Closing the Execution Gap in Pharma’s Commercial Model

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Pharma is leaving revenue and patients on the table because strategy isn’t reaching the field with the speed or clarity required to drive real-world action. Insights move too slowly and inconsistently to guide daily decisions, creating an execution gap that results in misaligned activity, wasted spend, and missed treatment opportunities.

The scale of commercial risk is substantial. According to research published in PubMed Central, the U.S. pharmaceutical industry spent an estimated $20.4 billion on detailing and direct marketing to physicians in a single benchmark year. While that structural investment is designed to shape prescribing behavior, its effectiveness depends entirely on whether the right insight reaches the right representative before the decision moment has passed.

The evidence suggests it frequently does not. A peer-reviewed analysis of 102 drug launches published in PubMed Central examined the systematic weakness in commercial forecasting and found that 55.9% of products had forecast errors exceeding ±50%, with projections often significantly overstating expected performance. The gap between what commercial teams project and what actually happens in the market is not an exception — it is the norm.

The consequences compound over time. When the cost of development failures is included, research published in PubMed Central estimates the mean expected cost of bringing a new drug to market at $879 million. By the time a product reaches launch, the organization has already absorbed that investment. Every quarter of misaligned execution is a quarter the market cannot return.

Taken together, these figures point to a consistent pattern: significant commercial investment, unreliable forecasting, and limited ability to correct execution once a product reaches the market.

This article draws on conversations with Philip Poulidis, CEO and Co-Founder of ODAIA, and Damion Nero, Global Head of Statistics at Daiichi Sankyo, where they examine why pharma’s commercial model is failing to translate strategy into field execution, and why AI-driven alignment, attribution, and workflow orchestration now define what a modern commercial engine must do.

This article examines three critical insights on how biopharma commercial leaders can use AI to restore alignment, eliminate waste, and accelerate real‑world impact across both strategy and field execution.

AI‑Driven Strategy–Execution Alignment: Anchor execution to real‑time patient and HCP signals so teams stop acting on stale models and delayed insights, closing the gap between brand intent and what actually happens in the field.

Attribution AI for High-Impact Repeatability: Isolate what truly drives prescribing behavior so leaders can cut low-impact engagement and systematically reinvest in the sequences that reliably lead to patient starts.

AI‑Embedded Workflow Orchestration: Put AI recommendations directly into the tools teams already use so guidance becomes part of daily decisions, reducing admin load and creating consistent, repeatable commercial behavior.

Listen to the full episode below:

Episode 1: Rethinking Pharma Commercial Targeting with AI – with Philip Poulidis of ODAIA​

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Guest: Philip Poulidis, CEO and Co-founder of ODAIA

Expertise: Artificial Intelligence, AI Commercial Execution, Enterprise Software Leadership, Product Strategy

Brief Recognition: Philip Poulidis is the CEO and Co-Founder of ODAIA. Prior to his current role, he spent 25 years scaling hardware and software businesses, including leading a $1B+ division at Marvell Semiconductor, founding Morega Systems, which AT&T acquired, and serving as SVP and GM of IoT at BlackBerry. Philip also co-founded Tartan AI, a machine-learning semiconductor company that was acquired by Samsung. He is widely recognized for his expertise in AI-driven commercial strategy, startup founding and scaling, and his work as an active investor and board advisor across digital health and AI startups.

Episode 2: Modernizing Targeting to Close the Field Execution Gap – with Damion Nero of Daiichi Sankyo​

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Guest: Damion Nero, Global Head of Statistics at Daiichi Sankyo

Expertise: Biostatistics & Real-World Evidence, Data Science & Machine Learning, Precision Medicine, HEOR & Health Technology Assessment

Brief Recognition: Damion Nero is the Global Head of Statistics for HEOR/HTA at Daiichi Sankyo. Prior to his current role, he served as Head of Data Science at Takeda and held Director-level positions at Pfizer, where he led real-world evidence science across oncology and precision medicine. He also served as Vice President of Data Science and Statistical Analytics at STATinMED Research. Damion holds a PhD in Bioinformatics from New York University, where he was awarded both a McCracken Fellowship and an NIH Minority Fellowship. ​

AI‑Driven Strategy–Execution Alignment

Philip Poulidis opens with the kind of clarity that immediately articulates the systemic frustrations felt by commercial leaders. He argues that pharma already has the data and the strategy. What it lacks is the ability to move intelligence through the organization fast enough for the field to act on it.

He argues that this is the point most leaders feel but rarely name: the system is not conceptually slow — it is operationally slow. Signals move through ingestion, transformation, review, and activation layers that were never built for real‑time commercial use. By the time guidance reaches the field, the moment has passed. The rep is already in the wrong office. The patient has already moved. The access condition has already shifted.

