The role of the Knowledge Manager in an AI world

How will the development of Artificial Intelligence affect the role of the Knowledge Manager?

There  is a lot of discussion on Artificial Intelligence as part of Knowledge Management, and the use of powerful computing to replace the reliance on experts. As discussed here, the expert, in a rule-based scenario, is seldom better than a smart computer, and the computers are closing the gap that remains. Is there still a role for KM and the Knowledge manager as the computers get smarter?

Take the vision below, from an IBM Watson TV commercial.

Here the company expert, Jack, is on holiday and is replaced by Watson, who can give advice just as good as Jack’s, and in some cases, in Jack’s own words.  Engineers using Watson can “access 30 years of experience in seconds” according to the commercial, which is exactly what we are seeking for in KM. Knowledge which used to live only in the expert engineer’s head is now available to all at the point of need. Knowledge, through the application of AI, has become common property, easily accessed.

The benefits of this use of AI are considerable (please note that I am not, in this post, addressing the use of AI to search for correlations and patterns in big datasets; I am looking more at the retrieval of actionable advice).

What AI is doing here is automating the supply chain for knowledge, and removing the bottleneck which the expert previous represented.  It represents some of the automated augmentation of knowledge work that will help increase the productivity of the knowledge worker, and help up meet Drucker‘s “50-fold productivity increase” challenge.

As a result, the knowledge workers get quicker access to better knowledge, the organisation is protected against the loss of experts and the risk of problems onsite, and more can be done with fewer people.

It is probably inevitable that the number of knowledge workers will decrease as this sort of AI-related augmentation is used more and more.  Think for example of the reduction in support-centre staff as the use of AI chatbots and technology such as Watson is used to answer customer queries, rather relying on human staff drawing on a Knowledge Base platform.

But what about the knowledge managers? Will they still have a job in the new world?

Yes, they definitely will.

AI like Watson in the video above interrogates structured and unstructured information, and rapidly retrieves an answer to a question, providingthat answer in the context of the enquirer. But someone has to make sure the knowledge is in the system already, and much of it is not.  Even if it is in the system, the AI needs the language to be able to understand the question and to retrieve possible answers, and to have algorithms tuned well enough to judge the right answer. So the Knowledge Manager may have some or all of the following jobs to do:

  • Capture the tacit knowledge in the first place. In many organisations, much or most of the crucial knowledge is still in people’s heads, and therefore completely inaccessible to AI. Some of it may never be captured. In the video above, how do you think Jack’s knowledge became accessible to Watson? Not through trawling Jack’s emails, but through a well-planned knowledge elicitation exercise, involving the skills of knowledge management.
  • Add the events which have not yet happened. This is another class of “knowledge not yet captured”. In the example above, the records will be full of data and information related to the normal running of the plant, but will rarely have much on operations “outside the envelope” – when something has gone wrong. I remember a knowledge capture session I did once with a senior engineer, and he told me his favourite way to train new guys was to give them out-of-the-box examples to work with. “Imagine pump 3 has stopped, pipe 7 is running at 500 degrees, and there’s smoke coming from the turbines. What do you do? Watson would not know what to do unless the knowledge manager puts examples like this into the dataset.
  • Clean the knowledge base. The biggest problem with AI is poor data, and AI starts with clean data. AI retriving knowledge starts with clean knowledge, and thats a job for the knowledge manager, as most knowledge bases I have seen are decidely unclean. For example a Watson-like AI acting as a chatbot answering customer queries needs a clean, reliable and constantly updated knowledge base just as much as contact centre agents do, and the knowledge manager makes sure that the knowledge base is managed well.
  • Build the ontology. Semantic search such as Watson’s relies on a really good ontology, so Watson can make sense of the question, and can identify classes of answers from the existing documentation. Who will write the ontology? The knowledge manager will, with the help of the experts.
  • Oversee the training. AIs need to be trained, and this can be a big job. As this KM World article points out, “it is reported that it took a core team of 20 researchers to build Watson’s Jeopardy-beating machine (along with a strong support team to aid in those efforts). Likewise, the AlphaGo team spent 18 months researching the very complex game of Go (with 20 core researchers publishing their paper in Nature)”.
  • Tune the algorithm. Not every AI has got the algorithm right, and a wrong algorithm can be a disaster. Microsoft shut down a bot called Tay after pranksters pushed it to make racist, sexist and pornographic remarks, for example.  At the time of writing, AI needs a lot of guidance before it can work on its own.
  • Continually improve the knowledge supply chain. New knowledge comes in all the time. This needs to be added to the knowledge base. The performance of the company needs to be tracked, and you need to look at the lessons. Sometimes the AI needs tweaking. All of these things are jobs for the knowledge manager. 

