The macro prize is real, and it hides in everyday work
McKinsey estimates generative AI could add 2.6 to 4.4 trillion dollars a year to the global economy, and that about three quarters of it sits in four functions: customer operations, marketing and sales, software, and R&D. The prize is not exotic. It is in the ordinary work people already do.
McKinsey's estimate of the annual value generative AI could add runs from 2.6 to 4.4 trillion dollars. The number is large and uncertain by design. The more useful detail is where it concentrates: about three quarters of it in four functions, customer operations, marketing and sales, software engineering, and research and development.
The same analysis notes AI could take on work that absorbs a large share of employees' time today, but that the gain only lands if the work is actually redesigned around it.
The value is not in a moonshot. It is in the daily grind of common business functions, which means most of it is claimable by ordinary companies, not just AI labs. The gate is whether the everyday workflow gets rebuilt, not whether the technology exists.
Look for value in your biggest, most common functions first. That is where the estimate says it hides.
A macro estimate is not your P&L. The value is claimable only if you redesign the work, not just add a tool.
The prize is broad-based. This is not a game only for tech firms.
Map where your people spend time in the four high-value functions, then rebuild one of those workflows.
Treat the estimate as a direction, not a promise, and chase specific, measured wins under it.
Sequence by function, not by hype. Customer operations and content are proven starting points.
Potential is not realized value. Most of the 2.6 to 4.4 trillion is still theoretical.
Estimates like this get quoted as fact. Cite it as a scenario, with the range.