nike was completely in step with the racial tensions in our society.  And for business readers, they claim $163 million in earned media, a $6 billion brand value increase, and a 31% boost in sales.

Fast forward to today. 

President Trump’s administration is doing all it can to impede voting by mail in the middle of a pandemic.  One could list the facts to support this claim but it’s not necessary and more importantly, gives credence to yet another distraction. 

There’s no question that voting is the central tent pole of a government by the people.  Pull out the free and fair election timber and democracy heaves a sigh and collapses.  

Blow the whistle.  Stop the game. 

Let’s bring UPS and FedEx onto the field.  You want to know “What Can Brown Do For You?” or what FedEx means by “We Live to Deliver”.

Easy…deliver voter’s ballots.  Step up and stand alongside the U.S. Post Office.

In the words of the musical Hamilton, this is the time for both firms to rise up, and help Americans’ not throw away our democracy’s November shot.  With their combined competence, these two icons of capitalism and democracy can make voting in a pandemic easy:

Putting special ballot drop boxes at every polling place
Deliver any ballot put in an existing FedEx/UPS drop box

Snuff out this made-up, yet dangerously real controversy with action that gets ballots in and counted …absolutely, positively.  The financial returns could even be great as well. 

You know what I’m saying…Just Do It!

We will get through this pandemic.  In six months, we’ve made enough progress that I can start leaning forward to peer around that future corner.  Early in the year, we were scrambling novices without any clues.  We don’t have that many answers yet, but we’ve got a bunch of clues as well as evidence that we will put this in its place.  The question is when.

It’s a major league tragedy that in the U.S. we’re playing AAA ball while so many others are in the Bigs.  If you’ve been struck directly or indirectly by COVID-19, that is no laughing matter.  Current therapies such as Remdesivir are a long way from what we need as is testing.  Until we have testing as easy and ubiquitous as those thermometer guns that are now pointed at our heads regularly, we’re behind the 8 ball.

But we’ll wiggle our way into some new place eventually and when we do, here are a few regressions that I’m looking forward to.

Thinking Inside the Box

I miss the box and all its familiar features.  Call an Uber without wondering who’s been in the back seat, touched the door handle, etc.  Walk up to someone without noting where the invisible six-foot bubble ends.  

Inside the box, I can live by all my habits: good and bad.  Habits are efficient whereas constantly inventing is draining.  I’d go so far as to consider myself creative, but I don’t mind not thinking either.  

Once back in the box, I’m sure I’ll stage some breakouts; but not every day.  That’s probably why folks say thinking outside the box vs. living outside it.  If you have the means (which many don’t – pandemic or otherwise) than being in your familiar box is not so bad.

Put Zoom in Its Place

I’m sure I’ll still find a use for my Bob’s Burgers Zoom background post-pandemic but I’m tired of looking for the closest resemblance to Paul Lynde in the Hollywood Squares layout.  Maybe the Zoom folks will come up with a peripheral vision feature where I can see the sidebar conversations going on, even eavesdrop a bit but until then, I’m too often frustrated by flat, two-dimension interaction.  

I’ll be glad to return to mingling.  Mingling is that unscheduled time before or after the meeting without a structured agenda.  Oh, you and I may have an informal mission during that time but for the most part, it’s a more random space.

Spontaneity

I’d like to walk into my place without washing my hands.  I’d even go for missing that moment when I forget and then fantasize, I’ve contaminated our protected nest because I forgot.  

My face has become increasingly emotionally challenged as I can’t touch it without feeling guilty.  It looks more withdrawn and lonelier but all I can do is explain why I’m being stingy with my touches.

You get the point.  We’re so careful that I’m looking forward to worry-free, unplanned moments.

Deliver Some Stuff, I’ll Get Some

Here the emphasis is on some.  I was committed to Amazon Prime pre-pandemic but I now I’m addicted.  I think that’s manageable, but I could use a little less Doordash and their equivalents.  Buying strawberries sight unseen guarantees you’re going to find some seriously dented fellas in the basket.  

And let’s not deceive ourselves on take-out either.  Take out is delivery only you’re the delivery service.  Soggy dinner is carrier independent.

And then there’s the substitution factor.  Order A and get B.  Or, it’s corollary, “out of stock”.  My son added 2 items to cross the free delivery barrier from one vendor and then only received the two added items because all that he really wanted was out of stock!

Break the 168 Hour News Cycle

When sheltered in place, there’s too much screen time.  It’s morphed the politics we should care about into TV wrestling.   Entertaining to a point but we’ve seen so much that it’s repetitive.  Donald Trump is the villain who taunts, screams, and acts out his role.  You know he’s the fake but it’s crazy easy to hate the bad guy, so we watch.

