Late 2022 was a turning point. ChatGPT landed, and many people suddenly realised what a few insiders already knew: AI was no longer a lab experiment. It was usable, affordable, and about to be everywhere.

Like every tech revolution, the hype had a purpose. It fuels investment, accelerates adoption, and pushes vendors to ship faster. Net-positive, overall. But it is also noisy. And for bid and proposal professionals, noise is expensive. When your next deadline is in 10 days, you do not need grand predictions. You need practical, affordable, genuinely effective help.

That is why the core question has shifted from “Should we adopt AI?” to “What is actually working?”

From my seat building AI RFP Software, and watching usage patterns daily, the biggest wins rarely come from flashy end-to-end promises. They come from hammering common bottlenecks, one by one, based on real demand.

Not platforms that force a rigid way of working and lock you in, but more like a flexible toolkit you run for the help you need, when you need it, on the step you are actually stuck on.

Here are three grounded use cases I see teams using every day (all through AI agents), where the value is both time saved and better outcomes:

1. From RFP + inputs to a 70-80% proposal draft
   Then teams review and complete. But the heavy lifting of structure, winning points, compliance coverage, and first-pass writing gets done in a few minutes.
2. From RFP + context to a bid/no-bid report
   Teams reduce rushed decisions by quickly surfacing risks, resource demands, compliance red flags, and “unknowns” that need clarification before committing.
3. From proposal draft to a structured “colour review”
   AI is surprisingly good at being the first reviewer and proposal professional for accepting/rejecting: finding gaps, weak claims, inconsistent terminology, missing evidence, and sections that do not map cleanly to requirements.

None of this is magic. It is applied assistance on repeatable steps. Now for the uncomfortable part: if your competitors are doing the same, where is the edge?

My view: stop fighting AI where it clearly beats you and double down on the parts it cannot own for the foreseeable future (if ever):

• Unique insights from real customer context
• Shaping the offer, not just describing it
• Interpreting requirements with judgment and instinct
• Aligning stakeholders and negotiating trade-offs
• Turning “compliance” into a compelling story
• People skills. People skills. People skills.

Beyond the prompt is not about doing less. It is about doing the right work again.