From Lab to Startup: Using SBIR Grants to Commercialize AI Research

Every year, thousands of AI papers get published, celebrated at conferences, and then quietly shelved. The gap between a promising model and a viable product is not primarily technical -- it is financial, strategic, and structural. The SBIR and STTR programs were designed precisely to bridge that gap, and for AI researchers willing to think like founders, they remain the most accessible path from lab bench to market entry.

Browse our SBIR Grants page for current opportunities across all federal agencies.

Why SBIR Works for AI Researchers (and Why Most Get It Wrong)

The Small Business Innovation Research program channels over $4 billion annually across 11 federal agencies, with awards structured in three phases. Phase I funds feasibility studies at up to $314,363. Phase II supports full prototype development at up to $2,095,748. Phase III -- the commercialization stage -- carries no fixed ceiling and draws from agency operational budgets rather than the SBIR set-aside.

For AI researchers, the STTR variant is often the natural entry point. Unlike SBIR, which requires the small business to perform the majority of funded work, STTR mandates a formal partnership with a research institution -- the university where your lab already operates. The institution must perform at least 30% of the work, and your company at least 40%. This lets researchers maintain their academic positions while spinning out commercial applications of their work.

The problem is that most academic teams write SBIR proposals the way they write journal papers. They lead with technical novelty, bury the market case in a perfunctory paragraph, and treat commercialization as a future concern rather than a present requirement. Federal reviewers are not peer reviewers. They want to know who will pay for your technology, how much they will pay, and what evidence you have that you have actually talked to them.

The Agency Landscape for AI

Not all agencies fund AI equally, and not all agencies evaluate proposals the same way.

NSF runs the broadest AI-specific SBIR program, with eight dedicated subtopics spanning computer vision, conversational AI, NLP, trustworthy AI, neuromorphic hardware, and sustainable AI for resource-constrained environments. NSF's Phase I acceptance rate hovers around 20% -- the highest among federal agencies -- and the program explicitly rewards technical novelty from unknown teams. If your research pushes genuinely new methods rather than applying existing architectures to a vertical market, NSF is your strongest bet.

DOD is the largest SBIR funder overall, and AI is woven throughout its solicitation topics. The Army's AI/ML Open Topic accepts proposals across areas including automated data labeling, anomaly detection, and biometrics. DARPA's SBIR topics are more targeted -- recent solicitations have focused on bias detection in defense AI systems, AI for veteran suicide prevention, and model robustness under adversarial conditions. DOD awards tend to be larger and move faster toward procurement, but reviewers expect demonstrated awareness of the military use case, not just academic rigor.

DOE funds AI applications tied to energy security, climate modeling, materials science, and grid optimization. The Office of Science SBIR program has included topics such as AI for malicious event detection in energy infrastructure. Phase I awards are capped lower at $200,000 over nine months, but DOE's Phase II can reach $1,100,000 over two years.

NIH has historically been a major funder of AI in biomedical contexts -- diagnostic imaging, drug discovery, clinical decision support. However, NIH is currently not accepting new SBIR or STTR applications due to the authorization lapse (more on that below).

The Reauthorization Problem

Here is the uncomfortable reality: the SBIR and STTR programs expired on September 30, 2025, and Congress has not yet passed a reauthorization. This is the longest lapse in the programs' 42-year history.

Existing awards continue to be funded. Phase III contracts remain active. But no agency can issue new solicitations or make new Phase I or Phase II awards until Congress acts. The House passed H.R. 5100, a clean one-year extension, but the Senate has stalled over competing proposals for security reforms and commercialization benchmarks. NSF has resumed processing previously submitted project pitches, and most SBIR watchers expect reauthorization to be attached to a broader spending vehicle in 2026.

What this means for AI researchers: the window is a preparation window. Build your team, incorporate the company, conduct customer discovery, refine your technical narrative. When solicitations reopen, agencies will compress timelines to make up for lost cycles, and the teams with ready proposals will move first.

Four Mistakes That Sink AI SBIR Proposals

Writing a paper instead of a proposal. Your Phase I narrative should spend more time on feasibility milestones and commercial hypotheses than on literature review. Reviewers already know the state of the art. They want to know what you will build, what you will test, and what a successful outcome means for your business.

Ignoring commercialization until Phase II. Every agency evaluates commercialization potential in Phase I. That means documented conversations with potential customers, a credible description of who pays and how much, and evidence that a market exists beyond your research community. A one-paragraph TAM estimate from a Gartner report will not cut it.

Treating STTR as free money for your lab. The STTR structure exists to accelerate technology transfer, not to subsidize academic research that would happen anyway. If your proposed STTR work is indistinguishable from your next grant cycle, reviewers will notice. The small business entity must have a genuine commercial mission, and the 40% work allocation means you need real engineering capacity outside the university.

Misreading the agency. An AI proposal built for NSF reviewers -- heavy on intellectual merit, light on operational requirements -- will struggle at DOD, where evaluators expect clear alignment with warfighter needs and acquisition pathways. Conversely, a DOD-style proposal that leads with the use case and glosses over technical risk will lose points at NSF, where intellectual merit is the primary evaluation criterion. Tailor the narrative to the agency, not the other way around.

Making the Jump

The researchers who successfully commercialize through SBIR share a common trait: they make the decision to build a business before they submit the proposal, not after they receive the award. That means forming the company, identifying a technical lead who will commit real hours, and engaging with potential users -- not just collaborators -- before the pitch goes in.

For AI researchers specifically, the current lapse creates a counterintuitive opportunity. Competitors who relied on SBIR momentum are stalled. The researchers who use this period to sharpen their commercial thesis, validate demand, and prepare airtight proposals will be positioned to capture disproportionate share when the programs restart.

Granted can help you identify which agencies and solicitation topics align with your research -- so when the doors reopen, your proposal is already in the queue.