The same goes for online.  Of course, there’s value but too much of anything makes you an addict.

What Did I Leave Out?We’re not going back to pre-COVID-19.  There are tons of new vistas that the pandemic has pulled forward faster than we were ready for.  We’ll keep many, invent more along the way, but it’ll also be nice to selectively regress.  Here’s to regression!

As restaurants, shops, and bars light up their “Open” signs, there’s one question that everyone from employee to customer will be asking: Is it safe to come in?

Outside heavy industry, safety training typically stops at fire drills and job-specific items such as arresting grease fires in the kitchen.  Customers don’t choose between Wal-Mart, Costco, or Target based on their safety profile. 

Until now.

In the Covid-19 era, leaders from Silicon Valley tech campuses to Billy Bob’s BBQ are challenged to establish virus safety as the new norm of the workplace.  Failure to do so will shut you down quickly, and more importantly, could cost someone their life. 

Implementing CDC guidelines is only step one.  Until virus safety is woven into the culture and evident in everyone’s behavior, your business, people, and customers are at risk. This piece uses best practices from an industry where safety has been core to the culture for decades to help readers understand what it will take to do likewise in your post Covid-19 workplace. 

From Hollywood to Houston

In the mid-Seventies, I left my academic position at USC for a management position inside Exxon.  My goal was to apply the organization behavior and strategy theories I taught to real-world problems.  It was time for this dog to eat his own dog food.

As a mildly reformed hippy and pescatarian, I expected serious culture shock moving from Hollywood to Houston.  What I didn’t see coming was Exxon’s safety culture. 

• Week One:  They gave me a card key and a hard hat and safety goggles…for a management job?
• Week Two:  Every meeting from top management to field toolbox meetings started with safety
• Week Three:  You couldn’t walk more than 100 feet without seeing another safety sign.
• Week Four:  At home, I received a manual from Exxon telling me how to stay safe in the garage, on a ladder, when bending, etc…I didn’t know my home was a war zone! 
• Week Four:  My boss asked if I had any accidents at home in my first month of work.
• Week Five:  My first meeting with serious Exxon big shots started with a half-hour review of on and off the job injuries & remediation plans.

With little industrial experience other than a summer job, I’d have to have been blind, deaf, and quite dumb to not get the message: Safety was a big deal. 

The kicker came between beers and chewing tobacco spits as a seasoned supervisor described the 1970 explosion at Exxon’s Linden, N.J. refinery.  In his words, the blast nearly wiped northern New Jersey off the map.  Luckily no one was killed.  The disaster caused Exxon to seriously expand their commitment to safety.

To this day, when my wife leaves dish towels on the bottom stair as a signal to carry them up to our washing machine, I shove them far to the side so no one slips on them. 

Creating a Safety Culture for Covid-19

An easy definition of culture is “how we work around here.”  Easy as that is, it doesn’t tell you much about how to re-shape your culture.  To do that, there are four levers worth pulling.  For brevity, I’ll highlight each and link it to my Exxon experience

1. Leadership – Starting every meeting with safety quickly conveyed Exxon management’s consistent focus. A simple structure that modeled leaders’ commitment every day.
2. Organization and People – Every Exxon plant and office had a safety committee across functions and levels, with budget, to engage and reinforce the safety message and practices. 
3. Process and technology – Safety impact was a key success criteria before adopting any new process or technology.  In the Covid-19 case, an analog could be making frequent handwashing and sanitizing a part of the daily routine.  It’s the bathroom cleaning log approach applied to everyone.
4. Metrics – Every incident, on and off the job, was tracked, examined, and remedial steps defined.

The last item bears special mention.  Covid-19 is exceptionally contagious and often asymptomatic.  While it might get to the edge of creepy at first glance, how your folks conduct themselves off the job will impact Covid-19 infections brought into the workplace.  No, you can’t dictate what folks do on their own time, but it’s totally within reason to educate them on the implications of keeping the consequences outside the workplace.

Safety is a Uniter; Not a Divider

In 1987, Paul O’Neill (George W. Bush’s first treasury secretary) was brought into Alcoa Corporation as their new CEO.  He followed a series of missteps that sapped customers and profits. 

At his first meeting with industry analysts, O’Neil, was grilled on his turnaround plan. O’Neill declared that while Alcoa’s safety record was better than many American companies, it was not good enough.  He pledged to make Alcoa the safest place to work in the United States.

