Dr. Ayesha Khanna is Co-Founder and CEO of Addo AI, a global artificial intelligence solutions firm, and Founder and CEO of Amplify, a media and education company helping leaders and professionals apply AI at work. One of the leading voices on AI as a hands-on tool for transformation, she has helped Fortune 500 companies and governments operationalize AI at scale, with clients including Visa, Telenor, Pfizer, Johnson & Johnson, and Mercy Health, over the last two decades.
She serves on the boards of Johnson Controls (New York Stock Exchange) and previously served on the Global Scientific Advisory Board of L’Oréal, Aveva (London Stock Exchange), and NEOM Tonomus in Saudi Arabia. She was named Reuter’s 10 Trailblazing Women in Enterprise AI (2026), an honoree of the global 100WomeninAI (2026), Edelman’s Top 50 AI Creators (2025), Salesforce’s 16 AI Influencers to Know (2024), and Forbes featured her as one of Southeast Asia’s groundbreaking entrepreneurs (2018). A graduate of Harvard, Columbia, and the London School of Economics, Dr. Khanna reaches more than 300,000 leaders and decision-makers across her platforms and newsletters. Her work has been featured in The New York Times, TIME, Harvard Business Review, and Forbes.
Recently, in an exclusive interview with CIO Magazine, Ayesha shared insights into how her 20-year journey from coder to Co-Founder & CEO of Addo AI and Founder of Amplify has been driven by a belief that AI only matters when it changes behavior and decisions. She views AI as shifting from the “novelty phase” to the “operating phase,” where the real value comes from turning demos into dependable capabilities that redesign workflows and governance. For leaders, her advice is to build AI literacy not around tools, but around sharper questions, human judgment, and the courage to learn inside the flow of work. The following excerpts are taken from the interview.
Every leader’s journey starts with a spark. What first pulled you into data and AI, and how did that early curiosity shape the path you took from coder to CEO?
What first pulled me in was the realization that data is not just information; it is a way of seeing systems that are otherwise invisible. As a coder, I loved the discipline of making logic work. But as I moved deeper into enterprises, cities, finance, and public systems, I saw that the real challenge was not writing elegant code. It was helping large organizations make better decisions under uncertainty. That changed my path. Coding taught me precision and humility. Leadership taught me that technology only matters when it changes behavior, incentives, and outcomes. My journey from coder to CEO has really been a journey from building models to building the conditions in which models can create value.
You describe yourself as an “Enterprise AI Operator,” not just a founder. What does “operator” mean to you day to day, and what do you love most about that role?
An operator is someone who lives in the reality between strategy and execution. In AI, that reality is messy. The data is incomplete, the workflows are political, the legacy systems are stubborn, and the business case must survive contact with the CFO, the regulator, the customer, and the frontline employee. Day to day, being an operator means asking: What decision are we improving? Who will use this? What happens if the model is wrong? How do we measure value? What needs to change in the process, not just the technology? What I love most is turning AI from an impressive demo into a new capability inside an organization. That is where the real magic is: not in the model alone, but in the operating change around it.
Addo AI was featured in Forbes in 2017 as one of Asia’s leading AI firms. Looking back, what was the moment you realized Addo had moved from “startup” to “market leader,” and what did that shift demand from you?
The shift happened when clients stopped asking us, “What is AI?” and started asking, “Can you help us redesign how this business function works?” That was the moment I knew we had crossed from curiosity to trust. The recognition was meaningful because it validated that serious AI innovation was coming out of Asia, not just Silicon Valley. But for me, the real signal was repeat enterprise work, larger mandates, and conversations with boards and CEOs about AI as a strategic capability. That shift demanded more discipline from me. As a startup, you can win on brilliance and speed. As a market leader, you win on reliability, governance, talent density, and the courage to say no to shallow AI theatre. We had to become not just inventive, but dependable.
This year, you launched Amplify to turn AI buzzwords into an advantage. What’s one trend you see in how leaders need to learn and adapt that traditional education isn’t teaching yet?
Traditional education still treats learning as something that happens before work, in a classroom, with a fixed curriculum. AI has broken that model. Leaders now need to learn inside the flow of work, continuously, with live problems and fast feedback. The most important skill is no longer memorizing frameworks; it is learning how to frame better questions, test assumptions, evaluate outputs, and redesign workflows around new capabilities. AI literacy is not “knowing the tools.” It is knowing where human judgment must remain, where automation adds leverage, and where an organization’s decision-making architecture has to change. That is what Amplify is built around: practical intelligence, not performative vocabulary.
Women remain underrepresented in AI and tech leadership. What’s one trend or shift you’re optimistic about that will change that picture by 2030?
I am optimistic because AI is widening the doorway into technology leadership. For too long, the industry acted as if the only legitimate path into tech was a narrow computer science pipeline. AI is changing that. Some of the most powerful AI use cases will come from people who deeply understand healthcare, education, finance, law, retail, operations, public policy, and human behavior. Many women already lead in those domains. If we give them the right AI fluency, capital, sponsorship, and platforms, they will not just participate in the AI economy; they will shape it. By 2030, I hope we will stop talking about women “entering tech” and start recognizing that the future of tech depends on domain experts becoming AI-native leaders.
Leading AI firms, boards, and charities means constant pressure. What’s one non-work activity or ritual that helps you reset and come back with energy?
My reset is deliberately simple: I step away from screens and move. A walk, especially without checking my phone, helps me clear the noise and reconnect the dots. AI is an intense field because everything is moving quickly and everyone feels they are behind. I need moments where I am not consuming more information but metabolizing what I already know. Some of my best strategic thinking happens after I have stopped trying to force it. The ritual is less about escape and more about creating mental space. You cannot lead transformation if your own attention is constantly fragmented.
Books, talks, and mentors shape how we think. What’s one book, podcast, or speaker that fundamentally changed how you approach technology, leadership, or advocacy?
One podcast I find consistently valuable is No Priors with Sarah Guo and Elad Gil. What I appreciate about it is that it stays close to the frontier without getting lost in hype. The conversations are not just about what AI can do in theory, but about who is building it, what is becoming commercially possible, where the bottlenecks are, and how quickly markets and organizations can absorb new capabilities. For me, that is the real leadership question. AI is not only a research story; it is a deployment story, a talent story, a governance story, and a business model story. No Priors sharpens how I think about the gap between invention and adoption. It reinforces a belief I have always held as an enterprise AI operator: the future does not belong to the people who can describe AI most dramatically. It belongs to the people who can translate it into useful systems, responsible decisions, and measurable advantage.
What is your biggest goal? Where do you see yourself in 5 years from now?
My biggest goal is to help people and organizations move from anxiety about AI to agency with AI. I want leaders to stop treating AI as a mysterious external disruption and start treating it as a practical capability they can understand, govern, and use responsibly. In five years, I see Amplify as a trusted global platform for AI capability-building, especially for professionals and women who need access to practical, career-changing AI skills. I also see myself continuing to work with enterprises and boards on AI strategy, because the next phase will be harder than the first. The novelty phase is ending. The operating phase is beginning. That is where I want to contribute.
If you could guarantee one outcome from Amplify for every professional who completes it, what would that outcome be?
I would guarantee confidence grounded in competence. Not vague excitement, and not fear disguised as caution, but the ability to look at a business problem and know how AI could help, where it could fail, and how to move from idea to implementation. A professional who completes Amplify should be able to ask sharper questions, identify useful use cases, work intelligently with technical teams, evaluate outputs, and communicate business value. Most importantly, they should feel that AI is not something happening above them or around them. It is something they can use, shape, and lead with.