Most large transformation programmes struggle to sustain their gains. Complexity re-emerges, workarounds become embedded processes and momentum dissipates once governance loosens. For global banks, this is compounded by regulatory fragmentation, legacy infrastructure and the difficulty of driving behavioural change at scale. Standard Chartered’s Brian O'Neill, global head of group transformation, holds a deliberately broad mandate spanning enterprise transformation, technology investment, process design and workforce capability — embedded within the bank's operating structure rather than operating alongside it. In practice, the mandate converges on three priorities aligned to the group’s strategy: sustaining performance through cost efficiency and resilience, accelerating product and service delivery, and creating operating leverage through improvements in client-facing processes. The objective, in his framing, is not to run a programme but to ensure the organisation never stops improving. Standard Chartered’s 2025 results provide the backdrop. The bank reported a 14.7% return on tangible equity — with underlying profit before tax up 18% to $7.9 billion, record operating income of $20.9 billion and $9.1 billion returned to shareholders against an $8 billion three-year target — surpassing its revised milestone a year ahead of schedule. The harder question, as Fit for Growth concludes in 2026, is what sustains that performance next. Beyond the savings number The lender's Fit for Growth programme delivered $754 million in run-rate savings through more than 300 initiatives by 2025. But O'Neill said that financial metrics tell only part of the story: “Fit for Growth has been very successful, but that's just one aspect of our transformation. What matters most is improving turnaround times, client service, and resilience.” He drew on two examples of process transformation. In wealth management, digitising client onboarding has eliminated manual paperwork and freed relationship managers to spend more time with clients. In commercial and investment banking, the same logic is being applied to trade finance, where document-heavy processes are being automated. The examples differ, but the underlying logic is the same. “The more automated and digitised we make these processes, the more scalable and resilient they are,” he said. “As we add new clients, we're able to accommodate this easily without extending turnaround times or creating pressure in the organisation.” The limits of top-down transformation Transformation at Standard Chartered is not an enforcement exercise. “You can't enforce culture. You have to influence it,” O'Neill said. Large-scale technology programmes still require rigour and discipline, but transformation succeeds or fails on whether people change how they think — client focus, efficiency and a willingness to challenge complexity baked into how people run their businesses day to day, rather than driven by programme mandates. “We're not perfect at that,” he said. External benchmarks reinforce why. McKinsey surveys put the success rate of large transformation programmes at below one in three; Bain's 2024 survey of more than 400 executives found that only 12% achieved their full transformation ambitions. The bank's response is to standardise operations where possible, localise where regulation requires, and give local teams the authority to challenge complexity themselves rather than waiting for central direction. Measurement tracks genuine improvement against activity — not just financial savings, but employee engagement, client experience and operational resilience. Building change that outlasts programmes O’Neill’s position is that transformation is an ongoing process because the environment never stops changing. “We must always be ready to respond to global events, market shifts and regulatory changes,” he said. What makes that sustainable is not programme architecture but behavioural change at the workforce level. “You can spend a lot of money and introduce new systems, but true transformation comes from the engagement of people. That's the most important factor,” O'Neill said. In practice, that means securing buy-in by tying transformation to outcomes for colleagues and clients rather than programme metrics. It means embedding continuous improvement as a mindset across business units rather than driving it from the centre. “You're never going to be able to sit in the centre and understand every process,” O'Neill said. “The people who understand that best are on the ground. We need to provide them with the tools to raise those points and to change them themselves.” The bank tracks impact across a range of metrics to catch unintended consequences alongside intended ones, and communicates the rationale for change at scale. “Humans naturally, to an extent, resist change, but they'll embrace it a lot more if they understand why it's happening,” he said. For O'Neill, the differentiator comes down to people. “Anyone can implement a big IT programme. But unless you can transform your workforce — have them leverage and capitalise on those new capabilities, and have the right skills, approach and cultural mindset — that will be what differentiates you from others.” AI governance through a risk lens AI deployment sits at the centre of what comes next. Under Fit for Growth, the bank invested in data infrastructure and AI enablement as structural productivity drivers. O'Neill's background in operational risk directly shapes how that investment is governed. The bank uses AI across two tracks: general productivity tooling deployed to colleagues across the organisation, and a larger set of programmes targeting core functional processes. On both, the governance requirement is the same; AI must be explainable, traceable, auditable and embedded within the operating model rather than existing as a standalone initiative. “If somebody queries a decision that AI has made, we need to be able to go back and evidence that and understand the steps it took to make that decision,” he said. He acknowledges the trade-off in speed. “But is it the right thing to do? Absolutely, because we want the right outcome for our clients.” The bank has formalised this through a Responsible AI Standard and a Responsible AI Council spanning data privacy, cybersecurity and model risk. The framework is operating at scale — with dedicated AI tools now embedded across client-facing functions from wealth management to transaction banking. A risk-trained mindset also reshapes what adequate testing looks like. Where human testing was previously done on a sample basis, AI now makes comprehensive testing possible. “Rather than saying no, it's much more about setting a much higher bar,” he said. For larger programmes, the bank maintains sign-off criteria before going live, with risk experts across cyber, data, operational risk and resilience providing final approval. The emphasis, however, is on embedding a controls-first mindset throughout the organisation, so that risk functions as a continuous discipline and not a compliance checkpoint at the end of a project. Operating at scale across regulatory regimes Operating across 54 markets means navigating a proliferating regulatory environment spanning data localisation, AI governance and technology requirements. O'Neill's view is that this is manageable rather than prohibitive. “All regulators want us to have the right cyber protection in place, to have the right protection around data and to provide resilient systems,” he said, noting that the specific regulations vary across markets, but the underlying intent is largely consistent. Technology risk has become a supervisory priority across major jurisdictions. In Singapore, the Monetary Authority's Veritas framework sets out principles of fairness, ethics, accountability and transparency for AI. India's Reserve Bank requires data localisation for payment systems. The UAE has its own data protection and AI governance frameworks. The practical implication is that the bank can maintain a largely standardised operating architecture while adapting at the edges. “Where we have to make amendments, they're tweaks rather than fundamentally different capabilities,” he said. Dedicated internal teams track regulatory changes across all markets, and the bank engages directly with regulators to explain its transformation agenda and seek feedback — treating compliance as a dialogue rather than a constraint. What comes after Fit for Growth O'Neill’s view is that AI should not be treated as a strategic priority in isolation. “Banks should prioritise the things that will help them deliver their strategic objectives. AI will form a part of that for many banks, but there's no silver bullet,” he said. Fit for Growth formally concludes in 2026. In its full-year results, the bank guided for statutory RoTE above 12% in 2026 and indicated that the search for productivity would continue beyond the programme's conclusion. For O’Neill, the discipline the programme was designed to instil — processes simplified, local teams empowered, impact measured — needs to outlast the vehicle that carried it. The more revealing test is not achieving strategic targets, but whether the behaviours built through Fit for Growth hold as the environment continues to shift through tariff uncertainty, geopolitical fragmentation and accelerating AI regulation. “There is no explicit end date to transformation,” O’Neill said.