Philip captures this failure mode:

“The problem is not strategy or data. The breakdown begins after the handoff, when strategy leaves controlled planning environments and enters fragmented execution. Teams interpret the plan differently. The market moves. Patients move. By the time insights come back, it is too late. AI finally lets brands move at the speed the market is moving.”

— Philip Poulidis, CEO and Co‑founder of ODAIA

Damion Nero makes this point explicit in his episode, noting that insights often reach the field a day or a week late because the infrastructure beneath them — even when cloud‑hosted — still runs on architectures that are 10 to 15 years old.

Ingestion, transformation, and activation are fragmented. Pipelines are patched together. Teams are “building the plane while flying it,” which leaves intelligence stranded in systems that simply cannot deliver it at the speed the market demands.

If the organization cannot move intelligence fast enough, it cannot execute the strategy it approves. AI‑driven alignment is not about more dashboards or more data science. It is about eliminating latency between signal and action so that field, medical, digital, and analytics teams operate on the same real‑time understanding of where therapeutic opportunity actually exists — and leaders can see within days whether the strategy is showing up in the field.

Nero reinforces that this gap is not driven by a lack of data, but by infrastructure and operational constraints. He notes that in many organizations, “it’s not that the information isn’t there—it’s that it’s a day late or a week late,” underscoring that operational latency, not strategy quality, is the limiting factor.

Attribution AI for High‑Impact Repeatability

Philip Poulidis opens this topic with a line that reframes the entire measurement problem in commercial pharma:

“You can have 80 touchpoints and still not move the needle. Activity doesn’t equal impact. Without knowing which sequence actually leads to a patient start, you’re just doing more of everything.”

— Philip Poulidis, CEO and Co‑founder of ODAIA

This is the dysfunction at the center of commercial performance: in practice, organizations often scale activity without clear evidence that it drives prescribing.

Philip’s point lands because it exposes a truth leaders already suspect: the organization is drowning in engagement data but still cannot answer the most basic question — what actually drives prescribing?

Without attribution, every function defaults to its own proxy metrics:

Sales metrics optimize for reach and frequency.

Marketing metrics optimize for impressions and volume.

Medical metrics optimize for scientific engagement.

Access metrics optimize for pull‑through activity.

None of these are inherently wrong — but none of them tell leadership whether the work is producing patient starts.

Damion Nero sharpens this from the operational side. In his episode, he explains that teams often measure what is easiest to access rather than what actually matters: “We’re measuring what we can access quickly, not what actually matters.”

He notes that legacy KPIs and outdated reporting cycles bury the real drivers of prescribing in lagging data, siloed systems, and models never designed for omnichannel behavior. The result is a commercial engine that is busy, visible, and measurable — but not reliably effective.

Philip and Damion agree that the path forward is not more dashboards; it is a shift from volume‑based measurement to sequence‑based evidence.

Attribution AI makes that shift possible. It isolates the specific actions and sequences that reliably precede a patient’s start, separating the high‑impact few from the low‑impact many.

When leaders can see which actions actually move prescribing, they stop funding noise and start scaling what works. Attribution becomes the mechanism that eliminates waste, restores strategic focus, and directs investment toward the sequences that reliably drive real‑world impact.

AI‑Embedded Workflow Orchestration

Damion Nero describes the daily reality of commercial execution: teams are navigating fragmented systems, manual processes, and guidance that arrives long after decisions are made. As he puts it:

“Most of the work happens in the gaps between systems, not inside them. People are stitching together information from CRM, email, dashboards, and shared drives just to decide what to do next. When the workflow itself is this fragmented, even the best strategy can’t show up the way it was intended.”

— Damion Nero, Global Head of Statistics at Daiichi Sankyo

Philip Poulidis extends this point from the systems perspective. He explains that commercial guidance often lives in decks, portals, dashboards, and email threads — all outside the moment of action.

This is the environment AI‑embedded workflow orchestration is designed to fix: instead of asking teams to pull insights from disconnected sources, AI places recommendations directly inside the tools they already use — CRM, call planning, email, field insights. Guidance becomes part of the action, not an extra step.

Nero emphasizes that the success of these systems ultimately depends on adoption and integration into daily work. He notes that many AI initiatives fail not because of model performance but because they remain “outside the ecosystem,” forcing teams to rely on fragmented tools or legacy workflows.

The impact is operational, not conceptual: execution becomes consistent, timely, and aligned with the sequences that actually drive prescribing.

Executives respond to this because the value is concrete and scannable. Workflow‑embedded AI must deliver four non‑negotiables:

In‑Flow Guidance — recommendations appear at the moment of action, inside the workflow.

Zero‑Friction Adoption — no new tools, tabs, or training burdens.

Consistent Execution — every rep follows the same evidence‑based sequence.

Reduced Admin Load — AI handles sorting, filtering, and prioritizing so humans can focus on engagement.

When intelligence is embedded directly into the workflow, teams stop improv

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