The best use case for generative AI in the enterprise is business continuity: covering for expert capacity you suddenly do not have. Holidays, sick leave, resignations and deadline collisions no longer have to cost you the deal. Here is the principle, and how to apply it before you need it.
The capacity problem every business has
Every organization, from a sole founder to a multinational, runs on a thin layer of expert capacity. When that layer is unavailable, the business does not slow down politely. Opportunities arrive on their own schedule, and they do not wait for your specialists to return. Most companies simply absorb the loss: the proposal not sent, the contract not reviewed, the deadline missed. It never appears on a profit and loss statement, but it is one of the most expensive line items a business carries.
Generative AI changes that equation. For the first time, expert-level drafting, analysis and review capacity is available on demand, at any hour, for a fraction of the cost of a single consultant day. The companies that treat GenAI as deadline insurance, rather than a toy or a threat, are the ones whose best people are already quietly proving the point.
The principle: coverage, not replacement
The principle is not that AI replaces your experts. It is that AI covers for them when they cannot be reached, and supplements them when they can. The distinction matters. A business that fires its specialists and hands everything to AI has misunderstood the tool. A business that lets deals die because its specialists are on holiday has misunderstood the moment.
I learned this directly one Nordic summer: my entire team was on holiday when a new client opportunity arrived with a 24-hour deadline, and generative AI carried the specialist workload that would otherwise have cost us the contract. The story is less important than the lesson, which is that the coverage was only possible because the working method already existed.
How to apply it before you need it
Deadline coverage fails if the first time you try it is during the emergency. Three preparations make it work:
- Choose and learn the tool now. Whichever frontier model you standardize on, someone senior must already know how it behaves on your real documents before the crisis, not during it. This is part of preparing your culture for AI, not an IT decision.
- Work in sections, keep the whole in view. Give the AI the complete context of the document or task, then delegate section by section. It will hold the overview, understand why each part exists, and volunteer improvements you had not considered.
- Keep human judgment as the final gate. The AI produces the options and the speed; you decide what represents the company. That division of labor is what makes the output trustworthy under pressure, and it mirrors the discipline of any real AI project.
Executives who build this muscle report the same pattern: routine expert tasks that consumed days now complete in a fraction of the time, and the emergency coverage capability comes free with the practice.
Frequently asked questions
What is the best use case for generative AI in the enterprise?
Business continuity: covering for expert capacity you suddenly do not have. Deadlines and opportunities do not wait for holidays, sick leave or resignations, and generative AI is the first tool that provides expert-level drafting and analysis on demand when your people cannot be reached.
Is this about replacing experts with AI?
No. The principle is coverage, not replacement. AI supplements your specialists when they are available and stands in when they are not. Firing experts misunderstands the tool; losing deals because experts are on holiday misunderstands the moment.
How should a company prepare for AI deadline coverage?
Three steps: standardize on a frontier model and learn its behavior on real documents before any crisis, practice delegating work in sections while the AI holds the full context, and keep human judgment as the final gate on everything that leaves the building.
What does deadline coverage cost compared to the alternative?
A fraction of a single consultant day, against the invisible cost of missed proposals, unreviewed contracts and lost deals. The expense never appears on a profit and loss statement, which is exactly why most companies never realize how much unavailable expertise costs them.
Thomas Anglero is a Strategic AI Advisor, keynote speaker and author of Intro to Artificial Intelligence. He has delivered over 450 keynotes across 30 countries for organizations including IBM, the WHO, the World Governments Summit and the European Commission. He founded the IBM Watson AI Lab for Cancer at the Oslo Cancer Cluster and closed over $500 million in enterprise transformation deals as CTO and Chief Innovation Officer at Cognizant.