AI Lead & Implementation Specialist | Context Engineer within Asset Management, Private Equity & Corporate Law
Some highlights from Jefferies “AI in Capital Markets” event in London this month. The importance of context engineering cannot be overstated—it’s an ever-growing operating discipline. When you explicitly combine PM intent, risk limits, high-quality data points, and sophisticated AI/ML understanding, you move from endless PoC/demo-grade outputs to highly effective insight generation. Ultimately, strategic decisions shift from trendy, sporadic solution creation to building a consciously curated portfolio of integrated, scalable services embedded in core workflows—with clear ownership, and cost-to-value oversight. Hype is ineffective; conscious curation is not! Personally, I think AI adoption will be one of the defining themes in financial services over the next 24–36 months, but broad deployment in investment research will lag until explainability, audit trails, and repeatability clear a materially higher bar. Most PoCs fail not on models but on governance and mechanics—implementation frameworks, evaluation metrics, data-leakage-proofing, red-teaming, bias controls, staged rollouts, and measurable guardrails will separate one-hit-wonder demos from production outcomes—and that’s where the alpha (and the accountability) actually is!
