Kick that can!
18 November 2024 (Paris, France) — Companies are embracing smart software. But not universally. And not quickly.
One question which, in my view, is getting little attention and that many, many corporations are posing: “What is the cost of changing an AI system a year or two down the road?”
The focus at this time is getting some AI up and running so an organization can “learn” whether AI works or not.
But a parallel development is taking place in software vendors enterprise and industry-centric specialized software. Examples range from a brand new AI powered accounting system, to Microsoft “sticking” AI into the ASCII editor Notepad. But many corporations are “waiting”.
If you have attended any of the top-table corporate events held over the last 2 months across the U.S., you know the question is out there.
As an example, let’s tally the costs which an organization faces 24 months after flipping the switch in a hospital chain which uses smart software to convert a physician’s spoken comments about a patient to data which can be used for analysis to provide insight into evidence based treatment for the hospital’s constituencies.
Here are some costs for staff, consultants, and lawyers:
- Paying for the time required to figure out what is on the money and what is not good or just awful – like dead patients
- The time required to figure out if the present vendor can fix up the problem or a new vendor’s system must be deployed
- Going through the smart software recompete or rebid process
- Getting the system up and running
- The cost of retraining staff
- Chasing down dependencies like other third party software for the essential “billing process”
- Optimizing the changed or alternative system.
The enthusiasm for smart software makes talking about these future costs fade a little.
I read “AI Makes Tech Debt More Expensive“. The author is correct: GenAI cannot handle complexity. It is a very good (and short) essay worth your time. I just want to highlight one paragraph (and I bolded two points, not the author):
In essence, the goal should be to unblock your AI tools as much as possible. One reliable way to do this is to spend time breaking your system down into cohesive and coherent modules, each interacting through an explicit interface. A useful heuristic for evaluating a set of modules is to use them to explain your core features and data flows in natural language. You should be able to concisely describe current and planned functionality. You might also want to set up visibility and enforcement to make progress toward your desired architecture. A modern development team should work to maintain and evolve a system of well-defined modules which robustly model the needs of their domain. Day-to-day feature work should then be done on top of this foundation with maximum leverage from generative AI tooling.
So ….
- Will organizations make this shift?
- Will the hyperbolic AI marketers acknowledge the future costs of pasting smart software on existing software like circus posters on crumbling walls?
Nope.
Those two year costs will be interesting for the bean counters when those kicked cans end up in their workspaces.
