Search used to give us a list of links and leave the reading to us. Artificial intelligence (AI) chatbots offer something different: ask a question in everyday language, get a summary, then ask a follow-up without starting over. That can be useful when you’re comparing options, learning an unfamiliar topic or trying to find the right words for what you need. Some search tools also provide links alongside their answers, making it easier to explore further. This important subject came to us from the Center for Democracy & Technology in their report, “Dark Patterns in AI Chatbots: A Taxonomy to Inform Better Design.”

The trouble is that a chatbot can sound certain when it is wrong. It may leave out an important qualification, misunderstand a source or present an unsupported claim as fact. Even systems that retrieve information from the web can produce answers that the cited material does not support. A link is helpful, but its presence alone is no guarantee that the answer is accurate.

There’s another cost to convenience. When an answer seems complete, we may stop before reaching the original reporting, research or expertise behind it. That matters most when details are disputed, changing quickly or likely to affect an important decision.

Chatbots have a valuable role in search: they can help us frame questions, sort through information and find a place to begin. The best habit is to treat the response as a starting point. Open the sources, check whether they support the specific claim and look for another perspective when the stakes are high. AI can shorten the path to an answer. We still have to decide whether it’s the right one.

Data Harmony is our patented, award-winning, AI suite that leverages explainable AI for efficient, innovative and precise semantic discovery of your new and emerging concepts, to help you find the information you need when you need it.

Better data starts with better thinking. Subscribe to Taxodiary for expert perspectives on the issues shaping information and AI.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.

Enterprise artificial intelligence (AI) adoption is moving at remarkable speed. Organizations are embedding AI into customer service, research, operations, analytics, content creation and decision-making – sometimes with a thoughtful strategy and sometimes because everyone else is doing it. This interesting topic came to us from Silicon ANGLE in their article, “Enterprise AI readiness trails the hype amid agentic rush.”

That urgency can produce real benefits. AI tools can help employees find information faster, summarize complex material, identify patterns, automate repetitive work and surface institutional knowledge that might otherwise remain buried in documents, emails or the minds of longtime employees. When paired with strong knowledge management, AI can make an organization’s collective expertise more accessible and useful.

But speed creates risk. AI cannot reliably retrieve, interpret or generate answers from organizational knowledge that is outdated, duplicated, scattered across platforms or lacking context. If the underlying information is unreliable, AI simply delivers unreliable information faster and often with enough confidence to make the problem harder to spot.

Rapid adoption also raises questions about ownership and governance. Who decides which sources an AI system can access? Who reviews the answers? How are confidential information, intellectual property and employee expertise protected? What happens when the system relies on a policy that was replaced three years ago but never removed?

Organizations should not treat knowledge management as a cleanup project to address after AI implementation. It is part of the infrastructure that makes enterprise AI useful.

Successful adoption requires accurate content, clear taxonomies, documented sources, defined ownership, strong access controls and regular review. It also requires employees who understand when to trust AI, when to question it and how to correct it.

Enterprise AI will continue moving quickly. Whether that is good or bad depends largely on whether an organization’s knowledge is ready to move with it.

If this got you thinking, there’s more where it came from. Subscribe to Taxodiary for the latest insights from our experts.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, uniquely positioned to help you in your AI journey.

Much of the conversation about artificial intelligence (AI) focuses on whether machines will replace human workers. But organizations often gain more value when AI is developed and applied as a tool that improves employee performance rather than eliminates employees altogether. This interesting, important and timely topic came to us from Nature in their article, “Why we must stop talking about artificial general intelligence — and instead build ‘pro-worker’ AI.“

AI-powered tools and applications can handle repetitive, time-consuming tasks such as organizing information, summarizing documents, scheduling activities, analyzing large datasets and preparing initial drafts. This gives employees more time to focus on work that requires judgment, creativity, empathy, collaboration and institutional knowledge.

A customer service representative, for example, can use AI to quickly locate relevant account information while still bringing human understanding to a complicated situation. A data analyst can use AI to identify patterns faster but must provide context and determine what those patterns mean. A communications professional can use an AI-generated draft as a starting point while shaping the final message for a specific audience.

In each case, AI enhances the worker’s abilities without replacing the qualities that make the worker valuable.

Organizations that take this approach can benefit from greater productivity, faster decision-making, improved consistency and more engaged employees. Workers are also more likely to embrace AI when they understand how it can reduce frustration and help them perform their jobs more effectively.

