AI 100 Trailblazer: Enterprise Knowledge - Taking AI Beyond a Prototype: At the Intersection of Knowledge and Data Management

Throughout the many AI solutions we have developed within both the public and private sector, we differentiate ourselves by working alongside our clients to weave KM and data management practices through knowledge graphs and a semantic layer and build user-focused AI solutions that are embedded within organizational processes, support the organizational mission, and reflect how people and processes fuel enablement and adoption.

AI 100 Trailblazer: Lucidworks - Your Trusted Partner for AI-Powered Search and Discovery

Lucidworks' total AI solutions seamlessly integrate cutting-edge generative AI models with over a decade of proven search expertise. We create personalized experiences for customers and employees, driving engagement and results across key applications like knowledge management, commerce, and service & support.

AI 100 Trailblazer- M-Files: Unleash the benefits of knowledge work automation and superior GenAI experiences

The M-Files metadata foundation drives superior AI experiences by creating a unique, customer-specific information model that surrounds all content, ensuring safe and high-quality results. With M-Files, organizations automatically get the mandatory enablers for successful AI deployment: connectivity, confidentiality and curation.

AI 100 Trailblazer: Northern Light optimizes GenAI for market and competitive intelligence research

Key to the accuracy of SinglePoint's GenAI responses is Northern Light's use of retrieval augmented generation (RAG); only sources from vetted content collections within the client organization's SinglePoint portal— business news, primary or licensed secondary market research, thought leaders' commentary, technology white papers, conference abstracts, or industry and government databases—are tapped for answers. There's virtually no risk of "hallucination" since SinglePoint does not rely on a commercial large language model's internet-based training data.

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.

The end of tech glory days

The tech industry's glory days may be fading a little, but this is not a time for despair. It's an opportunity for renewal. By shifting to a needs-driven approach, the industry can ensure its relevance in a rapidly changing landscape.

The third place of knowledge management

The third place I alluded to goes far beyond mechanistic KM or curated knowledge and takes us into the actual world of tacit knowledge. Here, knowledge comes from and often remains as personal experience, impressions, and intuition; it's undocumented and often hidden and elusive.

The flip side of generative AI: Extractive AI

Extractive AI takes a more comprehensive and transparent approach to machine intelligence.

Should we go back to paper-based KM?

The sheer volume of largely useless data we have accumulated across the years severely limits the ability of AI to work well, and it comes at a heavy environmental and financial cost.

IDC Spotlight: From Siloed Content to Streamlined Workflows – How a CCMS Powers Digital Transformation

How to Choose an AI guided Knowledge Management System?

CX Knowledge Manager Playbook

Extracting knowledge from your data:Learn how a semantic layer helps you find, access, integrate, and re-use your enterprise knowledge.