AI isn't hype anymore - it's fundamentally changing how decisions get made across industries. The potential is real, but so are the risks if implementation isn't thoughtful. Especially in pharmaceutical and healthcare contexts, where regulatory requirements, patient safety, and evidence standards demand more than just deploying the latest technology.
After 12 years navigating HEOR, pricing, market access, and value communication in pharmaceutical settings, I know where AI can add genuine value versus where it's just expensive complexity. And through focused study in responsible AI implementation, I'm building the technical knowledge to bridge that gap credibly.
Core focus areas
Responsible AI implementation
For pharmaceutical and healthcare organizations:
Your industry operates under strict regulatory frameworks - FDA, EMA, MHRA, and now EU AI Act compliance. You need AI strategies that account for:
- Evidence requirements and validation standards
- Regulatory submission processes
- Risk management frameworks
- Stakeholder communication (payers, clinicians, patients, regulators)
- Real-world evidence generation and analysis
I'm not selling generic AI transformation playbooks. I understand your operational reality - the governance structures, the approval processes, the evidence standards that shape pharmaceutical decision-making. My work focuses on where AI adds value within these constraints, not ignoring them.
For families and everyday users:
AI is moving from professional tools to everyday life. As a father of three, I'm committed to making AI literacy accessible - helping people understand what these tools can and can't do, how to use them thoughtfully, and how to talk about them with children.
This isn't about fear-mongering or blind adoption. It's about informed use: understanding capabilities, limitations, privacy implications, and ethical considerations in practical terms.
Healthcare AI strategy
Healthcare AI isn't just different in degree from other sectors - it's different in kind. The stakes are higher, the regulations stricter, the evidence requirements more demanding.
My pharmaceutical background gives me specific advantages here:
- Health economics expertise: Understanding cost-effectiveness frameworks, QALY calculations, budget impact models - the economic lenses through which payers evaluate new technologies
- Market access experience: Knowing how HTA bodies assess evidence, what payers actually care about, how to communicate value in stakeholder-specific language
- Regulatory navigation: Experience with FDA, EMA, NICE, MHRA frameworks - understanding approval processes and evidence requirements
- Cross-functional collaboration: Working across clinical, commercial, regulatory, and evidence teams - understanding organizational dynamics
I can help pharmaceutical and healthcare organizations think through:
- Where AI deployment makes strategic sense (and where it doesn't)
- How to build evidence packages that meet regulatory standards
- What stakeholders need to hear at each stage
- How to navigate the intersection of AI regulation and healthcare regulation
- Building internal capability without over-investing in hype
Multi-stakeholder analysis
One of the most valuable things AI can do is help you think through stakeholder perspectives more systematically. Not as a replacement for actual stakeholder research, but as a way to stress-test strategies and identify blind spots.
Through my MA coursework, I've been exploring how AI can facilitate more comprehensive perspective-taking - simulating payer objections, clinical concerns, regulatory questions, patient priorities. This isn't about generating content faster; it's about achieving higher quality analysis by considering more angles than you'd typically have bandwidth for.
In pharmaceutical contexts, this means:
- Anticipating payer objections before negotiations
- Stress-testing value propositions from multiple angles
- Identifying evidence gaps that different stakeholders might flag
- Preparing for regulatory questions
- Understanding where different markets will respond differently
The goal is strategic insight, not just efficiency gains.
What makes my approach different
Most AI consultants bring technical expertise and try to learn industry domains. I'm bringing 12 years of pharmaceutical domain expertise and building technical AI capability on top of it.
That means I can:
- Speak your language - understand HEOR, HTA, market access terminology without translation
- Recognize what's realistic in pharmaceutical operational contexts
- Distinguish AI hype from genuine value in your specific setting
- Navigate the intersection of AI regulation and healthcare regulation
- Focus on responsible implementation, not just deployment speed
I'm also committed to avoiding the consultancy trap of overselling complexity. Sometimes the right answer is "you don't need AI for this." Sometimes it's "start small and prove value before scaling." And sometimes it's "this will require significant change management before any technology solution makes sense."
Current development
Through my MA in AI & Digital Transformation at the University of Southampton, I'm developing expertise in:
- Machine learning fundamentals and model evaluation
- Responsible AI frameworks and governance
- AI regulatory landscapes (EU AI Act, UK approach, US frameworks)
- Practical implementation challenges
- Stakeholder communication strategies
I'm documenting this journey publicly through projects, insights, and case studies - building a portfolio that demonstrates both learning and practical application.