Why GovCon Growth Requires More Than AI and Public Data 

I’ve spent 30 years in the government contracting market, early in my career as an analyst conducting the research directly, and today I lead a team of more than 150 analysts who cover the entire market across the federal, state, local, and education sectors in the U.S. and Canada. Over the course of my tenure, I’ve only seen one other inflection point with comparable impact on how the market accesses and uses intelligence: the rise and eventual reach of the internet. AI is the first shift since then that feels this fundamental.

With this shift, a familiar question is resurfacing in a new form. When the internet became universal, the question was about access: what changes when public data suddenly becomes widely available online? The answer was a wave of platforms built to aggregate that data, clean it up, and make it searchable.

That dynamic hasn’t changed. Today, the question has shifted to usability and trust: what do you gain or lose when you layer AI on public data, whether through a generic LLM or a commercial offering? Interfaces have come a long way, and AI can surface answers quickly, but the fundamental question is whether public data is sufficient and reliable on its own. Because, let’s face it: public data is free.

The Problem Is Not the Interface or the AI. It's the Data It’s Using.

Federal public data is more incomplete than most people realize until they spend real time with it. Award announcements often lag by months. Incumbent contractors are rarely identified in a SAM notice. Cancellations don’t always make it into the system, and procurements quietly disappear. Agency Forecasts list programs that never materialize. For example, I’ve personally called government contacts listed in Agency Small Business Forecasts to ask about a procurement, only to have them tell me they had no idea what I was talking about.

An AI wrapper with a clean interface cannot fix any of this when the data underneath is incomplete and unverified. AI will process what it can find and give you an answer; often, a fluent and very convincing one. But it has no way to tell you which parts of its answer are grounded in accurate data, and which parts are built on information that is incomplete, stale, or flat out wrong. In a real pursuit, a confident, incorrect answer isn’t just unhelpful; it can also be very costly if acted upon. It sends your team down the wrong road, ties up pipeline capacity with false leads, and drains your pursuit budget on bids you were never going to win.

This is the part of the AI-on-public-data pitch that deserves more scrutiny, which goes beyond whether the interface is easy to use. The question is whether the underlying data is telling the truth (as they say in court, “the whole truth”) and whether the tool surfaces uncertainty and unknowns when it can’t answer confidently.

Being Early Is Not Just an Advantage. It’s Fundamental to Success.

Early in my career, someone told me something still true to this day: by the time it shows up on SAM.gov, you are already behind. I was skeptical. Then I spent years watching it play out, over and over again. I am not alone in saying this. Savvy business developers know this almost like a mantra. However, I often see companies and newcomers to the market treat SAM.gov as their primary business development tool, only to learn the hard way.

Here is what is happening on the other side of that solicitation before you ever see it. The agency has been scoping requirements, surveying the market, identifying likely vendors, drafting the statement of work, and, in many cases, speaking directly with companies to ensure their requirements are aligned with current industry capabilities. That process takes months, sometimes years. By the day the solicitation hits the street, the shape of the deal is largely set. The companies engaged throughout that process have influenced the requirements, built relationships with the program office, and established brand awareness, credibility, and trust that they can deliver on the buyer's needs.

Good market intelligence closes that gap by giving companies the time they need to build the relationships, shape requirements, and establish credibility with buyers long before the bid is submitted. It does that by identifying prospective and likely opportunities well before they are announced as a procurement, equipping companies with important information not typically included in a procurement announcement. A good market intelligence provider has analysts that directly engage with agencies and the people involved in the planning and procurement process. A tool that only scrapes public data and surfaces it faster is not closing the gap. It is just delivering the same late-stage information everyone else has in a better interface.

Some of the Most Important Information in This Market Is Not in Any Public Database

This is the hardest part to replicate, and it is worth being direct about. Without human intervention, critical decision-making context slips through the cracks, which leads to gaps in pursuit strategies.

Human intervention connects the dots between public data and the real world. A program manager returns a call because one of our analysts has been a reliable and knowledgeable contact for years. An industry day provides information about program direction that will not appear in any public document for months, if at all. A follow-up call after a schedule slips gives us insight into what is happening with a program that no AI could surface. An analyst tracks down the incumbent on a contract, the real contract value, and the timeline, through a combination of FPDS cross-referencing, direct outreach, and strong research practices.

That information is rarely in SAM, let alone cross-referenced effectively and consistently in USASpending, meaning AI cannot scrape it into existence. The relationships and judgment that produce it take years to build.

The Recompete Assumption Is Breaking Down

There is a specific area where the gap between AI-generated forecasts and reality has widened significantly recently, and it deserves its own attention.

One of the core models underlying many AI forecasting tools is straightforward: ingest contracts and task orders, examine the performance period, and surface what is coming up for expiration. Two years ago, that was a reasonable starting point. Today, it is one of the biggest risks of relying on an AI-only tool, often resulting in wasted time and resources.

The current federal contracting environment is changing faster than public data can track. Work that would have been recompeted in place is being pulled under different contract vehicles, restructured, or quietly eliminated. Even when work comes back to market, it is often bundled differently, routed through a different vehicle, or shifted to a different acquisition organization. The "recompete" of a contract held last cycle may look almost nothing like the new opportunity, or it may not exist at all.

An AI scraper looking at a period-of-performance end date has no way of knowing any of that. Yet without this context, companies are sending their BD teams to sort through hundreds of contract expirations and calling it pipeline.

The response I sometimes hear is that the AI surfaces the candidate, and humans confirm the reality. That sounds reasonable until you consider where the cost falls. Under this scenario, disproving a phantom recompete falls on your business development team, not the AI provider. For lean BD teams, which are often the teams most attracted to lower-cost tools, that opportunity cost is substantial. Business development professionals are expensive. Do you want them running down phantom leads, or do you want them building relationships with program offices, showing up at industry days, and positioning your company before the solicitation is written? AI cannot do any of that. But it can generate enough noise to keep your team from the activities that matter most.

What This Means for How You Spend Your Budget

A quick note on the pricing reality, because the honest version of this conversation is more useful than the marketing version.

If you are a very small firm doing occasional work in a single, narrow market segment and you are not running a proactive business development program, a lower-cost tool built on public data or even just the government source of the public data itself may be sufficient for your needs. There is no point in paying for depth and research you are not going to use.

But if you are running a BD program to drive growth, if early awareness of opportunities is part of how you compete, if you are expanding into new markets or chasing larger opportunities, the gap between what a public-data tool tells you and what is really happening in the market is not a minor inconvenience. It is a structural disadvantage. The companies that consistently succeed in this market know first, position earlier, and win more often. That kind of intel does not come from faster access to the same public data everyone else has.

Having watched companies compete for decades, I know what separates those who grow from those who stall and ultimately give up. Some learn the hard way, but the reality is that learning the hard way is entirely avoidable. That’s why the foundation matters: intelligence that has been validated and curated carries a weight that scraped public data will never match. The foundation is what makes the AI built on it worth trusting.

When evaluating solutions, I encourage you to ask questions beyond the interface. Ask where the data comes from. Ask how the forecasts are derived. Ask them how they verify the information they report, and whether it comes entirely from web-scraped public data. Ask whether there is a human who knows this market and who corresponds directly with agency decision makers to gather the information your BD team should not be spending time collecting themselves.

Those questions will tell you more than the demo will and help you assess whether it will truly be helpful and a force multiplier for your team, versus just another tool that will consume their time in other inefficient ways.