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

Quadrant charts (multidimensional scaling in two dimensions if you want to get all technical about it) are handy tools for visualizing patterns in data. They’re especially handy for oversimplifying complex situations, as several of you pointed out in response to last week’s column on determining which employee roles most and least lend themselves to remote work. Check out the comments: Where your desk should be – IS Survivor Publishing and scroll to the bottom.

And make no mistake about it. My quadrant-oriented analysis, based on the importance of process management and strong interpersonal relationships – was seriously oversimplified. To help de-over-simplify it, here are a few additional random thoughts and comments:

Pre-existing relationships: If relationship building drives the need for a role to be performed on-premises, why should teams that have already formed (and stormed, and normed) have to commute to their on-premises cubicles?

Answer: For pre-established teams, on-site face-to-face interactions might not matter, at least, not at first. But teams aren’t static entities. Over time, some members might “call in rich,” transfer to a different team, or be promoted to a different role. Meanwhile, new members might join and need integration into team functioning. Just my opinion: There isn’t yet a technological substitute for the chemistry of in-person team interaction for building the trust and shared understandings required for maximum team effectiveness.

We can’t be face-to-face anyway: In international enterprises, teams are often composed of members scattered across all latitudes and longitudes (well, maybe not all – few businesses employee Antarcticans or, for that matter, Guamians, but you get the idea). Bringing teams physically together can be too expensive, and the logistics too complicated to be practical.

It’s a key factor in the ability of SMBs (small-to-medium-size businesses) to compete with their larger competitors: They have the advantage of operating out of a single location, with no concerns about what, in an earlier column, I called the “soft bigotry of time zones” and the “softer bigotry of accents.” Cohesion is, in relative terms, easy.

It’s a competitive advantage because SMBs can, for the most part, bring team members together as needed, achieving alignment and trust more quickly and easily through proximity and similarity.

But this isn’t an unmixed blessing. SMBs often suffer from the disadvantage of excessive sameness. When everyone looks and largely thinks alike, “out-of-the-box thinking” and “not-in-the-company thinking” are the same.

Especially, SMB decision-makers are more prone than their larger competitors to assume their customers are just like them.

International or multinational/global businesses face the challenge of achieving the trust and alignment that happen naturally in an SMB, substituting sophistication in the use of collaboration technologies for the simplicity of physical presence.

SMBs face the obverse challenge.

Power: Employers are accustomed to having it; employees are accustomed to … well, to employers having it. But right now (nobody knows how long it will last), good employees are hard to find and know they have some leverage. So an employee’s preference for working remotely can’t just be written off as “That’s why they call it work.”

It’s akin to the early days of unionization. Employers then had to face the jarring realization that they couldn’t just call the shots anymore. Employees could and did negotiate with management from a position of significant strength.

Perhaps there are insights to be gleaned from this history.

Culture: Culture inflates the team-level characteristics of trust and alignment to enterprise scale.

Culture is far too big a subject to squeeze into a space this small. For today, consider that culture is, for the most part, a reflection of leader behavior. In on-premises SMBs the reflection is a high-fidelity image. The larger and more dispersed the organization, the more it’s like what you’d see in a fun-house mirror.

Bob’s last word: If achieving trust and alignment at a team level is hard when interactions are limited to what collaboration technologies allow, promoting a company-wide culture in a large, dispersed enterprise is orders of magnitude harder.

And yet, without a healthy and cohesive culture, leaders might as well relegate themselves to an operating model in which workgroups, departments, and divisions interact as if they’re all outsourcers, delivering their work products to their (gawd) “internal customers’” inputs while negotiating service level agreements that look a lot like arm’s length contracts.

Bob’s sales pitch: My ManagementSpeak cupboard is bare. Keep your ears open and send in your favorite euphemisms, obfuscations, and oxymorons. What you’ll get in return is the admiration of your fellow members of the KJR community (if I can attribute you as the source), or the secret satisfaction that comes from anonymity (if I can’t).