Generative AI Ushers in a New Era for Insights: Knowledge Management 3.0

From physical documents to intelligent, natural-language summaries: generative AI has brought Knowledge Management (KM) a long way since its humble beginnings. Once labor-intensive, the internet streamlined KM for global enterprises. However, generative AI changed the game by delivering fully-formed answers to consumer insights questions. As generative AI evolves, the next era of KM could replace human-created documents with real-time data inputs.

Information has always been one of the most valuable tools a business can wield. Since the advent of worldwide internet access, corporate KM has leveraged increases in raw computing power and software sophistication to deliver faster, more meaningful insights relating to consumer behaviors, market trends and business opportunities.

With generative artificial intelligence (AI) software rewriting the rules once again, knowledge management has entered a third era where natural language processing can return complex, multi-faceted answers to specific questions, instead of just compiling a trove of documents for a worker to sort through.

As we uncover the power of KM 3.0 systems, and continue developing even greater capabilities, it’s important to remember how we got here and recognize the amazing power of today’s solutions.

A History of Knowledge Management Technology

Prior to the 1980s, knowledge management was functionally a manual labor process, even if it was conducted at a desk or on an early computer screen. This Knowledge Management 1.0 (KM 1.0) era required a lot of time and validation to collect trustworthy information from print sources and internal documents, with the goal of helping businesses determine the best marketing approaches and product decisions.

Collaboration required physically transferring documents or floppy disks to colleagues, so not only could a research project take weeks or months to arrive at conclusions, but the data leveraged could actually be out of date before action is taken.

Once the internet arrived, KM 2.0 processes began offering far greater speed alongside access to more sources, enabling faster and more data-based responses to support planning and profitability. This era lasted several decades and saw incremental improvement to capabilities, but the core function was always a search engine.

Knowledge and insights management workers would generate keywords related to the desired topic, and the internet search engine or internal intranet would parse their stored data and display a list of documents or files with relevant information. Given the broad nature of this approach, it often still left workers with mountains of information to sift through and largely relied on data that could be weeks old.

When KM 2.0 emerged in the mid-2000s, we began to see software companies build powerful research platforms that could integrate with the internal systems of enterprise businesses, giving them consolidated access for as many users as needed.

These dedicated solutions were often offered through a Software as a Service model (SaaS), where the business subscribes to the service and can access the latest tools which are constantly being refined and improved. The end results could include a link to the pertinent section of a document, but ultimately remained a basic list.

AI-Based Knowledge Management 3.0: A Game Changer

Around 2021, a change began to unfold in the technology community that fundamentally altered how people interact with computers and data. This is when the public first began to gain access to generative AI and natural language search engines that don’t require keywords and can deliver advanced responses to complex questions.

According to recent research by Harvard Business Review, KM platforms based on this technology are ushering in a wave of new opportunities for businesses and workers to increase the effectiveness and value of knowledge management by increasing “question velocity, question variety and question novelty.”

This means a company can use AI to train algorithms so they are more adept at answering a larger variety of questions, revealing patterns in vast data sets, and providing analyses that are all but impossible for humans to complete.

Consequently, they found users of AI-enhanced platforms asked 79 percent more questions, asked different or more involved questions 94 percent of the time, and asked unique questions that revealed novel connections and changed the trajectory of teams, organizations or industries 75 percent of the time.

Gone are the days of poring over individual documents to seek out specific data points – now KM 3.0 results can be delivered in fully-formed plain language explanations that go much deeper into data than ever before. The best KM 3.0 platforms offer a conversational process in which complete questions return complete answers, and can even use audio and video files as sources by automatically transcribing them for inclusion in the data set.

According to Forrester Research, this evolution in simplicity has changed how KM can be used, by reframing research platforms from a bucket of data we have to sift through into a magnet that can locate anything we ask it to, and even combine disparate information into new ideas or understandings.

Combining data from multiple sources, the AI-based KM 3.0 software can deliver intelligent summaries in response to multi-faceted questions. A sample result could include a brief summary or bulleted list of relevant facts in complete sentences, links to source documents and even integrated prompt-tuning that provides suggested language to improve results. Users can also directly help improve the AI by providing feedback on the quality of answers and suggestions.

The Future of Real-Time Data with KM 4.0

KM 3.0 platforms will continue to grow and change in the coming years as AI advances. Ultimately, though, in the decades to come we expect an even greater shift to occur in how research is conducted – one that transitions from using prepared documents as the main source material to integrating raw, real-time data inputs into generative AI search results.

Even KM 3.0 generative AI still relies on human-created documents as input material, which can, in certain instances, be months or years old. As AI development continues to advance, the next generation of KM platforms – KM 4.0 – may bypass or supplement this intermediate stage of human-created document inputs by integrating directly with live internal databases, even social media feeds, and more – so each question can be answered in real-time with in-the-moment information.

Additional automated tools will enable users to turn these on-demand insights into executive reports for easy sharing or presentations – making the entire process more speedy, real-time, and streamlined.

A Future Full of Possibilities

Leading KM platform developers are already hard at work leveraging generative AI and other automation tools in novel ways to improve the accuracy and impact of searches – and while KM 3.0 has only just begun, KM 4.0 is already on the horizon.

The folks at Harvard Business Review liken the future prospects of expanded AI on “cognition and how we think” to the introduction of web browsers that made the internet exponentially more useful by enabling low-cost global information transmission.

In plain terms, AI will be able to do more of the analytical “thinking” that can help draw conclusions, while people will be tasked with determining the right questions and choosing what actions to take based on the results.

They further posit that the fear of AI replacing workers is somewhat overblown, and that the more likely reality is that workers or organizations who embrace AI will replace those who do not.

As the technology develops and other innovations make new directions possible, knowledge management could become the most important differentiator between competitors, or perhaps even the most important core business concern to rival intellectual property. After all, there’s a reason we’re all familiar with the axiom ‘knowledge is power’.