AI works. This is how.

Two companies. The same brief: rebuild customer service around AI. The same models. The technology company goes live within six months. The large bank is counting in years.

That is not an anecdote. That is the pattern in Stanford’s Enterprise AI Playbook, which examined 51 successful AI deployments from the inside. The difference between weeks and years never lies in the technology. It lies in the organisation.

Last month I wrote: AI works, most organisations do not. This month, the good news. The companies that can do it do strikingly similar things. So it can be copied. Not just by DBS and Freeport-McMoRan, the usual suspects from McKinsey’s playbook. Also by a truck maker from Illinois.

The model is the easy part

Ask the people who have done it what was hardest. They rarely name the technology. Stanford counted: 77 per cent of the biggest obstacles were invisible. Change management. Data quality. Process redesign.

And 61 per cent of the successful projects had a failure behind them first. Always the same pattern: AI let loose on a process that was already broken. A flawed process cannot be rescued by AI. It just fails faster.

What made the second attempt work? A CEO who took ownership. Not parked with the CTO. Weekly check-ins, not quarterly reports.

And this: in none of the 51 cases was anyone punished for a failed AI attempt. Read that sentence again. Is that true where you work?

Oh, and the resistance did not come from the shop floor. The most frequently cited brake was the staff functions. Legal, HR, Risk, Compliance. Precisely the people who say they are protecting you.

Map the work, not the tools

Why can’t managers see where AI really changes their business? Researchers from INSEAD and Harvard Business School, in the latest HBR, call it the mapping problem.

They ran an experiment with 515 companies. All got the same tools, the same training, the same budget. Half got one thing extra: examples of how others had redesigned their work around AI. That half found 44 per cent more applications. Won paying customers more often. Generated nearly twice the revenue. Not through better technology. Through better looking.

Robert Oh, chief digital and information officer at truck maker International Motors, watched it happen in his own company. Dozens of enthusiastic experiments. Tasks got faster. The business did not. Technology demos, not innovation. The Financial Times has a word for it: AI tourism. Decide you need an AI strategy first, then go looking for a problem. So he put the executive team round the table, mapped the processes from sales to service, identified 21 opportunities and scrapped sixteen. One project went from idea to production in twelve weeks. Payoff: millions a year.

Measure growth, appoint an owner, finish the job

This summer Wharton surveyed more than 1,400 people in large American enterprises. Eighty per cent see a positive ROI on AI. Only 28 per cent see a strongly positive one. What separates the two? Three things.

One: what you measure. Those who judge AI on growth, such as revenue, conversion or output, report strong returns almost three times as often as those who count hours saved. Yet “hours saved” is still the most popular yardstick. You measure what is easy, not what matters.

Two: who carries it. A genuine Chief AI Officer: 58 per cent strong returns. The task added to a sitting executive’s plate: 24 per cent. Nothing arranged: 8 per cent. But beware, Bain partner Florian Mueller warns in the FT: a Chief AI Officer can be a great builder but also a dangerous alibi. If staff hear about AI only from the AI chief, it is a project. If they hear it from the CEO and the whole executive team, again and again, it is a strategy.

Three: whether you finish. Those who complete the redesign, instead of bolting AI onto a handful of processes, score 67 per cent. Those organisations reorganised their management. They did not lay off more people than the rest. Let that sink in.

What this asks of people

This is where it touches my profession. An insurer in the Stanford study put it bluntly: we lack people who understand the process and the AI. And who can bring the two together. That profile is scarce. It also appears in almost no job description.

Wharton shows one more thing. 39 per cent of managers say their organisation is aligned on AI. Of their employees, 26 per cent say so. That gap runs through every industry, every company size, every department but one. At the top, they think everyone is on board. On the floor, they are waiting for the briefing.

Leaders who can close that gap will be worth their weight in gold in the years ahead. Are you looking for them yet?

In short: AI works. The organisations where it works do not have better models. They fixed the process first, mapped the work, measured for growth, appointed a single owner and finished the job. With people who understand both process and technology.

You are welcome to exchange thoughts on this — on the leaders who can carry an AI transformation from process map to result, and on finding and selecting the best, right candidates for leadership roles in your organisation.

Warm regards,

Aegeus

PS On this subject in particular, it seems only right to say so: I write these newsletters with the help of AI. I decide the topic, the argument and the tone; the AI helps me with desk research, checking figures against their sources and sharpening the text. Exactly as described above: it only works if you keep hold of the process yourself.