What the 2025 State of Marketing AI Report Really Means for Biotech & Life Sciences

The 2025 State of Marketing AI Report shows a decisive gap between AI adoption and organisational readiness — a problem magnified in biotech and life sciences where accuracy and compliance are critical. AI literacy, governance, training and structured workflow design are now essential infrastructure. This article breaks down the key findings for scientific organisations, introduces the Human + AI + Human model, and outlines a rigorous, evidence-aligned framework for adopting AI safely and effectively.

Why Human + AI + Human is now the only scalable operating model for science-led organisations

The 2025 State of Marketing AI Report (Marketing AI Institute & SmarterX) is one of the clearest looks to date at how AI is transforming marketing — not in theory, but in real operational practice. Across nearly 1,900 respondents, the findings are unambiguous:

• AI adoption is accelerating rapidly.

• Organisational maturity is not keeping pace.

• And in sectors like biotech and life sciences, where rigour and compliance matter as much as speed, this gap creates both opportunity and risk.

Below is an analysis built specifically for scientific teams, commercial leaders, and founders operating in high-stakes, complexity-driven environments.

1. AI adoption is scaling faster than capability

The Report shows a striking acceleration:

• 40% are actively experimenting

• 26% are integrating AI into workflows

• 17% are already transforming their roles

• Only 17% remain in early exploration

But this adoption sits on fragile foundations:

• 75% lack an AI roadmap

• 63% have no generative AI policy

• 60% have no AI ethics framework

In biotech and life sciences — where accuracy, regulatory alignment and scientific credibility are critical — operating without structure is not a minor oversight.
It is an exposure.

2. Training is the missing infrastructure

For the fifth consecutive year, the #1 barrier to AI adoption is:

“Lack of education and training.” (62%)

Supporting data:

• 68% of teams receive no formal AI training

• 62% receive no prompting training

• Most rely on ad-hoc self-teaching

For scientific organisations built on validated processes and SOPs, this insight is critical:

AI cannot be safely or effectively adopted without structured training and capability building.

Training isn’t a nice-to-have. It is the foundation of AI maturity.

3. Workforce impact is shifting toward oversight, not replacement

The report shows:

• 53% believe AI will eliminate more roles than it creates

• Only 21% worry about their own job

This reflects what we’ve already seen in scientific R&D: automation transforms tasks, not people.

**Jobs are bundles of tasks. Most tasks can be AI-assisted.

Human judgement remains essential.**

This underpins the Human + AI + Human model:

1. Human: Set direction, accuracy and standards

2. AI: Accelerate repetitive, generative or analytical work

3. Human: Validate, refine and decide

A model perfectly aligned with scientific workflows.

4. SMEs — not enterprises — may have the advantage

Contrary to expectation, the report reveals that SMEs:

• Are the most likely to be in the Scaling AI phase

• Are the most likely to offer prompting training

• Adopt tools faster and with less friction

For biotech and life sciences SMEs, this is significant:

You don’t need enterprise budgets to become AI-forward — just clarity, governance and capability.

Agility is an advantage.

5. The #1 outcome organisations want: time

Across all respondents:

82% want AI to reduce repetitive, manual, analytical tasks.

This is highly relevant to biotech and life sciences teams, where:

• Workflows are documentation-heavy

• Content is technical

• Commercial cycles are long

• Teams are small

• Accuracy is essential

AI provides:

• Throughput

• Consistency

• Speed

• Clarity

• Analytical depth

This is why the Report positions AI as “critically important” to marketing effectiveness.

6. AI maturity requires infrastructure — not tools

One of the most important findings: Teams with roadmaps, policies, governance and training are twice as likely to be AI-mature. Tools alone do not create transformation. What creates transformation is:

Human Direction → AI Acceleration → Human Oversight

The Human + AI + Human model.

Safe. Scalable. Evidence-aligned. Designed for science-led organisations.

7. The rigorously-designed AI adoption model for scientific organisations

To align with scientific decision-making principles, AI adoption should follow a structured, analytical sequence:

A. Problem-Based Identification

Start with validated commercial and operational constraints:

• Bottlenecks

• Accuracy-dependent workflows

• Underpowered demand generation

• High documentation load

• Slow content production

AI should only be applied where it solves a real problem.

B. Use-Case Scoring (value × feasibility × readiness × risk)

A model familiar to scientific leaders — mirroring how R&D, clinical and operational decisions are evaluated.

This ensures:

• Alignment

• Transparency

• Reproducibility

• Prioritisation

C. Prioritise low-risk, high-value opportunities

Examples include:

• First-draft generation

• Technical summarisation

• Slide deck structuring

• Reporting automation

• Scientific content consistency

These build confidence without risking accuracy or compliance.

D. Model ROI and time-to-value

Scientific organisations expect:

• Efficiency metrics

• Cost avoidance

• Time saved

• Output increased

• Error reduction

AI should be evaluated with the same rigour as any major operational initiative.

8. What biotech & life sciences teams should do now

Based on the data, the path is clear:

1. Create an AI roadmap

2. Define policies, ethics & governance

3. Deliver structured AI training

4. Adopt Human + AI + Human workflows

5. Deploy AI-assisted inbound & outbound systems

6. Start small → measure → validate → scale

This is how to adopt AI safely, effectively, and confidently.

Final Thought: AI Requires Evidence, Not Enthusiasm

The 2025 Report confirms:

• AI adoption is accelerating

• Teams are behind the curve

• Training and governance gaps are significant

• SMEs have agility advantages

• Efficiency gains are too strong to ignore

• Transformation requires structure

For biotech and life sciences, the core question is no longer:

“Should we use AI?”

but

“How do we use AI in a way that multiplies human expertise — safely, responsibly, and at scale?”

The answer begins with literacy, governance and the Human + AI + Human operating model.

Build AI Capability the Right Way

If your organisation is exploring AI adoption — safely, responsibly and in a science-aligned way — learn more about CTRL//SHIFT, our AI Training & Transformation Programme for human-first organisations:

FAQs

1. Why is AI adoption uniquely challenging in biotech and life sciences?

Because scientific content is complex, accuracy is essential, and workflows are highly regulated. AI must be deployed within clear governance, training and validation frameworks.

2. What is the Human + AI + Human model?

It’s a safe, scalable operating model where humans set direction, AI accelerates repetitive/analytical work, and humans validate and decide. It aligns naturally with scientific processes.

3. What is the first step for biotech organisations adopting AI?

Building an AI roadmap — a structured plan linking AI to commercial, operational and scientific goals, supported by governance and training.

4. Can AI be used for scientific content?

Yes, but with oversight. AI can accelerate drafting, summarisation and structuring, while humans ensure scientific accuracy, nuance and compliance.

5. Why is AI training so important?

The 2025 Report shows training is the #1 barrier to adoption. AI literacy is essential for safe, compliant and effective use — especially in regulated sectors.