What do you get when a military intelligence officer works with pharma?
You get decision advantage instead of reports.
This week on Life Sciences Today
My guest on Life Sciences Today this week was Tony Page, Senior Vice President of Insight Analytics at Within3.
What happens when a former military intelligence officer turns his attention to pharma launches?
You stop thinking in terms of AI-driven dashboards and start thinking in terms of decision advantage.
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Why pharma moves too slowly
On this episode of Life Sciences Today, Tony Page of Within3 explains why pharma still moves too slowly, how fragmented data weakens strategy, and why better launch intelligence starts with asking better questions.
In this conversation, Tony Page, SVP of Insight Analytics at Within3, brought a special perspective to the show: 15 years in military intelligence, followed by a career adapting intelligence methods for business and pharma.
His core point was simple but powerful: pharma does not suffer from lack of data.
Pharmasuffers from fragmented data, siloed teams, and slow decision cycles.
We structured the discussion around my usual 5 questions.
What was your journey?
He traced a path from military intelligence and strategic forecasting into pharma, where he saw an urgent need for continuous intelligence instead of occasional competitive reports.
How does Within3 create value?
By connecting strategy, key questions, data collection, and AI analysis into one process-driven platform.
How does the platform help customers capture value?
It gives medical affairs and brand teams faster, more actionable answers by integrating CRM data, social listening, claims data, meeting reports, and more into one system. The business model is SaaS licensing.
What does Tony want to do for buyers in 2026?
Help them make faster, more confident strategic decisions with a launch intelligence system built around real questions, not passive reporting.
What is Within3’s moat?
A combination of network analytics, domain-specific AI, and years of learning from real client data and workflows.
The Anti-Pattern
The biggest anti-pattern, Tony argued, is the rush to build internal AI tools before clearly defining the business problem. That creates delays, drains resources, and blocks adoption of solutions that already work.
As Tony said to me on the pod:
Too many companies are asking for a “faster horse” instead of defining where they actually need to go.
Watch the show here
About me
I’m a pharma-tech founder who learned hard lessons the hard way. Over 22 years, I built five companies: four exits, one glorious flop. Customers ranged from Israeli medical device startups to Verily, Amgen and Walmart. 70+ clinical trials. 14 FDA.
My latest is OpenCRO - risk analysis for AI-enabled medical device companies.
Instead of generic cybersecurity playbooks, I do a 2 hour workshop with the client where people write down threat scenarios on paper cards. Liberating for people used to filling out Excel checklists.
OpenCRO agents take the stories, build the threat model and write analysis showing how to reduce reputation, revenue and regulatory risk.
If you are running an AI-enabled device company - I’d love to hear your story.