AI adoption creates potential; realizing value requires changing how the business works.
That distinction has become central to my preparation for Faster Isn’t Smarter. People can use AI regularly, produce work faster, and still struggle to show what improved for the customer or the organization. Adoption is an achievement. It also leaves an important management question unanswered: what turns that activity into value?
The AWS report Reimagine: Turning AI into Value explores this gap through executive interviews and Amazon’s internal experience. One useful distinction is between measuring usage and developing the capability to work differently. Knowing that people opened a tool tells us very little about whether they challenged an assumption, redesigned a process, or produced better work. These are practitioner observations rather than proof of a universal formula, but they raise questions worth bringing into leadership reviews.
Adoption measures still have a purpose. They can reveal where access, confidence, relevance, or support is missing. The problem begins when we ask those measures to establish something they cannot: whether the business is better off.
Consider an illustrative customer-support workflow. AI helps employees draft replies more quickly. First-response time improves. More cases are marked complete.
Then follow the work downstream. Do customers contact the company again because the answer was incomplete? Are experienced employees spending more time checking recommendations? Has the queue moved from drafting to approval?
The improvement may be real. Its value depends on what happens across the complete workflow.
This is why I keep returning to a simple exercise: follow one piece of AI-assisted work to the person who receives it. Ask what became easier, what still requires judgment, and what new work appeared. A team’s productivity gain can create additional capacity—or additional work for someone else.
Ania W. Masinter’s HBR playbook, Prioritizing AI Investments That Create Real Value, makes a related point: identifying opportunities requires examining how work crosses functional boundaries. The investment decision needs to include workflow redesign, customer needs, and where competitive advantage is changing.
For leaders, that means assigning ownership beyond the tool deployment. Someone needs the authority to change the handoffs, resolve conflicting priorities, and remain accountable for the eventual outcome.
Time saved needs a destination.
Suppose a team releases several hours of capacity each week. Those hours could support additional customer demand, reduce a backlog, improve service, or make room for work that has repeatedly been postponed. They could also disappear into more meetings, more output, or fragmented gaps that are difficult to use.
The business case therefore needs a decision about what happens next. Who will redirect that capacity? What should improve? How will we know?
Multiplying estimated hours saved by salary cost may help describe potential capacity. It does not establish a cash saving. The value story needs to account for what the organization actually does with the time, alongside implementation, AI operating costs, checking, and rework.
This also connects to a distinction I have been exploring in my recent writing: AI for efficiency and AI for opportunity.
Efficiency asks how we can do existing work better or faster. Opportunity asks what becomes possible when a constraint changes.
A team that can examine customer feedback more easily might use that capability to produce its existing report faster. It might also test whether it can identify unmet needs that previously remained buried across thousands of interactions.
That second possibility requires imagination and evidence. What constraint has changed? Which customer need could we now address? What is the smallest experiment that would tell us whether the opportunity matters?
If every AI investment must justify itself through immediate labor savings, we risk narrowing the portfolio before exploring what could create growth.
The pace of learning matters as much as the pace of execution.
AI can help us make the next decision before we understand the consequences of the last one. A faster reply is visible immediately. Repeat contacts, customer trust, and the effects of a policy change may take longer to assess.
AI can help shorten some feedback loops. Other outcomes still need time to emerge.
This is where the graduated autonomy ladder in my conference work becomes useful. AI can observe, advise, prepare work for approval, act within defined bounds, or operate under delegated authority. The appropriate role depends on the decision, the consequences, and the evidence available.
Drafting a response and authorizing a refund require different permissions. Expanding authority should follow demonstrated performance, enforceable limits, and a workable route back to human review. There is no requirement for every workflow to reach maximum autonomy.
Making these choices part of everyday management matters. In Transformations That Work, Michael Mankins and Patrick Litre emphasize embedding transformation into the company’s operating rhythm, involving middle managers, and managing people’s capacity for change. Those lessons are directly relevant when employees are expected to learn new tools while continuing to deliver their existing work.
For the next AI review, I would bring together the team reporting a productivity gain, the team receiving its output, and the leader accountable for the overall result. Choose one workflow. Establish the baseline, the intended benefit, how released capacity will be used, and when enough evidence should exist to decide what happens next.
I’m particularly interested in the handoff between an AI team demonstrating a gain and a business leader making that gain useful. In your organization, who owns that handoff—and what have they needed the authority to change?