Successful adoption requires more than purchasing a collection of shiny new tools. Organizations must provide training, establish clear policies, protect sensitive data and involve employees in deciding how AI should be used. They should also measure whether the technology is improving outcomes, not merely whether it is reducing labor costs.

Replacing workers may offer an appealing short-term calculation, but it can also mean losing expertise, relationships and experience that are difficult to rebuild. Organizations that use AI to extend human capabilities create something far more valuable: people who can work more efficiently without sacrificing the insight, accountability and humanity that technology cannot provide on its own.

Go beyond the buzzwords. Subscribe to Taxodiary for informed perspectives on data, AI, taxonomy and the technologies changing how information works.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, uniquely positioned to help you in your AI journey.

Artificial intelligence (AI) is only as reliable as the data behind it. When an AI system produces an inaccurate, biased or simply baffling result, one of the first questions should be: Where did the data come from? Data lineage helps answer that question.

Data lineage is the documented journey of data throughout its lifecycle. It shows where data originated, how it moved between systems, who accessed it and what happened to it along the way. Think of it as a family tree for data – complete with origins, relationships, transformations and perhaps a few questionable relatives.

This visibility is essential to effective data governance. Governance establishes the policies, responsibilities, standards and controls for managing data. Lineage provides evidence that those expectations are actually being followed.

Together, they help organizations answer practical questions like, “Is this data coming from an approved source?” and “Were required quality checks performed?”

Without lineage, data governance can become theoretical: a lovely collection of policies sitting on a digital shelf while data continues wandering freely through the organization. Without governance, lineage may document the journey but do little to ensure that the journey is safe, accurate or appropriate.

Data lineage can also work against governance when it is incomplete, outdated or treated as a one-time documentation project. Modern data environments change constantly. Systems are replaced, fields are renamed, datasets are combined and new AI tools are introduced. If lineage records do not reflect those changes, they can create false confidence. Organizations may believe they understand their data while relying on a map that no longer matches the territory.

For AI, accurate lineage is especially important. AI models often rely on enormous volumes of data drawn from multiple sources. If that data contains errors, duplicated records, outdated information or embedded bias, the model may amplify those problems in its results.

Lineage allows teams to trace questionable AI output back through the data pipeline. They can identify whether the problem came from the original source, a transformation rule or the way datasets were combined. This makes it easier to correct the problem and explain how the model reached its conclusions.

Strong lineage also supports regulatory compliance, model monitoring and responsible AI. Organizations can demonstrate which data was used, whether they had permission to use it and how it was prepared. That level of transparency is increasingly important as customers, regulators and employees demand greater accountability from AI systems.

Data lineage does not guarantee perfect data or flawless AI. Nothing does. But it gives organizations the visibility needed to find weaknesses, enforce governance policies and improve results.

When AI produces an answer, data lineage helps organizations show their work and determine whether anyone should trust it.

The biggest challenge is that most organizations have little knowledge on how AI systems make decisions and how to interpret AI and machine learning results. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact and its potential biases. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.

Better data starts with better thinking. Subscribe to Taxodiary for expert perspectives on the issues shaping information and AI.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.

Artificial intelligence (AI) has and continues to reshape the job market college graduates are entering. While concerns about automation are justified, AI is also creating opportunities for graduates who learn how to work with it rather than compete against it. This timely and important topic came to us from NPR in their article, “Many recent grads say AI is making it harder to get a job. Economists aren’t so sure.”

One advantage is greater productivity. AI tools can help new employees research topics, analyze information, write code and complete routine administrative work faster. Graduates who understand how to use these tools effectively may contribute sooner and take on more complex responsibilities. AI is also creating jobs in areas such as data science, cybersecurity, machine learning, governance and technology oversight.

The shift could make some careers more accessible. A graduate does not necessarily need advanced technical training to benefit from AI. Marketing professionals can analyze campaign data, designers can explore concepts and financial analysts can evaluate possible scenarios. Used well, AI can help employees expand their capabilities across many industries.

There are disadvantages, however. Many routine tasks traditionally assigned to entry-level employees can now be automated. This may reduce the number of junior positions available and remove some of the work through which graduates once developed experience. Employers may also expect new hires to accomplish more with less training because AI tools are available.

Over-reliance presents another risk. Graduates who use AI without questioning its output may repeat errors, overlook bias or possibly most important, weaken their own critical-thinking skills.