There are also circumstances where AI doesn’t yet work well.

“Machine learning works best in an environment with rules and huge numbers of data points. That might work with cars driving through heavy traffic governed by laws, or with achieving the best price for selling a big block of shares. It might not work well in deciding where to invest a hedge fund’s money, for example, or recommending products to customers without much previous data to go on. The minute things get fuzzy—either due to a lack of rules, an unclear evaluation of success or a lack of data—artificial intelligence performs poorly”.

In a fuzzy and complex world, you are out of the realm where experts, expert systems and AI function well. Here you need the knowledge networks and the communities of practice, who can draw on their collective tacit experience. And helping build and sustain these networks is part of the role of the knowledge manager. AI can do nothing with tacit knowledge.

The number of knowledge managers in the AI world may well increase, not decrease.

Knowledge managers, and allied disciplines such as content managers and data scientists, will quite possibly be in greater demand if the use of AI increases as some commentators predict. For example, according to the bloomberg article quoted above 

“These limitations (of AI) mean it’s not yet clear that the cost of automation will be offset by savings in human capital. Hiring a data scientist can cost more than $200,000, according to Bloomberg News. Flight-bookings company Amadeus has 40 of them. Siemens says it has more than 200 A.I. specialists running various projects. And even Silicon Valley has its grunt workers: Facebook is hiring 3,000 content moderators, on top of 4,500 existing ones. A.I. cheerleader Amazon has 341,000 employees—three times the number it had in 2012”.

AI does not mean that knowledge management is dead, and the knowledge manager is out of a job. It adds a new and powerful technology to the knowledge managers arsenal, and changes the nature of some of the knowledge manager’s tasks and adds new ones, while other tasks remain just as they were.

It looks like AI is not going to replace the knowledge managers, content managers and data scientists, at least not in the short term!

View Original Source (nickmilton.com) Here.

Can you deliver Knowledge Management with no technology?

There are a few cases – very few – where you can build a Knowledge Management progam without the help of technology.

  Low-tech Twitter
We often talk about a Knowledge Management Framework as a mix of Roles and Accountabilities, Processes, Technologies, and Governance – the four legs on the Knowledge Management table.

But can you get along with any of these missing?

Can you do Knowledge Management with no technology, for example?

The purpose of KM technology is to allow people to talk when they can’t get into the same room at the same time, and to allow knowledge to be stored and developed beyond the constraints of human memory.

So it would be possible to have a technology-free application of KM for:

  • a small group, who were all co-located and could talk at any time;
  • meeting frequently to discuss knowledge, and keeping that knowledge tacit and undocumented;
  • debriefing quickly while memories are fresh, ensuring knowledge did not need to be stored for a long time;
  • dealing with rapidly-changing knowledge which would be out of date by the time it is documented;
  • or knowledge they need to internalise quickly, which they do through conversation rather than reading;
  • which they will re-use quickly, before they forget the details. 
This is rather like Action Learning, and you could argue either that Action Learning is a technology-free application of Knowledge Management where the knowledge remains only in tacit form, or that Knowledge Management is a technology-enabled extension of Action Learning using technology to spread the learning process through space and time across multiple teams and locations.

