The Culture Shift Happening Behind the AI Boom

Why women in AI are redefining how work actually works

AI is getting a lot of attention right now, but the center of the work is still innately human. That theme ran through the entire Women in AI event in London. Yes, we talked about agentic systems, drug discovery breakthroughs and the tools that are reshaping our sector. But the real conversation was about people. How we work together, how we build confidence, how we navigate ambiguity and how we create careers that feel meaningful.

This matters for biopharma and healthcare more than ever. Science is advancing fast, and the systems around it are still catching up.

A disengagement problem hiding in plain sight

Organizational psychologist Dr Michelle King opened with a striking data point from a global study of 125,000 employees in 40 countries. Seventy-seven percent of employees are disengaged at work.

Not bored, disconnected. From purpose. From autonomy. From each other.

When people are disengaged, even the strongest science struggles to cut through. Michelle broke it down even further with a few key insights:

• Relationships are the biggest predictor of fulfillment at work.
• Nine in ten people would give up significant lifetime earnings for more meaning.
• Most twenty-somethings avoid workplace social situations because they feel intimidating.
• Roughly 82 percent of today’s roles depend on collaboration, while workplaces are getting flatter, more diverse and more ambiguous.

The future of work won’t be defined by better dashboards or faster tooling. It will be shaped by environments where people feel safe, valued and connected.

Women are already leading into that future

In a study of 875 leaders focused on the future of work, respondents were asked which skills will matter most in the years ahead. Top answers featured adaptability, emotional intelligence, resilience, leading inclusively and achieving results through others.

Then she asked the same group which skills women already demonstrate today. Women held four of the top five future skills. Men held one. The competencies that will matter most are already showing up in how many women lead today.

“You cannot copy outdated leadership models and expect to thrive. You have to define for yourself what good looks like.”

AI removes friction, but humans create meaning

The second session shifted into practical implementation, led by Zoe from Merz Therapeutics. She showed how her team is using AI inside a pharma organization to cut manual work, speed up insight generation and give people more time for higher value thinking.

Her team:

• Cut £50,000 in outsourced costs by building payer materials and BIA models in house using AI tools.
• Reduced manual work across medical, access and sales.
• Turned conference summaries that used to take five hours into a workflow completed in minutes.
• Used NotebookLM to turn complex datasets into podcasts they could review on the commute.
• Freed up roughly half a day per week for strategic work.

And the biggest theme from her team’s feedback was simple: AI frees your mind so the rest can follow. Safeguards were just as important as the outputs. The team reviewed citations, watched for hallucinations, and used corrective feedback to train the models. They added a local policy to protect patient confidentiality and commercial sensitivity with human judgment guiding everything the team has built.

The strongest outcomes were not about speed. They were about clarity. Less operational noise. More cognitive space for decisions, ideas and strategy.

What agentic AI actually looks like

The final discussion explored AI’s role in drug discovery. Emma Slade detailed how her team at Tangram Therapeutics is building multi agent systems designed to:

• Read and critique scientific literature
• Break complex biological questions into arguments and counterarguments
• Score therapeutic targets across biological, commercial and feasibility dimensions
• Evaluate hundreds of TI pairs in parallel
• Reduce months of manual work into hours

These systems aren’t replacing scientists. They are augmenting them.

“You can speed up a portfolio analyst, but you cannot automate a gut instinct that has been earned over twenty years.”

AI will take drug discovery from fifteen years to something faster. But every major decision will still require a human with deep expertise and the confidence to challenge, verify and contextualize.

Tangible takeaways directly from the room

So how does this translate to in everyday work? It means doubling down on the things that help you think clearly, collaborate well and keep momentum. These are our takeaways:

1. Engagement is a leadership issue, not a motivation issue. If 77% of employees are disengaged, the fix isn’t perks or posters. It’s better relationships, clearer expectations and workplaces where people feel valued and safe to contribute.
2. Human skills are becoming the real differentiators. Adaptability, emotional intelligence, resilience, inclusive leadership and the ability to get results through others are outperforming traditional “command and control” habits.
3. Younger talent needs clarity and psychological safety, not perfection. Early career employees struggle most with ambiguity. They need explicit guidance, not assumptions, and environments where questions aren’t treated as incompetence.
4. AI works best when it expands capacity, not replaces it. The highest value isn’t speed — it’s clarity. When AI removes manual noise, people think better, decide faster and spend more time on actual strategy.
5. Humans must stay in the loop. Fact-checking, refining, challenging and adding context is what makes outputs trustworthy. AI handles the grunt work. Humans handle the judgment.
6. The informal network is your hidden infrastructure. The 12–20 people you rely on for advice, context and support shape your career more than org charts do. Invest in those relationships, they make everything else move.

What this means for pharma teams right now

We have new scientific capability but unchanged human dynamics. If anything, the gap is widening. The message echoed across every speaker: AI accelerates the science. People accelerate the work. When employees feel safe to ask, challenge, explore, collaborate and learn, everything moves faster. When they feel isolated or overwhelmed, even the most powerful tools fail.

The future of work in pharma is not defined by the models we build. It is defined by the environments we build around them.

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