Measure the work
before you let AI change it.

On 29 July, C.H. Robinson filed its second quarter results with the SEC. The freight broker credits what it calls its Lean AI strategy with productivity improvements of over 60% “since the end of 2022” in its two main businesses. The same document shows average headcount down from 12,858 to 11,471 in a year, North American volume up 1.5% while the industry’s shipment index fell 3.3%, and adjusted operating income up about 20%.

The phrase I keep coming back to is the date. C.H. Robinson picked a starting line, kept counting from it, and now reports against it every quarter. Most companies I meet cannot do that for a single workflow, because nobody wrote down how long the work took, how often it went wrong, or what one finished unit cost before the AI arrived. Without that, a CFO is left defending AI spend with anecdotes and survey scores.

37%

Respondents who attribute any EBIT impact to AI, flat on 2025, per McKinsey, 2026.

25%

AI initiatives that delivered the expected ROI, according to 2,000 CEOs surveyed by IBM, 2025.

8%

Organizations reporting established AI ROI, while 95% have an AI strategy, per KPMG, Q1 2026.

6%

Executives who saw payback on a typical AI use case within a year, per Deloitte, 2025.

People feel faster while the P&L stays flat

The clearest version of the gap comes from The state of AI in 2026, which McKinsey published in August from a survey of 1,719 people in May and June. Eight in ten respondents say AI has improved their own productivity. Only 37% say it has contributed anything to their organization’s EBIT, essentially the same share as a year earlier, even though more companies are now scaling AI. The high performers, those who attribute at least 5% of EBIT to AI, are stuck at about 6% of respondents.

The other big surveys land in the same place. KPMG’s Global AI Pulse, 2,110 executives in 20 countries, found that 64% report meaningful business value and only 8% report established return on investment. IBM’s 2025 CEO study holds the oddest pair of numbers: 68% of CEOs say their organization has clear metrics to measure innovation ROI, and the same CEOs say only 25% of their AI initiatives delivered the return they expected. Having a metric on a slide and having a number from before the change are two different things.

The difference between the 6% and everyone else is partly about measurement. McKinsey reports that high performers are twice as likely as others to have defined processes to measure the impact of their AI initiatives, and far more likely to redesign workflows instead of adding AI to existing ones. The second habit depends on the first. You cannot tell whether a redesigned workflow beats the old one if nobody recorded the old one.

Without a before, the after cannot be audited

Deloitte asked 1,854 executives in Europe and the Middle East about AI returns in the summer of 2025. Most said a typical use case reached satisfactory ROI in two to four years, against the seven to twelve months they expect from other technology. The report names the measurement problem directly: “fragmented systems and siloed platforms make it challenging to track before-and-after impact.” Executives added that AI usually arrives alongside data cleanup, team changes and process fixes, which makes its share of any gain hard to isolate.

That second point is the one finance teams underrate. When a company rolls out an AI tool, cleans its vendor master and reorganizes the AP team in the same quarter, every one of those projects will claim the improvement. A baseline taken before any of them started, plus a team that changes later, is the only way to split the credit with a straight face.

Gartner made the same argument to CFOs in May, using a survey of 204 finance leaders in which 63% said AI implementation in 2025 was slower than expected.

They must not mistake activity for impact.

He was talking about counts of pilots, tools rolled out and use cases in production. Those are the numbers most AI dashboards report, because they are easy to collect after the fact. Hours, errors and unit cost are hard to reconstruct after the fact, so they have to be collected first.

The credible reports all name a starting point

The public examples I trust share one feature: a stated before. C.H. Robinson fixes its start at the end of 2022 and puts the productivity claim in the same filing as headcount, volume against the market and operating margin, so an analyst can check one against the others. Its CEO, Dave Bozeman, also adds that the strategy “needs to result in better service,” and cites customer satisfaction scores next to the productivity number. The release does not spell out exactly how productivity is calculated, which is the one thing I would want added.

