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Young talented programmer reading code on computer screen. Generative AI will transform knowledge management.

Knowledge management is an example of why generative AI will soon be commonplace in enterprise applications

A leading IT analyst firm recently published a market assessment that predicts generative AI-enabled applications will achieve general market acceptance in less than two years.  That’s unusually rapid adoption for an advanced technology that was first offered commercially only a year ago.

Generative AI finds a home in enterprise knowledge management

Generative AI-enabled enterprise applications are on the fast track to mainstream market adoption thanks in large measure to early adopter use cases like automated document summarization for market research and competitive intelligence content, a specialized implementation of knowledge management. Consulting firm BCG places this type of use of generative AI in the “improving efficiency” category, one of three they have identified to define the value generative AI can deliver within the enterprise.

In The Generative AI Dossier: A selection of high-impact use cases across six major industries, the Deloitte AI Institute describes the value of generative AI for next-level market intelligence:

“By harnessing Generative AI’s capacity to read and summarize vast amounts of relevant material, companies can expedite market research and gain concise insights for effective decision-making in new markets…

“Generative AI enables rapid market research by efficiently reading and summarizing extensive volumes of pertinent material, presenting the information in a readily understandable format for market research teams.”

And not only for market research teams, but also for decision-makers in all functional areas across an organization, including strategic planning, marketing, and product development.  The power of a generative AI-produced executive summary of findings from a search query directed at authoritative documents housed in a company’s vetted research collection cannot be overstated.  Because it draws upon reliable source material, such a summary represents a superb starting point for a business research investigation — far better than anything the search industry has delivered to date – and points the researcher directly to documents that contain the most on-point information for their topic of inquiry.

Ensuring accuracy in generative AI knowledge management applications

Bear in mind this business research application of generative AI in the enterprise calls for accuracy, not creativity, in writing.  One way that Northern Light ensures accuracy in our implementation of generative AI – we use OpenAI’s GPT 3.5 Turbo large language model in our SinglePoint™ knowledge management platform for market and competitive intelligence – and avoids gen AI’s much-discussed “hallucination” problem is by setting the “temperature” to zero.  In this way, we’re instructing the generative AI algorithm to stick to the facts contained in the source documents and take no linguistic liberties when summarizing the information.

Finally, it’s important to note that using generative AI to answer business research questions in an enterprise environment is not the same thing as logging into Chat GPT.  The stringent accuracy that market and competitive intelligence research requires, and the data security and copyright compliance that enterprises demand, mean an enterprise generative AI application for business research (as for many enterprise use cases) must clear a high bar.

Generative AI has a bright future in enterprise knowledge management

The fact that a leading IT analyst is so optimistic about generative AI-enabled applications in the enterprise suggests that smart people and organizations are quickly figuring out how to navigate around the technology’s potential pitfalls and put it to good use.

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