Closing the Execution Gap in Pharma’s Commercial Model

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Pharma is leaving income and sufferers on the desk as a result of technique isn’t reaching the discipline with the velocity or readability required to drive real-world motion. Insights transfer too slowly and inconsistently to information each day selections, creating an execution hole that outcomes in misaligned exercise, wasted spend, and missed remedy alternatives.

The scale of business danger is substantial. According to analysis published in PubMed Central, the U.S. pharmaceutical trade spent an estimated $20.4 billion on detailing and direct advertising and marketing to physicians in a single benchmark 12 months. While that structural funding is designed to form prescribing habits, its effectiveness relies upon solely on whether or not the proper perception reaches the proper consultant earlier than the resolution second has handed.

The proof suggests it steadily doesn’t. A peer-reviewed evaluation of 102 drug launches published in PubMed Central examined the systematic weak spot in industrial forecasting and located that 55.9% of merchandise had forecast errors exceeding ±50%, with projections usually considerably overstating anticipated efficiency. The hole between what industrial groups venture and what really occurs in the market is just not an exception — it’s the norm.

The penalties compound over time. When the price of growth failures is included, analysis published in PubMed Central estimates the imply anticipated price of bringing a brand new drug to market at $879 million. By the time a product reaches launch, the group has already absorbed that funding. Every quarter of misaligned execution is 1 / 4 the market can’t return.

Taken collectively, these figures level to a constant sample: vital industrial funding, unreliable forecasting, and restricted potential to right execution as soon as a product reaches the market.

This article attracts on conversations with Philip Poulidis, CEO and Co-Founder of ODAIA, and Damion Nero, Global Head of Statistics at Daiichi Sankyo, the place they look at why pharma’s industrial mannequin is failing to translate technique into discipline execution, and why AI-driven alignment, attribution, and workflow orchestration now outline what a contemporary industrial engine should do.

This article examines three crucial insights on how biopharma industrial leaders can use AI to revive alignment, eradicate waste, and speed up actual‑world impression throughout each technique and discipline execution.

• AI‑Driven Strategy–Execution Alignment: Anchor execution to actual‑time affected person and HCP alerts so groups cease performing on stale fashions and delayed insights, closing the hole between model intent and what really occurs in the discipline.
• Attribution AI for High-Impact Repeatability: Isolate what actually drives prescribing habits so leaders can lower low-impact engagement and systematically reinvest in the sequences that reliably result in affected person begins.
• AI‑Embedded Workflow Orchestration: Put AI suggestions straight into the instruments groups already use so steering turns into a part of each day selections, decreasing admin load and creating constant, repeatable industrial habits.

Listen to the full episode under:

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 present position, he spent 25 years scaling {hardware} and software program companies, together with main a $1B+ division at Marvell Semiconductor, founding Morega Systems, which AT&T acquired, and serving as SVP and GM of IoT at BlackBerry. Philip additionally co-founded Tartan AI, a machine-learning semiconductor firm that was acquired by Samsung. He is widely known for his experience in AI-driven industrial technique, startup founding and scaling, and his work as an energetic investor and board advisor throughout digital well being 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 present position, he served as Head of Data Science at Takeda and held Director-level positions at Pfizer, the place he led real-world proof science throughout oncology and precision drugs. He additionally served as Vice President of Data Science and Statistical Analytics at STATinMED Research. Damion holds a PhD in Bioinformatics from New York University, the place he was awarded each a McCracken Fellowship and an NIH Minority Fellowship. ​

AI‑Driven Strategy–Execution Alignment

Philip Poulidis opens with the sort of readability that instantly articulates the systemic frustrations felt by industrial leaders. He argues that pharma already has the information and the technique. What it lacks is the potential to maneuver intelligence by the group quick sufficient for the discipline to behave on it.

He argues that that is the level most leaders really feel however hardly ever title: the system is just not conceptually gradual — it’s operationally gradual. Signals transfer by ingestion, transformation, overview, and activation layers that had been by no means constructed for actual‑time industrial use. By the time steering reaches the discipline, the second has handed. The rep is already in the mistaken workplace. The affected person has already moved. The entry situation has already shifted.

Philip captures this failure mode:

“The drawback is just not technique or information. The breakdown begins after the handoff, when technique leaves managed planning environments and enters fragmented execution. Teams interpret the plan in a different way. The market strikes. Patients transfer. By the time insights come again, it’s too late. AI lastly lets manufacturers transfer at the velocity the market is shifting.”

