Transitioning from CRM to intelligent engagement
Life sciences organisations are investing heavily in modern engagement platforms, but the results fall short.
The issue usually isn’t the technology itself. It’s how organisations adjust their operating models, data, and day-to-day ways of working around it.
Progress without impact
After years of investment in omnichannel strategies and AI-enabled platforms, many life sciences organisations still struggle to show a clear, measurable impact from their customer engagement efforts.
The technology itself has moved forward. Today’s platforms support more connected, data-driven interactions across commercial, medical, and patient-facing functions. On paper, the shift away from traditional CRM to more intelligent engagement models is already well underway.
Yet, the reality tells a different story. Many transformation efforts fall short – not because the technology is lacking, but because organisational structures, processes, and working habits evolve more slowly. As a result, insights remain fragmented, and engagement models never fully come together.
Access to technology is no longer a limiting factor. The real constraint is embedding it effectively in complex, regulated environments.
Platforms such as Agentforce Life Sciences illustrate how far the technology has progressed. At the same time, they highlight a gap – between what is technically possible and what organisations are set up to deliver. Whether that gap can be closed ultimately determines if transformation creates real value.
Beyond CRM: How engagement is shifting
One of the more significant changes in recent years is how CRM is being redefined. It’s no longer only a place to store or track interactions. Increasingly, it’s expected to act as a system of engagement – supporting personalised, data-driven interactions, enabling new commercial models, and connecting more seamlessly across the wider organisation.
At the same time, the competitive landscape is transforming. The market used to revolve around a single dominant vendor. Now, it’s becoming more of an ecosystem, where outcomes depend on how well organisations bring together different technologies, partners, and capabilities.
Expectations around engagement are also changing. It’s no longer centered solely around physicians. Life sciences organisations now operate within a wider ecosystem that includes payers, patient advocacy groups, healthcare networks, and digital channels – all of which influence access, adoption, and outcomes.
Today’s platforms allow organisations to connect interactions across multiple touchpoints and build a more complete picture of relationships.
In reality, many organisations still treat these systems like traditional CRM tools. Setups often remain centred on individual users or channels, rather than reflecting the broader network.
This becomes particularly visible when organisations try to scale new engagement models. Initiatives that combine commercial outreach with medical insights or patient support often fail to move beyond pilot stages. Without a shared understanding of how functions contribute, coordination remains difficult.
Shifting to intelligent engagement therefore requires more than new tools. It involves redefining what “customer engagement” means in life sciences – moving from linear interactions to more connected, multi-directional relationships.
Where organisational structures slow transformation
Even when the strategic direction is clear, many transformation efforts often lose momentum at the organisational level. These platforms are designed to connect functions, yet, most organisations are still structured around separation.
Usually, the objectives, governance models, and data ownership of commercial, medical, clinical, and market access teams are usually different. These divisions exist for regulatory reasons, but they make it harder to turn a unified engagement strategy into something that works in practice.
This tension becomes visible early on. Decisions that appear straightforward from a technical perspective – such as defining a shared view of a healthcare professional – quickly turn into alignment challenges across teams and regions.
In one global programme, a company introduced a new engagement platform to unify interactions across commercial and medical teams. While the rollout progressed as planned, adoption remained limited until governance structures and shared processes were established. Only then did the platform begin to deliver tangible value.
New technology gets introduced, but everyday ways of working tend to stay the same. Teams have access to new capabilities, yet, still fall back on familiar routines. The result is a modern platform layered onto a legacy operating model.
Organisations that make progress tend to focus on governance early on – who owns what, how teams work together, and how engagement is coordinated. Without that clarity, even well-designed platforms tend to fall short.
Data and compliance as core bottlenecks
Organisational alignment sets the direction. Data and compliance often determine how quickly progress can be made.
Most life sciences companies operate across multiple systems, regions, and regulatory contexts, each with its own data structures and identifiers. Bringing this into a consistent, usable foundation is rarely straightforward.
It requires clear decisions about ownership, standards, and data quality – often across multiple teams and markets.
Data preparation – cleansing, mapping, and harmonisation – can consume a significant portion of the timeline, particularly when dealing with fragmented healthcare professional records.
Regulation adds a further layer. Frameworks such as GxP, 21 CFR Part 11, GDPR, and the emerging EU AI Act shape how data – and increasingly AI – can be developed, deployed, and governed.
Even with strong platform capabilities, organisations still need to adapt these requirements locally. What works in one market rarely carries over as-is.
Research from Gartner suggests that many CRM initiatives in life sciences underperform, often because data and compliance challenges are underestimated.
Without a reliable, shared data foundation, more advanced capabilities – including AI-driven insights, and next-best-action recommendations – remain difficult to operationalise.
What organisations must get right
Looking at transformation efforts that deliver results, a consistent pattern emerges: they are treated primarily as organisational change, with technology playing a supporting role.
A key step is to broaden the definition of customer engagement. Instead of focusing on individual interactions, leading organisations examine how different stakeholders fit into a more broader engagement model.
Getting teams aligned is a big part of this. Shared governance, common data definitions, and clear responsibilities require effort upfront, but they make collaboration much easier later.
Data also needs ongoing attention. Treating it as a one-off migration rarely works. Organisations that invest early in data quality and ownership are better positioned to use more advanced capabilities over time.
New features only create value when they become part of everyday workflows. AI-driven recommendations or automated compliance checks don’t have much impact unless teams actually use them in practice.
Introducing a platform is only one part of the equation. What matters more is how well organisations adapt their structures and ways of working around it.
From potential to real impact
The move from traditional CRM to intelligent engagement shows how much the technology has advanced in recent years. Platforms such as Agentforce Life Sciences reflect that progress.
At the same time, the next shift is already emerging. AI and connected data are beginning to change how engagement is designed and executed across the life sciences value chain.
For now, adoption remains cautious. In a highly regulated industry, factors such as compliance, usability, and integrations still take priority. This doesn’t diminish the role of AI – it defines the conditions under which it can succeed.
The organisations that move ahead will be the ones that manage to embed AI within these constraints, instead of treating it as something separate.
Right now, the bigger limitation isn’t the technology itself. It’s how consistently it’s being used. Where operating models stay fragmented and legacy assumptions persist, even advanced platforms struggle to deliver their full value.
For many organisations, the transition is already in motion. Rolling out new systems is usually not the hardest part. The bigger shift lies in how engagement is structured and executed. Those that address this early are far more likely to move beyond incremental gains and towards a more connected, adaptive model.
About the author
Roman Bevz is principal IT domain consultant for life sciences at Avenga. He works with organisations across the sector on AI-driven transformation, with a focus on customer engagement, data strategy, and governance. With experience spanning both clinical and commercial functions, he supports technology adoption initiatives that help life sciences companies move towards more connected and effective engagement models.