Success in an AI-influenced workplace will require more than knowing how to enter a prompt. Graduates will need strong communication, judgment, creativity, adaptability and subject-matter knowledge. They must understand when AI can help and when human experience and accountability matter more.

If this got you thinking, there’s more where it came from. Subscribe to Taxodiary for the latest insights from our experts.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.

Data analysts and data scientists both work with data, but they typically answer different kinds of questions. This interesting topic came to us from Boston University in their article, “Data Analyst to Data Scientist: What Actually Changes in the Work.“

A data analyst focuses primarily on understanding what has happened and what is happening now. Analysts collect, clean and organize data, then use tools such as spreadsheets, SQL and visualization platforms to identify trends and create reports or dashboards. Their work helps leaders answer practical questions.

Strong data analysts do more than produce charts. They interpret findings, recognize inconsistencies and translate numbers into information people can use to make decisions.

Data scientists often work further into the future. In addition to analyzing existing information, they build statistical models and machine learning systems that predict outcomes or automate complex decisions. A data scientist might forecast customer behavior, develop a fraud-detection model or create an algorithm that recommends content. Their work commonly requires programming, advanced statistics and experience working with large or unstructured datasets.

The roles can overlap considerably, especially in smaller organizations. An analyst may build predictive models, while a data scientist may create dashboards or conduct straightforward business analysis.

The clearest distinction is often the purpose of the work. Data analysts turn existing data into understandable insights that support decisions today. Data scientists use data to build models, test possibilities and anticipate what may happen next. Both roles are essential and both depend on trustworthy data, clear questions and the ability to communicate what the numbers actually mean.

Tap into decades of expertise in data, taxonomy, metadata and emerging technologies. Subscribe to Taxodiary.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.

Data analytics used to spend much of its time answering a familiar question: What happened? Analysts gathered data, built reports, spotted trends and helped organizations make sense of the past. Artificial intelligence (AI) has made that work faster, but it has also changed what organizations expect from it. This interesting and important topic came to us from HPC Wire in their article, “AI Is Forcing Analytics Teams Into a New Role.”

AI tools can sift through large datasets, flag unusual patterns and forecast outcomes instead of waiting for a custom report. That gives analysts more time to tackle the harder questions: Why is this happening? What might happen next? And what should we do about it?

The shift sounds exciting, and it is. But a faster answer is not automatically a better one. AI can identify a correlation without understanding the circumstances behind it. It can build a convincing forecast from incomplete data. It can even make a questionable finding look polished enough to pass along without a second thought.

That makes human judgment more valuable, not less. Analysts need to understand where the data came from and explain what the findings mean for the people making decisions. They also need to know when the data cannot support a confident answer.

AI has changed data analytics from a discipline focused largely on producing reports to one that can help guide decisions in real time. The best analysts will use that speed to ask better questions, test the answers and make sure the story the data tells is one the evidence can actually support.

Better data starts with better thinking. Subscribe to Taxodiary for expert perspectives on the issues shaping information and AI.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, uniquely positioned to help you in your AI journey.

Everyone wants to talk about what AI can do. Fewer people want to talk about where it gets its data, who maintains that data or whether anyone can find the latest version of it. Fair enough. Data management is not the flashy part. It is, however, the part that keeps the flashy part from confidently getting things wrong. This interesting topic came to us from Gov Insider in their article, “Why AI governance starts with data management.”

Data management covers the everyday work of collecting, organizing, storing and making data usable. When that work is done well, artificial intelligence (AI) has a better chance of finding relevant information and producing useful results. When it is done poorly, an AI system may pull from outdated records, duplicate information or missing context. A polished answer can still be a wrong one.

That is where data governance comes in. Governance sets the expectations: Who owns the data? Who can access it? How should it be used? How do we know it is accurate? Data management puts those expectations into practice through processes people actually follow.

The two need each other. A governance policy that nobody carries out is just a document. A well-organized collection of data without clear rules can create its own problems, especially when AI makes that data easier to search, combine and share.

Before an organization asks what AI tool it should buy, it is worth asking a less exciting question: Can we trust the data we are about to give it? The answer may save a lot of cleanup later.

The future of AI depends on how content is prepared today. Access Innovations partners with organizations to turn metadata, semantics and structure into AI-ready infrastructure that protects meaning and enables confident innovation.

Go beyond the buzzwords. Subscribe to Taxodiary for practical insights and expert perspectives on data, AI, taxonomy and the technologies changing how information works.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, uniquely positioned to help you in your AI journey.