Analysts looking for increased sales and profit strategies didn’t react well.  After a couple of more questions on inventory and capital ratios, O’Neill berated the analysts:

I’m not certain you heard me. If you want to understand how Alcoa is doing, you need to look at our workplace safety figures. If we bring our injury rates down, it won’t be because of cheerleading or the nonsense you sometimes hear from other CEOs. It will be because the individuals at this company have agreed to become part of something important: They’ve devoted themselves to creating a habit of excellence.

Analysts left the meeting distraught. One later said:

The board put a crazy hippie in charge and he’s going to kill the company. I ordered my clients to sell their stock immediately, before everyone else in the room started calling their clients and telling them the same thing.  It was literally the worst piece of advice I gave in my entire career.

Had he and his clients bought Alcoa stock that day and held it until O’Neill’s retirement in 2000, they would have seen net income increase five times while Alcoa’s market grew by $27 billion.

Using Safety to Align and Accelerate Change

What O’Neill demonstrated at Alcoa, and I saw at Exxon, is that safety touches everyone. Unlike sales quotas or production goals, safety gets everyone operating to a common improvement goal where rank rarely matters. Safety also embodies caring whereas most business goals are grounded in dollars.

Put yourself in the shoes of your favorite restaurant owner trying to re-open.  Every customer will be watching server and kitchen practices as they test “is it safe here?”   Staff will be watching how customers comply with new practices and be understandably ansy when they have to confront non-compliance.

The good news is we are in this together: customers, employees, and suppliers.  No one wants to pass on Covid-19 no matter how cooped up they’ve been.  But don’t mistake the most visible new habits such as masks and social distancing as the finish line. 

Creating a safe Covid-19 culture requires putting time into defining and pulling the four levers described.  How well you do that will define how fast, effective, safe, and profitable, your opening up becomes. 

Or be like Paul O’Neill and consider that crafting Covid-19 safety might be as enlightening as it is necessary.

My friend Bob forwarded an opinion piece from today’s New York Times entitled: How long Will a Vaccine Really Take? 

As the author of Fast Cycle Time, I’m rarely against faster development (as long as it delivers value). Bob’s note triggered me to take a deeper look at the underlying barriers and opportunities for speed for a Covid-19 vaccine.

Speed = Value Delivery

The drug approval process is all about balancing risk and benefit.  For regulators, risk containment drives decisions more than the benefits gained.  All drugs have side effects.  Dial-in the public’s reaction be it anti-vaxxer or potential recalls, it’s safer for regulators to say “no” when any hint of a problem surfaces. A bias towards no is a bias towards slow

But just skipping steps rarely increases speed safely.  For example, to speed Covid-19 antibody testing to market, the FDA uniquely allowed test manufacturers’ to go to market without first being authorized.  According to the FDA website, only one test in more than seventy released has been authorized.   

Not surprisingly, there have been many issues with antibody test results including false positives and false negatives. Being falsely told that you have antibodies could cause someone to venture in public thinking they immunity when they didn’t. 

Speed without value can be more dangerous than helpful. 

The Complexities of Life Science

Vaccine development is not like designing a car or new software.  In both these cases, the gap between what’s already known and what’s needed to complete the project is normally quite small – think of designing a keyless door entry or adding a new button to a web site.

In drug and vaccine development, the gap between what’s known and what’s needed to succeed is far larger.  The advances in cancer drugs, AID/HIV, etc. evolve slowly as the knowledge gap is whittled down.

Speeding up drug development without increasing the potential for missed complications is also governed by humans response time to drug therapy.  You can track but you can’t speed up patients’ reactions to a drug the way a supercomputer can speed up testing a software program. 

The optimistic findings in the recent Remdesiver trial that forecast a reduction in Covid-19 hospital stays from 15 to 11 days required minimally 15 days to reach that conclusion.  As these are averages, some patients were tracked longer than 15 days.  When you multiply that over multiple study sites with staggered start and end times, you can see why trials can take so long.

FDA Approval is a Starting Point

It’s important to note that the drug approval process only gives initial confidence in the safety and efficacy of new drugs.  Once released, the population using the drug expands exponentially.  That will be particularly true for the Corona virus as the user base is potential all human life on Earth.  Therefore, any harmful issues found after release would impact a much larger population.  In that case, the cure could become worse than the disease.    

Using a fast track approach responsibly would heighten requirements for post-release studies.  Post-release studies are hard to get funded because there’s minimal economic upside for the manufacturer in comparison to getting the next new drug approved.  With a disease of this scale, any fast-paced approach should also include funding for large-scale post-release studies.

