Who gets access to opportunity?   

“Since we started in 2008, we have reached 300,000 women entrepreneurs in lower and middle-income countries across the world. We support women to build on the skills they already have: leadership, resilience, empathy.

“If a fortunate woman can benefit from these tools, imagine what could happen if we put them in the hands of women who have capacity, foresight and resilience.”

AI wasn’t on my horizon when I started the foundation, but I knew even then that digital tools were helping me as a small legal business owner. I thought, if a fortunate woman can benefit from these tools, imagine what could happen if we put them in the hands of women who have capacity, foresight, the urge and resilience to use them to expand and grow.

We conducted a survey of nearly 3,000 women entrepreneurs in low to middle-income countries. We found that almost half of the women surveyed were already aware of AI and what it could mean for their business. However, 20% of the women hadn’t yet heard about AI’s possibilities.”

Who is responsible when systems fail?

“AI transformation is tough and there will be failures and expensive mistakes, so it requires honesty, emotional intelligence and communication.

It all starts with mindset and culture, especially at the top. If the leadership isn’t courageous and committed, nothing else matters.

“If the leadership isn’t courageous and committed, nothing else matters.”

Responsible AI is not optional. If you treat it like a nice-to-have, you’re holding a ticking time bomb. At Microsoft, we saw many real cases, internal and external, where ethical risks with AI became real problems.

It’s reputational, financial, legal risk – and the public often doesn’t hear about it because of confidentiality or PR suppression. Responsible AI is as essential as cybersecurity.”

What does trust look like at scale? 

“If a person makes a mistake, it’s just a human mistake. If AI does it, it’s front-page news. That’s the cultural challenge around trust. 

But peer experience changes things. A doctor says: “I used this tool and it helped me with a tough case.” Or: “It suggested an option I hadn’t even considered.” And suddenly colleagues pay attention.

“Anyone can make an algorithm; that’s not the hard part. The real challenge is making it trustworthy, explainable and equitable.”

In 2020, I was asked to curate Mayo Clinic’s 150 years of data to build a multimodal dataset for AI. Since then, we’ve created hundreds of predictive algorithms and several foundation models that do what humans can’t. 

My father-in-law died of pancreatic cancer in 2014. It’s usually found too late, invisible on scans. Now Mayo Clinic has an AI model that can help specialists detect it at stage 0 – up to three years before clinical diagnosis.”