Agentic artificial intelligence promises systems that can do more than answer questions. AI agents can interpret goals, make decisions, use tools, retrieve information and take actions with limited human intervention. But giving AI greater autonomy raises an important question: Does it actually understand the data it is acting on? This topic was brought to us by the article, “Semantic layers welded to knowledge graphs… then shoe-horned into agents,” from Medium.

That’s where semantic layering becomes critical. A semantic layer sits between raw organizational data and the people or systems using it. It provides shared definitions, relationships and business context ensuring that terms such as “customer,” “revenue,” “active account” or “risk” mean the same thing across databases and applications.

For agentic AI, that context can become the difference between simply accessing data and using it intelligently.

An AI agent asked to identify declining customers and initiate retention efforts, for example, must understand what qualifies as a customer, how “declining” is measured, which data sources are authoritative and what actions it is permitted to take. A strong semantic layer gives the agent a structured map for interpreting that environment.

The intersection also introduces challenges. Semantic layers must remain accurate as data, terminology and business rules change. Conflicting definitions across departments must be resolved. Governance, permissions and provenance become increasingly important when an AI system can act on its interpretation rather than merely report it.

Organizations must also avoid assuming semantic context eliminates AI errors. Agents still require guardrails, monitoring and appropriate human oversight.

As agentic AI evolves, semantic layering may become less of a data-management convenience and more of an essential infrastructure layer helping ensure autonomous systems understand not just what the data says, but what it means.

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.

Government organizations hold some of the largest and most sensitive collections of data in the world. As agencies increasingly adopt artificial intelligence (AI), protecting that information requires more than traditional cybersecurity. It requires governance built specifically for an AI environment. SC World brought this interesting and important topic to our attention in their article, “How to Evaluate Enterprise AI Security and Governance Platforms.”

AI platforms create new opportunities to analyze vast data libraries, uncover patterns and improve public services. But they also introduce new questions: What data can an AI system access? Who determines how it can be used? Can sensitive information inadvertently appear in an AI-generated response? And how can an agency explain how a system reached a conclusion?

Effective AI governance begins with understanding the data itself. Agencies need clear classification, ownership, access controls and retention policies across structured and unstructured information. AI should not become a shortcut around existing permissions. A user or an AI agent acting on that user’s behalf, should only be able to retrieve information the user is authorized to access.

Security must also extend beyond the data repository to the AI platform. Agencies should evaluate how vendors store prompts and outputs, whether organizational data is used for model training, how information is encrypted and logged, and what happens when data moves between systems.

Finally, governance cannot be a one-time compliance exercise. AI systems, models and regulations will continue to evolve. Continuous monitoring, auditing and clearly defined accountability are essential.

For government organizations, the goal is not simply to deploy AI securely. It is to create an environment where innovation can occur without sacrificing public trust, privacy or stewardship of the data entrusted to them.

Keeping data safe and whole is important. Making data accessible is as well. Whatever you are searching for, it is important to have a comprehensive search feature and quality indexing against a standards-based taxonomy. Choose the right partner in technology, especially when your content is in their hands. Access Innovations is known as a leader in database production, standards development and creating and applying taxonomies.

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.

Much of the conversation about artificial intelligence (AI) in education has centered on generative AI: students using ChatGPT to write essays, teachers experimenting with AI-generated lesson plans and administrators debating where the technology belongs. But behind those highly visible applications, another form of AI has been quietly finding its place in classrooms for years: machine learning.

Machine learning systems identify patterns in data and use those patterns to make predictions, recommendations or decisions. In education, that capability can support everything from personalized learning platforms to identifying students who may need additional help.

Adaptive learning systems, for example, can analyze how a student performs across assignments and adjust the difficulty, pace or type of material presented. Instead of every student moving through exactly the same lesson at the same speed, technology can help educators provide more individualized learning experiences.

Machine learning can also help teachers spot patterns that may otherwise be difficult to see. A student may perform well overall but repeatedly struggle with one concept. Another may show a gradual decline in participation or assignment completion. Analytics can surface those changes earlier, giving educators another tool for determining when intervention might be helpful. The important word, however, is tool.

Machine learning does not understand a student in the way a teacher does. Data may indicate that a student’s performance has changed, but it cannot necessarily explain why. A teacher may know that the student recently changed schools, is struggling with the material, learns differently or simply had a terrible week. That distinction becomes even more important as generative AI enters the classroom.

