Tia White
Most enterprises now have an AI strategy on paper and very little of it in production. The board wants returns, the engineering organisation is still rewriting pilots, and personalisation, agents and generative AI are stuck behind unresolved questions on data, privacy and operating model. The gap between AI ambition and AI in revenue is now the defining technology problem of the cycle.
Tia White is General Manager of AI and Machine Learning at Amazon Web Services, where she helps enterprises move generative AI and personalisation from pilots into production systems that change how customers are served and how revenue is earned.
Full Profile
Why organisations work with Tia White
- Operator perspective from inside a hyperscaler. She runs go-to-market, product and engineering for an AWS AI portfolio tracking over USD 25M in profit, including some of the fastest-growing services at AWS.
- A track record of taking generative AI and personalisation from theory into customer-facing product. She led the introduction of Personalized Search, Next Best Action and generative AI capabilities into Amazon Personalize.
- Cross-sector enterprise fluency. Before AWS she held senior technology roles at Wells Fargo, Capital One and JPMorgan Chase, leading regulated digital transformation programmes in financial services.
- A specific point of view on what separates AI investments that compound from AI investments that stall, grounded in current AWS customer work rather than vendor abstraction.
- A credible voice on building the technical leadership bench behind AI, including women and Black technologists, that boards are now being asked to account for.
Biography highlights
- General Manager, AI and Machine Learning, Amazon Web Services.
- First Black woman hired as a GM in Machine Learning, AI and Analytics at AWS.
- Director of Engineering at a Fortune 100 company before age 30.
- Prior senior technology roles at Wells Fargo, Capital One and JPMorgan Chase.
- Led the launch of new Amazon Personalize capabilities including Personalized Search, Next Best Action and generative AI features; speaker at AWS re:Invent 2022 on personalisation.
- Featured in Forbes; keynote and emcee credits include SXSW and the Grace Hopper Celebration. Board and advisory roles include Rewriting the Code.
Biography
Generative AI has moved faster than most enterprise operating models can absorb. Inside AWS, Tia White runs the AI and Machine Learning business that customers turn to when a pilot has to become a product. She owns go-to-market, product and engineering for an AI portfolio tracking over USD 25M in profit, including some of the fastest-growing services at AWS.
Her work at AWS includes leading the next generation of Amazon Personalize, with features such as Personalized Search, Next Best Action and generative AI capabilities now used by enterprises to lift engagement and revenue. She has spoken at AWS re:Invent on personalisation as an operating discipline rather than a marketing feature, and is regularly cited by AWS as a voice on what Gen Y and Gen Z customers expect from personalised experiences.
Before AWS she held senior technology roles at Wells Fargo, Capital One and JPMorgan Chase, including the leadership of public cloud migrations in financial services. She reached Director of Engineering at a Fortune 100 company before the age of 30. That financial services background gives her a working answer to the questions regulated industries are now asking about generative AI, data, privacy and risk.
She was the first Black woman hired as a GM in Machine Learning, AI and Analytics at AWS, and uses that platform to address the harder question behind most AI roadmaps, which is whether the organisation has the talent and the leadership bench to deliver them. She has been featured in Forbes, has keynoted and emceed at SXSW and the Grace Hopper Celebration, and sits on boards including Rewriting the Code.
Key speaking topics
- Generative AI in the enterprise
- Personalisation as a revenue and engagement discipline
- AI agents and agentic workflows
- Machine learning in regulated industries
- Cloud migration and digital transformation
- Responsible AI and bias mitigation
- Building diverse technical leadership in AI and engineering
Ideal for
- CTOs, CIOs and Chief Data and AI Officers moving from AI pilots into production
- CMOs and Chief Customer Officers investing in personalisation, recommendation and customer engagement
- Boards and executive committees in financial services, retail and regulated industries setting AI strategy
- CHROs and DEI leads responsible for the technical talent pipeline behind AI
Audience outcomes
- A clearer read on which generative AI use cases pay back and which do not, drawn from live AWS customer patterns.
- A working view of personalisation as an operating capability that touches data, product and engineering, not a marketing campaign.
- Sharper questions for the board on AI risk, privacy and the operating model around generative AI.
- A concrete sense of what the technical leadership bench behind a serious AI roadmap looks like, and where most organisations are short.
Talks
A working session on where generative AI and large language models actually fit inside an enterprise and where traditional machine learning still wins.
Key takeaways:
- A practical filter for separating GenAI use cases from classical ML use cases.
- Patterns from AWS customers on moving GenAI from pilot to production.
- The data, privacy and change-management questions that decide whether a GenAI programme ships.
How leaders are using machine learning and AI to compress decision cycles, personalise customer experience and rewire the operating model.
Key takeaways:
- How to identify the highest-value ML and AI use cases in your business.
- What real-time, data-driven decision-making looks like when it works.
- The organisational change required for an ML programme to outlast its sponsor.
An accessible explanation of what AI agents are, where they are being deployed today, and what it takes to run them safely inside an enterprise.
Key takeaways:
- What an AI agent is, in plain terms, and where it differs from a model or a copilot.
- The technology and process scaffolding required to deploy agents responsibly.
- Privacy, governance and change-management questions leaders should be asking now.
An introduction to federated learning as a way for regulated organisations to train AI models across institutions without moving sensitive data.
Key takeaways:
- How federated learning works and why it matters for healthcare, financial services and government.
- The privacy and compliance properties that make it commercially relevant.
- What regulated leaders should be asking their AI teams about federated approaches.