Parallel execution

Parallel processing is a standard fast cycle time strategy.  It works best when the prior phase has a higher probability of success since a failure in the prior phase turns most concurrent work in later phases to scrap. Parallel processing works best with incremental innovation such as repurposing an existing drug than creating a new molecule.

For those reasons, overlapping the second and third phases of drug development is better than phase one as one can leverage what was learned in phase one.

Recruitment Challenges

Recruiting people for a study’s drug and control arm is a major time sink.  Getting people to sign up, fill out consent forms, etc. is a challenge as is keeping track of them throughout the study.  Phase 3 is typically the largest challenge because it involves the most study sites and people. 

The good news is there are plenty of people at multiple stages of infection with the Corona virus.  This creates a large and easy to identify pool of potential trial participants that is also highly motivated:  quite unusual.

Scaling Manufacturing

Vaccine production is difficult due to the nearly infinite combinations of biological variability in starting materials, the microorganism itself, the condition of the microbial culture etc.  Testing comparative batches of product also has high variability. 

Scaling is dependent on growth rate (e.g. it’s hard to speed up yeast action when baking bread) and equipment size which in itself can thwart progress.  What works at lab scale often has surprises when done at huge production vessels. 

Building a manufacturing plant in parallel as noted in the Times piece would save time.  The key question is how many “risk-buy” decisions one’s willing to make.  Risk-buy decisions make bets on process requirements, vessel sizing, plant layout, control equipment, etc. before one knows for sure.  While it’s faster, the likelihood of scrapping and replacing predicted needs increases. 

Knowing this upfront is helpful.  Rather than designing a plant for cost minimization; design the plant with an eye towards anticipated adaptability.  Minimize decisions that create “hard points” (costly to modify) and maximize flexible elements that may be removed or re-scaled. 

Faster Value Depends on Faster Learning/Experimentation

The data collection required to track thousands of trial participants and all the details of when they took the drug, their reactions, complications, etc. is not trivial.  Though workflow software makes this faster, much ends up being done on paper, faxes, etc.  Having a strong and common IT infrastructure across multiple study efforts is as exciting as plumbing but, its impact is enormous. 

Researchers will learn as much from other trial failures and successes as their own.   The easier it is to move data within and between studies, the faster a vaccine will be found. 

The Biggest Barrier to Speed is Already Solved

With estimates for the cost of developing a new drug exceeding $1b, companies are cautious about choosing and launching new programs.  The fact that the Corona virus created a global pandemic provides an impetus that rarely exists in a single firm. 

Instead of going through an endless series of management review and small scale proof-of-concept experiments to start development, the Corona virus begins with a loud “all hands on deck”. 

Let’s get after it.

Could you design a better Petri dish for the Corona virus?

Brace yourself if you are one of the first to return to the post-Corona office.  Corona safe 1.0 slams open office design into reverse gear because it swaps density for distance as it throttles community contact.

Welcome Back….And Please Stay Away from Me!

Here’s a sample of what you can expect in the post-Corona office:

• Signage explosion – Underfoot, on the walls, at every entrance and exit, signs will remind you to keep your distance, wash your hands, clean your work surface, etc.
• Barriers – Clear particle barrier shields will be augmented by furniture and planters placement to inhibit any herd-like movement or gathering.
• You’re the Janitor –Janitors will do evening deep-cleaning, so you do the rest. You’ll be wiping down shared surfaces regularly.
• Mask and sterilization dispensers – HR will sponsor mask decoration contests and sanitizer stations will be omnipresent
• Vending/coffee sequestration – Nix to community touch attractions.
• Automated or always open doors – door handles are viral network hubs to be avoided.
• Continuous Bio-metric testing – Expect to be regularly sniffed starting with distant temperature scanning.
• Fewer community spaces – Nice to see you but who was sitting here a moment ago?
• Fewer large meetings – “I just don’t feel safe” will be the new key to meeting avoidance.

Is it worth it?

Ronald Coase won a Nobel Prize by demonstrating the rationale for the modern organization is lower transaction costs.  Having people in the same tent used to be far cheaper than coordinating outsiders.  The original Ford plant started with raw iron ore and turned out complete cars.  Outsourcing and globalization emerged when technology dropped the cost of outside coordination…and that’s just the economic argument.

In reality, work is a social container as much as it is a production engine. The social pressure to get back to work builds on the economic motivations.  This is particularly true for small personal service businesses where interaction, including with the customer, is the product.  Even those who don’t miss their job miss the folks with whom they work.