Students now have tools capable of explaining concepts, generating practice questions, translating information, brainstorming ideas and providing immediate feedback. Educators can use the same technology to develop instructional materials, differentiate lessons and reduce administrative work.

But AI also introduces significant challenges. Schools must address academic integrity, student privacy, data security, algorithmic bias and unequal access to technology. Educators also face a more fundamental question: What does learning look like when producing an answer is no longer necessarily evidence that a student understands the material?

The answer is unlikely to be banning AI entirely or handing education over to algorithms. Instead, AI may push education toward something it has long valued: critical thinking. Students will increasingly need to evaluate information, question outputs, recognize bias, verify sources and explain how they reached a conclusion, not simply produce the correct answer.

Machine learning can identify patterns. Generative AI can produce content. Neither replaces the human relationships, judgment and curiosity at the heart of education.

The classroom of the future may have considerably more technology in it. The challenge will be making sure that technology helps students learn how to think, rather than simply thinking for them.

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.

For information science professionals in higher education, data quality is hardly a new concern. Long before generative artificial intelligence (GenAI), machine learning and advanced analytics entered the conversation, the principle was simple: information systems can only be as trustworthy as the data they contain. This important topic came to us from IT Brief and their article, “Modern data teams: key roles, structures and data quality.”

What has changed is the scale and the stakes. Universities now manage increasingly interconnected ecosystems of student records, research data, digital repositories, learning platforms and AI-enabled tools. In this environment, inaccurate, incomplete or poorly described data does more than create an inconvenient report. It can distort research findings, undermine institutional decisions and reduce confidence in the systems scholars, administrators and students depend upon.

For information science professionals, data quality also extends beyond whether a value is technically correct. Context matters. Provenance matters. Metadata, controlled vocabularies, standards and semantic consistency matter. Data must be understandable and usable by people and increasingly by machines operating far removed from the system where that information originated.

AI makes this especially important. Large-scale models and automated systems can process enormous quantities of information quickly, but speed does not compensate for weak data foundations. In fact, automation can amplify existing inconsistencies and errors at unprecedented scale.

Higher education therefore needs information science expertise more than ever. Data quality is not simply a technical cleanup task performed before analysis. It is an ongoing discipline of stewardship, governance and context.

Search has become more intelligent, personalized and diverse, leveraging technologies to deliver faster and more accurate results across a wide range of platforms and devices. Making the content findable is important to knowledge management.

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) can process millions of data points in seconds, identify patterns humans might miss and automate tasks that once consumed hours. That makes AI an extraordinarily powerful tool for data analysis. But a tool is exactly what it is. Tech Target brought this topic to our attention in their article, “Will AI replace data analysts: A year and a half later.“

AI can analyze data. It cannot fully understand why the data matters. Data analysts bring something essential to the process: context. They understand the organization, its goals, its customers and the questions that actually need answering. An AI system might identify an unexpected drop in sales, for example, but an analyst determines whether that change reflects seasonality, a flawed dataset, shifting customer behavior or something the organization needs to address immediately.

Analysts also know when to question the data itself. AI models work with what they are given, which means incomplete, biased or poorly structured data can produce convincing but misleading results. Human expertise remains critical for recognizing anomalies, challenging assumptions and determining whether an insight makes sense in the real world.

Rather than replacing analysts, AI has the potential to make them significantly more effective. It can handle repetitive data preparation, accelerate exploratory analysis and generate visualizations worth investigating. That gives analysts more time for interpretation, strategic thinking and communication.

The future of data analysis isn’t humans versus AI. It is analysts who know how to use AI effectively. Organizations still need people who can ask the right questions, recognize when an answer doesn’t add up and translate numbers into decisions. AI can make the analyst faster. It can make the analyst more capable. But human judgment is what turns analysis into understanding and understanding into action.

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.

Organizations have never lacked data. The bigger problem has been figuring out what to do with all of it, especially the information that doesn’t fit neatly into rows and columns. This interesting and important topic came to us from CDO Magazine in their article, “Why AI Changes Unstructured Data: Data X-Ray’s Kyle DuPont on Metadata Intelligence.”