11^th Year Company Has Been Recognized for Innovation in Knowledge Management

Access Innovations Inc. has been named to the prestigious KM World List of Trend Setting Products for 2026. This year marks the 11^th year the company has been recognized for its innovation in the knowledge management industry.

“Knowledge management products continue to evolve, mainly due to rapidly evolving AI-based technologies that call for new, updated strategies and that match up with user expectations. This year’s KM World Trend-Setting Products list identified products that deliver actual results, and highlights the ones that are truly innovative, transformative, and worth watching,” said Marydee Ojala, KM World’s Editor in Chief.

“We are excited and honored to be included on the list again this year. We strive to continually improve our award-winning Data Harmony software suite, which builds explainable AI to improve search. Also, we recently launched our next-generation platform – accessinn.ai, offering streamlined ontologies optimized for AI,” explained Heather Kotula, President and CEO of Access Innovations. Some other companies on the 2026 list include Adobe, Anthropic, IBM, and Salesforce.

This summer, Access Innovations released version 3.18 of its Data Harmony software and  DH Web, the first fully web-supported version of the software. The new releases incorporate features that make the software seamless to use, including more flexible navigation and active and intuitive icons that link directly to functions. DH Web also reflects features that have been requested by users.

The company’s new Accessinn.ai platform creates API-accessible ontologies that offer tagged content for use in RAG, Agentic Workflows, LLM s and other related applications. The platform also ensures content can be retrieved to generate appropriate, accurate and well-grounded responses.

Built using industry and international standards, accessinn.ai offers ready-to-implement authoritative ontologies in manufacturing, science, business and finance, engineering, education, health and medicine computers, information and communication, federal and state regulations, news, politics and humanities. It also offers custom ontology development tailored to specific organizational content or workflow.

“We are proud to have been at the forefront of knowledge organization and semantic content management for nearly five decades. By bringing ontology-based systems into the era of AI, we continue our mission to make knowledge organization accessible, interoperable and powerful for the next generation of AI solutions,” Kotula said.

Anyone interested can request a free trial of Data Harmony by visiting www.accessinn.com and clicking on the DH Suite tab. They can also register for a free demonstration of available ontologies at accessinn.ai.

About Access Innovations, Inc. – www.accessinn.com, www.taxodiary.com, www.accessinn.ai

Access Innovations empowers clients to realize their search goals by leveraging AI and helping clients build accurate and explainable AI that increases search precision by more than 90 percent and their team’s productivity by more than seven times.

About KMWorld – www.kmworld.com

With more than 25 years of market coverage experience serving both technology professionals and executive management, KMWorld guides more than 50,000 IT and business professionals at organizations across North America involved in the evaluation, recommendation, and purchase of enterprise technology products and services. KMWorld Magazine is free to qualified subscribers and is published bi-monthly. 

Risk management has traditionally relied on historical records, professional judgment and a fair amount of educated guessing. Data analytics changes that by helping organizations identify patterns, measure exposure and recognize potential trouble before it becomes a reality, an expensive one. Express Computer brought this important topic to our attention in their article, “From reactive to predictive: How AI and data analytics are redefining business risk management.”

By analyzing operational, financial, customer and market data, organizations can detect unusual activity, identify and assess vulnerabilities and model possible outcomes. Analytics can reveal where risks are concentrated, which warning signs deserve attention and how one decision may affect the rest of the organization. It turns risk management from a periodic exercise into a continuous, evidence-based process.

Artificial intelligence (AI) takes those capabilities to a new level. AI systems can process far more data, and far more quickly, than human teams. Machine learning models can identify subtle relationships, flag anomalies and update risk predictions as conditions change. This is especially valuable in areas such as fraud detection, cybersecurity, supply-chain disruption and regulatory compliance.

But AI also introduces additional risks. A model trained on incomplete, biased or poorly governed data may produce confident recommendations that are fundamentally wrong. Automated decisions can be difficult to explain, and overreliance on AI may cause organizations to overlook context that exists outside a dataset.

The strongest approach combines analytics, AI and human oversight. Data analytics provides the evidence. AI increases speed, scale and predictive power. People contribute judgment, accountability and an understanding of consequences.

Better data starts with better thinking. Subscribe to Taxodiary for expert perspectives on the issues shaping information and AI.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, uniquely positioned to help you in your AI journey.