Post-Corona safety requirements increase the transaction costs of running offices while concurrently diminishing the community and coordination value.  If we stay further apart in the post-Corona office, why are we so anxious to get back together again?  Maybe we need to learn team spreading vs. team building?

The Way Forward

As noted in my previous post, the Corona crisis can accelerate the adoption of innovation by removing alternatives.  Rather than transforming the current office to comply with Corona safety requirements, resist that instinctual response and invest in making work more efficient in a hybrid office-remote model.

Over the years, many Silicon Valley companies migrated to enable selective remote work principally based on employee lifestyle preferences.  The migration has been a messy accommodation more than a purposeful strategy that’s actively pushed, integrated, and advanced technology.

The evidence for that is visible in the “freemium” sales models tool makers such as Slack employ.  They offer a free version that provides enough value to pull bottoms-up purchases by local managers.  The hope is that if enough adopt, the pressure for organization-wide IT support and customization will push switching to a higher service, enterprise version.

While remote working tool makers have all the right “community” and “collaboration” buzz words in their sales pitches, we are at the very beginning of understanding what it takes to build a distributed social-technical work system.  Most organizational practices, procedures, and norms are grounded in physical presence.  For example, the vast majority of supervisory and managerial training implicitly assumes physical proximity.  References to remote working are mostly afterthoughts.

We need video systems to evoke healthy arguments along with polite presentations.  We need a remote equivalent to jumping up to a whiteboard that’s as frictionless as the real thing.  We need ways to have hallway conversations beyond the chatbox and without a physical hallway.

You get the point.  We want to go back to the office because the technology and infrastructure that replaces it aren’t there yet.  Rather than just falling back; consider leaning into defining what’s coming next: the intentional hybrid office.

Once discovered, the novel Coronavirus caught our immediate attention because of its virality and mortal threat.   Without action, experts believed it could outrun our healthcare system; space, staff and equipment.  “Flattening the curve” was the only strategy available to slow the virus from spreading at its natural pace.

The next normal will be about defining how and how much social and economic functioning we can restore without threatening public health.  That will depend on developing therapies that significantly diminish the mortal threat balanced by the risks of economic hardship.  Since most Americans don’t have significant financial cushions, not going back to work, regardless of how risky that is, might not be a viable choice.

That said, opening businesses doesn’t mean customers will show up.  Consumers, employees and employers will assess the personal risks of returning.  Risk could be reduced by new data that suggests our initial fears weren’t warranted.  A highly effective vaccine will thrust us quickly into defining the true, new normal.  However, with over 35,000 deaths in the U.S. alone, what is considered safe will have a new definition.

Until then, the gift of crisis is that it forces us to challenge conventional thinking.  Each challenge will also create a new opportunity.  This article highlights what we have learned on this journey to date, what we should expect and define some of the opportunities.

How We Flattened the Curve

Here’s a short list of what we’ve learned while flattening the curve

1.  Cooperation Works; Competition Impedes

Stuck at home, we’ve all watched more news than normal.  I was struck by the courage and compassion of healthcare workers, public safety officers as well as all the supporting cast who delivered goods and re-stocked shelves at personal risk.  Humans naturally pull together but the degree and commitment is constantly re-affirming.

That said, the insanity of states fighting each other and the Feds for protective equipment, ventilators and other newly scare resources was debilitating.  We all enjoy the tension of good sports competition but it has no place in crisis.  Winning is surviving.  The doctor depends on the nurse, who depends on the respiratory therapist, and so on.  Saving loved ones is not a game.

2. Good Governance Matters

Who heard of Dr. Anthony Fauci before Covid-19?  Now a public sector superstar for his knowledge and political grace, his rise to prominence reminds me of Joni Mitchell’s line in Big Yellow Taxi where she decries paving over paradise to put up a parking lot with “you don’t know what you’ve got ‘til it’s gone”.

Governance is how we agree to dole out authority to get stuff done across multiple stakeholders.  At times, it requires a bit of ruling to keep the herd together, but mostly it relies on sharp leadership backed up by coordinating the myriad of execution details.

Public sector governance is always tougher and less efficient than the private sector because the charter is broader, the breadth of stakeholders is wider, and most importantly; public leaders never have the option that CEOs have to say, “this is not a democracy.”

Just as you can’t throw a fire department together once the fire is blazing, we’ve torn down too much public capability and capacity under the illusion that either it can be built or outsourced when we need it.

3. Truthiness Doesn’t Beat the Truth

From Stephen Colbert’s lips to inclusion in the Oxford dictionary, truthiness is “the quality of seeming or being felt to be true, even if not necessarily true.”