Unstructured data includes emails, documents, PDFs, images, videos, social media posts, customer reviews, meeting transcripts and countless other forms of content. It represents the majority of data organizations create, but historically, analyzing it at scale has been difficult, expensive and time-consuming.

Artificial intelligence (AI) is changing that. Natural language processing (NLP), machine learning and generative AI can analyze unstructured content to identify patterns, topics, relationships, sentiment and meaning. Instead of simply searching documents for keywords, AI can help organizations understand context. Thousands of customer comments can become insights about emerging concerns. Years of reports can reveal recurring themes. Meeting transcripts can become summaries, action items and searchable organizational knowledge.

Generative AI and retrieval-augmented generation (RAG) take this further by allowing people to interact conversationally with large collections of unstructured information. Employees can ask questions and receive answers synthesized from documents that previously required hours of searching.

But unlocking unstructured data also introduces challenges. AI does not automatically make messy, outdated or poorly governed information trustworthy. Organizations still need metadata, taxonomies, access controls, data quality standards and governance to ensure AI is working with the right information.

AI is making unstructured data far more accessible, but accessibility is not the same as reliability. The opportunity isn’t simply to process more data. It’s to transform previously untapped information into knowledge and knowledge into better decisions.

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) may get the headlines, but its effectiveness still depends on something far less glamorous: data. That is where FAIR data becomes increasingly important. This interesting topic came to us from Nature in their article, “Advancing FAIR data towards comparable, organized, predictive AI-ready data for community validation.”

FAIR stands for Findable, Accessible, Interoperable and Reusable. Introduced as a set of principles for scientific data management, FAIR is not simply about making data publicly available. It is about ensuring data can be discovered, understood, exchanged and used appropriately by both people and machines. That last part makes FAIR particularly relevant to AI.

AI systems need more than enormous quantities of information. They need data with context, consistent structures, meaningful metadata and clear relationships. Findable data helps systems locate relevant information. Accessible data establishes how it can be retrieved. Interoperable data allows information from different sources and systems to work together. Reusable data includes the context, provenance and permissions necessary to use it correctly again.

In other words, FAIR principles help transform a collection of data into something AI can actually work with effectively. But the relationship works both ways.

AI can also help organizations make their data more FAIR. Machine learning and generative AI can assist with metadata generation, classification, entity recognition, data mapping and identifying relationships across previously disconnected datasets. AI-powered tools can uncover missing metadata, inconsistent terminology and duplicate information that make data difficult to discover or reuse.

There is an important caveat: automation does not automatically create trustworthy data. AI can just as easily amplify poor classifications, missing context and existing biases. Human oversight, governance and standards remain essential.

As AI becomes embedded in more organizational systems, FAIR is evolving from a data-management best practice into an AI-readiness strategy. Because smarter AI does not begin with a smarter algorithm. It begins with data the algorithm can find, understand and use.

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.

Cloud computing has been one of the defining technologies of the digital era, giving organizations access to computing power, storage and applications without requiring them to build and maintain everything themselves. Now artificial intelligence (AI) is changing that equation again.

AI is not simply another workload moving to the cloud. It is influencing how cloud environments are designed, managed, secured and scaled while cloud computing is simultaneously making increasingly powerful AI accessible to more organizations. The result is a relationship in which each technology is accelerating the other.

One of the biggest advantages of cloud-based AI is accessibility. Training and running sophisticated AI models can require enormous computing resources. Cloud platforms allow organizations to access GPUs, specialized AI chips and scalable storage without investing millions in their own infrastructure.

That scalability is particularly valuable because AI demand can fluctuate dramatically. Organizations can increase computing resources for model training or high-volume inference and scale back when demand decreases.

Cloud platforms are also helping democratize AI. Prebuilt models, APIs and AI development tools allow organizations without large teams of data scientists to experiment with natural language processing, machine learning, generative AI and intelligent automation.

AI is improving the cloud itself as well. AI-powered systems can monitor performance, predict resource demand, automate workload allocation, identify unusual activity and optimize energy consumption.

The combination is powerful, but it is not simple.

AI workloads can consume significant computing resources, creating unpredictable cloud bills. Organizations attracted by the flexibility of cloud AI can quickly discover that experimentation at scale becomes expensive.

