Your Enterprise Knowledgebase Was Built for Search, Not Action

The next stage of enterprise knowledge management will not be defined only by larger repositories or better search. It will be defined by whether the knowledge layer can explain what is true, where it applies, who is responsible for it, and what it is allowed to do. A knowledgebase built for search helps people find information. A knowledge system built for action helps a company use that information safely.

Stop Calling It Automation: AI Is a Knowledge Augmentation Layer, not a Replacement

Organizations that treat AI as an augmentation layer—one more component in a socio-technical system built on semantic grounding and human accountability—will get the compounding advantage everyone else is trying to buy with a bigger model. That's the bet worth making. Not AI instead of KM, but AI inside KM, on KM's terms. 

The Age of the Citizen Developer: Mitigating Risk While Cultivating Enthusiasm

As untrained coders adopt AI, organizations must balance risk mitigation with fostering innovation. The organizations that succeed will not be those that restrict citizen developers, but those that channel their activity within well-defined guardrails and enforceable governance frameworks. When governance enables innovation rather than reacting to it, enterprises can capture AI's value without exposing themselves to unnecessary risk.

Semantic Layers Bring Answers to Problems KM Is Designed to Solve

With a semantic layer framework, an organization can actually spot where they lack explicit knowledge and information, or where people are asking questions for which explicit answers don't exist.

Europe Needs to Stop Building Regulatory Moats and Start Building Markets

For Europe to cultivate world-leading AI companies, it needs to build the foundational conditions that have made ecosystems such as Silicon Valley so productive. This also means attracting and retaining talent through easier immigration, greater mobility across borders, and simpler equity compensation structures that make joining an early-stage startup genuinely rewarding. Without people willing and able to take risks on new ventures, there is no growth or innovation.

You Don’t Need 47 Agents

The most powerful multistep execution isn't a chain of specialized agents. It's a single model with enough context to plan, execute, and recover—informed by everything it's learned from every prior execution.

Agentic AI and the Evolution of Finance: How Smarter Systems Are Powering Usage-Based Models and Enterprise Growth

Agentic AI marks a shift from passively recording business activity to actively driving it. Those who embrace this shift early will do more than automate tasks—they'll build a trusted, intelligent infrastructure that accelerates not only efficiency but also agility, strategy, and scale.

Conversational AI interfaces and human-AI collaboration to transform legal knowledge management in 2026

This next year will see the knowledge management function take a direction that focuses on a more advanced and mature way of leveraging AI.

Leaders predict AI to continue permeating all aspects of KM in 2026

AI continues to be the topic du jour for various aspects of knowledge management, and 2026 looks to be no exception as leaders in the industry look ahead

Why Diverse Data Leads to Smarter Business Decisions

To truly unlock the business growth capabilities of data and AI, strategy must be grounded in diversity and aligned with an organization's values. When inclusive data is supported by ethical, diverse teams and a culture of accountability, the result is not just smarter business, but also meaningful impact for society as a whole.

Embracing Digital DNA for Business Transformation

By adopting a mindset of agility, adaptability, and knowledge sharing, companies will be able to solve customer pain points, deliver exceptional experiences, foster loyalty, and continue to anticipate changes and meet customer needs with a digital-first mindset.

Bridging the Knowledge Gap in Manufacturing: Securing Institutional Expertise for the Future

The manufacturing sector is at a crossroads. While investments in new technologies and infrastructure are essential, they must also be utilized to preserve and share institutional knowledge. By adopting a comprehensive KM strategy that includes centralized data management, digital innovation, and a culture of knowledge sharing, manufacturers can safeguard their expertise and secure long-term success.

Navigating the IT Landscape: Balancing Hybrid Cloud and Cloud Repatriation to Stay in Control of Your Infrastructure

To secure their data, certain industries have strict regulations regarding data storage, requiring data to be kept in specific geographic locations or under stringent security measures. This is yet another reason for organizations nowadays to switch back to on-prem resources.

Experts predict AI will continue impacting KM in 2025

AI continues to disrupt the knowledge management space and experts in the field predict that it's a trend that still hasn't reached its full potential, yet. In 2025 there's more room for improvement.

Integration impasse: Why organizations can’t wait for data integration before deploying AI

The need for comprehensive data management will always be important, and there are many other benefits of digital transformation, but CIOs don't need to delay GenAI projects until the completion of a giant data centralization effort. By adopting a more flexible approach that incorporates GenAI and next-generation BI tools, businesses can navigate the complexities of modern data ecosystems while driving innovation and maintaining a competitive edge in an AI-driven world.

The transformative role of AI in the next generation of records management

While there are many ways AI will disrupt and advance the records management process, these four key applications will make the biggest impact: automating document classification and tagging, records retention and data hygiene, leveraging natural language processing for record analysis and predictive analytics for records management.

Navigating the risks and challenges of AI (quickly): Create an AI governance program

A strong AI governance program is essential to ensuring compliance and reducing risk. An equally important benefit is that by developing the governance program at the same time the AI application is being developed, issues can be identified early, thus avoiding system redesign or rework on the tail end.

Democratizing software development with no code/low code

By enabling greater productivity and accelerated software development timelines, no code/low code is on the rise.

What you should know about cross-border data transfer laws

Multinational companies are generally aware of data transfer laws, but smaller ones just embarking on looking beyond country borders may not be.

Microsoft’s Copilot: A force multiplier for KM

Generative AI (GenAI) applications will increasingly transform organizations' IT platforms. Companies of any size that opt to create robust apps on their own, however, are in for a protracted, complex, and expensive experience.There's a better way: Buy into what I call a GenAI ecosystem from a vendor in whose tech you are already invested. These ecosystems are comprised of the sum of services customers mostly need to build and launch robust apps.