Life sciences companies are under mounting pressure to accelerate development, scale globally and capture the potential of AI. To meet this challenge, the industry is moving into platformization 2.0 — platform-based operating models augmented with AI. This shift is creating new workflows, new ways of working and new classes of digital assets, from data ecosystems to AI platforms. In turn, these need to be governed, managed and scaled responsibly.

Managed service providers (MSPs) must move beyond traditional IT support. Success now depends on broader scope, new skill sets and new metrics to operate effectively in open ecosystems where AI is embedded. This post explores platformization 2.0, the new ground it creates for MSPs and how leaders can evaluate partners to capture value with confidence.

Platformization 2.0 and life sciences operations

The life sciences industry has always been partitioned by design. Early waves of digitalization — fueled by automation and SaaS adoption — streamlined discrete functions but left core silos intact. As pressure to accelerate development with scale, companies began adopting platform strategies, modernizing monoliths into cloud-native systems that enable unified operations at scale without compromising compliance.

Since late 2022, AI has become the catalyst for the next phase — Platformization 2.0. Generative AI alone is projected to unlock $60 billion to $110 billion in annual value across the pharma value chain. This second stage of platformization is defined by three characteristics:

• Integrated intelligence — embedding AI into back- and front-office workflows, making platforms adaptive and context-aware.

• Advanced data foundations — improving accessibility and quality, accelerating decision-making with grounded truth.

• Knowledge and ontology layers — enabling reuse, regeneration and knowledge as a core competency.

In short, platformization 2.0 moves life sciences beyond isolated, custom-built tools toward integrated, standardized and continuously managed platforms that embed compliance, scalability and AI-readiness. For MSPs, this opens new ground to upgrade services and redefine their role in pharma operations.

The new ground for managed services

The opportunity of platformization 2.0 for MSPs expands beyond uptime and infrastructure management. AI-infused platforms, robust data foundations and knowledge-driven models are creating new categories of assets, processes and risks. This opens new ground where MSPs need to differentiate.

There are five domains that stand out:

Managed modernization
This is no longer a one-off program but a continuous journey. MSPs must manage multi-year modernization roadmaps, ensuring every upgrade — from cloud migration to platform release cycles — is sustainable, compliant and value-accretive over time.

Data and AI platforms as a service
This involves the continuous management of data and models, including managed lakehouses with FAIR governance, AI-ready knowledge development, AI/ML lifecycle operations, GenAI copilots for functional domains and GxP-ready validation of AI outputs.

Managed AI risks
As AI adoption scales, risks multiply. From AI evals for bias detection, tackling hallucinations and LLM tracing, to token cost management, to regulatory non-compliance. MSPs must treat AI risk management as a first-class service.

Specialized pharma platform support
Pharma relies on specialized platforms across R&D, preclinical data processing, clinical trials, manufacturing, regulatory and commercial functions. MSPs must operate these platforms as managed services and ensure interoperability. They also need to embed audit-ready compliance and deliver scalability across the entire life sciences value chain.

Enterprise technology operations
This involves multi-cloud management with compliance and cost optimization, secure self-service environments for scientists and analysts, AI-assisted modernization, AI-enabled SDLC pipelines and cybersecurity and privacy services at scale.

As Platformization 2.0 pushes life sciences beyond isolated, custom-built tools toward integrated, standardized and continuously managed platforms, the bar for managed services rises. Partners are no longer judged only on uptime, MTTR or cost per ticket. They’re expected to deliver sustained business value by operating platforms, data and AI at scale. 

There are four key priorities for the next-generation of MSPs:

• Reducing cost and complexity by replacing bespoke integrations with standardized workflows embedded in platforms.

• Increasing AI adoption at scale with compliance built into data pipelines, model validation and outputs.

• Increasing speed to market by shortening development and regulatory cycles through automation and reusable platforms.

• Increasing scale without headcount growth by leveraging automation to expand capacity while controlling cost.

This evolution reframes MSPs as operators of the digital backbone of the life sciences sector. The winners will be those who continuously modernize.

Evaluating new managed service providers in life sciences

Leaders should assess providers across seven dimensions with a number of key questions: Can they modernize reliably? Can they infuse AI responsibly? And can they help us scale efficiently — while also improving compliance and speed?

Table 1 outlines the five dimensions for evaluating life sciences managed service partners.

Table 1. Five dimensions of evaluating Managed Services Partners as platform and AI technologies advance.

Moving forward

AI in the life sciences industry is an exciting opportunity. What sets today apart from other periods of change is the speed at which transformation is unfolding. Legacy modernization, platformization and AI adoption are converging.

To capture the upside, life sciences companies will need managed service partners that can do more than maintain systems. They must guide incremental modernization journeys, infuse AI responsibly, data orchestration and operate new workloads with resilience and compliance built in.

The winners will be those who treat managed services as a strategic lever — with a clear strategy for where to apply AI, how to scale it safely and which partners can deliver AI-enabled operations at global scale.