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By Arthur Runno
I don’t make this argument lightly. I spent nearly two decades in the trenches of proposals, running compliance matrices, managing color teams, and chasing perfection. My entire career was built on the old way of doing things. And yet I believe it’s time to let go of that model.
Yes, I now lead a company in the proposal AI space — but whether firms adopt my platform or another, the point stands: AI is here, and it changes everything. What matters isn’t clinging to process for its own sake but helping companies win more while spending less. The future belongs to teams willing to leave behind the rituals of the past, embrace AI-first workflows, and cut their cost per bid dramatically while increasing win rates. That’s not theory, it’s the new reality of GovCon.
AI didn’t just speed up proposals. It destroyed the old economics of bidding. And yet most GovCon firms are stuck chasing work as if it still costs millions to compete. This disconnect is draining pipelines, burning out SMEs, and lowering win rates. It’s time to rethink the bid/no-bid decision for the AI era.
For decades, capture and proposal leaders treated every bid like a million-dollar gamble. The cost of developing a competitive proposal for a major federal opportunity could run into the millions once you added in staff hours, consultants, color-team reviews, graphics, facilities, and executive attention. Because each bid carried such a heavy price tag, bid/no-bid gate reviews were strict, selective, and often political. Pursuit discipline was baked into the economics. Executives thought hard before green-lighting a major effort, because the sunk cost was staggering if the bid failed.
This shaped the culture of business development. Chasing fewer, better opportunities was considered wisdom. Proposal teams were seen as costly corporate assets whose time had to be guarded carefully. Economics enforced discipline, sometimes imperfectly, but at least consistently.
Artificial intelligence has fundamentally altered that math. What once took hundreds of hours of labor, compliance matrix development, boilerplate drafting, formatting, and even red-team reviews can now be accelerated, automated, or at least heavily assisted. Drafting a 200-page proposal no longer requires a war room staffed 24/7. The cost per bid has collapsed.
This should be liberating. In theory, AI should free companies to compete more strategically and cost-effectively, empowering lean teams to deliver more with less. It should also allow senior leaders to reserve their attention for the truly strategic bids, not just the ones their staff had time to prepare. But that’s not what has happened.
GovCon shops are still behaving as if it’s 2005. They chase every opportunity as though each bid still carries a multi-million-dollar price tag. Leaders equate “number of proposals” with “business development success,”even though that logic belonged to the old economics. Now, when bids get cheaper, the temptation is to pursue far more of them. The result? Diluted pipelines, burned-out SMEs, and declining win rates.
Worse, leadership attention is squandered on low-probability pursuits. In many companies, executives still demand the same formal reviews and theater of color teams, but spread across twice as many bids. The gate reviews that once protected resources have become rubber stamps. The old bid/no-bid model hasn’t caught up with the new reality, and the cost isn’t measured in dollars anymore. It’s measured in wasted focus, lower pWin, and human burnout.
The traditional gate review was designed to prevent wasting millions on un-winnable opportunities. But if the marginal cost of creating a proposal is a fraction of what it was, those gates start to feel like legacy rituals. They aren’t stopping waste — they’re slowing down adaptation.
What’s missing is not discipline altogether, but discipline of a different kind. We no longer need guardrails to prevent million-dollar mistakes. We need smarter frameworks to prevent teams from flooding the field with cheap but un-winnable bids.
Consider two mid-tier firms responding to the AI era. The first doubled its bid volume but never modernized its workflow. It layered AI outputs on top of the same color-team rituals, endless rewrites, and bloated reviews. The result was predictable: more drafts, the same bureaucracy, and exhausted SMEs validating material that should have been automated away.
The second firm went the opposite way. It redesigned its process around AI. Compliance scans, fit/gap checks, and red-team simulations were automated up front. Drafts reached SMEs 80–90% complete, so their role was to refine and add nuance, not build from scratch. They submitted fewer proposals overall, but their win rate rose sharply—and their teams had more energy for the bids that mattered.
The lesson? AI doesn’t eliminate the need for people. It eliminates the old workload, letting SMEs concentrate on high-value tasks. AI is not the enemy of proposal professionals—it’s the amplifier of discipline and strategy.
