 | Life Sciences companies are racing to modernise their enterprise systems. They upgrade ERP platforms, connect manufacturing to planning, and move legacy landscapes to the cloud. But too many of these projects start with a tool instead of a question: what is the business actually trying to achieve? Vikas Dhiman, Project Manager Life Sciences at Sopra Steria, explains why Enterprise & Supply Chain is ultimately a business capability, not a technology one, and where organisations get it wrong. |
The backbone behind the business
In Life Sciences, supply chain ultimately exists to ensure that the right product reaches the patient reliably. For Vikas, Enterprise & Supply Chain starts with connection, not systems. It links planning, procurement, manufacturing, quality, warehousing and distribution, all areas that depend on each other, and whose underlying systems and data need to be connected too. In Life Sciences, there’s an added layer: quality and compliance.
“It’s not only about getting the product from A to B efficiently,” Vikas says. “We also need to maintain quality, traceability and reliable data throughout the process.” As he puts it: “I see Enterprise & Supply Chain as the backbone that lets an organisation operate as one connected enterprise, rather than a collection of separate functions.”
“It’s not only about getting the product from A to B efficiently. We also need to maintain quality, traceability and reliable data throughout the process.”
Fragmentation is the real obstacle
Complexity is what makes this urgent today. Life Sciences companies typically operate across global sites, suppliers, external manufacturing partners and distribution networks, and yet they are expected to respond faster to disruption, control costs and maintain reliable supply. Technology is moving quickly too, and organisations want more real-time visibility into what’s happening across their operations. And in Life Sciences specifically, there’s a patient at the end of every supply chain, which makes reliability and continuity of supply non-negotiable.
The obstacle standing in the way, more often than not, is fragmentation. Over the years, organisations have implemented many different systems — ERP, manufacturing, quality, laboratory, supply-chain applications — that each work well on their own but don’t always communicate with each other as effectively as they should.
Vikas sees the same pattern in his own project work: “Even when a company is actively trying to integrate two major systems, such as a manufacturing platform and an ERP core, it’s rarely straightforward, because each system carries its own history, logic and dependencies.”
Three developments stand out for him against that backdrop: the shift away from complex legacy environments towards integrated, increasingly cloud-based platforms; the growing importance of good-quality, connected data to support better visibility across planning, manufacturing and inventory; and AI and automation, particularly around forecasting and planning. On that last point, he’s careful to frame it correctly: the real question is no longer simply where AI could be used, but where it can actually solve a business problem and create measurable value.
When migration meets regulation
Nowhere is the complexity more visible than in a large-scale system migration. Vikas points to the kind of complexity seen in large-scale Life Sciences transformations, such as organisations moving from legacy ERP environments to SAP S/4HANA – a shift many pharma companies across the industry are now undertaking. He has seen across transformation programmes how unclear business processes and requirements can make major ERP migrations significantly more complex than necessary.
For a large organisation, this is far from a simple system swap. In Life Sciences, such migrations can involve GxP-relevant data and processes that support product quality, patient safety and regulatory compliance. This makes data integrity and controlled migration particularly important. Most of the data involved is regulated data that affects patient safety, data integrity and the overall product lifecycle, and that ultimately gets shared with regulatory bodies such as the EMA in Europe or the FDA in the US.
“That regulatory pressure changes how migration has to be approached,” says Vikas. “If data is moved incorrectly or something is missed in the process, it creates a chaos that reaches all the way to patient-specific data, and from there, straight into regulatory territory.” Data integrity, making sure whatever exists in the source system is correctly carried over to the target system, becomes a core principle rather than an afterthought. Major upgrades typically mean extra checks too: regulators will often inspect or audit a system after a significant change, to confirm the organisation’s systems remain compliant even as they evolve.
AI on a leash
Despite all the hype, Vikas is honest about where things stand: AI adoption in Life Sciences enterprise systems is still at an early stage. The reason is risk. Pharma companies work directly with patients, so there is little room to experiment freely. Regulation reflects this caution too. In regulated Life Sciences environments, AI needs to be introduced with appropriate governance, validation and human oversight based on its intended use and risk. For higher-impact applications, human review remains particularly important.
That doesn't mean nothing is happening. Sopra Steria has already built an AI-enabled tool that reviews audit trails. It will soon go live with a client, and it will save a lot of manual review time. More broadly, Vikas sees pharma companies using AI most in research. There, it has the potential to accelerate parts of the traditionally long R&D lifecycle, from early research to bringing medicines to patients. For now, though, AI in enterprise and supply chain is a tool used carefully, under supervision. It is not yet a replacement for human judgement.
“AI could help accelerate parts of the traditionally long R&D lifecycle – from early research to bringing medicines to patients.”
The case for clarity
A common thread in Vikas’s perspective is that organisations too often treat transformation as a purely technical exercise. What matters first, he argues, is being clear on what the business actually wants from a tool. “The technology exists to support the business, not the other way around,” he says. Companies that are clear on what they’re trying to achieve can choose from plenty of tools already on the market. Companies that aren’t clear end up making expensive choices that don’t fit their needs. Or they invest heavily in capability they never actually needed.
Across large transformation programmes, clear business processes and requirements are critical. When these foundations are established early, technology decisions and implementation become much more effective.
“What makes Sopra Steria’s approach distinctive, in Vikas’s view, is combining broad technology expertise — enterprise platforms, cloud, data, AI — with a deep understanding of Life Sciences business processes, quality and compliance. The right expertise gets assembled around the client’s actual need, not a predefined tool. One concrete example is that clients don't have to translate generic SAP expertise into their regulated reality themselves. With Sopra Steria, technical skill and sector understanding come as one.”
“The technology exists to support the business, not the other way around.”
Towards the intelligent, predictive enterprise
Looking further ahead, Vikas expects Enterprise & Supply Chain in Life Sciences to become significantly more digitalised, connected and intelligent. Today’s enterprise systems mostly execute transactions and follow predefined processes. The next generation, he believes, will do much more: spot exceptions, predict problems before they escalate, and help people decide what to do about them. Imagine a system that flags a potential material shortage early, understands which production activities it could affect, and helps a planner weigh up alternatives. This is the kind of predictive capability he expects to become standard.
That shift doesn’t remove the need for oversight. If anything, it raises the stakes. As systems take on more autonomous decision-making, organisations still need to understand the data behind each decision. They need to understand the controls around it, and who ultimately remains accountable. The future Vikas describes isn’t simply "more AI." It’s AI used deliberately, on top of trusted data and genuinely connected processes. That combination is what turns today’s fragmented pillars into tomorrow's intelligent, predictive enterprise.
His advice for organisations preparing for that shift echoes where he started: get clear on the business problem first. Build the right processes, bring the right skills together, and only then choose the tools to support it.