If your bid team could submit twice as many proposals tomorrow, would your business be better off? TIP. AI-generated narration.
Sometimes the hardest part of a bid is explaining why you shouldn't write it.
Imagine it's late afternoon. The team is already stretched. Another opportunity arrives. The fit looks weak, and similar bids have gone nowhere. But someone says, if we don't bid, we'll never know.
That is an understandable pressure. Leaders need a pipeline. Saying yes keeps a possibility alive. Yet someone else may carry the cost: another late night, less attention to an existing client, or less time on an opportunity the business is better placed to win.
This is an illustrative scene, drawn from a familiar leadership tension. The person authorising work may not experience what producing it demands. If we reward the number of proposals submitted, faster bidding can reinforce that pressure.
In a professional services workshop we ran, someone suggested training Copilot to be the bid manager. Perhaps useful. But it was a solution. The outcome the team actually wanted was to win more of the right business.
That distinction opened up different questions. Which opportunities deserved attention? What did the customer value? Where should scarce expert time go?
In another workshop, people described the difficulty of finding case studies. Some were scattered. Others had never been captured. Better search could help with the first problem. It couldn't retrieve evidence that nobody had recorded.
These were real workshop insights, anonymised here. They revealed problems and possible changes. They did not establish improved win rates.
Two films offer useful ways to think about this. Moneyball shows statistical analysis challenging established ideas about value. The Big Short shows people going out to investigate whether the reassuring story matches reality. They're illustrations, not proof of a business method. But the questions travel well: are we measuring what matters, and have we checked what's actually happening?
AI can help prepare that investigation. Give it relevant evidence and agreed business criteria. Ask for the strongest case for bidding and the strongest case for declining. Ask it to separate facts from assumptions, identify gaps and explain what would change its recommendation.
Don't begin by telling it the answer you want. And don't treat a confident percentage as a reliable forecast. Check where it came from. Are past opportunities really comparable? What's different now?
AI can offer some distance from internal politics. It can also inherit bias from its training and mirror the way you frame the question. Asking it to be independent is useful. Testing its reasoning is essential.
You can then rehearse the conversation from different leadership perspectives. What might a commercial director question? What would delivery need to know? Those simulated reactions help preparation; they aren't evidence of what real people think. Speak to them.
The recommendation becomes constructive: here's the evidence, here's the uncertainty, and here's where we propose putting the effort instead. A leader may still choose to bid for a strategic reason. Make that reason explicit, resource the choice and revisit the assumption later.
This is where mindset and method come together. Curiosity helps us question the request. Customer conversations help us understand the problem. Small experiments test assumptions. Review connects what we learned to the next decision. Psychological safety lets the team raise an unwelcome finding, and leadership remains accountable for acting on it.
Learning to use AI matters. Learning how to question, investigate and decide matters more. We develop those capabilities through real work, so the approach can be used again.
For your next bid review, ask what evidence would change the decision, and where the time would go if you declined. Record the reasoning and come back to it.
Efficiency creates room. Effectiveness determines what we do with it. Before asking AI to help you bid faster, ask how it could help you choose better.
