But with that power comes a growing concern: How do we ensure AI doesn’t distort the historical record?

Some historians and scholars have advocated for stronger governance around the use of AI in historical research, particularly when it comes to data integrity. History depends on evidence, context, provenance and careful interpretation. AI systems, however, can generate inaccurate information, introduce bias, misinterpret historical language or present conclusions without clearly identifying their sources.

The quality of the underlying data presents another challenge. Historical collections are rarely complete or neutral. Records may reflect the perspectives of those who had the power and resources to create and preserve them, while marginalized voices may be limited or absent. Training AI on these collections without understanding those gaps can reinforce existing biases and potentially give them new authority.

Governance can help establish safeguards. Clear standards can help researchers understand where information originated, how it was transformed and when AI contributed to an interpretation. Scholars also need mechanisms for identifying errors and maintaining the integrity of original source material.

The goal isn’t to keep AI out of historical scholarship. Used responsibly, it can expand access to archives and reveal valuable new insights.

But AI should be a tool for examining history, not rewriting it.

As historians increasingly incorporate AI into research, preserving data integrity will be essential to ensuring that new technological capabilities deepen our understanding of the past rather than quietly altering it.

Melody K. Smith

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

Sponsored by Access Innovations, where smarter AI starts with structured, meaningful, well-governed knowledge.

Traditionally, taxonomists have focused on organizing information through taxonomies, thesauri, ontologies, metadata schemas and other structured vocabularies. Their responsibilities often included identifying concepts, establishing preferred terms, creating hierarchical and associative relationships, maintaining consistency and helping improve search and retrieval.

Those responsibilities remain important. But AI has expanded both the tools available to taxonomists and the consequences of their work.

Machine learning and natural language processing can now analyze enormous collections of documents, identify recurring concepts, detect relationships, recommend tags and assist with classification. Generative AI can accelerate activities that once required significant manual review, including proposing synonyms, generating descriptions and mapping terminology across datasets.

That automation changes where taxonomists provide the greatest value.

Instead of spending as much time manually tagging individual assets or identifying every potential term, taxonomists can increasingly evaluate and refine machine-generated recommendations. Their expertise becomes essential in determining whether an AI-generated relationship actually makes sense, whether terminology reflects the language of the organization and its users and whether classifications are sufficiently accurate and consistent for their intended purpose.

In other words, the work is moving from simply creating structure to governing intelligent systems that use structure.

AI has also made taxonomy even more important beyond traditional search applications. Taxonomies and ontologies can provide semantic context for knowledge graphs, retrieval-augmented generation, enterprise search, recommendation engines, analytics and AI assistants. Organizations need reliable ways to define concepts and relationships so AI systems can better understand what their information means, not merely recognize patterns within it.

That puts taxonomists in a new position with a new perspective. Their responsibilities may now include evaluating AI-generated metadata, developing training and validation datasets, monitoring automated classification, identifying bias or inconsistencies and defining human-review processes.

Arguably the most important, AI has increased the importance of judgment.

An algorithm can suggest that two terms are related. A large language model can propose a definition. An automated classifier can assign a document to a category. But none of those outputs automatically makes the result correct, appropriate, unbiased or useful.

Taxonomists provide the human expertise needed to ask questions like: Does this relationship make sense? Is this terminology accurate? Whose language is represented? What context is missing? How will this classification affect retrieval? Should the AI be trusted to make this decision automatically?

As AI becomes embedded in more information systems, these questions become more consequential.

The future taxonomist, therefore, is unlikely to be someone replaced by AI. It is someone whose responsibilities have moved higher in the information value chain. Routine work may become increasingly automated, but the need for semantic expertise, governance, quality assurance and human judgment is growing.

AI can generate terms, suggest relationships and classify content at extraordinary speed. But speed is not the same as understanding. The taxonomist’s evolving role is to provide that understanding and to ensure that the intelligence organizations build on top of their information is grounded in structure, context and meaning.

Access Innovations helps organizations prepare their content for AI by preserving meaning, attribution, and trust before it ever enters a model. That foundation makes responsible, reliable AI not just possible, but sustainable.

Melody K. Smith

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

Sponsored by Access Innovations, where smarter AI starts with structured, meaningful, well-governed knowledge.

One of AI’s greatest strengths is its ability to analyze enormous amounts of information quickly. Medical images, laboratory results, genetic data, patient histories and research findings can be examined for patterns that may be difficult for humans to identify. In diagnostic testing, AI can help clinicians recognize subtle warning signs, potentially identifying diseases earlier and giving patients more treatment options.

The potential becomes especially significant with complex conditions such as Parkinson’s and Alzheimer’s diseases. Researchers are using AI to analyze brain imaging, biomarkers, genetics, patient symptoms and disease progression. By finding connections across these different types of data, AI may help scientists better understand why these diseases develop, identify people at risk earlier and determine which treatments are most likely to benefit individual patients.

