Measure the before: banks that baseline are four times as likely to show a return

A named workflow tied to a revenue line, which is rarer than it should be. The claim is management's, and pricing and market conditions also move E&S growth, so treat it as a strong signal, not proof. The useful part for a mid-market firm is the shape: pick the choke point where business waits (submissions), speed it up, and watch the growth line.

Short post or one-slide graphic, "Where does business wait?": the Hanover example, then three questions for any regulated firm. What is the slowest handoff between a prospect and a decision? How long does it take today? What would you watch to know it got faster?

Adoption is far ahead of measurement, and the dividing line is cheap: write down how long the task took before AI and how long people spend checking the output now. Among banks that baseline, 57% have a financial number. Among the rest, 14%. This is association, not cause, but it costs almost nothing to test.

LinkedIn post, "What was the task time before?": lead with the 57 versus 14 split, add the call-summary example (summary in seconds, checking time unmeasured), end on one question for the reader's last pilot.

Expectation is running years ahead of results, and results track company size: 1% of finance chiefs below $1 billion report significant returns versus 17% above $5 billion. For a mid-sized firm that is a planning fact. Budget for a measurement ramp of up to two years and name one accountable owner, because only 13% have one.

Carousel or short video, "The two-year ramp": three slides on 10% today, 54% expecting it in 24 months, and the size gradient. Close with the one-owner question: who answers for this AI initiative by name?

Build speed is the easy part to show and the easy part to mistake for ROI. What we can state is input quality and time to build; what we cannot yet state is replies, meetings booked or revenue. Saying exactly where the evidence stops is the point.

Build-in-public post or workshop exercise, "Speed, quality, return": three columns for one AI build. Mark what was measured (hours, records cleaned), what was not (outcomes), and the date by which it will be.

Risk teams now count over-investing in the wrong AI use case as an enterprise risk, next to cyber. Gartner's advice is a portfolio view: tie each investment to a strategic priority, validate readiness before scaling, and measure value by its effect on decisions, not only productivity. That is the pilot-to-P&L problem, stated as a risk.

Carousel, "Three questions before the next pilot": does it tie to a priority, are we ready to run it, how will we know it worked. Pair with a one-line ask for the risk or compliance lead: who signs off that the benefit case is real?