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|In: Strategy and Tactics|Last Updated:

Does your organization have a climate change problem?

No, no, no, no, no. I’m not asking if your organization is or will be affected by anthropogenic climate change, or if it has a plan for dealing with it.

No, what I’m asking is about a parallel, namely:

While in spite of overwhelming evidence, some people still doubt climate change is real and potentially devastating, by now that’s an ever-shrinking minority. And yet, as a society we’re still unwilling to take the steps needed to address the problem.

A likely reason: “solution aversion,” (and thanks to Katharine Hayhoe, Chief Scientist for The Nature Conservancy, for bringing this phenomenon to my attention).

Solution aversion is what happens when the solution to a problem is so onerous that our minds run away from it screaming “Murmee murmee murmee murmee” to drown out the voices insisting the problem has to be solved.

So when I ask if your organization has a climate change problem, I’m asking if it’s facing an emerging situation that threatens its existence or viability, except that it isn’t really facing it at all. It’s refusing to face the situation due to solution aversion.

The problem might be that your customers are aging and you have no strategy for replacing them with others whose life expectancy is greater.

It might be that your product architecture has painted you in a metaphorical corner, preventing your design engineers from adding the features your product needs to be competitive.

Closer to IT’s home, an “unplug the mainframe” initiative was chartered and budgeted with goals in line with its title: the plan is to migrate all of the hundred or so mainframe-hosted batch COBOL programs in your applications portfolio with a hundred or so cloud-hosted batch COBOL programs.

Which means that when IT finally unplugs the mainframe, all of the business managers who had put their plans on hold for two years will discover that the converted applications, having preserved their batch-COBOL legacy, are no more flexible than their big-iron ancestors. Which in turn means that by the time business plans become business realities they’ll be four years out of date.

If you think your organization’s decision-makers are succumbing to solution aversion, the obvious question is what you can do about it. The obvious answer is to try to persuade them to deal with their climate-change problem by putting together a solid business case.

The obvious answer is, sad to say, the wrong answer. You aren’t going to resolve this with evidence and logic, just as you aren’t going to solve it by tearing your hair out in frustration while saying, through gritted teeth, “That’s just kicking the can down the road.”

The only way to overcome solution aversion is to figure out an alternative solution that doesn’t trigger the aversion reaction. Usually, this means figuring out ways to nibble away at the problem in convenient, non-threatening ways.

In the case of actual climate change this might mean starting with painless steps like replacing incandescent bulbs with LEDs, and making your next car a plug-in hybrid.

In the case of mainframe unplugging it might mean identifying a small number of the mainframe batch COBOL applications that, by rewriting them in a microservices architecture would generate an 80/20 benefit in terms of improved flexibility and future business agility.

Bob’s last word: My usual formula for persuasion starts with selling the problem. There’s no point in designing a solution until decision-makers and influencers agree there’s a problem that needs solving. And it’s only after everyone has agreed on the solution that it makes any sense to take the third step – developing an implementation plan.

The role of having a plan in a persuasion situation is to give decision-makers and influencers confidence that the solution can, in fact, be successfully implemented.

This week’s guidance doesn’t violate this formula so much as it augments it. It’s intended for situations in which the most plausible solution … actually, plan, but the folks who coined the term “solution aversion” didn’t ask for my input … “un-sells” the problem.

So it should be called “plan aversion,” but let’s not quibble. What matters is recognizing when your organization has a climate-change problem so you can find ways to finesse the plan.

Bob’s sales pitch: CIO.com just posted the eighth and last article in my IT 101 series. It’s titled “The CIO’s no-bull guide to effective IT” and it both summarizes and serves as a tour guide to the previous seven entries. Whether you’re new to IT management or are a seasoned CIO, I think you’ll find value in the collection.

Also: Remember to register for CIO’s upcoming Future of Work Summit February 15th through 17th, where, among an extensive program, you can hear me debate Isaac Sacolick on the business readiness of machine learning. Our session is scheduled for February 16th, 2:50pm CST. Don’t miss it!

