AI & Technology Strategy
We help boards and leadership teams cut through the noise around AI and emerging technology — identifying where genuine value lies for your business, what the risks are, and what a credible adoption roadmap looks like. Advisory grounded in real institutional experience, not theoretical frameworks.
The gap between AI ambition and AIOps reality at scale is rarely technical. It is organisational. Boards approve strategies that assume ML models in production behave like proofs of concept in a notebook. They do not. The monitoring infrastructure, the feedback loops, the model governance, and the retraining cadences required to sustain AI at institutional scale are an order of magnitude more demanding than the original build — and almost never costed in.
Sebastian's AI strategy work at HSBC was grounded in that reality. The group's first AIOps blueprint was written not as an aspiration document but as an operational specification: what the platform needed to look like, what the team needed to be able to do, and what the multi-cloud architecture had to support. That work has since informed advisory to mid-market organisations at earlier stages of the same journey.
Engagements typically follow three phases: a structured diagnostic of current AI maturity and the gap to operational capability; a written blueprint defining the target architecture and governance model; and, where required, contingency-based execution support to hold the programme to account through delivery.
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