Key Takeaways

  • Buyers are prioritizing data integration, AI readiness, and workflow modernization as foundational enablers
  • Healthcare and pharma teams are using digital tools to shorten research cycles and strengthen patient engagement
  • Evaluating talent models, partner ecosystems, and ongoing governance now plays a central role in successful programs

Problem to Solve

Most teams stepping into digital transformation in pharma and healthcare tend to start with a similar pain point: the data is there, but it is scattered, slow to use, and difficult to apply in meaningful ways. Even organizations with strong electronic health record (EHR) adoption struggle. Although the U.S. healthcare sector sits at about 95% EHR usage according to the Office of the National Coordinator for Health IT in 2023, it still grapples with interoperability and analytics that can support real clinical or commercial decisions.

Pharma faces its own pressure. Many leaders internally benchmark the time-to-market reduction of 20% to 30% achieved by fully rewiring with digital and analytics, a finding McKinsey reported in 2021. No one expects it to be easy, but it signals a direction that is hard to ignore. R&D teams face substantial pressure from this trend, particularly as AI in drug discovery and development is expected to achieve a compound annual growth rate of over 20% through 2030, per IDC's 2023 analysis.

Healthcare systems, on the other hand, are still contending with habits formed during the COVID-19 acceleration. Forrester reported in 2021 that 79% of organizations accelerated their digital plans due to the pandemic. Telehealth, remote monitoring, and cloud-based data platforms moved from ideas to expectations. Some of those investments worked well; others created new silos. So here we are in 2026, and many buyers are trying to sort out what should stick, what needs to be rebuilt, and what might come next.

Evaluation Approach

When organizations evaluate strategies, they often start with two deceptively simple questions. What outcomes are we actually chasing? And what data or system barriers stop us from getting there? Sounds basic, but buyers frequently skip straight to selecting platforms before mapping the real issues.

Executives tend to anchor decisions around a few themes:

  • How interoperable can our data be, and what standards like HL7 FHIR will we need to support
  • Where AI or automation meaningfully reduces manual research or clinical workload
  • Whether existing cloud or on-prem systems will scale as new analytics layers are added
  • How talent will adapt, especially when data engineering and domain expertise need to meet in the middle

Occasionally, a buyer will pause and ask something that seems almost rhetorical. Are we building a digital capability or buying one? It matters, because the two paths require different governance structures and cost models. It is also where consultancies like Maxima Consulting sometimes enter the conversation, especially for teams that need a mix of IT consulting and workforce augmentation rather than a single platform purchase.

Implementation Considerations

Once a direction is chosen, implementation usually splits into stages. Not a perfect sequence, more like overlapping phases that each carry some friction.

The foundational work typically includes data readiness. Buyers look at integration pipelines, metadata quality, and decisions about standardization. Some teams adopt HL7 FHIR early, while others wait until downstream workflows are more defined. That said, delaying too long can create bottlenecks later.

After that, buyers evaluate where AI-enhanced workflows fit. In pharma R&D, this could start with target identification or trial recruitment. Healthcare systems tend to aim first at care coordination, telehealth optimization, or imaging analytics, especially where vendors like Philips have proven the value of connected patient monitoring platforms. The interesting thing is that organizations often underestimate the cultural shift here. Scientists and clinicians do not automatically trust new tools, and implementation teams need to plan for that.

Security, compliance, and quality management also matter. ISO 13485 requirements can guide medical device teams as they incorporate software-enabled components. Pharma groups consider how automated validation or real-world data from sources like Roche and Flatiron Health can meet regulatory expectations.

There is usually some messy middle where legacy systems surprise everyone with hidden dependencies. Buyers who treat this phase as a discovery layer instead of an obstacle tend to navigate it more smoothly.

Outcomes to Measure

Instead of chasing a single success metric, most buyers track directional improvements across several categories. Not everything moves at the same pace, so blended indicators often create a clearer picture.

Common measures include:

  • R&D cycle efficiency, especially in early discovery
  • Clinical workflow time savings, even if only certain functions see the gains
  • Quality and access to real-world evidence
  • Patient engagement and care continuity, particularly when remote monitoring is involved
  • Manufacturing and supply chain visibility, which Deloitte highlighted as a major priority for competitive advantage in its 2023 report

Teams also look for softer signals. Are clinicians asking for new data dashboards instead of avoiding them? Are regulatory conversations smoother? Is IT spending shifting from maintenance to capability building? These questions may not yield perfect numbers, but they help leaders understand whether the transformation is taking hold.

Buyer Takeaways

Buyers tend to walk away from early evaluations with a few shared insights. One is that digital transformation in pharma and healthcare is less about tools and more about sequencing. Starting too big can be as risky as starting too small. Another is that talent strategy deserves equal weight alongside technology selection. Partner ecosystems matter, especially when internal teams are stretched thin or lack specialized experience in data engineering or compliance-heavy tech stacks.

There is also an appreciation that transformation is iterative. Healthcare and pharma organizations that accept this usually sustain momentum more easily than those that expect a perfect blueprint from day one. And, maybe most important, the organizations that stay close to market research tend to make more grounded decisions. Whether it is Deloitte noting that 64 percent of life sciences executives see digital and analytics as the primary competitive driver through 2028, or IDC's projections about AI growth, the landscape continues to shift quickly. Staying informed helps buyers calibrate expectations and reduce surprises.