Most enterprises that build software are proudly flying the flag of AI/ML. "We're technology leaders!" their leaders crow in annual reports and at conferences. At the same time, any objective observer usually sees a lack of common sense in the operation of the company's systems. It often appears that, far from needing beyond-human artificial intelligence, they could use some insect-level functioning instincts that get things done. What's going on? Can it be fixed?
The Industry-standard way to fix the problem
The usual fix to the problem is to completely ignore the fact that there's a problem in public, while following something like these proven strategies:
- Brag, loudly and often, about you and your organization's commitment to AI/ML. The commitment is serious; it's deep and it's broad!
- Talk about the initiatives you've funded and the top experts you've hired.
- Talk about the promising things you've got in the works.
- Use extra phrases to demonstrate your seriousness, things like "1-to-1 personalization" and "adaptive processes" and "digital-first transformation."
- Put your top executives with fancy titles out there to follow the same strategy, using their own words.
I've given a detailed example of how a top healthcare insurance company follows this strategy while operating at a sophistication level that is best described as "hey, this electronic mail stuff sounds neat, let's give it a try."
Sometimes one of these organizations puts something in practice that works. It typically takes a great deal of time and effort to find and modify the relevant production systems. The efforts that are mostly likely to make it into production are those that can be done with the least amount of modification. For example, minimal-effort success can sometimes be achieved by extracting data from production systems, subjecting it to AI/ML magic and then either feeding a new system or making it effective with just a couple of insertion points.
The Obstacles to AI/ML Success
The obstacles to AI/ML success have two major aspects:
- The typical practice of leap-frogging all the predecessors to AI/ML to maximum sophistication.
- The extensive, incompatible existing production systems into which AI/ML power has to somehow be inserted.
A good way to understand these obstacles is to imagine that you're in a world in which boats are by far the most important means of bulk transportation. In other words, the world in which we all lived at the start of the 1800's. Suppose by some miracle a small group has invented nuclear power and has decided it would be a great way to provide locomotion to large boats instead of the sails and wind power then in use. What prevents the amazing new technology from being used?
Easy: the boats were designed for sails (with masts and all that) and have no good place to put a nuclear engine, and no way to harness its power to make the boat move. The strong steel and other materials required to make a turbine and propellers doesn't exist. You can demonstrate the potential of your engine in isolation, but making it work in the boats available at the time won't happen. You can spend as much time as you like blaming the boats, but what's the point?
The solution is clear by studying boat locomotion: there were incremental advances in boat materials and design, and the systems used for powering them. Paddle wheelers have been around for over a thousand years. Here's a medieval representation of a Roman ox-powered paddle wheel boat.
For serious ocean travel, the choice became the large sail boat, as in this painting of boats near a Dutch fortified town:
Suppose you had a nuclear engine of some kind and were somehow able to make it with materials that were generally not available in the 1600's. How would you use it to power the sail boat? The very thought is ridiculous. The problem is that the boats have no way to accept or utilize the nuclear engine.
How to overcome the obstacles to AI/ML
What would a sensible person do? Exactly what real-life people did in history: incrementally make boats suitable for more powerful means of locomotion, and make more powerful means of locomotion that would make boats go more quickly. Practically. You know, in real life.
That means, among other things, once steam power was created, gradually make it suitable for powering ships with sails -- using the sails to conserve coal when the wind was strong, and using coal to power paddles when the wind wasn't blowing. Then, after materials advanced, invent the screw propeller -- which didn't happen until the late 1800's -- to make things even better. Eventually, the engine and the ship would converge and be suitable for the introduction of nuclear power.
This is an excellent model for understanding how to overcome the obstacles to powering existing enterprise applications with AI/ML:
- The AI/ML can only be jammed into existing systems with great effort and by making serious compromises.
- With a few exceptions, simpler methods that can make real-life improvements should be devised and introduced first, with the portion of AI/ML gradually increasing.
- The existing enterprise applications are like wooden sailing ships, into which generation-skipping advanced locomotion simply can't be jammed.
- Evolve the applications with automated decision-making in mind, first putting in simple methods that will produce quick returns.
- The key to AI-friendly evolution is to center the application architecture on metadata in general, and in particular with metadata for workflow.
The important thing is this: increase the "intelligence" of your applications step by step, concentrating on simple changes for big returns. Who cares whether and to what extent AI/ML is used to make improvements? All that matters is that you make frequent changes to improve the effectiveness, appropriateness and personalization of your applications. Experience shows that relatively simple changes tend to make the greatest impact. See this series of posts for more detail.
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