What does a successful AI project actually look like?
Short answer: narrow, integrated, measured. The projects that pay off pick one high-value workflow, rebuild it around the tool, and track a metric that existed before AI did.
The few that worked narrowed to one high-value workflow and redesigned it around the tool, instead of bolting AI onto the old process (MIT Project NANDA; McKinsey, 2025).
Narrow, integrated, measured. The moonshot dies; the boring one compounds.
›transcript
- What a winning AI project looks like.
- The moonshot dies. The boring one compounds.
- One workflow, wired into the tools people already use, measured.
- Run the free, interactive course at heimlandr.io/ai-reality-map.
// the deep dive
A good first project is almost boring: one well-chosen workflow, AI wired directly into the tools people already use, and a metric that existed before AI did (handling time, error rate, throughput). MIT's 2025 work found the 5 percent that captured real value did exactly this: they narrowed to one high-value workflow, customized deeply, and started at the edges before scaling into the core. McKinsey, testing 25 organizational attributes, found redesigning the workflow had the single biggest effect on whether gen AI moved EBIT, and that the clearest returns showed up in unglamorous back-office automation, not the flashy front office. The feedback such a project generates becomes proprietary data that makes the next version better. That compounding loop, not a launch, is what turns into an advantage.
// chapter faq
What makes an AI project succeed?
Workflow redesign, more than anything else McKinsey tested across 25 attributes. The winners wire AI into the tools people already use, start at the edges, and measure with a number the business tracked before AI existed.
Which AI use cases show real returns first?
The unglamorous ones. McKinsey found the clearest returns in back-office automation, not the flashy front office: document handling, support triage, reporting, quality control. Boring compounds.
How long until an AI project pays back?
Longer than the pitch says. Deloitte found only 6 percent of organizations saw payback in under a year; most reached satisfactory returns in two to four years, against seven to twelve months for conventional tech.
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