The pressure to adopt AI in government contracting is intense. Every vendor promises faster drafts, compliance automation, and transformative results. But as highlighted in Fergal McGovern’s LinkedIn article, “We Need AI.” For What, Exactly? leaders should pause and reconsider their approach.
Inspired by UK Bid Consultant Darrel Woodward’s insight that “many organizations want bid technology and AI, but they can’t explain why.” Without understanding the real problem you’re solving, you’re just chasing technology for its own sake. This summary captures key insights from the article. It breaks down this “problem-first” framework for making smarter decisions about AI adoption.
Why do so many companies buy AI tools without knowing what problem they’re solving?
The answer lies in where “people, process, and automation” collide under pressure.
Leaders face immense expectations to boost win rates. When a VP of Capture or Proposal Director sees a GenAI platform promising to “write 80% of your first draft,” it looks like decisive action. It feels like transformation.
But here’s the disconnect: the real issue is rarely writing speed. More often, it’s a fractured capture process. Win themes fall flat because customer discovery was rushed. Subject matter experts are unavailable when you need them most. The solution isn’t compliance-checked because it was never properly defined.
In these situations, organizations are attempting to patch fundamentally human and process-driven problems with expensive software. That rarely works.
What’s the biggest risk of using AI in government contracting proposals?
Government contractors operate in a uniquely unforgiving environment where accuracy isn’t just important, it’s mandatory.
This is where many AI tools create more problems than they solve. A proposal draft generated at lightning speed means nothing if it contains hallucinated facts, misstated capabilities, or slightly inaccurate past performance details. These aren’t minor mistakes. They’re compliance violations that can disqualify your entire submission.
What happens next reveals the true cost. Instead of saving time, your capture managers, proposal writers, and SMEs spend hours re-reading every sentence, validating claims, and fixing errors in the AI-generated content. Ironically, they’d have finished faster starting with a proven, human-vetted template.
This pattern undermines repeatability. Rather than creating a consistent, high-quality process, you’ve introduced an unpredictable system that delivers varying quality with each use.
How can I tell if I’m solving the right problem before purchasing AI technology?
Start by forcing your team through the “5 Whys” exercise before evaluating any vendor. This exercise cuts through the noise and reveals whether technology can actually address your root cause.
Usually, the actual problem isn’t slow writing. It’s unclear solution clarity during capture. No AI platform will fix that. What you need is a mandatory, structured solutioning milestone early in your process.
Should I focus on what I want to buy or what I want to achieve?
Always define the outcome you need, not the output you think you want.
Consider the difference:
- Technology-first thinking: “We need to purchase a GenAI tool for compliance matrices.”
- Outcome-first thinking: “We need a reliable, 100% accurate method to ensure every Section L and M requirement is addressed in our proposal, and our reviewers can validate coverage in under two hours.”
The second approach opens up possibilities. Your solution might be a sophisticated AI platform. Or it might be an improved template, a more rigorous writing guide, or a non-negotiable peer-review checkpoint.
It might even be a targeted tool for one discrete task like converting RFP requirements into a structured spreadsheet, combined with a human-led process that guarantees accuracy. The technology choice matters far less than the process you build around it.
In the age of AI proposals, who’s the most valuable person in the room?
It’s not whoever’s running the latest AI platform. The real value comes from human judgment – the leader who can pause the momentum and ask, “What are we actually trying to fix here?”
Real transformation in government contracting doesn’t come from purchasing AI platforms. It comes from fixing the human-driven, collaborative processes that create winning bids. Those processes may incorporate technology or they may not, but they absolutely require rethinking how people work together.
Conclusion
Before your next technology purchase, remember this: rather than building an “AI strategy”, focus on building a “problem strategy”. Define what’s broken. Use the 5 Whys. Specify the outcome you need, not just the tool you think will deliver it.
Then and only then, evaluate whether AI is part of the answer.
To explore the full conversation behind these insights, check out the full article on LinkedIn.