Klarna’s 2024 announcement of its customer service assistant is older and widely quoted, and it reads like a baseline report. Resolution time fell from 11 minutes to under 2. Repeat inquiries, the support version of rework, dropped 25%. Customer satisfaction was on par with human agents. The $40 million profit figure was labeled an estimate for the year ahead, which is honest. Each claim compares the new state to a measured old one, and one of them measures quality alongside speed.

The research version is a study by Erik Brynjolfsson, Danielle Li and Lindsey Raymond, first released in 2023 as an NBER working paper. They followed 5,179 support agents as an AI assistant was rolled out to different groups at different times. Because they had each agent’s output before access and a group that had not yet received it, they could say with some confidence that issues resolved per hour rose 14% on average and 34% for novice and low-skilled agents, with little effect for the most experienced. A CFO does not need an economist to borrow that design: measure first, roll out in waves, and compare.

Four numbers per workflow are enough

A baseline does not need a data warehouse. For each workflow you plan to change, I would record four numbers, all tied to one clearly defined finished unit: a paid invoice, a closed ticket, a signed contract, a shipped order, a closed month.

Hours per unit. Total human hours spent on the workflow in the period, divided by units finished. Include review, chasing and handoffs, which are usually where the time goes.

Cycle time. Elapsed time from the trigger to the finished unit, taken from system timestamps where they exist. Report the median and the slowest tenth, since the slow tail is what customers and auditors notice.

Error and rework rate. The share of units that come back: reopened tickets, credit memos, correcting journal entries, contract redlines after signature. This is the number AI can quietly make worse while hours improve, so it belongs in the baseline from day one.

Cost per finished unit. Fully loaded labor for those hours, plus software, outside services and the cost of rework, divided by units. Once AI is in the workflow, add token and license costs and the human time spent checking its output. I made the case for pricing AI this way in an earlier note; the baseline is what gives that unit cost something to be compared with.

A baseline a CFO can run in three weeks

This is the method we use. It fits inside two to four weeks, depending on how many workflows you take on, and it needs one analyst from FP&A, the process owner and a few hours from the people who do the work.

Week one: choose and define. Pick the three to five workflows where AI money is about to be spent. For each one, write a single sentence defining the finished unit and when it counts as done. Agree on the period you will measure, ideally a normal month with no close or peak season distortion, or adjust for it. Name the controller who will sign the result.

Week two: collect. Pull counts and timestamps from the systems that already hold them: ERP, ticketing, CRM, document management. For hours, ask the team to log time on the workflow in 15-minute blocks for ten working days, or use work sampling, where people record what they are doing at a few random moments each day. Pull rework from the system records listed above and ask the team which kinds of rework the system misses.

Week three: price and freeze. Turn hours into cost with the loaded rates finance already uses. Calculate the four numbers per workflow and put them on one page with the definitions, data sources and known gaps. The process owner and the controller sign it, and the page is filed where the audit trail lives. If possible, hold one team, site or region back for the first wave of the rollout, so you have a group to compare against.

Week four, if you need it: set the rules. Agree in writing how savings will be counted. My rule is that a saving counts only when it shows up in one of three places: fewer hours bought (overtime, contractors, open requisitions left unfilled), more finished units for the same cost, or lower rework cost. Set review dates at 90 and 180 days, measured exactly the way the baseline was.

The decision rule that follows is simple. No AI business case gets approved without a signed baseline for the workflow it targets. That one rule changes the conversation with vendors, internal champions and the board, because every claim afterward is a comparison against a number someone signed before anything changed.

What to do this quarter

For the CFO: pick the one workflow where the next AI purchase is most likely, run the three-week baseline before the contract is signed, and attach the one-page result to the purchase request. Then ask for the same page from every team that already rolled out AI without one, and accept that their first honest measurement starts today.

For the CEO: ask for AI results in the board pack in the same four numbers, against a stated start date, the way C.H. Robinson reports them to its shareholders. If a team cannot show a before, give it three weeks to get one before you approve its next request.