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|In: Cognitive Enterprise|Last Updated:

In case you missed the news, Israeli scientists have taught a goldfish how to drive.

Well, not exactly. They placed it in a bowl with various sensors and actuators, and it correlated its initially random movements to which movements moved it toward food.

The goldfish, that is, figured out how to drive the way DeepMind figured out how to win at Atari games.

This is the technology – machine-learning AI – whose proponents advocate using for business decision-making.

I say we should turn over business decision-making to goldfish, not machine learning AIs. They cost less and ask for nothing except food flakes and an occasional aquarium cleaning. They’ll even reproduce, creating new business decision-makers far more cheaply than any manufactured neural network.

And with what we’re learning about epigenetic heritability, it’s even possible their offspring will be pre-trained when they hatch.

It’s just the future we’ve all dreamed of: If we have jobs at all we’ll find ourselves studying ichthyology to get better at “managing up.” Meanwhile, our various piscine overseers will vie for the best corner koi ponds.

Which brings us to a subject I can’t believe I haven’t written about before: the Human/Machine Relationship Index, or HMRI, which Scott Lee and I introduced in The Cognitive Enterprise (Meghan-Kiffer Press, 2015). It’s a metric useful for planning where and how to incorporate artificial intelligence technologies, included but not limited to machine learning, into the enterprise.

The HMRI ranges from +2 to -2. The more positive the number, the more humans remain in control.

And no, just because somewhere back in the technology’s history a programmer was involved that doesn’t mean the HMRI = +2. The HMRI describes the technology in action, not in development. To give you a sense of how it works:

+2: Humans are in charge. Examples: industrial robots, Davinci surgical robots.

+1: Humans can choose to obey or ignore the technology. Examples: GPS navigation, cruise control.

0: Technology provides information and other capabilities to humans. Examples:Traditional information systems, like ERP and CRM suites.

-1: Humans must obey. Machines tell humans what they must do. Examples: Automated Call Distributors, Business Process Automation.

-2: All humans within the AI’s domain must obey. Machines set their own agenda, decide what’s needed to achieve it, and, if humans are needed, tell them what to do and when to do it. Potential examples: AI-based medical diagnostics and prescribed therapies, AIs added to boards of directors, Skynet.

A lot of what I’ve read over the years regarding AI’s potential in the enterprise talks about freeing up humans to “do what humans do best.”

The theory, if I might use the term “theory” in its “please believe this utterly preposterous propaganda” sense, is that humans are intrinsically better than machines with respect to some sorts of capabilities. Common examples are judgment, innovation, and the ability to deal with exceptions.

But judgment is exactly what machine learning’s proponents are working hard to get machines to do – to find patterns in masses of data that will help business leaders prevent the bad judgement of employees they don’t, if we’re being honest with each other, trust very much.

As for innovation, what fraction of the workforce is encouraged to innovate and are in a position to do so and to make their innovations real? The answer is, almost none because even if an employee comes up with an innovative idea, there’s no budget to support it, no time in their schedule to work on it, and lots of political infighting it has to integrate into.

That leaves exceptions. But the most acceptable way of handling exceptions is to massage them into a form the established business processes … now executed by automation … can handle. Oh, well.

Bob’s last word: Back in the 20th century I contrasted mainframe and personal computing systems architectures: Mainframe architectures place technology at the core and human beings at the periphery, feeding and caring for it so it keeps on keeping on. Personal computing, in contrast, puts a human being in the middle and serves as a gateway to a universe of resources.

Machine learning is a replay. We can either put machines at the heart of things, relegating to humans only what machines can’t master, or we can think in terms of computer-enhanced humanity – something we experience every day with GPS and Wikipedia.

Yes, computer-enhanced humanity is messier. But given a choice, I’d like our collective HMRI to be a positive number.

Bob’s sales pitch: CIO.com is running the most recent addition to my IT 101 series. It’s titled The savvy CIO’s secret weapon: Your IT team | CIO .

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