HSBC CIO Stuart Riley on Leading with Data and AI
Stuart Riley stepped in as HSBC's Group CIO in February 2024 and, within eight months, his remit expanded to include data and innovation alongside a seat on the Group Executive Committee. That move signals trust in a leader who pairs hands-on depth with enterprise scale. His brief is clear: build an efficient, resilient, innovative digital bank while simplifying a complex IT estate.
HSBC now runs more than 600 AI use cases across fraud detection, customer support and core operations. Riley cuts through the noise with a simple view: "Whilst some overestimate AI's short-term impact, I believe many significantly underestimate its long-term potential." That balance-disciplined delivery today, compounding gains tomorrow-guides decisions on platforms, data and AI.
From scale operator to change agent
Before HSBC, Riley spent 14 years at Citigroup, rising to Co-CIO and overseeing technology for more than 50,000 people across 100 countries with an annual budget north of US$4 billion. He led Citi Velocity, a market-leading suite of digital products adopted across the industry, and later ran Institutional Client Group Technology.
In his Co-CIO role, he pushed early on AI and invested in private cloud infrastructure to strengthen data foundations. That experience-building platforms, shipping client-facing products and managing global scale-now informs HSBC's Digitise strategy.
Simplifying the stack while scaling AI
Riley's agenda is pragmatic: rationalize platforms, standardize data, and deploy AI where it reduces risk, improves service or lowers cost. The aim is fewer systems, clearer ownership and faster change cycles. He's pairing platform simplification with safe scaling of AI, anchored by strong data controls and measurable outcomes.
The shift is cultural as much as technical. Product thinking, shared services and reusable components replace siloed builds. AI becomes part of core workflows, not a side project.
Enterprise AI: an operating model executives can use
- Data foundation first: define golden sources, lineage and access patterns; remove duplication; automate controls.
- Platform over projects: offer common services (data, identity, model ops) to speed delivery and reduce risk.
- Pragmatic cloud: match workload to environment; use private cloud for sensitive data and performance where needed.
- Funding and KPIs: tie investment to cost-to-serve, cycle time, risk reduction and revenue impact; review quarterly.
- Responsible AI: embed model monitoring, explainability, and human override; standardize approval paths.
- Talent and teams: blend engineers, data staff, product managers and risk partners; keep leaders close to code.
Entrepreneurial start, engineering roots
Riley began by founding TAG Consulting in 1996, giving him a bias for speed and pragmatism. He moved into investment banking tech at RBS as Technology Manager for FX eCommerce, then spent seven years at Deutsche Bank in senior roles across Rates eCommerce, Sales Technology and Global Finance & Foreign Exchange Technology.
Those years built his reputation for shipping complex, client-facing platforms while building followership across distributed teams. The engineering mindset stayed intact as scope grew.
Impact beyond the bank
Inside HSBC, he serves as Executive Sponsor for Strive UK, supporting colleagues from different economic backgrounds. Beyond the bank, he is an expert member of Teach First's Technology Committee, helping drive technology transformation to address educational disadvantage.
He joined the board of Quantexa in February 2025, reflecting his focus on decision intelligence and data-driven risk insights. He also advises on environmentally sustainable cloud computing at HiveNet and contributes to BP's Digital Advisory Council as the energy major advances its net-zero transition.
What executives can take from this
- Think in compounding systems, not one-off projects. Platform choices set the pace for years.
- Measure what matters: cycle time, defect rates, risk outcomes and unit economics-not vanity metrics.
- Scale AI where you have data clarity and accountable owners; avoid experiments without production paths.
- Keep leadership close to the technical coalface; it shortens feedback loops and improves decisions.
- Use governance to enable speed: standard patterns, reusable controls and pre-approved components.
The bottom line
Riley's approach is simple and effective: simplify the stack, treat data as a strategic asset, and industrialize AI with clear accountability. That mix of hands-on depth and systems-level leadership gives HSBC a credible path to a leaner, smarter bank.
If you're planning your own AI roadmap, explore practical resources for finance leaders here: AI tools for finance.
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