More data doesn’t translate into better decisions 

The case for decision-centric commercial intelligence in an AI-enabled pharma environment  

Blog
June 24, 2026

Christian Bowers

Principal, Pharmaceutical & Life Sciences Strategy & Analytics, PwC US

Phil Sclafani

Principal, Pharmaceutical and Life Sciences Commercial Advisory, PwC US

Pharmaceutical and life sciences companies have invested heavily in data, analytics, and AI, yet many still struggle to turn insights into timely, confident action. As the volume of commercial intelligence grows, the challenge is achieving decision clarity. Organizations that shift from analytics-centric to decision-centric intelligence can better identify opportunities, align teams, and act with greater speed and precision.

Key takeaways:

More insights don't guarantee better decisions. Many pharma organizations struggle to translate growing volumes of data into timely commercial action. 
Decision-centric intelligence focuses on creating increased confidence in choosing next-best actions. Leading pharma companies are moving beyond dashboards to proactive recommendations that drive faster decision-making. 
Context drives confidence. Connecting commercial, access, and market signals helps teams understand what's driving performance and where to intervene. 
Execution is the differentiator. Coordinated action across commercial functions and channels enables faster responses and greater business impact.

The decision gap and the end of the static dashboard

Pharmaceutical companies are not struggling with a lack of data. They are struggling with translating insight into timely, confident action.

Despite significant investment in analytics and AI, many commercial teams face a familiar challenge: more insight, but slower decisions. Competing signals, proliferating dashboards, and fragmented ownership often delay alignment. By the time decisions are made, the opportunity to influence the market may have narrowed. AI is accelerating this dynamic. While it increases the volume and speed of insights, it also contributes to insight overload—and does not inherently resolve ambiguity or prioritize action.

Most existing commercial analytics platforms used today were not designed to solve this problem. They excel at aggregating data and optimizing workflows but remain largely observational—leaving teams to interpret signals, align-on implications, and determine next steps manually.

Competitive advantage will belong to organizations that create decision advantage: deciding earlier, aligning faster, and acting with confidence in moments of uncertainty. By continuous real-world signal integration, our clients have been able to narrow forecast variance by 15-25%.

From analytics maturity to decision advantage

Many organizations still measure progress through analytics maturity—investing in data platforms, AI tools, and integrated ecosystems. These capabilities are essential, but they are not sufficient on their own. Analytics maturity can amplify ambiguity if it is not anchored to clear decision processes.

Existing pharmaceutical and life sciences commercial analytics platforms are designed to help organizations do things right. But they are not designed to ensure organizations are actually doing the right things— or doing them fast enough.

As a result, even highly mature analytics environments can struggle to answer important commercial questions, such as:

Where are we losing patients in the access journey—and what should we do about it this week?
Which accounts require immediate field intervention?
What is driving shifts in competitive momentum and how should we react?
Which friction points, if addressed, would materially change our trajectory?

Traditional analytics can describe these dynamics, but it rarely translates them into clear, prioritized action.

Closing the gap requires rethinking how commercial insight platforms truly create value — shifting from simple insight delivery to decision enablement.

Leading organizations are focusing on three capabilities:

Traditional analytics requires users to search for answers. Decision-centric intelligence surfaces them.

An effective intelligence engine continuously scans signals across prescriptions, payer dynamics, engagement activity, and field inputs to detect emerging risks and opportunities. It translates those signals into clear, prioritized recommendations—delivered with context and urgency.

For example, early prescribing behavior from a physician may trigger a tailored engagement strategy—coordinating digital outreach, field action, and educational touchpoints—without requiring manual orchestration. The role of the commercial team shifts from analysis to oversight and refinement.

Instead of asking, “What is happening?” teams are guided toward “What should we do next?” For example, clients we worked with reduced disease diagnostic delay by an average of 2-18 months, expanding addressable patient capture by 20-30% across technical analysis.

Aggregation alone does not drive decisions. Leaders need to understand the drivers of performance—not just the metrics.

Decision-centric systems connect behavioral signals (rep activity, digital engagement), structural factors (payer dynamics, access barriers), and real-world utilization (claims, referrals) to isolate root causes. They quantify the relative impact of factors such as access friction, competitive pressure, engagement gaps, or patient drop-off.

For example, a decline in prescriptions in a priority territory may initially appear to be a field execution issue. In reality, integrated signals may reveal a recent payer policy change driving increased prior authorization denials, redirecting the appropriate response from field redeployment to access strategy intervention.

When recommendations are grounded in clear, explainable drivers, teams are more likely to trust them and act quickly.

This level of synthesis is what enables confident decision-making.

Even when the right decision is identified, execution often breaks down.

Commercial decisions typically span multiple functions—field teams, marketing, market access, and patient support. Without coordination, insights stall, actions fragment, and impact is diluted.

A decision-centric intelligence engine closes this gap by translating decisions into coordinated action:

Triggering field engagement at the right moment
Aligning digital and personal promotion
Sequencing interventions across channels
Embedding feedback loops to continuously learn and adapt

The result is not just faster decisions—but faster, more consistent execution at scale. We have seen clients shift 25-40% of field call activity from low-yield to high-yield accounts through dynamic signal-based prioritization.

A new playbook for commercial leadership

The shift from analytics-centric to decision-centric commercial intelligence is already underway. Organizations that make this transition will be better positioned to act earlier, align faster, and compete more effectively in increasingly dynamic markets.

At PwC, we are helping clients accelerate this shift, combining strategy, data, and advanced analytics to build intelligence engines designed for decision-making. Our demonstration modules showcase what this looks like in practice.

Connect with us to explore how a decision-centric approach could be applied to your commercial organization.

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