Why Startups Need an Ecosystem Before They Need Enterprise Customers

One of the more fascinating opportunities I encountered recently involved a highly academic organization developing an AI-driven platform intended to transform how biomedical research and pathology data are interpreted. The science was exceptional. The team consisted of world-class researchers. The technology held genuine promise to accelerate biomarker discovery, improve translational research, and ultimately support more precise drug development.

The challenge wasn't the science. It was the commercialization strategy.

During our discussions, I proposed that the company think less like a software vendor and more like an ecosystem builder. Rather than viewing pharmaceutical companies as the primary destination for their technology, I argued that they should simultaneously cultivate partnerships across three interconnected groups:

• Academic medical centers generating novel discoveries.

• Instrument and platform manufacturers embedding the technology into existing laboratory workflows.

• Pharmaceutical and biotechnology companies seeking validated solutions at scale.

Each group reinforces the others.

Academia generates credibility and new use cases. Instrument companies create distribution and workflow integration. Pharmaceutical companies bring validation, funding, and commercial demand. Together they create a flywheel that becomes increasingly difficult for competitors to replicate.

In my experience, this is how durable technology platforms are built.

The leadership ultimately chose a different direction, and I wasn't selected for the role. My impression was that the organization viewed direct partnerships with large pharmaceutical companies as the fastest path to commercialization, while I believed that building an ecosystem first would create far greater long-term leverage.

There is nothing inherently wrong with pursuing large pharmaceutical partnerships early. In fact, they can provide valuable validation and resources. But they also introduce risks that many first-time platform companies underestimate.

Large organizations move deliberately. Pilot studies expand. New requests emerge. Internal stakeholders change. Success metrics evolve. The startup often invests significant time customizing its platform, educating multiple teams, and responding to an expanding list of technical questions.

Meanwhile, the startup's most precious resource—time—continues to disappear.

Whether intentionally or simply through normal enterprise dynamics, there is also the possibility that large organizations internalize what they learn during these collaborations and ultimately decide to build their own implementation or pursue alternative approaches. Regardless of the outcome, the startup may find itself heavily committed to one customer relationship while making little progress expanding its broader market.

This is why diversification matters.

A healthy ecosystem creates multiple sources of validation, multiple revenue paths, multiple product integrations, and multiple channels through which customers discover the technology. Every new partnership increases the value of every other partnership.

That is the essence of network effects.

Today's AI companies frequently describe themselves as "platform companies," yet many still pursue commercialization as if they were selling a single product to a single customer. Those are fundamentally different business models.

Platform businesses win because they become connective tissue across an industry.

In life sciences, that connective tissue often includes researchers, clinicians, software developers, laboratory instrument companies, CROs, diagnostics companies, cloud providers, pharmaceutical companies, and regulators. Each participant contributes to making the platform more valuable for everyone else.

The companies that recognize this early build momentum.

The companies that don't often spend years searching for the perfect niche while competitors quietly assemble ecosystems around them.

AI alone is rarely the competitive advantage.

The ecosystem surrounding the AI is.

For founders, this creates an important strategic question:

Are you building software for customers?

Or are you building an ecosystem that customers eventually can't afford not to join?