Clinical trials have become more complex, longer, and data-intensive. But the way many clinical-development decisions are made hasn't evolved at the same pace.
A decision made during protocol design may influence country allocation. Country allocation affects site identification strategy. Site selection influences startup, enrollment, cost, and ultimately study execution.
Yet too often, the context behind those decisions doesn't travel with the study.
Instead, information moves between teams through meetings, spreadsheets, individual systems, and institutional knowledge. One team may make a well-informed decision, only for the next team to reconstruct the assumptions behind it.
That is more than a technology problem. It’s an operating-model problem and one that H1 and Novo Nordisk are working together to address.
Novo Nordisk developed StudyHub around the realities of clinical development, connecting workflows across study design, country allocation, site selection, startup, budgeting, execution, and resource planning. It has already been deployed at enterprise scale, with seven digital products supporting more than 1,500 regular users.
Now H1 is building on that foundation as part of its partnership with Novo Nordisk, combining Novo Nordisk's clinical-development expertise with H1's healthcare data, technology and AI capabilities.
The opportunity extends beyond making individual tasks faster. It’s about creating a connected decision environment across clinical development.
The problem isn't a lack of data
Clinical-development teams have access to more information than ever: claims data, epidemiology, investigator and site intelligence, historical trial performance, operational data, internal sponsor data and numerous third-party datasets.
The challenge is turning it into context that teams can use when making decisions.
Consider site selection.
Knowing that an investigator has relevant therapeutic-area experience is useful. But that information becomes far more valuable when it can be evaluated alongside patient availability, competitive trial activity, historical site performance, geographic considerations, operational feasibility, and a sponsor's own experience with that site.
The same principle applies across the study lifecycle.
Country-allocation decisions can benefit from patient availability, historical performance and local insights. Protocol feasibility can influence downstream operational planning. Study-execution data can become an input into future planning rather than disappearing into another silo once a trial ends.
Connecting those signals creates something more valuable than aggregation: continuity.
AI needs context before it can deliver impact
This distinction becomes even more important as clinical-development organizations adopt AI.
AI can dramatically reduce the effort required to analyze data, compare scenarios, and surface recommendations. But AI operating on fragmented information risks accelerating fragmented decision-making.
The foundation matters.
H1's approach to connected clinical development is built on a unified clinical data layer that brings together a sponsor's proprietary information, H1 healthcare intelligence, and other relevant third-party data.
On top of that foundation sit applications that support clinical users throughout the trial lifecycle, from study design and country allocation to site identification, execution, portfolio intelligence and cost management.
The objective isn't to automate every decision. It’s to preserve the connective tissue between decisions.
An assumption made during study design should remain visible as teams assess feasibility. The rationale behind a country strategy should be available when sites are evaluated. Operational outcomes should inform future studies and portfolio planning.
AI then becomes part of a continuous decision system rather than another standalone tool teams need to consult.
Technology alone won't solve the problem
There is another lesson from the StudyHub experience that may prove equally important.
Clinical-development transformation cannot simply be configured into software.
Every sponsor has different data, governance structures, operating models, definitions of success and ways of moving between global strategy and local execution.
That makes co-creation critical.
The technology has to reflect how clinical teams actually work: who participates in a decision, which evidence matters, how tradeoffs are evaluated, and where human expertise is most valuable.
The goal is not to remove people from clinical development. It is to reduce the manual work surrounding them so their expertise can be applied to decisions, exceptions, and judgment rather than gathering information and reconciling spreadsheets.
From individual tools to connected intelligence
For years, clinical-development technology has largely advanced one workflow at a time.
The next phase may look very different.
The H1 and Novo Nordisk partnership provides a glimpse of what happens when the industry stops asking how AI can improve an individual clinical task and starts asking how data, AI, and human expertise can work together across the entire development lifecycle.
For sponsors, that could mean faster planning, more consistent decisions, greater transparency, and an ability to learn across studies instead of starting over with each one.
And for patients, the stakes are higher. Every unnecessary delay between clinical development and a successful trial can extend the time before a promising medicine reaches the people waiting for it.
The real opportunity for AI, then, isn't simply to make today's clinical-development processes faster.
It's to build a better way of making the decisions behind them.