After writing last week about predictable costs, I was delighted to see a recent CIO magazine article quoting a consultant harkening back to his days as a telecom manager:
“I spent 25 years in telephony where every minute had a price, and agentic AI is the first software I’ve seen that bills like a phone call and gets budgeted like a license,” says CJ Combs, AI strategy executive at Columbus.
Not only is there now this gap between billing and budgeting; the article goes on to note another key difference: Agentic AI’s costs are unpredictable because the technology’s behavior is unpredictable. With telecom in a mature enterprise, you pretty much knew within a reasonable range how much your users were going to consume, based on historical trends. Maybe agentic technology will mature enough, or we’ll simply get enough experience, to predict how much effort goes into accomplishing the tasks end users set the agents on. But we’re not there yet.
Which is ironic. Normally we think of machines as predictable and humans as variable. AI agents may not exactly be capricious, but it’s safe to say their behavior is not fully understood at this point.
The telecom comparison is adjacent to another point that the CIO article discusses, as does this article in a publication called Becker’s Health IT. The latter offers a case study describing a health system’s attempt to unify three discrete communications systems: one for patient texting, one for clinician texting, plus Microsoft Teams and email. It’s a great account of a deployment that didn’t go as expected, but still yielded valuable lessons, and you should definitely read the whole thing.
The health system’s ultimate decision to stop short of fully unifying the systems grew out of issues with change management among the various constituencies, according to the article, which quoted a corporate leader, “Change management had to come from three areas, not just IT, but clinical and operations as a trifecta.”
This was a valuable lesson the enterprise will apply to its AI decision-making, according to the article. Not coincidentally, it’s also an AI lesson the previously-cited CIO article emphasized, quoting one expert:
“The biggest AI cost overruns rarely come from technology,” says Abhijit Kakhandiki, SVP and GM of digital business automation at BMC Software. “They come from organizations deploying AI without redesigning processes, ownership models, and governance.”
Enterprise Connect experts have been stressing the importance of change management going back to the earliest days of Unified Communications, ever since user adoption became an issue you could no longer take for granted. AI in general and agentic AI in particular are poised to pull even more business processes and end users directly into implementations that either impact or are driven by communications/collaboration systems. This will deepen the change management challenge and require that IT decision-makers work even more closely with business leaders and develop a fuller understanding of their users’ needs.
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