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Article by: Australian Bid Director Dave Gorham
AI in bid development promises faster turnaround, consistent formatting, and draft content generated in seconds. For procurement teams and bid writers juggling multiple live opportunities, that's genuine relief from the grind of document production.
But as adoption accelerates, so does a more nuanced conversation: one that separates the real value AI brings from the risks of overreliance.
The case for AI in bid development
At its best, AI takes a lot of the grunt work off skilled bid writers. It's good at structuring responses to evaluation criteria, drafting capability statements, summarising lengthy specifications, and maintaining a consistent tone across long documents.
For organisations with well-documented case studies and project history, this can dramatically cut timelines. It also helps writers avoid starting from nothing; having something to work from, rather than a blank page, is a real advantage.
The quality of your inputs sets the ceiling
However, AI is only as good as what you feed it. Thin, generic, or poorly organised collateral (weak case studies, vague methodology documents, and patchy project data) produces outputs to match. You’ll have well-written paragraphs that say nothing at all.
Evaluators read hundreds of submissions every year and will spot templated, hollow content immediately. If an AI-generated response can't clearly explain why your organisation is the right fit for that specific project, it hasn't done its job, no matter how polished it looks.
The lesson isn't that AI fails. It's that AI amplifies what already exists. Good inputs produce good outputs. Weak inputs produce well-formatted mediocrity.
Linking an expensive AI platform to an existing bid library with the same old case studies that lack evidence, relevant and differentiation isn't going to help win - it will just mean you had to put less effort into lose.
What AI simply cannot do
Beyond content quality, some things sit beyond the reach of even the best prompting. AI can't prioritise. Live bids require constant judgement calls: which sections carry the most weight with evaluators, where to push back on unclear scope, which themes to lead with given what you know about the client.
AI can lay out the options, but it can't read the room. Pre-bid briefings, industry days, and client conversations convey signals no document captures: what's frustrating the client about their current provider, what the panel cares about versus what the RFP says, and where sensitivities lie. An experienced person notices these signals and adjusts accordingly. AI has no access to that context, and even if it did, it couldn't act on it in the moment.
Winning bids come from understanding people: their pressures, their goals, what success looks like to them. Good bid writing speaks directly to that and builds trust. No amount of instruction turns AI into something that understands what it's like to sit across the table from someone who genuinely needs a problem solved.
The new roles emerging from AI
The organisations getting the most out of AI aren't the ones with the flashiest and most expensive tools. They're the ones who understand exactly where the technology's usefulness ends and where human judgement needs to take over.
While ‘prompt engineers’ are now ubiquitous, one new pattern is emerging more often: a role that exists because AI can produce content faster than anyone can check it's actually good. Speed without a check on quality just means bad content, delivered faster. Someone needs to be constantly asking whether a response genuinely answers the question or just sounds like it does.
The people who do this well aren't necessarily the most senior person on the team. They're the ones most across the detail: comfortable switching between big-picture thinking and line-by-line editing, willing to push back when something isn't good enough, and able to keep going through a process that wears most people out.
As AI takes on more of the writing workload, this kind of role becomes less of an optional extra and more of a necessity. The teams building it into their process now are the ones likely to turn AI's speed into quality responses, not just faster ones.
AI raises the floor. Humans raise the ceiling. And the humans doing that work increasingly include someone whose job is making sure speed never comes at the cost of substance.