The Procurement Trap: Why Government AI Projects Die Before They Start

The Problem Starts Before the RFP

I've worked with enough government and public-sector teams to recognize a pattern. Leadership gets excited about AI. A working group forms. Slides get made. Then someone says, 'We need to put this out to bid.'

And that's usually where the initiative dies — slowly, quietly, buried in procurement timelines.

This isn't a criticism of contracting officers. They're doing exactly what the rules require. The problem is that most agencies are trying to procure AI the same way they procure laptops or security software. Defined specs. Lowest compliant bid. Multi-year contract. Done.

AI doesn't work that way. And forcing it into that mold produces expensive failures.

What I Actually See in the RFP

I've reviewed public-sector AI solicitations that specify the model vendor, the output format, and the use case — all before a single workflow has been analyzed. The agency has essentially made every meaningful technical decision upfront, in a document written by people who haven't deployed AI in production.

Then the winning vendor inherits those decisions. They build to spec. Leadership gets a demo. The demo looks fine. Six months later, the system isn't being used because it was built around a procurement artifact, not an actual workflow problem.

That's not a vendor failure. That's a structural one.

Three Things That Actually Move the Needle

1. Separate the discovery from the deployment contract.

Before you write an RFP for an AI system, you need a short, bounded engagement to map the workflow, assess your data readiness, and identify where AI can realistically help. This doesn't have to be expensive. It has to be honest. A 60-to-90-day discovery phase changes everything — you're no longer guessing at specs, you're writing from evidence.

If you're not sure where to start, our free AI Readiness Assessment is designed specifically for this moment. It gives you something concrete before you commit budget.

2. Write outcome-based requirements, not technology requirements.

Instead of specifying that a vendor must use a particular large language model, specify what the system must do: reduce manual document review time by X percent, flag anomalies in Y category with Z accuracy threshold. Outcome language gives vendors room to propose the right solution. It also gives your team something measurable after go-live.

3. Build evaluation into the base contract, not a future option period.

I've watched agencies award AI contracts with no formal evaluation mechanism until Year 3. By then, you've spent two years with a system that may be drifting, hallucinating in low-visibility ways, or simply not being used. Governance isn't an add-on. It belongs in the base period of performance.

The Speed Myth

Here's something I tell every government client: moving fast in AI doesn't mean skipping steps. It means sequencing them correctly.

A lot of agencies are under pressure to show AI progress — from leadership, from oversight bodies, from constituents. That pressure is real. But rushing to deploy without workflow analysis and data readiness is how you get a six-figure contract and a tool nobody trusts.

The agencies I've seen move fastest are the ones that did the foundational work first. They knew their data. They mapped their workflows. They defined what good looked like before they signed anything. When the contract started, there was nothing to figure out. The team just built.

What This Looks Like in Practice

One agency I worked with was planning to deploy an AI-assisted case management tool. Initial procurement was moving toward a full platform purchase — roughly $800K. We paused and did a proper discovery phase instead.

What we found: 60% of the intended use case could be handled by a lightweight automation layer that already existed in their infrastructure. The remaining 40% did need AI — but a targeted, auditable implementation, not a platform. Final contract scope dropped significantly. And because we'd done the workflow mapping, the vendor they selected had clear requirements to build against.

That's what discipline over enthusiasm looks like in a government context.

If You're Planning an AI Procurement This Year

Start with an honest assessment of your data and your workflows before you write a single requirement. Our ASCEND framework was built specifically to help organizations sequence this work correctly — so what gets procured actually gets used.

Government AI can work. I've seen it. But it requires the same rigor you'd bring to any critical infrastructure decision. The model is the easy part. The procurement structure is where projects are won or lost.