Artificial Intelligence will change Knowledge Management as we know it

I recently came across this blog post by a start-up that is developing an Artificial Intelligence that is being trained to read and write at the level of a specialized human analyst and produce briefings in human language based on a set of different information resources. It’s just one example of many different companies that are currently working on this challenge. The obvious clients are intelligence agencies, governments, or news agencies, but eventually this will enter all of our everyday work very soon.

I thoroughly believe that this is what knowledge management in large organizations will look like in 10-15 years from now. In my organization, we’re challenged daily to consolidate the key lessons and insights from all our country-level programmes and experiences, lest meaningfully combine them with information, trends and insights from the larger development sector. We complain that we’re overwhelmed by the information overload that social media, Yammer and knowledge networks impose on us, and retreat to focusing on a narrow set of information that confirms our biases, pretending we know what we need to know, when in fact we always only have a small piece of the puzzle. Artificial Intelligence promises to overcome this dilemma, as it will have immediate access to all information available, and can do the necessary analysis for us.

We might not be quite there yet to make this practical for organizations like UNDP, but we’re getting closer and closer. Last year we as UNDP KM team at HQ engaged with a well-known AI systems provider, and while both organizations were not quite ready yet to commit partnering on an AI system that can make sense of unstructured texts, trends, insights and lessons in the development sector, will have to get real about this soon if we as an organization want to be ready for what is to come. To quote the same article above: “With technology that can read and write, you have the flexibility to generate custom insights in any format or level of detail. If you’re a subject matter expert, Primer can tell you a detailed story that takes your knowledge into account. If you’re new to a subject, it can generate an introduction to get you up to speed quickly. If you have an interest in a particular angle on the story, or a geographic lens that you want to zoom in on, the insight can be customized for you. Imagine the possibilities if you had one thousand analysts working for you, all day, every single day. What questions would you ask, what kinds of briefings would you have them prepare?”

Now, technology can always only be one part of the solution. It is important to keep in mind Dave Snowden’s adage that if you have $1 to invest in KM, invest 99 cents in connecting your employees over shared opportunities and 1 cent on content. Connecting people has always been (and will always be) at the center of knowledge management, where we try to connect staff to those who have the skills, capacitating them to identify the right people, enable them to collaborate and research in real time and turn the result of that into actionable insights. It’s why other UN organizations have often looked to UNDP for KM advice, because it regularly chose to make strategic investments in connecting and fostering networking among its staff, being the first UN agency to pioneer email-based knowledge networks in 1999, the first to introduce organization-wide corporate social networking with its award-winning platform Teamworks in 2009, and continuing that trajectory with Yammer (among other things) today. Connected people are the ‘operating system’ of any meaningful KM effort that allows real-time collaboration within a human context that can be turned into actionable insights.

But what forefront thinkers in the AI space tell us about AI’s implication for governments is true for international organizations like UNDP as well: AI will relieve knowledge workers form drudgery tasks, split up our work into automated tasks (e.g. research, collation) and human tasks (value-based decision making, social interactions), and augment the capacities of knowledge workers by adding layers of real-time and predictive analysis that humans couldn’t do by themselves. Together with many of my KM colleagues who are much more skeptical about AI than I am, I also believe the focus will be on augmentation, not replacement. Nonetheless, all indications suggest that we are at the beginning of a revolution of how knowledge work looks like, and organizations like UNDP will be affected internally by both the benefits and risks. The only way to get ourselves ready for it, is by doing what the innovation community always does: Striving to get our feet wet early, and learn, learn, learn.


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