Pharmaceutical and biotech companies are under mounting pressure to justify artificial intelligence investments, but the harder question is whether anyone is measuring if it actually works.
A recent market survey of brand and commercialisation leaders, spanning global top 20 pharma firms down to small specialty companies, reveals a striking disconnect between AI enthusiasm and expectations.
More than 80% of patient and market access professionals said it is appealing when a vendor positions itself as tech- or AI-forward in its offerings.
However, when those same respondents were asked what they actually expect from a tech-forward partner, 61% said smarter, more adaptive execution rather than AI specifically.
ConnectiveRx executives Cindy Baksh and Steve Randall argue the real work begins before any AI decision, with organisations needing to define what “better” actually looks like first.
The survey respondents identified their top three markers of partner success as reduced patient abandonment, increased new prescriptions, and high execution accuracy across programmes.
Baksh and Randall are clear that technology alone delivers none of those outcomes, with execution strategy doing the heavy lifting and AI playing a supporting role where appropriate.
Organisational redesign is identified as the primary driver of AI value, with reworked roles, handoffs, and decision rights essential to preventing AI from becoming what the executives call “shelfware.”
Without that structural groundwork, even well-resourced AI deployments consistently fail to move speed-to-therapy and pull-through metrics in any meaningful direction.
On benefits verification specifically, the executives argue that probabilistic outputs are simply unacceptable, with direct payer-source data required to anchor electronic benefits verification processes.
AI should instead be deployed to score confidence levels and flag discordant results, keeping human judgement at the centre of high-stakes patient access decisions.
Targeted use cases identified as appropriate for AI deployment include rapid identity-based patient screening with human review pathways built into the process from the outset.
Pattern detection for copay misuse and buy-and-bill payment anomalies also represents a strong fit, improving both accuracy and decisioning speed in complex reimbursement environments.
The broader argument from ConnectiveRx is that the industry’s obsession with AI adoption is arriving ahead of the strategic clarity needed to make it deliver genuine commercial value.