Executive analysis of where technology changes the strategic equation in the life sciences, and where it doesn't.
Societal expectations about pricing, access, data use, and fairness now shape what pharma companies can credibly do. This article looks at five narratives executives should treat as strategic design constraints, and how they affect growth, pricing resilience, and stakeholder trust.
Read the articleMany life science organizations have more AI pilots than AI impact. This article explains how to choose high-leverage use cases that connect to strategy and build durable advantage, rather than adding more proofs of concept.
Read the articleWhat patient data exists, who owns it, and why most of it is not ready for AI. A practical map for leaders deciding where data can support strategy and where the assumptions need closer scrutiny.
Read the articleHow AI-enabled launches actually move therapy initiation and adherence. This article looks at where human judgment remains essential and what that means for launch and patient support strategy.
Read the articleInitiatives designed around an average patient can miss the people who matter most to outcomes. This article explains why life science leaders should look deeper at variation before committing to their next initiative.
Read the articleRolling out a platform is not the same as changing how work gets done. This article examines the gap between technology access and real workflow use, and why leaders often overestimate their digital progress.
Read the articleStrong models do not guarantee adoption. This article looks at how trust shapes AI use in the life sciences and what leaders can do to build confidence among the people expected to rely on it.
Read the articleA look at Sanofi’s leadership transition and what it signals about strategy, executive change, and competitive positioning in large pharma.
Read the articleSmall patient populations, complex diagnostics, and fragmented data make rare disease a demanding test for AI. This article explores why lessons from rare disease increasingly matter for portfolio-wide strategy.
Read the articleWhat does it take for life science leaders to succeed with AI? This article outlines the leadership qualities that matter most across strategic orientation, personal capability, and organizational execution.
Read the articleA breakdown of ten strategic priorities that surfaced at the J.P. Morgan Healthcare Conference 2026, from portfolio resilience and AI integration to pricing pressure and talent.
Read the articleSurgical robotics is shifting from standalone devices to integrated, data-driven platforms. This article looks at what that transition means strategically and why AI readiness matters for organizations evaluating the next generation of surgical technology.
Read the articleThe path from scientific breakthrough to everyday value in genomics was longer than early expectations suggested. This article draws lessons from that history for leaders setting expectations about AI.
Read the articleTechnology strategy that ignores how physicians make decisions rarely succeeds. This article explores how physician experience should shape technology and care strategy in the life sciences.
Read the articleUsing sepsis early warning systems as an example, this article examines technology readiness, organizational foundations, and why promising AI can stall when the surrounding conditions are not in place.
Read the articleA biotech’s AI pilot that did not scale offers useful lessons about organizational readiness. This article looks at what leaders should assess before expanding an AI initiative.
Read the articleCommon pitfalls that keep AI initiatives in the life sciences from delivering value, and the questions leaders should ask before committing further investment.
Read the articleAn aging population is often framed as a burden on health systems. This article explores how it can also reshape portfolio and growth strategy across the life sciences.
Read the articleWhy the right data, structured for a specific decision, matters more than volume. This article examines fit-for-purpose data as a differentiator for AI in the life sciences.
Read the articleLessons from Babylon Health’s collapse: overstated technology claims, partnership risk, and why strategic due diligence matters when evaluating AI-driven health companies.
Read the articlePatient centricity is often treated as a value statement. Where it produces measurable returns across development, access and lifecycle decisions, and how to build it into strategy from the start.
Read the articleExpedited FDA pathways are growing. How market leaders pair regulatory strategy with technology and partnerships to choose the right pathway and improve their odds.
Read the articleDecades of underrepresenting women in research left gaps in data, products and care. The business case for closing the women's health gap and what it means for portfolios.
Read the articleSynthetic data can ease privacy, access and sample-size limits, but only for the right questions. Where it creates value, the risks it adds and how leaders should govern it.
Read the articleReal-world evidence now informs decisions from development to market access. Where it adds strategic value across the lifecycle and which data challenges to plan for.
Read the articlePharma partnerships often stall on data sharing and coordination. How AI and federated learning change the partnership equation, with a recent industry platform as a case study.
Read the articleThe gold standard is not always the right standard. When fit for purpose beats best in class, and how leaders can choose the right bar for each decision.
Read the articleStrong strategy depends on how a team thinks together, not just on its smartest members. This article looks at the bad apple effect, where one disruptive behavior can drag down a whole team, and what leaders can do to protect the conditions that good strategic decisions need.
Read the articleUnchecked assumptions quietly derail life science strategies. Why leaders accept them, how outside perspective tests them and what leaving them unexamined costs.
Read the articleLoss aversion and the certainty effect shape how executives weigh innovation bets. What prospect theory reveals about leadership choices, and why smaller companies often move faster.
Read the articleMost strategies fail in execution, not design. Five operational questions to test before committing, from capabilities and resources to timing and sequencing.
Read the articleA state ban on AI therapy is a signal for the whole industry. The risks it exposes and the governance choices life science leaders should make now.
Read the articleAI is only as reliable as the data beneath it. Why data quality has become a strategic issue for AI in pharma and biotech, and the cost of building on data that is not ready.
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