Is Moving From One-Off Solutions To A Technology Platform The Best Fit For Your R&D?

By Jenna Phillips, Clinical Trial Transformation Expert, PA Consulting

As efficiency mandates shape every part of the pharma industry, leading pharma companies are shifting their technology approach in R&D from the use of fragmented, stand-alone digital tools to integrated technology platforms that unify data and operations across discovery, development, and manufacturing. The platform approach is attracting visionary leaders because of the digital upgrades it promises, as well as its potential to drive measurable gains in speed and quality alongside reductions in cost, in a field where every day counts and delays can cost millions.

What Is Tech Platformization, And How Can It Help?

The essential difference in a platform approach compared to legacy point solutions is the end-to-end integration of data and experience. Complete integration across recruitment, labs, trial sites, and the manufacturing floor delivers real-time data flow to stakeholders, reduces bottlenecks, and powers faster, more data-driven decision-making. Upon effective data integration, case studies show that study start-up times can shrink from weeks to days when data is connected and accessible across the enterprise. For example, Mayo Clinic research involving 765 trials found that unified data integration increased patient participation and condensed start-up and recruitment timelines by making real-time eligibility and site activation data accessible across all stakeholders.

It is important to note that while AI can be an important component in platformization, it is not sufficient on its own. Rather than dabbling with isolated automation, organizations such as AstraZeneca, Pfizer, and Roche are embedding AI and machine learning into core R&D processes at a platform level, using integrated tooling and data to accelerate protocol design, recruitment, and predictive analytics to identify clinical risks before they happen, leading to significant operational cost savings and better trial outcomes.

Many leaders who embrace a platform-driven approach use the term “quality by design” to refer to the objectives of their technology platforms across R&D operations. Modern platforms facilitate rigorous automated data validation that reduces late-stage surprises, regulatory setbacks, and manual rework in every part of the R&D organization.

5 Steps To Introduce Platformization And Improve R&D Efficiency

1. Decommission to accelerate.

The biggest barrier to technology platformization is organizational inertia rather than the technical implementation challenges. Bold leaders realize, though, that legacy silos and outmoded systems block effective data flows and, in turn, slow decision-making. Decommissioning old tools, rather than running them in parallel because they’ve always worked and employees know how to use them, enables a clean break that frees human and financial resources to implement and adopt scalable cross-functional platforms that adapt and grow with business needs in a future-facing way.

2. The toughest digital frontiers are actually human; build to accommodate their needs.

Not every component of the R&D ecosystem is easy to digitize. The unpredictable nature of translational science, especially at the critical Phase 2 transition, remains an important bottleneck that even the best AI or tech platform cannot fix overnight. The most human attributes of clinical operations, such as patient recruitment, still depend greatly on trusted doctor-patient relationships and collaboration with site stakeholders and their often-siloed data. These necessary carve-outs from platformization present real obstacles. Platform strategies help here by centralizing site data, streamlining eligibility screening, and standardizing protocol amendments. But human elements will still require innovative solutions that are tailored to accommodate each organization and the requirements of each workflow.

3. Embed data validation and governance.

Platforms unlock value only if data on which they are built is reliable, clean, and regulation ready. Building cross-functional governance teams and institutionalizing automated validation routines ensure excellence is repeatable, audit trails are robust, and surprises are minimized. This is a strategic differentiator in an era of increasingly complex, multi-source clinical data.

4. Focus on human-AI collaboration rather than human replacement.

Automated digital platforms can flag risks, standardize documentation, identify eligible patients for a trial, and optimize time to insight, but they cannot replace the human judgment needed for patient-centric trial design or regulatory interpretation. Leading organizations are blending human expertise with AI-driven insights, creating a feedback loop that continuously improves protocols and outcomes. When designing and implementing end-to-end platforms, it is a mistake to completely cut operational stakeholders out of the loop.

5. Track and benchmark impact.

At the start of a platformization journey, set measurable performance objectives, such as 20% reduction in start-up timelines, 10% increase in recruitment speed, and reduced cycle times for protocol amendments and regulatory query cycles. Track these metrics consistently to demonstrate the real-world impact of the transition to an integrated platform approach.

Leading organizations are already seeing results. Roche has reported significant reductions in development timelines through the use of AI-enabled integrated R&D processes, while ICON has partnered with Microsoft to connect data and workflows across clinical operations, helping streamline activities such as trial feasibility, site selection, and study monitoring.

Positive examples like these can help sustain workforce engagement and momentum throughout what is often a challenging transformation journey. They also provide a tangible reminder of the value of efficiency initiatives, which can sometimes feel impersonal when framed solely around technology and productivity gains.

How To Get Started With Platformization

The move to platform-based, AI-augmented technology is both an IT upgrade as well as a strategic mandate for accelerated, more reliable, and cost-effective R&D.

Organizations that want to leverage platform technology to achieve increased efficiency should consider the following:

• Conduct a thorough assessment of legacy systems and build a timeline for decommissioning any that are redundant, outdated, or ineffective. This can be a difficult transition to make, especially when existing technologies and ways of working are comfortable for teams to use.
• Prioritize R&D use cases in which integration and automation will have the greatest near-term impact, such as patient recruitment, protocol simulation, and end-to-end clinical data flows.
• Invest in cross-functional data governance and validation infrastructure that can give confidence to digital and business leaders that the systems are trustworthy and deliver the right results. This governance approach also benefits from ongoing performance benchmarking and robust communications that sustain buy-in and momentum.
• Align people, processes, and technology under the platform vision. A technology platform is a tool like any other and should be used by people to meet their needs.

Embracing platformization in pharmaceutical R&D enables seamless data integration, accelerates timelines, and reduces costs, ultimately allowing companies to bring innovative therapies to patients faster and more reliably.

In today’s complex, data-driven drug development landscape, these efficiency gains are not just advantageous but are essential for staying competitive and delivering on promises to patients.

About The Author:

Jenna Phillips is a clinical trial transformation expert at PA Consulting.  She uses her background in public health and behavioral science to help organizations bring to market new product and service offerings that drive value for customers, patients, healthcare practitioners, and other stakeholders.