It’s important to note that not all bidding environments are equal. For Indefinite Delivery/Indefinite Quantity (IDIQ) vehicles and task orders, the economics are different. Once a company has earned a seat on the vehicle, the heavy lift is done. The cost per task order response is relatively low, and the timelines are short. In this environment, bidding nearly every task order can be rational, especially when AI handles the heavy lifting andSMEs only refine or validate key sections.
Teaming shifts the dynamic even further. With AI generating a 90% draft in hours, a company can hand that package to a partner for completion. In return, that partner may contribute labor categories, niche expertise, or past performance you need to strengthen the bid. Here, speed and readiness are not just efficiency, they become a business development lever for securing work share.
The danger comes when firms confuse this IDIQ logic with full-and-open pursuits. Treating a billion-dollar prime contract like a 10-day task order sprint is reckless. AI lowers costs across the board, but leaders must distinguish between the sprint of a task order—where volume makes sense under a modernized process—and the marathon of a new award, where strategy and selectivity remain paramount. Conflating the two is one of the biggest risks facing GovCon today.
Old vs. New Workflow: Shipley vs. AI-First
Old Model (Shipley Workflow):
- Proposal Manager reviews the RFP line by line.
- Creates an annotated outline.
- Builds the compliance matrix.
- Runs a kickoff meeting to distribute assignments.
- Writers and SMEs start drafting from scratch.
By the time a team has a first draft, hundreds of hoursare already burned.
New Model (AI-First Workflow):
- Receive the solicitation.
- Upload documents → generate annotated outline + compliance matrix automatically.
- Verify and adjust the outline.
- Generate the draft proposal.
- Draft is complete before kickoff, with SMEs and writers tagged directly in their sections.
What once took weeks of coordination now takes hours— and the “hot pink team” draft is ready before kickoff even starts.
$25M Case Study: Cost Per Bid Before and After AI
Traditional Proposal (Old Model):
- Capture & solutioning: 1,500 hrs
- Proposal writing & management: 2,000 hrs
- SME input & validation: 1,000 hrs
- Graphics, compliance, layout: 500 hrs
- Reviews & rework: 500 hrs
Total ~5,500 hrs × $100/hr = $550,000.
AI-First Proposal (New Model):
- Capture & strategy: 400 hrs
- Proposal writing: 400 hrs
- SME validation: 400 hrs
- Graphics/compliance: 150 hrs
- Reviews & rework: 150 hrs
Total ~1,500 hrs × $100/hr = $150,000.
Savings per $25M proposal = $400,000.
Scale that across 200 proposals/year → $80M in SG&A saved annually.
What This Means for Government
For contracting officers and evaluators, AI doesn’t mean a flood of sloppy submissions. Done right, it means:
- More compliant proposals – structures and evaluation criteria mapped line by line from the solicitation.
- Fewer errors – compliance gaps flagged up front instead of late in the process.
- Clearer narratives – SMEs spend less time formatting boilerplate and more time sharpening technical and management approaches.
- Integrity preserved – proposals still reflect real past performance and capabilities, not AI “hallucinations.”
- More competition – lower cost per bid means more qualified firms can afford to compete.
- Better value – broader competition puts downward pressure on prices and improves outcomes for agencies.
In short: AI-assisted proposals don’t undermine the system —they help government get more competition, better compliance, and lowerprices.
Comparison: Two Companies in the AI Era
The companies that survive the coming GovCon downturn won’t be the ones that flood the field with proposals. They’ll be the ones that redefine what a smart pursuit looks like: leveraging AI for speed and compliance while being ruthless about opportunity selection. Success now belongs to lean, disciplined teams who know when to say no, and who know when a task order sprint is worth the run.
AI has broken the economics of bidding and GovCon hasn’t noticed. The winners will be those who embrace AI-first workflows, modernize their bid/no-bid discipline, and give SMEs back their time. For industry, that means lower costs and higher win rates. For government, it means more competition, stronger compliance, and better value. Those who fail to adapt may discover that in the new economics of bidding, the real price of indiscipline is extinction.