Bullshit and posturing politics wastes time.  The way we’ve flattened the curve and will ultimately define the new normal, will be based on leveraging science.  In or about the 17^th century, the scientific method evolved and accelerated the quality of human life by gathering data to systematically build hypotheses, collect data and test if it works.  Rinse and repeat.

That’s why viral and anti-body testing is key.  The reason the underling Coronavirus is called “novel” is we’re just starting to learn about it.  Resolving crisis thrives on faster learning; not blame or rhetoric.

4. Social Science is Harder; Not Softer Science

The only thing we knew at the start was the Coronavirus was highly contagious and seemingly more deadly than anything we’d seen.  The immediate solution was to stay further away from each other.  That has challenged our lives since we’re intensely social creatures.  No handshaking, kissing; and please wear a mask dear.  It’s a bit creepy but there are no other alternatives for now.

Shelter-in-place isn’t a long-term solution.  People are desperate to connect. The pain and grief of having a loved one isolated in an ICU, near-death and by themselves is unimaginable.  On a much smaller scale, the isolation required after a positive contact or test within a family is tough enough.

Now, flash forward to when scientists develop a safe and effective vaccine.  Will we be willing to tolerate the anti-vaxxer’s who have refused measles vaccination?

We need answers that fit our social needs as well as our biological ones.  As I’ll describe next, therein are the opportunities and requirements that define the next normal.

What Comes Next?

How many Zoom video meetings or cocktail parties have you been to?  For readers younger than myself (e.g. most of you) Zoom didn’t invent video conferencing; it’s been a viable technology since at least the 1980s.  And adoption hasn’t been terrific because unless you’re special, we all multi-task on conference calls but we’d rather not be seen doing it.

Crisis accelerates the adoption of technology because it eliminates the alternatives that inhibit adoption, or dramatically increases the urgency for adoption.  When shelter-in-place cut off staff meetings, elective medical visits, and closed bars, people quickly lowered their video conferencing adoption bar.

Having done so, you can bet that working remotely, applying for a car loan by video and more will accelerate.  So will adjunct devices that provide what used to be only available physically.  An early yet simple example is Zoom’s background feature.  Instead of seeing your cluttered home office, you can use one of Zoom’s virtual backgrounds.

But let’s dig deeper.  With a vaccine at best a year away at scale, what is secure will be redefined.  Until now, knowing a person’s temperature from fifteen feet away had few applications.  Until there’s a foolproof vaccine, airport security will quickly incorporate temperature sensing.  Mounted on top of today’s metal detectors will be a temperature scanner that reads your temperature as you approach.  If you’re over 99 degrees, you’ll be pulled out of line for secondary screening.

Despite the huge need for viral testing now and absent a potent vaccine, what we’re using now won’t cut it.  Security will broaden from insuring identity and detecting bombs, guns and knives to include viral testing.  No one will wait for results in the lobby for 5 days or be willing to be invasively swabbed.  We’ve got to get to something closer to spit-in-the-tube sampling with instant readouts.

What is safe will also redefine what’s a clean hotel room.  Clean towels and sheets will be joined by certified sanitization.  There is already a UV disinfecting robot that claims to wipe out a virus in two minutes.  Cruise ships will need ways to ensure customers that they won’t become viral Petri dishes.

Concerns for supply chains will drive increased investment in 3D printing technologies.  Companies went offshore for cheaper labor.  In the Covid-19 world, just-in-case strategies that cope with supply chain interruptions will be as important as just-in-time’s cost savings.  More capital will accelerate 3D printing expansion beyond plastic and simple metals as well as drive higher throughput.

The New Normal is Over the Horizon 

A friend reminded me that the Chinese symbol for crisis and opportunity are the same.  This first phase of coping with Covid-19 has shaken us.  The next normal will build on what we’ve learned, and raise new challenges.  What’s is clear; we’re not going back.

For example, what seem like a series of strategic pivots to an entrepreneur, an investor could view as an endless quest for a sustainable business model.  As I’ve mentioned before, pivoting and rotating in place are two sides of the same coin.

Having listened to countless pitches over the last fifteen years, what makes these errors particularly irksome is 1) how frequently they occur; and, 2) how easy they are to avoid.

1. Confusing effort with progress and therefore, valuation

Long hours, diving catches and heroic wins are all part of the entrepreneurs’ journey.  From their perspective, no one is sacrificing more nor working harder.  They measure progress by looking back in time and comparing where they are now to where they’ve been.

Investors don’t share that personal experience nor the effort it took.  Investors compare this startup to other ventures they’ve seen at a similar stage.  Investors are more interested in what the next inflection point is and when they’ll reach it.

