How Richmond Pharmacology

The challenges

Richmond Pharmacology, a large Contract Research Organisation based in London, was facing a challenge familiar to many healthcare companies in 2025: AI was everywhere, but clarity was nowhere.

The leadership team knew AI could transform clinical research operations. They'd seen the headlines about achieving 80% time reductions and dramatic cost savings. But they were also aware of a sobering statistic: 80% of AI implementations in healthcare fail to deliver meaningful ROI.

The specific challenges keeping them cautious were:

• Technology hype versus business reality: Every vendor was promising AI miracles, but which use cases would actually deliver value for a CRO of their size and focus? They needed to separate genuine opportunities from expensive distractions.

• Implementation risk: They'd heard cautionary tales of companies spending millions on AI pilots that never scaled beyond proof-of-concept. How could they avoid becoming another failure statistic?

• No clear framework: Their team had technical questions, strategic questions, and operational questions but no structured way to evaluate where AI would create genuine competitive advantage versus where it would just add complexity.

• Change management concerns: Even if they identified the right technology, how would they actually drive adoption across their organisation? What governance structures needed to be in place?

• Resource allocation: With multiple potential AI applications across clinical operations, data management, pharmacovigilance, medical writing, quality assurance, and regulatory affairs—where should they invest first to maximise ROI?

Richmond Pharmacology needed more than vendor pitches. They needed an honest, evidence-based assessment of what actually works in AI implementation for clinical research, delivered by someone who understood both the clinical domain and the technology deeply.

Our Approach

We designed a comprehensive strategic workshop specifically for Richmond Pharmacology's leadership team:

Phase 1: Industry landscape assessment

Rather than starting with technology capabilities, we began with brutal honesty about the industry's AI implementation record. We presented real data: the 80% failure rate, the common pitfalls, the specific reasons companies fail to move from pilot to production. This established realistic expectations and helped leadership understand that success requires more than just buying the right software.

Phase 2: Domain-driven opportunity mapping

We structured the analysis around six key domains relevant to CROs: Clinical Operations, Data Management, Pharmacovigilance, Medical Writing, Quality Assurance, and Regulatory Affairs. For each domain, we presented:

• Real-world case studies with verified metrics

• Specific use cases ranked by implementation difficulty and potential ROI

• Examples from comparable-sized CROs (Simbec-Orion, Quotient Sciences, Syneos Health)

This wasn't theoretical—every example came from published studies or verified implementations.

Phase 3: The "What Failure Looks Like" analysis

We dedicated substantial time to examining why AI implementations fail. We identified six common failure patterns and illustrated each with real examples. Critically, we emphasised "second-order effects", hidden consequences of AI implementation that companies often miss until it's too late.

This section helped the leadership team develop the right questions to ask vendors and internal teams, moving beyond surface-level technology demonstrations to genuine evaluation of implementation feasibility.

Phase 4: Building good foundations

We outlined the four essential foundations for successful AI implementation:

1. Data readiness: Not just having data, but having it in usable formats with proper governance

2. Validation frameworks: How to validate AI systems in regulated environments

3. Oversight structures: Governance models that balance innovation with compliance

4. Risk management: Identifying and mitigating risks before they become expensive problems

For each foundation, we provided specific frameworks and checklists that Richmond Pharmacology could immediately apply to their evaluation process.

Phase 5: From pilot to scale

We presented a clear six-phase deployment pathway (Assessment → Pilot → Validation → Limited Deployment → Full Deployment → Optimisation) with realistic timelines (6-12 months for full implementation). We explained what successful organisations did differently to move beyond perpetual pilots to actual operational integration.

Phase 6: Customised recommendations

We concluded with specific recommendations tailored to Richmond Pharmacology's position, including:

• Prioritised domains for initial investment based on their strengths (their 250,000+ volunteer database made patient recruitment optimisation particularly attractive)

• Immediate actions they could take in the next 0-6 months

• A realistic assessment of opportunity versus the risk of inaction as competitors accelerate their AI capabilities

Throughout the workshop, we maintained focus on a critical principle: business objectives should drive technology decisions, not the other way around. Every recommendation was tied back to measurable business outcomes rather than technological impressiveness.