Data presents another challenge. AI depends on large quantities of quality information, raising questions about where that data resides, who can access it and how it moves between systems. Privacy, cybersecurity, regulatory compliance and data sovereignty become increasingly important when sensitive information is used to train or interact with AI models.

Organizations must also consider vendor dependence. Building AI systems around proprietary cloud services can make migrating workloads difficult and expensive later.

Several technologies are already reshaping what cloud computing looks like. Edge computing moves some processing closer to where data is created, reducing latency and limiting the need to send everything to centralized cloud environments. Specialized AI processors are improving performance while potentially reducing the enormous energy demands associated with AI workloads.

At the same time, hybrid and multi-cloud architectures are giving organizations greater flexibility over where models, applications and data operate. AI agents may push this evolution even further as autonomous systems dynamically request resources, interact with applications and execute increasingly complex workflows across cloud environments.

Even cloud management itself is becoming more intelligent. Emerging AIOps technologies use machine learning and automation to detect problems, anticipate failures and optimize infrastructure before humans need to intervene.

The future of cloud computing is not simply bigger data centers with faster processors. It is infrastructure that increasingly observes, predicts, adapts and acts. That creates enormous opportunities for organizations, but it also makes cloud strategy inseparable from AI strategy, data governance, security and cost management.

AI may be in the clouds, but organizations still need their feet firmly planted on the ground. The technology can provide extraordinary computing power and flexibility. The real advantage will belong to organizations that know how and when to use 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.

Artificial intelligence (AI) may be advancing at breakneck speed, but one stubborn truth hasn’t changed: AI is only as good as the data behind it. RT Insights brought this topic to us in their article, “Why AI Systems Are Only as Good as the Data Being Fed into Them“.

This is not new news. “Garbage in, garbage out” predates generative AI by decades. Organizations have long understood that incomplete, inconsistent, outdated or poorly structured data produces unreliable results. Yet many are investing heavily in sophisticated AI tools while continuing to wrestle with the same underlying data problems.

Why? Part of the problem is that data quality is rarely glamorous. A new AI platform generates excitement. Cleaning metadata, standardizing terminology, eliminating duplicates and establishing governance? Not so much. Those foundational tasks require time, resources and collaboration across departments, often without an immediate, flashy payoff.

Organizations also tend to underestimate how fragmented their data environments have become. Years of disconnected systems, inconsistent naming conventions, departmental silos and accumulated “we’ll fix it later” decisions create problems that AI doesn’t magically solve. In fact, AI can amplify them.

Poor-quality data can lead to inaccurate predictions, misleading recommendations, incomplete search results and confidently delivered answers that are simply wrong. The more organizations rely on AI to support decisions, the greater the consequences become.

The irony is that organizations don’t necessarily need better AI first. They need better foundations.

That means treating data quality as an ongoing organizational responsibility rather than a cleanup project. Governance, metadata, taxonomies, standards and stewardship must become part of the AI strategy itself.

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.

Organizations today have access to more data than ever before. Customer behavior, operational performance, financial trends, market activity and employee insights can all be measured and analyzed. But having data and making good decisions with it are two very different things. This interesting topic came to us from IDB in their article, “How Can Governments Turn Data into Better Decisions?“

The first step is identifying the questions that matter. Collecting everything simply because it can be collected often creates noise rather than clarity. Organizations should begin with specific business goals and determine which data can help them understand progress, identify problems or uncover opportunities.

Data quality is equally important. Decisions based on incomplete, outdated, inconsistent or poorly structured information can create more risk than relying on intuition alone. Strong data governance, shared definitions and consistent standards help ensure people across an organization are working from reliable information.

Context also matters. A dashboard may show that sales dropped, website traffic increased or customer engagement changed, but numbers rarely explain why on their own. Combining quantitative data with institutional knowledge, customer feedback and human expertise helps organizations understand what the numbers actually mean.

Artificial intelligence (AI) and advanced analytics can strengthen this process by identifying patterns, relationships and anomalies that might otherwise go unnoticed. They can also help organizations analyze larger volumes of information more quickly. But technology should support decision-making, not replace judgment.

Ultimately, becoming data-driven is not about producing more reports. It is about creating a culture in which reliable information reaches the right people at the right time and is translated into action.

The most valuable data is not the data an organization collects. It is the data that helps someone make a better decision.

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.