AI is also accelerating drug discovery. Traditional development can take years as researchers evaluate thousands of possible compounds. AI-supported systems can narrow those possibilities, predict how potential drugs may interact with biological targets, and help researchers prioritize the most promising candidates for further testing.

The goal is not simply faster healthcare. It is better-informed, more precise healthcare.

AI alone will not cure Parkinson’s, Alzheimer’s, cancer, or other serious diseases. Its findings still require scientific validation, clinical trials, quality data and human expertise. But by helping researchers ask better questions, recognize patterns sooner and explore possibilities faster, AI could significantly shorten the distance between scientific discovery and meaningful treatment.

For patients facing diseases once considered nearly impossible to conquer, that acceleration could make an extraordinary difference.

AI only works as well as the structure behind it. Access Innovations helps organizations prepare their content for AI by preserving meaning, attribution and trust before it ever enters a model. That foundation makes responsible, reliable AI not just possible, but sustainable.

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.

The potential benefits are enormous. An ASI could analyze problems at a scale and speed humans simply cannot match. It might accelerate medical breakthroughs, develop solutions to climate challenges and uncover scientific discoveries that would otherwise take generations. Organizations could gain extraordinarily powerful tools for modeling scenarios and making decisions.

But “smarter than us” also raises an uncomfortable question: What happens when we are no longer the most capable intelligence making those decisions? Does Skynet become a reality?

An ASI given poorly defined objectives could pursue them in ways its creators never intended. Even seemingly reasonable goals can produce harmful results if context, ethics and human values are missing. There are also fears surrounding concentration of power. Who owns or controls an ASI? A government? A handful of technology companies? And who determines the rules it follows?

There are more immediate concerns as well. The pursuit of increasingly capable AI could intensify cybersecurity threats, misinformation, workforce disruption and reliance on automated decision-making long before true superintelligence arrives.

ASI remains hypothetical. We do not know whether it will emerge, when it might happen or what form it would take. But that uncertainty is precisely why the conversation matters now.

The challenge isn’t simply creating intelligence greater than our own. It is ensuring that our governance, data, safeguards and human judgment become sophisticated enough to manage what we create.

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.

Melody K. Smith

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

Sponsored by Data Harmony, harmonizing knowledge for a better search experience.

Artificial intelligence (AI) has dramatically expanded what predictive analytics can do. But, as with most things AI, more powerful does not automatically mean more accurate.

AI and machine learning can process enormous volumes of data, recognize complex relationships and continuously refine predictive models as new information becomes available. Organizations can move beyond relatively static forecasts toward predictions that are faster, more detailed and increasingly responsive to changing conditions.

That creates significant opportunities. Businesses can better anticipate demand, personalize customer experiences, detect fraud and identify emerging problems before they become expensive ones. AI can also uncover patterns humans might never think to look for.

But AI introduces challenges as well. Predictive models are only as reliable as the data feeding them. Poor-quality, incomplete, outdated or biased data can produce predictions that are confidently wrong. AI can also make increasingly complex models difficult to explain, creating a dangerous situation in which organizations trust a prediction without understanding how it was reached.

There is also a fundamental limitation: prediction is not certainty. AI may identify that two things frequently occur together without understanding why.

AI has made predictive analytics extraordinarily powerful, but it hasn’t eliminated the fundamentals. Good predictions still require quality data, strong governance, human expertise and healthy skepticism.

When content is properly structured, enriched and governed, AI becomes an asset rather than a risk. Access Innovations gives clients the tools and expertise to make their content AI-ready while keeping control over accuracy, access and provenance.

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.

AI maturity describes how effectively an organization moves from experimenting with AI to integrating it strategically, responsibly and sustainably across the business. It isn’t measured by how many AI tools an organization buys or how quickly employees start using them. It is reflected in how well AI connects to data, people, processes and business objectives.

Organizations typically mature through stages. Early efforts often involve experimentation: testing generative AI, automating individual tasks or launching isolated pilot projects. As experience grows, successful applications become more intentional and integrated. Governance develops. Data practices improve. Employees gain AI literacy. Organizations establish clearer measures of success and begin deploying AI across workflows rather than using it as a collection of standalone tools.

Eventually, mature AI becomes less about the technology itself and more about organizational capability. Problems arise when adoption moves faster than maturity.

Organizations may deploy AI without adequate data quality, governance, security or oversight. Different departments may adopt disconnected tools with little coordination. Employees may not understand when to trust AI outputs or when to question them. Automation can scale inefficient processes rather than improve them, while poorly governed systems can amplify errors and inconsistencies.

Data remains one of the most important measures of AI maturity. Organizations need reliable, structured and well-governed information that AI systems can find, interpret and use in context.

AI maturity, then, isn’t a destination. As models, technologies and business needs evolve, organizations must continually reassess their capabilities.

AI only works as well as the structure behind it. Access Innovations helps organizations prepare their content for AI by preserving meaning, attribution and trust before it ever enters a model. That foundation makes responsible, reliable AI not just possible, but sustainable.

