A convincing technology demonstration can make a difficult task look effortless. A few instructions produce a polished report, a property description or an impressive shortlist. The harder question is what happens when the tool meets the untidy information and competing priorities of an ordinary working day.

Joe Sokolic, founder and CEO of Sokolic Property Group, believes property businesses should judge AI through clearly defined operational problems.

“Start with the piece of work that needs improving,” says Sokolic. “If we cannot describe the problem and how we will measure improvement, we are not ready to judge the solution.”

Consider a brokerage that spends considerable time preparing client shortlists. The task may involve finding available properties, checking specifications, comparing requirements and assembling a presentation. An AI pilot could focus on drafting the initial comparison from approved records.

Before introducing it, the business should understand the existing process. How long does preparation take? How often do brokers have to correct information? Which missing details cause follow-up work? Those observations provide a baseline against which a trial can be assessed.

Sokolic argues that the cost of checking must be included. A draft produced in seconds may still require extensive verification. If errors are difficult to detect, the apparent time saving may disappear when the full workflow is considered.

“The measurement should include the work required to make the output dependable,” he says. “That is the output the client receives and the business stands behind.”

A useful pilot would have a limited scope, a named owner and a clear review date. The team could compare several completed cases, record corrections and ask whether the final shortlist became more useful. It should also identify cases where the tool added little value.

Staff feedback matters because the person using the system may encounter friction that a manager never sees. Re-entering the same information, checking conflicting records or moving between disconnected applications can make an apparently efficient tool cumbersome in practice.

The decision after a trial should remain open. The business may expand the workflow, simplify it or stop using it. A pilot has served its purpose if it reveals that the proposed approach is unsuitable before wider adoption.

For Sokolic, disciplined experimentation is part of leadership. It allows a firm to move with intent while keeping the standard of service visible.

“A successful AI project should be easy to explain in business terms,” he says. “We should be able to say which problem improved, what evidence supports that conclusion and who benefits from the change.”

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