Sunday, 23 April 2017

The depreciating value of human knowledge

Automation is just one facet on the broader spectrum of AI and machine intelligence. Yes, it's going to affect us all (it already is with the increasing emergence of intelligent agents and bots), but I think there is a far deeper issue here that - at least for the majority of people who haven't become immersed in the "AI" meme - is going largely unnoticed. That is, the very nature of human knowledge and how we understand the world. Machines are now doing things that - quite simply - we don't understand, and probably never will. 

I think most of us are familiar with the DIKW model (over-simplification if ever there was), but if you ascribe to this relationship between data, information, knowledge and wisdom, I think the top layers - knowledge and wisdom - are getting compressed by our growing dependencies on the bottom two layers - data and information. What will the DIKW model look like in 20 years time? I'm thinking a barely perceptible "K" and "W" layers!

If you think this is a rather outrageous prediction, I recommend reading this article from David Weinberger, who looks at how machines are rapidly outstripping our puny human abilities to understand them. And it seems we're quite happy with this situation, since being fairly lazy by nature, we're more than happy to let them make complex decisions for us. We just need to feed them the data - and there's plenty of that about! 

This quote from the piece probably best sums it up:

"As long as our computer models instantiated our own ideas, we could preserve the illusion that the world works the way our knowledge —and our models — do. Once computers started to make their own models, and those models surpassed our mental capacity, we lost that comforting assumption. Our machines have made obvious our epistemological limitations, and by providing a corrective, have revealed a truth about the universe. 

The world didn’t happen to be designed, by God or by coincidence, to be knowable by human brains. The nature of the world is closer to the way our network of computers and sensors represent it than how the human mind perceives it. Now that machines are acting independently, we are losing the illusion that the world just happens to be simple enough for us wee creatures to comprehend

We thought knowledge was about finding the order hidden in the chaos. We thought it was about simplifying the world. It looks like we were wrong. Knowing the world may require giving up on understanding it."

Should we be worried? I think so - do you?
Steve Dale

Wednesday, 12 April 2017

Hail to the Chief - creating faux senior roles is no alternative to a grounded strategy

I've been advising a client that is devising a new knowledge strategy.

Here's a snippet of a recent phone conversation...

Client: 'We're thinking of appointing a Chief Knowledge Officer. We need to show that the strategy has some real clout behind it'.

Me: 'So will this Chief Knowledge Officer have a seat on the main board? If not, how many levels down will the role be positioned?' (The board has only 3 members, CEO, Finance/HR and Operations directors)

Client: 'No, it will be at senior manager level' (that's 3 levels down from the board)

Me: 'I think you should wait to see what the knowledge strategy requires, before creating roles. I'm going to send you an article from a recent Harvard Business Review. Let's have another conversation when you've read it'.

The HBR article I emailed was 'Please Don't Hire a Chief Artificial Intelligence Officer'
I asked my client to simply substitute 'KM' for 'AI' and 'Chief Knowledge Officer' for 'Chief AI Officer'.

Try this yourself with the following paragraph from the article and you'll see why...

'However, I also believe that the effective deployment of AI in the enterprise requires a focus on achieving business goals. Rushing towards an “AI strategy” and hiring someone with technical skills in AI to lead the charge might seem in tune with the current trends, but it ignores the reality that innovation initiatives only succeed when there is a solid understanding of actual business problems and goals. For AI to work in the enterprise, the goals of the enterprise must be the driving force.
This is not what you’ll get if you hire a Chief AI Officer. The very nature of the role aims at bringing the hammer of AI to the nails of whatever problems are lying around. This well-educated, well-paid, and highly motivated individual will comb your organization looking for places to apply AI technologies, effectively making the goal to use AI rather than to solve real problems'.
The problem with creating 'Chiefs' is that they imply clout, but often have none. Witness the number of Chief Knowledge Officer jobs that were created around the turn of the century and how many remain today. I can't think of one. 

Before any roles are created, it's essential that those with real clout understand how organizational learning or knowledge transfer can help them achieve their personal objectives and solve 'actual business problems'. Get that right and you're more than halfway to your strategy. Creating hollow roles are probably unnecessary nails.

Friday, 7 April 2017

Motivating deep experts

Every now and again you hear something that is so simple, you wonder why you hadn't thought of it before. I had one of those moments listening to an superb Knowledge and Innovation Network webinar yesterday. Ian Corbett was presenting on 'Helping experts become catalysts for knowledge and Innovation'. KIN members can see Ian's slides on Memberspace in the Management Buy-in special interest library.

Ian, originally a geologist by trade, has done a lot of research on 'expertise' and is now applying it to charitable education projects in South Africa, where he lives.

The 'aha' moment during Ian's talk came when he was explaining how to get the best from deep experts or technical teams. The defining characteristics are:

  1. They value face-to-face interaction (plays to their inner ego)
  2. Low tolerance for admin and passing fads
  3. They seek innovation, not reuse
  4. They want autonomy

Pretty obvious when you think about it eh?

Yet how often do managers acknowledge these simple needs? KIN had a good look at intrinsic motivations at the recent Spring Workshop on Behavioural Economics. Looking at these 4 motivational factors, they might nicely define what intrinsic motivation means for a deep expert.

Next time you are working with a group of experts, what will you do to act on, or at least acknowledge these?
Source: APQC

KIN members can see Ian's slides on Memberspace in the Management Buy-in special interest library.