Melody K. Smith

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

Sponsored by Data Harmony, harmonizing knowledge for a better search experience.

AI did not create the data problem. It simply made it much harder to ignore.

For years, organizations have struggled with fragmented information, inconsistent terminology, disconnected systems, weak metadata and poor governance. Those problems were inconvenient when humans were doing most of the searching, interpreting and decision-making. People could compensate for gaps, recognize context, question suspicious results and sometimes simply know where the right information was hiding.

AI changes the stakes.

Organizations now want AI-supported analysis that can identify patterns and surface insights. They want decision intelligence that combines data with sophisticated models to recommend actions. Increasingly, they want agentic and autonomous workflows capable of completing tasks with limited human intervention.

All of those ambitions depend on one thing: the quality, structure and accessibility of the underlying data.

An AI system cannot reliably reason its way around information that is incomplete, contradictory, poorly classified or inaccessible. More powerful models do not magically repair weak metadata. An autonomous workflow built on inconsistent terminology can simply make the wrong decision faster and at scale.

That is why many organizations discovering limitations in their AI initiatives do not actually have an AI problem. They have a data problem that AI has exposed.

The answer is not necessarily another model, platform or shiny new AI tool. It is the less glamorous work that data professionals have advocated for all along: improving data quality, establishing governance, creating and maintaining taxonomies and ontologies, managing metadata, connecting information across silos, defining authoritative sources and ensuring information can be found and understood in context.

These practices become even more important as organizations move from AI experimentation toward operational implementation. The greater the autonomy given to technology, the greater the need for trustworthy information underneath it.

This also means organizations should stop treating data preparation as a preliminary step that can be checked off before an AI project begins. Data is infrastructure. It requires ongoing stewardship, governance and investment.

AI has certainly changed what organizations can do with their information. It can help uncover relationships humans might miss, accelerate analysis, improve discovery and automate increasingly sophisticated processes.

But it has not changed the fundamental requirement for success. Good AI requires good data.

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.

But in the legal field, faster is only useful if the information is accurate, trustworthy and defensible. That makes data governance especially critical.

Legal work depends on sensitive, confidential and often privileged information. When AI systems interact with that data, organizations need clear policies governing where information comes from, who can access it, how it is classified, how long it is retained and whether it can be used to train or inform AI systems.

Data quality matters just as much. AI can produce impressive answers from unreliable information and sometimes confidently invent information that doesn’t exist. In legal settings, an incorrect citation, outdated regulation, misclassified document or incomplete record can have consequences far beyond an embarrassing chatbot response.

Strong data governance also creates traceability. Legal professionals need to understand not only what an AI system produced, but what information contributed to the result. Well-managed metadata, taxonomies, access controls, versioning, provenance and retention policies help create that accountability.

AI has enormous potential to make legal work more efficient and accessible, but it cannot eliminate the responsibility to manage information properly. If anything, it raises the stakes.

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.

AI systems can process enormous volumes of information at remarkable speed, uncovering patterns and connections that traditional technologies might miss. That creates opportunities for better decision-making, automation and even stronger cybersecurity. AI-powered tools can identify unusual activity, detect potential threats and respond to security incidents faster than humans alone.

But there’s another side to that capability: AI makes data more accessible, more valuable and potentially more vulnerable.

Generative AI has introduced new concerns about what information employees enter into AI tools, how that data is stored or used and whether sensitive or proprietary information could inadvertently become exposed. AI can also make cyberattacks more sophisticated by helping bad actors automate phishing, impersonation, social engineering and other attacks.

Preparing for what comes next requires organizations to think beyond traditional cybersecurity.

Strong data governance must be part of the foundation. Organizations need to understand what data they have, where it resides, who can access it, how it is classified and whether it should be available to AI systems in the first place.

Most importantly, organizations should stop thinking about AI strategy and data protection as separate conversations. They are deeply connected.

AI only works as well as the structure behind it. Access Innovations helps organizations prepare their content for AI by preserving meaning, attribution and trust before it ever enters a model. That foundation makes responsible, reliable AI not just possible, but sustainable.

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.

RAG improves AI responses by retrieving information from trusted sources before generating an answer. Rather than relying solely on what a large language model (LLM) learned during training, a RAG system can search a curated collection of current, relevant content and use that information to ground its response.

That creates an important opportunity for publishers. High-quality RAG depends on high-quality source material. Authoritative articles, research, technical documentation and other professionally published content provide the reliable foundation these systems need. The publisher’s role shifts from simply delivering information directly to readers to also ensuring that trusted knowledge can be accurately discovered, attributed and used by AI systems.

The key phrase, however, is implemented correctly. RAG systems should respect content rights, licensing agreements, access controls and attribution. They also require strong metadata, taxonomies and content structures that help AI identify the right information and understand its context.

Publishers have spent decades establishing credibility, editorial standards and subject-matter authority. RAG does not eliminate the value of that work. It creates another channel through which it can be recognized and delivered.

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.