Start with the work, not the tool.
A recent Wall Street Journal report describes technology leaders seeing value in AI while struggling to measure exactly where the returns come from. That uncertainty deserves attention. Organizations need to know whether an investment is helping, what it costs to sustain, and whether the benefits justify continuing.
At HyperChimp, we believe that evaluation should begin before deciding on ChatGPT, Claude, Gemini, or another AI platform. For the small businesses and nonprofits we work with, it starts with understanding the work people need help doing.
Take a routine customer request. Someone reads it, finds the relevant information, drafts a response, gets approval, and follows up. AI might shorten the drafting step considerably. But if the request still sits in an approval queue for days, the customer may notice little difference.
The team has saved time on a task. Whether the service has improved is a separate question worth measuring.
Answer five questions before investing.
What needs to improve? Identify a specific problem: missed follow-ups, slow responses, repeated data entry, or information that is difficult to find.
What happens today? Record the time, effort, delays, and errors involved. Include the work people do to correct mistakes.
What would success look like? Choose an outcome that matters to the people doing or receiving the work.
Who is responsible? Give someone ownership of reviewing results, handling exceptions, and deciding whether the approach needs to change.
What will the recovered capacity make possible? Decide how the organization and its people will benefit from any time saved.
Let the people doing the work shape the evaluation.
The people doing the work belong in these conversations from the beginning. They can explain why an apparently simple step takes so long, which exceptions require care, and where a proposed shortcut could create another problem.
Their experience should shape both the design and the evaluation.
A useful measure might be the time from receiving a request to resolving it. Another might be the number of cases reopened because the first answer was incomplete. Asking employees how much checking the AI system requires can reveal costs that a software usage report misses.
For example, say a nonprofit answers donor questions by email. Before changing anything, it records how long a reply takes and how often a donor has to write back. A month after the new workflow starts, it measures the same two things. If replies go out faster but staff now spend that time checking drafts, the result should say so. Our guide to choosing a first nonprofit workflow walks through picking the right task.
Count the full investment and choose what happens next.
Financial discipline matters, too. Subscription fees are only part of the investment. Implementation, training, oversight, and maintenance all belong in the calculation. Time saved should be described accurately: it creates capacity, but it does not automatically become cash savings.
Then comes a leadership decision that deserves more attention: what happens to that capacity? A team could use it to give customers more thoughtful help, clear a backlog, or finish its work within reasonable hours. Those outcomes require deliberate choices. If every minute recovered simply becomes another demand, the organization should be honest about what has improved—and for whom.
Start with one manageable workflow. Establish a baseline and a review date. Measure the results, listen to the people involved, and adjust or stop when the evidence calls for it.
HyperChimp’s commitment is to build technology that leaves people more capable of doing meaningful work. We believe an AI investment should be evaluated against that commitment as carefully as its budget.
Have a process that is consuming too much of your team’s time? HyperChimp can help you understand what is getting in the way, define a worthwhile improvement, and determine where AI can help.