Organizations tend to have an enormous blind spot; it has to do with time and the investment of it. Organizations and those who manage them want to see milestones and love to see things finished. Projects get finished. Products get shipped, whether physically or electronically.
A transformation is neither of these things, and transformation is what is required for us to move ahead in the new environment created by the many and varied uses of what is generally called AI.
Project or Product
The definition of product, according to Dr.Mik Kersten, is closer to how we should be looking at things now than other definitions, but still falls short in my estimation. We don’t build or run a transformation. It may or may not be planned. It may originate from either inside or outside an organization.
Perhaps the best way to see this is to look at a transformation that has occurred in relatively recent history: the smartphone and its associated technologies. The smartphone has transformed the way we communicate, navigate, investigate, purchase, pay, play games, bank and transfer money, make restaurant and hotel reservations, and so on. In fact, it’s hard to find an aspect of our daily lives that the smartphone has not affected. Our lives now look substantially different from how they did in the pre-smartphone world.
The Next Transformation
The advent of AI, whether strategically adopted into workflows by an organization, embedded in the software we use every day, or the “Shadow AI” individuals are using outside the purview of the organization itself, ushers in a transformation of work and the workplace, likely in as dramatic a way as the smartphone has transformed our daily lives. We are already seeing many areas of work moving from being task-driven. The tasks themselves can be handed off to one AI tool or another, ostensibly so that humans can work on the more creative and imaginative aspects of business. (A note on this later.)
Let’s look at one example of this task handoff: after-call work in IT support. Let’s suppose we have a small service desk with 5 agents. The agents average resolving 20 cases each workday. The agents also average 4 minutes for after-call work (completing the notes and marking the case resolved). The new version of their ITSM tool embeds Generative AI, and one of its capabilities is to complete each case summary in a matter of seconds. Each case now takes about 3 minutes and 30 seconds less to mark resolved.
| After-Call Work (ACW) Before AI | After-Call Work with AI |
| 20 cases x 4 minutes = 80 minutes per day | 20 cases x 30 seconds = 10 minutes per day |
| 80 minutes per agent x 5 agents = 400 min. | 10 minutes per agent x 5 agents = 50 min. |
| Total ACW = 400 minutes per day | Total agent time saved on this task = 350 minutes (5 hours and 48 minutes) |
Each of the 5 agents in our example now spends over an hour per day on after-call work. Management must now decide how best to use that extra time, and perhaps it’s learning better and better ways to use emerging technology. This is just one small example of how work is changing.
Is this time-saving a project? No. Is it a product? We can call it a “product of the technology” perhaps, but not in the sense that it is any part of “build, run and enhancement” except that support may be more efficient.
Transformation Is Complex
This change, and hundreds of others like it across the organization, are ripples in the transformation that is occurring and will continue for years to come. Ripples, which radiate outward and interact with objects and other ripples, are an appropriate analogy. Their motion follows the principles of fluid dynamics and appears simple when only one pebble is thrown into a pond; when many pebbles of various sizes and weights are tossed into the water at different times, the pattern of ripples becomes incredibly complex. Amplification, interference, and cancellation come into play, and it takes some high-level math to predict the results.
Add to this complexity the unpredictable actions of the people involved (picture them dipping paddles into the rippling pond), and the outcomes become quite blurry and nearly impossible to predict in a traditional, linear way. We need new ways of thinking, just as atomic physics did to begin to understand the interactions of subatomic particles, in light of quantum mechanics.
I expanded on this thinking in SymphonyAI’s crowdsourced paper, How to Turn ITSM Risks into Growth Opportunities in 2026, which gathered perspectives from 17 ITSM industry voices on the risks leaders are underestimating as AI becomes embedded in organizations. The paper explores related themes across people, processes, technology, and value delivery, and is worth reading alongside this piece.
“Every business process reflects the constraints that existed when it was first devised.” — Maria Cardow, CIO, LevelBlue
As the constraints change or vanish, the way businesses run must change and will change, with or without input from us. But we cannot predict these changes with any degree of certainty. We find ourselves facing a version of Heisenberg’s Principle as new ways to use AI are surfacing every day; we might be able to see where we are, but we can’t see where we are going, or how fast.
The simple choices, like how to use the agents’ saved time in our example, should be made carefully, but quickly, before what comes next arrives, before the constraints evolve, before the ripples collide. Luckily, there are ways AI can assist us in making those decisions, too.
We have arrived at a time when change’s speed and complexity exceed our abilities to manage them: What cannot be managed must be led.
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