Innovation · Impact · Trends by Andre Correia

Perspectives

From 2015 research to today

Written for leaders in pharma and biotech. Not a republish of the past, but a dialogue with it.

The line that still holds

Life-sciences companies innovate for a living and regulate for survival, and that dual nature did not change. What changed is the speed of platforms, the volume of data, and the expectation that IT is a business partner, not a cost centre keeping the lights on.

When I completed my MBA research at Bayes Business School (formerly Cass), respondents were broadly at par with peers on cloud, mobility, and analytics, rarely ahead. Caution was rational. Today, lagging is less about awareness and more about orchestration: too many pilots, not enough operating-model clarity.

Mapping the trends: 2015 to today

How the themes in the book read against what executive leaders discuss now.

Cloud
2015

Opportunity vs. security; S&M workloads more acceptable than R&D or manufacturing data.

Today

Hybrid and sovereign cloud; validated pipelines; AI factories in controlled environments.

Mobility
2015

Managed devices; selective mobile access; consumerisation emerging.

Today

Zero-trust and identity-first access for field, lab, and executive teams across borders.

Data & analytics
2015

BD&A influenced decisions; talent and ownership gaps; IT rarely seen as data owner.

Today

GenAI, lakehouses, RWE; governance and quality as bottlenecks; responsible AI as compliance frontier.

Operating model
2015

CoEs and SSCs; unification and standardisation as default.

Today

Federated standards: global where it matters, local where it must; product-minded data teams.

Five shifts since the book

Hybrid by default

The debate moved from public or private cloud to validated pipelines, regional sovereignty, and who owns the AI stack in GxP contexts.

Identity over devices

Mobility is less about handing out laptops and more about zero-trust access to trials, CRM, and lab systems, often from anywhere.

Data as product

Lakehouses, ontologies, and real-world evidence teams treat data as a managed product, with legal and scientific owners, not just IT.

GenAI under governance

Copilots and LLMs are entering workflows. The hard part is traceability, bias, and fit with validation, not the demo.

Operating model reset

Centres of excellence remain, but agility now demands federated standards: global where it matters, local where it must.

Talent redefined

The gap is no longer data scientists alone. It is product-minded engineers who speak quality, regulatory, and commercial language.

Questions for executive leaders

Conversation starters for briefings and workshops. For tailored facilitation, see advisory.

Where does value sit?

Which workloads must stay on-prem or in sovereign regions, and which can move to shared platforms without compromising IP or validation?

Who owns the data product?

Are scientific, legal, and commercial owners named for each critical dataset, or is accountability still fragmented across IT projects?

What is our AI guardrail?

Where are copilots allowed, what must be human-reviewed, and how do we prove traceability to regulators and partners?

Are we pilot-rich, scale-poor?

How many experiments lack a path to operating-model change, funding, and measurable outcomes in the next 12–18 months?

Pharma and biotech: same discipline, different pace

Large pharma still leads on global operating models, shared services, and heavy validation. Biotech and mid-size sponsors often move faster on cloud-native tooling and partnerships, but face the same questions on data residency, quality systems, and who signs off on platform choices.

The 2015 study focused on pharma, yet the lessons transfer to other regulated sectors. Financial services share privacy and audit intensity; tech and telecom move faster but offer reference patterns for platform economics.

What the book got right, and what to update

Still right: security, privacy, and unclear accountability slow adoption; commercial functions often move faster than R&D; ROI narratives matter to secure funding.

Update the lens: big data is now AI at scale; mobility is embedded in how trials and field teams work; cloud is assumed. The question is which workloads, under which controls.

For the original survey findings, recommendations, and executive-summary depth, download the eBook (email access).

Continue the conversation

Shorter takes appear on LinkedIn. Advisory and speaking engagements are available for leadership teams in pharma and biotech.

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