You Can Build AI In House.
But Should You?

AI is no longer an experiment in pharma. It is an executive mandate tied to growth, efficiency and competitive advantage. Most large pharmaceutical organizations have already committed to investing. The harder question is how to translate that investment into measurable commercial impact without creating more risk, cost or complexity.

The challenge leaders face

Building AI internally promises control and independence. In reality, many organizations underestimate what it takes to succeed.

Common challenges include:

• Long development cycles that lag behind rapidly evolving technology
• Rising costs tied to specialized talent, maintenance and governance
• Compliance and audit gaps that increase regulatory and brand risk
• Disconnected tools that add complexity instead of improving performance

Too often, AI becomes another siloed initiative rather than a scaled commercial advantage.

What you will gain from this POV

This is not theory. It is a practical guide for senior leaders accountable for outcomes, budgets and risk. By accessing the full POV, you will get:

• A clear view of the cost of building AI versus owning enterprise grade platforms
• A framework for embedding compliance, audit readiness and governance from the start
• Insight into how to unify data, content and execution rather than layering new tools
• Guidance on models that preserve ownership and accelerate time to impact – see ours

Pressure test your AI strategy

The leaders pulling ahead are not waiting years to see results. They are making intentional choices now about what to build, what to own and where partnership creates advantage.

If AI is already a priority in your organization, this POV will help you validate or challenge your current approach before tradeoffs become locked in.

Download the full POV to understand what it really takes to build, own and scale AI in pharma and decide whether building alone is truly the advantage it appears to be.