I know of one example like this – where a project was in the middle of intensive negotiations with a hot government, and conducted Knowledge management through a series of After Action reviews in the team “war room” with everyone present. The knowledge could then be internalised and taken into the next negotiation without the need for documentation. However even then, the team used the war-room whiteboard as a form of technology to build up a map of the negotiation process and the negotiating stances of the governmental departments.

So it is possible for a small team to use something like Action Learning as a technology-free form of KM. However as soon as the conversation needs to reach beyond the immediate team, or where knowledge has to be stored for more than a few days, technology needs to play its part.  “Play it’s part” is the key point – technology is one of the four enablers of KM, and cannot deliver KM on its own. What it can do is extend the reach of learning discussions, and provide a place where the content of those discussions can be stored and developed over time. 

While there are a few cases where no technology is needed for KM, in the vast majority of cases Technology is one of the four main enablers, forms one of the four legs on your KM table, and will require one quarter of your focus during KM implementation.

View Original Source (nickmilton.com) Here.

Which Knowledge Management technologies add most value?

Interesting results are coming through from the Knoco 2017 Knowledge Management survey, including this plot of comparative KM technology value.

We asked the survey participants to rate these different types of technology by the value they have added to their KM program, including in the question the option to choose “we do not use this technology” or “it’s too early to tell”.

The chart above shows these technologies in order of value from left to right, as a stacked area chart, with the weighted value shown as a blue line (this line would be at 100% if all the participants that used this technology claimed it had “high value” and at 0 they all claimed it had no value).

The top of the grey area represents the usage percentage for these technologies, as the light grey area above represents people who do not use this technology. The top of the green area represents the percentage of people who said this technology had added “large value”.

288 people answered this question.

The technology types are listed below in order of usage, and in order of value.

Technology type in order of usage 
(most common at the top)
Technology type in order of value delivered  when used (most valuable at the top)
Best practice repository
Document collaboration
eLearning
People and expertise search
Enterprise search
Enterprise content management
Portals (non-wiki)
Video publication
Question and answer forums
Blogs
Lessons Management
Microblogs
Brainstorming/ideation/crowdsourcing
Wikis
Social media other than microblogs
Expert systems
Data mining
Innovation funnel
Semantic search
Enterprise search
Best practice repository
Document collaboration
Enterprise content management
eLearning
Portals (non-wiki)
People and expertise search
Question and answer forums
Lessons Management
Expert systems
Brainstorming/ideation/crowdsourcing
Microblogs
Video publication
Social media other than microblogs
Wikis
Semantic search
Data mining
Innovation funnel
Blogs

Comparison of usage and value

There is a strong correlation between usage and value. This could represent a tendency for the more valuable technologies to get the greatest use. This is a perfectly valid interpretation.  An alternative argument would be to say that technologies deliver more value the more they are used. Technologies at the top of the list are mainstream technologies, used frequently, and delivering high value. Technologies at the bottom of the list are less mainstream, and deliver less value to the companies that use them, because those companies make less use of these technologies. This is also a plausible interpretation.

Even with this interpretation, we could still look for “Good performing” technologies which deliver more value than their popularity would imply, and “Poor performing technologies” which deliver less value than their popularity would imply.

Under this interpretation, the best performing technologies are Enterprise Search and Expert Systems (both of them 6 places higher in the Value list than the Usage list) and the worst performing technologies in terms of value per use are Blogs.

This does not necessarily mean Blogs are a bad technology; it probably means they are not being used in ways that add KM value.

Changes since the 2014 survey

We saw very similar results in the 2014 survey, again with Blogs being the poorest performing technology given their usage figures, and again with the best performing technologies in terms of value vs use being Enterprise Search and Expert Systems.

Those technologies which have most increased in use between 2014 and 2017 are Microblogs and video publication, and not surprisingly these have also seen the greatest increase in value delivery as well. The technology which has decreased in use the most over the last 3 years is the innovation funnel technology.

View Original Source Here.

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