Technologists, in particular, are prone to measuring progress internally.  They’re problem solvers at heart and recall every bug, speed, stability and capacity improvement.  Investors get excited by external success such as marketplace traction and competitive positioning.

Valuation works the same way.  Ultimately, the market determines what a company is worth; not how much effort was put in by the startup team.

When pitching investors, use your past progress to build confidence in your ability to execute but focus your pitch on defining the external inflection points and the path to reach them.

2. Confusing having answers with gaining investor confidence

There’s nothing that irks me more than an entrepreneur who has a quick answer to every question investors throw their way.  While they’re intending to demonstrate how diligently they’ve identified and mitigated risks, when every answer starts with, “we’ve looked at that, here’s how we’re handling that” it actually worries investors.

Experienced investors know that the path forward rarely plays out as originally laid out.  For starters, everything seems to take longer and cost more than planned.  More importantly, the most valuable technical and market fit experiments illuminate new paths versus confirming current direction.

Investors’ questions test how startup leaders think and respond to unknowns.  There’s a thin line between being prepared and unintentionally arrogant.  A good response is to describe your current approach and the assumptions that need to be tested.   Better to ask the investor for their take on pivotal concerns than always having an answer.

3. Confusing technology with customer value

Value is what a customer gets out of a product or service.  Technology is a means for delivering it. We frequently see pitches that confuse cool technology with customer value.  This shows up as “science projects” or “technology push” companies.

Science projects have potential, but they’ve barely left the lab.  Stability, scaling, cost and supply issues have yet to be addressed.  Technology push projects have left the lab but are still searching for a viable customer use case.  A common clue is when the entrepreneur cites innumerable potential uses but hasn’t tested or achieved product-market fit with any.

Entrepreneurs would do better if they start by putting themselves in their customers’ shoes and ask, “What is the job they’re struggling to do?”  Getting that job done faster, cheaper or better is what matters.  Only in selective cases such as pharmaceuticals, is technology itself, a likely end.

Even when technology will obviously help do their job better, technology adoption is not a given.  Be it a pad of paper or an electronic spreadsheet, customers have a current solution.  It may be slow, costly or underperforming but like a bad habit, it’s the devil they know.

Habits are tough to change.  Adopting a new technology might look obvious to the entrepreneur but by itself, it doesn’t account for the real and imagined switching costs.  Ask yourself what do customers have to give up to adopt the new solution?

4. Confusing early traction with sustainable momentum

Landing that first customer is an exciting and enticing achievement.  They validate the entrepreneur’s thesis but only to a point.  Early adopters, by definition, do not represent the mainstream market.  For whatever reasons, they see enough value to offset the risk of buying from a young, unproven company.

Early adopters often take a leap because of a prior connection to the startup. Perhaps they worked with the founder in a prior setting.  That connection provides the trust that compensates for the startup’s immaturity.  It also signals why they are not representative of the broader market.  Truly new customers don’t share that trusting history.

Investors want customer validation that extends beyond past relationships.  At a minimum, early adopters are more impressive to investors when they’ve increased their buying significantly beyond initial orders.  Even more impressive are referrals that come from early adopter recommendations.

Entrepreneurs should leverage their past relationships but recognize their limited reference value.  Use them to bridge into truly new customers or volume.

5. Confusing market entry strategy with different business models.

It’s rare that a startup has the credibility to sell to large enterprises.  They begin with baby steps but often underestimate the challenges they’ll face moving upstream.

For example, many SAAS companies start with a “freemium” strategy hoping to convert satisfied users into paying customers.  Alternatively, entrepreneurs will initially target small and medium size businesses (SMB) and then try to move up-market.

Both approaches can work.  The challenge is keeping focused on making the second step.  Current customers provide feedback and ask for improvements that if addressed too aggressively, can anchor the startup too firmly in the initial market.  What was a stepping stone morphs into a blocking boulder.

Not only are the requirements of up-market customers more robust, it’s difficult to consider those who had the guts to invest early in your firm as disposable.  It sounds harsh, but scaling requires focusing on the requirements of the next horizon.

For most, the answer is not to try and penetrate the enterprise market earlier.  The exception would be if one has the financial backing and strategic footing to do so; go for it.  That’s usually limited to serial entrepreneurs with deep connections.

Rather it’s important to recognize that without discipline, early customers can quietly lure an entrepreneur too deeply into the entry market.  To use SMB as a launching pad, startups have to be willing to jettison early adopters when greater opportunities present themselves.

