From AI Curiosity to AI Confidence: Nishtha Jain on Building a More Human Future for AI in Healthcare
“It is important to recognize that shaping AI’s impact on people and patients is as ‘technical’ as choosing the right model architecture.” Meet today’s Woman Dreamer, Nishtha Jain, an AI & Digital Transformation Executive in healthcare innovation with experience spanning CSL, Takeda, and the Healthcare Businesswomen’s Association (HBA). In this interview, Nishtha shares what it truly takes for AI to be adopted and scaled in healthcare, how leaders can determine whether their organizations are “AI-ready,” and how women can establish credibility and successfully lead in an AI-driven world. She also shares the one way she personally uses AI in her daily life that she now “can’t imagine going back” without. We are excited to share Nishtha’s story as part of our Women in AI series at Women Who Win!
Tell us your story. You are currently a Global Search and External Innovation Leader at CSL, and prior to that you were the Head of Innovation and Digital Technology at Takeda Pharmaceuticals and President of Corporate Relations at the Healthcare Businesswomen’s Association. How did you first discover your interest in the intersection of healthcare and technology, and what key lessons have shaped your leadership journey so far?
I sometimes joke that I grew up wanting to be a doctor and somehow ended up becoming a translator between doctors, data scientists, and executives. My interest in the intersection of healthcare and technology really started with a simple frustration: I could see how much human effort went into caring for patients, and how often our systems, data, and tools got in the way instead of helping.
I began my career closer to the healthcare data side, learning how real‑world data, and analytics shape decisions about which therapies reach which patients. Over time, I moved across the BioPharma value chain in biotech to large pharma organizations which gave me an end‑to‑end view of how a molecule, a clinical trial, a safety signal, and a patient story are all connected by data. That holistic view is what ultimately led me to my role as Head of Innovation and Digital Technology at Takeda, where I focused on using AI and digital to improve R&D quality and the patient experience at scale.
Two lessons have shaped my leadership journey. First, technology is never the hero, people are. My guiding question is always, “Does this help people?” whether that’s a patient, a study site, or a scientist. Second, bold innovation has to coexist with deep responsibility. My TEDx talk “The Digital Drug: Why Social Media Needs Safety Labels” came from seeing how technology can hijack our attention and well‑being, and it reinforced for me that ethical design and digital wellness must sit at the core of any AI and tech agenda.
In parallel, I served as HBA volunteer for 8 years and last role with HBA was President of Corporate Relations at the Healthcare Businesswomen’s Association, I see every day how inclusive leadership and sponsorship can change who gets to sit at the table where these technologies are designed and governed which ultimately changes the outcomes we create.
2. You’ve led AI-driven transformation across R&D, medical, and commercial functions in biopharma. Looking back, what is one key lesson you’ve learned about what it truly takes for AI to be adopted and scaled in healthcare organizations?
If I had to distill years of AI programs across R&D, medical, and commercial into one lesson, it is this: AI scales only when it is wired into real workflows and has a real business value tied to solution. Many organizations start with “AI tourism” pilots that photograph well but don’t attach to a critical process, a clear owner, or a business outcome. Ultimately, any transformation is people first and technology second, so people need to be on the journey from day otherwise adoption will fail.
In my work, I’ve found three things matter most. First, start from the problem, not the model: is this about prediction, personalization, or productivity, and what exact decision are we trying to improve? what I call the “Three Ps” lens. Second, bring cross‑functional teams together early: quality, clinical, safety, medical, tech, and compliance need to co‑design the solution so that validation, risk, and user experience are baked in from day one. Third, treat change management as a first‑class workstream training, new roles, KPIs, and governance because AI is ultimately a behavior change program masquerading as a technology program.
3. How do you empower your teams to think about whether their org is "AI ready"? And what are the key challenges with AI readiness in pharma/R&D in particular?
When I talk to my teams about whether an organization is AI‑ready, I rarely start with models or tools. I start with three questions: Do we have high‑quality, findable data? Do we have accountable owners for processes and decisions? And are leaders prepared to change how work gets done if the AI shows a better way? Those questions quickly reveal whether we have the foundations for AI to stick.
In pharma and R&D specifically, there are some unique readiness challenges. We operate in highly regulated environments where validation, auditability, and explainability are non‑negotiable, so governance must be designed up front, not as an afterthought. Our data is fragmented across legacy systems, geographies, and partners, which means investments in data platforms, ontologies, and harmonization are as important as the AI itself. And finally, scientific and quality cultures are rightly risk‑aware; the opportunity is to reframe AI as amplifying human expertise, not replacing it, showing clinicians, statisticians, and quality leaders how these tools can remove friction so they can spend more time on high‑judgment work.
Practically, I empower teams by giving them a simple, structured way to assess use cases: What problem are you solving? What data do you need? Who will use it and how will success be measured? Where are the regulatory and ethical guardrails? When teams can answer those questions clearly, they move from “AI curiosity” to “AI confidence.”
4. You’re deeply involved in women's professional communities such as the Healthcare Businesswomen’s Association and Chief. For women leading AI without coming from a purely technical background, what are the most effective ways to establish credibility? What are the skills you think women need to develop to lead organizations with AI successfully?
I spend a lot of time with women leaders at HBA and Chief who are driving AI agendas. The first thing I tell them is: your value is in asking better questions and shaping the why and where of AI, not in writing the Python script yourself.
To establish credibility, three behaviors go a long way. One, become fluent in the business value of AI for your domain, know the top use cases, the risks, and the metrics that matter, and be able to explain them in plain language to the C‑suite and to frontline teams. Two, build strong partnerships with your technical counterparts and data teams; show up prepared, ask precise questions, and co‑own outcomes instead of treating AI as a “black box” you merely sponsor. Three, be visibly principled on ethics, bias, and inclusion, who is in the room when AI is designed, whose data is used, and who benefits, because these are leadership questions, not technical ones.
In terms of skills, I emphasize strategic storytelling with data, AI literacy (enough to challenge vendors and internal teams constructively), stakeholder management across legal, regulatory, and IT, and the ability to lead through ambiguity. Women are often already doing this work intuitively; the shift is to name it, lean into it, and recognize that shaping AI’s impact on people and patients is as “technical” as choosing the right model architecture.
5. To end on a fun note,what’s one way you’re personally using AI in your daily life that you now can’t imagine living without?
On a personal note, one of the ways I now use AI daily and can’t imagine going back is as a sort of “reflection partner” for my digital life. After spending so much time researching digital wellness for my TEDx talk “The Digital Drug,” I became very intentional about how I use technology and attention.
Today, I use AI to summarize long articles and research papers, to help structure talks and keynotes, and even to prototype ideas for my writing and LinkedIn content, so I spend less time on blank pages and more time refining the story and message. I also use simple AI‑powered prompts to “pause” before reacting to the constant stream of digital inputs turning my own PAUSE framework into a daily practice so that technology serves my focus and values instead of the other way around.