Commercial teams are running yesterday's plays. AI just changed the game

There's a moment in elite sport when a team stops reacting and starts anticipating. Pep Guardiola doesn't just watch where the ball is. His teams are trained to read where it will be three passes from now. In Formula 1, race strategy is no longer a gut call at the pit wall. It's a simulation run thousands of times before lights out, updated in real time as conditions shift.

Life sciences commercialisation is arriving at exactly that moment. The teams that figure this out early are making the old playbook irrelevant for everyone still running it.

The gap between organisations that talk about AI and those that genuinely deploy it commercially is widening faster than most leadership teams realise.

Industry is still addicted to the rear-view mirror

For years, industry talked about data-driven marketing as an aspiration. CRMs, dashboards, digital channels, analytics talent; companies across the sector invested in all of it and called it transformation. But most of that was rear-view mirror work: understanding what happened last quarter, which rep had the best call rate, which campaign drove the most clicks. Good hygiene, but not good enough anymore.

The organisations worth watching aren't the ones with the fanciest AI stack. They're the ones that have stopped asking "What happened?" and started asking "What should we do next, and why?" That shift sounds simple. Operationalising it is where most commercial organisations are still stuck.

The marketing content engine is broken

Ask any VP of Marketing what slows them down and there’ll be the same answer: content creation is too slow, too siloed, and too disconnected from the science it's supposed to communicate.

Medical affairs and commercial teams produce variants of the same core narrative for HCPs, payers, and patient groups in processes that were never designed to talk to each other. The result is inconsistency, latency, and good science failing to land with the right audience at the right moment.

GenAI, deployed against approved content and governed brand guidelines, changes this. Production cycles that took weeks compress to days. More importantly, the humans in the loop are finally free to focus on what they're actually good at: clinical nuance, stakeholder tone, strategic emphasis. The machine handles the scaffolding. The expert handles the substance. That's not a cost story; that's a capability story.

Sales reps are flying partially blind

Morgan Stanley deployed an AI assistant to its wealth advisors, not to replace them, but to surface the right research, the right talking points, the right next action at the right moment. The result was measurably better client engagement. AI removed the cognitive overhead getting in the way of the advisor being at their best.

The same dynamic is playing out in pharma field force effectiveness. VPs of sales operations are moving beyond Next Best Action nudges in a CRM toward systems that synthesise HCP prescribing signals, digital engagement history, and patient flow data. That creates dynamic territory intelligence. The rep walks in knowing why this conversation, why this physician, and why now. The sales leader who resists this kind of intelligence is handicapping their team.

Analytics and IT can claim the strategy seat

For most of the last decade, analytics and IT leaders have been treated as the infrastructure layer, not the intelligence layer. Business asks, they build. The strategy conversation happens elsewhere. AI changes the seat at the table, but only if they take it.

Teams might be spending up to 80% of their time on data preparation while business requests pile up. Queries that should take hours stretch into days. Problems like these are now solvable, thanks to knowledge graph chatbots connected to sales analytics. They reduce analyst intervention by 50% to 60% on recurring queries. GenAI query agents scale campaign ROI assessments across brands without adding headcount. Automated deck generators compress forecasting cycles from days to hours.

But none of this happens without analytics and IT leaders embedded in the commercial strategy conversation from the start, governing AI outputs so the business can act on them with confidence. That matters most as agentic AI moves from pilot to production. Select organisations have already deployed them, including agents that autonomously personalise a physician's digital journey, trigger follow-ups, and automatically adjust budget allocation. With poor data governance, though, these agents are a compliance liability instead of a growth asset.

The real prize: Decision systems that never sleep

The problem with omnichannel was never the aspiration. It was that the underlying machinery was built for broadcasting, rather than deciding. Siloed teams and disconnected technology meant HCPs received the same message three times from three different corners of the organisation.

What leading companies are building now is fundamentally different: decision systems. These are AI-orchestrated engines that continuously ingest signals from physician behaviour, digital engagement, field interactions, and market dynamics, translating them into real-time commercial action. Channel, message, timing, and resource allocation adjust continuously, not quarterly when it’s too late.

With these decision systems, physician engagement is earned instead of burned. Campaigns optimise themselves, and field interactions are crafted with the full picture. Revenue outcomes compound because every signal makes the next decision smarter. It's a new commercial operating model.

Three things every commercial leader needs to stop doing

1. Stop approving AI pilots in isolation. A marketing AI pilot disconnected from the sales ops data model and IT governance framework isn't a pilot. It's an expensive hobby. The organisations winning here have commercial, analytics, and technology leadership in the same room making connected bets.
2. Stop conflating AI literacy with AI strategy. Getting a team comfortable with AI tools is hygiene. Deciding which commercial processes to reimagine, and which human capabilities to invest in because of AI, is strategy. Only one of those conversations belongs in an all-hands.
3. Stop waiting for perfect data. The data will never be perfect. The question is whether it's good enough to generate a better decision than the one that would be made without it. Often, it already is.

The window won't stay open forever

In banking, institutions that moved early on AI-driven decisioning built advantages that multiplied. The competition found themselves structurally disadvantaged in cost, talent, and customer experience. Catching up required cultural rewiring on top of investment.

Pharma commercialisation faces the same. Organisations that build intelligent decision systems in the next 18 to 24 months will develop commercial muscles that are genuinely hard to replicate. Not because the technology is proprietary, but because organisational learning and the confidence to act on AI-generated insight compound over time.

The playbook is changing. The teams rewriting it right now will define what commercial excellence looks like for the next decade. You know your organisation will eventually get there. The question is whether you'll lead the way or spend years chasing it.

About the author

Tejas Arur is principal and head of marketing at Axtria. A marketing leader with over 25 years of experience building and scaling growth engines that drive business transformation across global enterprises and high-growth environments, he has led brand, demand generation, digital, analytics, and sales enablement aligning narrative, platforms, and performance into integrated growth systems. Arur operates at the intersection of insight and action, helping organisations translate evolving market dynamics into sustained growth.