Minds over Money

In sum, your best investors are like the Farmers Insurance spokesperson.  They know a thing or two because they’ve seen a thing or two.  Your company needs the fuel that dollars provide but you’ll get a far better return if you engage investors’ intellect and experience along with their checkbooks.

Trump’s emotional appeal worked well for securing the mandate to change but it’s killing him in execution.  By relying purely on positional power without a change strategy, he’s demonstrating what happens when you wing it.

I’ve gathered rules of thumb for change leaders from mentors, clients and students.  If Trump’s more successful than I expect, I’ll need to re-think them but until then, here are a few that Trump’s ignoring.

1.  Stay alive

Trump’s thin skin, emotional tweets and bullying approach makes enemies out of who might be friends.  Dissing anyone who has a different perspective is not only offensive, it transforms change leaders into targets.  The steady drip of leaks is not a sign of love.

2.  Avoid walking uphill

You can climb mountains or walk around them in the valleys.  Choosing replacing and repealing Obamacare put Trump head-to-head with the Democrats and approximately 20 million enrolled people.  Alternatively, Trump could have selected infrastructure which would be much harder for Democrats to oppose.

3.  Don’t use one when two could do it

If change is your product, building market share is your goal.  Growing share beyond early adopters is essential for any change to gain critical momentum and achieve escape velocity.  Relying on repetitive campaign rallies to excite the already committed base doesn’t expand the market.

4.  Informal, cultural routines can kill you

Private sector leaders frequently deride those in the public sector as slow moving bureaucrats.  They are right because running a public organization is fundamentally different from running a private business.  Democracy is not efficient but like capitalism, it’s the best we’ve got.

The first job for any change leader is to scrutinize the forces that hold the current state in place.  Don’t think for a minute that formal authority matters as much as the invisible cultural norms.  Cultural change doesn’t happen by fiat.

5.  Change needs dedicated resources…and a plan

In practice, change is usually added involuntarily onto already full plates.  The rookie mistake is to just get started over relying on the goal’s importance instead of mapping out a strategy.  To make change at scale, that plan needs resources: people, time and money.  Would you try to remove and replace your firms’ customer relationship management system with a slogan?

Speed for Success

Some have suggested that Trump is being daring and decisive effectively following Facebook’s mantra of move fast and break things.  Problem is, that Facebook abandoned that mantra in 2014.

“We used to have this famous mantra…What we realized over time is that it wasn’t helping us move faster because we had to slow down to fix these bugs and it wasn’t improving our speed.”

Change leaders go slow at the start to finish fast.

Ideas in good currency often get abused.  When pivoting is abused, it becomes rotating.  An undisciplined, throw it against the wall and see what sticks strategy for which pivoting becomes the legitimizing framing.  It’s as though founders expect building a business is more like hitting a Las Vegas jackpot than the iterative hard work of serving customers.

Rotating is more likely when attention to customer acquisition trumps delivering an outstanding customer experience.  It manifests itself as the modern day equivalent of the shifty salesperson with a lot of glitz and promises at the front end that evaporate quickly.  Internally, deals signed are touted; not deals delivered.  Customer churn is high and you’ll find scant measures or sustained concern for customer satisfaction in these firms.

Rotating in place is full of activity but on a comparable basis, generates little forward momentum.  Other than a few self generated press clippings spurred by each pivot, you’ll rarely hear about rotating companies from leading industry analysts. Establishing a market beachhead requires a persistence presence and linear progress

Rotating companies have high empathy for those who work in them because it’s stressful.  But, they also have low empathy for customers.  Deeply enriching one’s understanding of customer needs is the basis for a good pivot.  You can’t achieve product-market fit without it. That requires patience, listening and engagement.  Rotating companies focus on sales numbers.

They are more like drive-by assassins who follow emerging customer herds looking for kill shots.  These herds are created when new technologies and market trends such as financial and advertising tech disrupt traditional practices.  Mega cap incumbents know the world is changing but they’re not quite sure what to do and thus are more susceptible to try anything smelling new.  No one wants to be the last wildebeest in the herd the lion is chasing.

There’s ample evidence from innovation authorities such as IDEO that a series of rapid prototypes is a quicker path to a good customer solution. And keep in mind, the cost of product experimentation is far lower than business model experimentation.

Every time a firm changes its business model, it is effectively resetting itself to stakeholders.  Seed investors no longer recognize the company and may wonder what’s going on.  Employees have to learn new routines. Customers who bought the company’s last value proposition are more likely to be confused by the new one

This isn’t an argument to hang on to a flawed premise.  It’s an argument that pivoting is closer to an organ transplant than a joint repair…it’s not something to do that often.