— Philip Poulidis, CEO and Co‑founding father of ODAIA

Damion Nero makes this level specific in his episode, noting that insights usually attain the discipline a day or per week late as a result of the infrastructure beneath them — even when cloud‑hosted — nonetheless runs on architectures which are 10 to fifteen years previous.

Ingestion, transformation, and activation are fragmented. Pipelines are patched collectively. Teams are “constructing the airplane whereas flying it,” which leaves intelligence stranded in methods that merely can’t ship it at the velocity the market calls for.

If the group can’t transfer intelligence quick sufficient, it can’t execute the technique it approves. AI‑pushed alignment is just not about extra dashboards or extra information science. It is about eliminating latency between sign and motion in order that discipline, medical, digital, and analytics groups function on the similar actual‑time understanding of the place therapeutic alternative really exists — and leaders can see inside days whether or not the technique is displaying up in the discipline.

Attribution AI for High‑Impact Repeatability

Philip Poulidis opens this subject with a line that reframes the whole measurement drawback in industrial pharma:

“You can have 80 touchpoints and nonetheless not transfer the needle. Activity doesn’t equal impression. Without realizing which sequence really results in a affected person begin, you’re simply doing extra of every little thing.”

— Philip Poulidis, CEO and Co‑founding father of ODAIA

This is the dysfunction at the middle of business efficiency: in apply, organizations usually scale exercise with out clear proof that it drives prescribing.

Philip’s level lands as a result of it exposes a fact leaders already suspect: the group is drowning in engagement information however nonetheless can’t reply the most elementary query — what really drives prescribing?

Without attribution, each operate defaults to its personal proxy metrics:

• Sales metrics optimize for attain and frequency.
• Marketing metrics optimize for impressions and quantity.
• Medical metrics optimize for scientific engagement.
• Access metrics optimize for pull‑by exercise.

None of those are inherently mistaken — however none of them inform management whether or not the work is producing affected person begins.

Damion Nero sharpens this from the operational aspect. In his episode, he explains that groups usually measure what’s best to entry slightly than what really issues: “We’re measuring what we are able to entry rapidly, not what really issues.”

He notes that legacy KPIs and outdated reporting cycles bury the actual drivers of prescribing in lagging information, siloed methods, and fashions by no means designed for omnichannel habits. The result’s a industrial engine that’s busy, seen, and measurable — however not reliably efficient.

Philip and Damion agree that the path ahead is just not extra dashboards; it’s a shift from quantity‑based mostly measurement to sequence‑based mostly proof.

Attribution AI makes that shift doable. It isolates the particular actions and sequences that reliably precede a affected person’s begin, separating the excessive‑impression few from the low‑impression many.

When leaders can see which actions really transfer prescribing, they cease funding noise and begin scaling what works. Attribution turns into the mechanism that eliminates waste, restores strategic focus, and directs funding towards the sequences that reliably drive actual‑world impression.

AI‑Embedded Workflow Orchestration

Damion Nero describes the each day actuality of business execution: groups are navigating fragmented methods, handbook processes, and steering that arrives lengthy after selections are made. As he places it:

“Most of the work occurs in the gaps between methods, not inside them. People are stitching collectively info from CRM, e-mail, dashboards, and shared drives simply to determine what to do subsequent. When the workflow itself is that this fragmented, even the greatest technique can’t present up the manner it was meant.”

— Damion Nero, Global Head of Statistics at Daiichi Sankyo

Philip Poulidis extends this level from the methods perspective. He explains that industrial steering usually lives in decks, portals, dashboards, and e-mail threads — all exterior the second of motion.

This is the surroundings AI‑embedded workflow orchestration is designed to repair: as an alternative of asking groups to drag insights from disconnected sources, AI locations suggestions straight inside the instruments they already use — CRM, name planning, e-mail, discipline insights. Guidance turns into a part of the motion, not an additional step.

The impression is operational, not conceptual: execution turns into constant, well timed, and aligned with the sequences that really drive prescribing.

Executives reply to this as a result of the worth is concrete and scannable. Workflow‑embedded AI should ship 4 non‑negotiables:

• In‑Flow Guidance — suggestions seem at the second of motion, inside the workflow.
• Zero‑Friction Adoption — no new instruments, tabs, or coaching burdens.
• Consistent Execution — each rep follows the similar proof‑based mostly sequence.
• Reduced Admin Load — AI handles sorting, filtering, and prioritizing so people can give attention to engagement.

When intelligence is embedded straight into the workflow, groups cease improvising round system limitations and begin executing the technique the manner management meant — reliably, repeatedly, and at scale.