By: Melina Carter
Over the past five years, enterprise AI has evolved from a cutting-edge technology into a central component of industries such as finance, legal services, insurance, and healthcare. However, the startup landscape feeding these sectors remains challenging. In regulated markets, even minor errors—such as a hallucinated output or compliance misstep—can significantly impact an AI vendor’s trajectory. For investors, this creates a dilemma: the greatest opportunities may lie in enterprise AI, but the risks can be concentrated and unpredictable.
Traction Fund, an investment platform focused on enterprise and applied AI startups, has developed a unique solution to address these challenges. Its reverse-diligence model leverages a network of senior executives—CIOs, COOs, CFOs, CISOs, General Counsels, and operations leaders—who assess startups not just as investors but from the perspective of potential customers.
This approach, though straightforward, has far-reaching implications.
Why Traditional VC Diligence Falls Short in Regulated AI
Typically, venture capital firms evaluate startups based on financial, product, and market analysis. However, in the field of enterprise AI—particularly where compliance, data governance, workflow reliability, and integration complexity are critical—external diligence is often insufficient.
A model’s performance in a demo rarely provides investors with insight into:
- Deployment challenges
- Internal stakeholder adoption
- Integration timelines
- Data-security limitations
- Regulatory considerations
- Total cost of ownership
These aspects can only be fully assessed by enterprise buyers—executives responsible for managing high-stakes operations and who understand what is necessary to implement tools in complex environments like global banks, U.S. law firms, or public-sector organizations.
Traction Fund’s innovation lies in making these executives the first line of evaluation, rather than an afterthought.
The Reverse-Diligence Model
Central to this approach is Traction Club, a curated network of over 70 senior leaders from Fortune 500 companies, leading law firms, global financial institutions, and regulated industries. Before Traction Fund makes any investment, these executives evaluate AI vendors from a buyer’s standpoint.
Some of the key questions they consider include:
- Can this model be deployed safely in our environment?
- Does it improve on our current workflows or vendors?
- Is the integration cost reasonable?
- Will end-users adopt this solution?
- Does it meet compliance, audit, and security standards?
- Would we consider piloting this solution today?
If the answer is “yes,” something remarkable happens: these executives often commit to becoming early customers or pilot partners during the due diligence process. This accelerates revenue generation, which can be challenging for founders at the pre-Series A or pre-Series B stage, as it occurs even before the investment is finalized.
Traction Fund executives note that around 70% of investments begin with direct requests from executives: “We need a tool that solves X. Can you find a company that offers this solution?” The fund sources globally, narrowing the field to startups with solid technical foundations and clear enterprise potential.
In some instances, startups receive enterprise introductions or pilot opportunities, even if the fund chooses not to invest. This dual value of the model is one of its core strengths.
A New Operational Layer Between Startups and Enterprises
The fund’s internal strategy and operational planning have recently been led by Ulvi Rashid, its Finance and Investment Director. His structured diligence framework—developed in collaboration with Traction Club executives—has turned the process into a repeatable methodology that reduces risk for investors while accelerating early revenue for founders.
“Most enterprise AI startups fail not because the technology is flawed, but because the buyer dynamics are misunderstood,” explains one senior banking CIO who participates in Traction Club. “The approach Traction Fund uses allows us to test vendors earlier and avoid implementation pitfalls.”
Through this reverse-diligence model, startups benefit from:
- Real enterprise feedback from actual buyers
- Pre-validated use cases
- Revenue opportunities during diligence
- Clearer product-market fit
- Direct insights into buyer decision-making processes
- Enterprise references for future sales
For founders, this approach may help accelerate enterprise adoption, potentially shortening the typical go-to-market timeline by several months compared to traditional methods.
However, it is essential to acknowledge that, like all investment strategies, the process involves inherent risks. The startup landscape is unpredictable, and there are no guarantees that any specific model or startup will achieve long-term success, particularly in complex, regulated industries. Despite this, Traction Fund’s model offers a favorable framework for reducing risk and facilitating more informed, responsible AI adoption in sensitive sectors.
For the growing AI ecosystem, Traction Fund’s approach could serve as a blueprint for responsibly deploying innovation across some of the world’s regulated industries.
Disclaimer: The content in this article is for informational purposes only and does not constitute financial, investment, or legal advice. The views expressed are those of the author and do not reflect the opinions of any associated organizations. Investments in startups, particularly in regulated markets, involve significant risks, and past performance is not indicative of future results. Readers are encouraged to conduct their own research and consult with qualified professionals before making any investment decisions.
