5 Ways Life Sciences Leaders Are Recalibrating for the Rest of 2026

Life sciences executives are feeling 2 very different things at once: weary about the economy but confident in their own companies.

Deloitte's midyear analysis of leadership sentiment set out to explain that gap, along with where artificial intelligence (AI) deployment is and is not translating into measurable value. Pete Lyons, MBA, vice chair and US Life Sciences leader at Deloitte, dug into the findings with The American Journal of Managed Care^® (AJMC^®).

This transcript has been lightly edited for clarity.

AJMC: The report finds a wide gap between how leaders feel about the global economy and how they feel about their own company. What's driving that disconnect, and is it unusual compared with prior years' surveys?

Lyons: The disconnect reflects how leaders interpret this moment. When they look at the global economy, they see real macro headwinds, persistent pricing and access pressure, competitive intensity, geopolitical volatility, technology shifts, and regulatory uncertainty.

When they look at their own companies, they see progress they can influence directly: pipeline advancement, operational improvements, scientific platforms, and the ability to execute with more precision. That is why the sentiment gap is so wide. In our survey, just 9% of leaders said they felt more positive about the global economy than 6 months ago, while 62% said they felt more positive or much more positive about their own company’s outlook.

AJMC: Biopharmaceutical and medical technology leaders flagged different top risks for the second half of 2026. Should payers and health systems expect those 2 subsectors to behave differently in contract negotiations or product launches this year as a result?

Lyons: Yes, the report suggests biopharma and medtech are facing different pressure points, and that should show up in how they approach both negotiations and launches in the second half of 2026.

Biopharma leaders are more focused on regulatory and reimbursement shifts, clinical uncertainty, pricing pressure, and competitive developments that can change the economics of a launch quickly. Medtech leaders, by contrast, are more exposed to macro conditions, provider capital spending, utilization trends, and operational risks that affect adoption and purchasing decisions.

AJMC: Seventy-one percent of respondents say AI deployment has advanced, but only 45% report measurable improvement, and just 13% see measurable improvement at scale. What's the biggest barrier you're seeing between AI adoption and AI value—is it measurement infrastructure, organizational change management, or something else?

Lyons: The biggest barrier is less about deploying AI and more about turning AI into a measurable business process that delivers value. Many organizations have advanced implementation, but if AI is simply layered on top of existing workflows, they may see activity without meaningful performance improvement. The companies getting value are the ones redesigning workflows, assigning accountable owners, and setting up a clear baseline-to-benefit case so they can measure impact over time.

There is also a real operating model challenge. Scaling AI requires more than technology; it depends on workforce upskilling, workflow redesign, the right technical talent, and the data and infrastructure needed to support adoption. In other words, AI value is not created at deployment. It is created when the way the business works changes.

AJMC: The report notes that AI capability-building is one of the top reasons companies are turning to partnerships. For managed care organizations evaluating partnership pitches from biopharmaceutical or medical technology companies this year, what questions should they be asking to separate genuine AI capability from partnership announcements that are more about signaling than substance?

Lyons: What managed care organizations should be pressing for is clinical and operational evidence, not just proof of concept. If a biopharma or medtech company is pitching an AI-enabled partnership, the key questions are straightforward: What patient population did this work in? What outcomes actually improved? What happened to cost, access, and care quality? And critically, did those results hold up in real-world deployment?

If a company cannot answer those questions with specificity, that is a signal. Managed care organizations should also look closely at incentive alignment. The most durable partnerships are the ones where the life sciences company succeeds when outcomes improve, because that is what creates credible, measurable value rather than signaling.

AJMC: Finally, the report frames the back half of 2026 as a test of whether companies can convert confidence into "disciplined, measurable action." What would you consider the clearest early signal—something you'd expect to see by Q3 or Q4 earnings calls—that a company is successfully making that conversion vs falling short?

Lyons: The clearest early signal is specificity. By Q3 or Q4, companies that are truly converting confidence into disciplined, measurable action will talk less about transformation in the abstract and more about what changed, what improved, and what they can measure.

You should expect to see explicit prioritization decisions, including what they are stopping or deprioritizing, along with concrete metrics such as productivity savings, margin impact, launch milestones, pipeline readouts, market-access wins, AI use cases with measured outcomes, and partnership progress. The discipline signal is when executives move beyond broad investment language and start tying resources to milestone velocity, operational performance, and strategic tradeoffs.