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Governance
Information and technology governance is a subset discipline of corporate governance, focused on information and technology and its performance and risk management.

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Features

The KMWorld AI 100: The Companies Empowering Intelligent Knowledge Management

It's easy to become overwhelmed, even awestruck at the amount of information about AI, particularly GenAI, being thrown at us on a daily basis. The ability of AI technologies to process vast amounts of data, recognize patterns that humans can't see, and generate new knowledge and insights boggles the imagination. The challenge faced by knowledge managers is determining what is actually useful and will have staying power.

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.

ViewPoints

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.

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The flip side of generative AI: Extractive AI

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

The trust problem with GenAI

2023 has been the year of ultra-hyping GenAI, and who is paying for this deluge of marketing? Technology vendors that want us to buy it. Again, it's impressive stuff, but when we shift from selling to buying and ultimately using it, many tough questions need to be asked.

Get your game on: KM skills needed for reliable use of LLMs

There is no questioning that generative AI is here to stay, but its use in mission-critical work has some way to go before it can be trusted and let loose.

Are you data-driven or knowledge-driven?

We no longer need to blindly accept the output of even the most sophisticated AI/ML platforms. In fact, we should not consider any artifact, whether produced by humans or machines, as valid knowledge unless it contains not only supporting data and analyses, including provenance, but also an explanation of the underlying plausibility.

Knowledge Management Whitepapers

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.

Unified Data Layer: Transforming Data Choas into Actionable Insight

2024 KMWorld Guide to KM Trends, Products, and Services

Governance Companies and Suppliers
Governance Directory