PIMFA Guest Blog SOLVE Part 2 of 3 – Operations: Reshaping the Core

Operations: Reshaping the Core

Operations determine whether AI can function safely, consistently, and at scale.

Operational readiness for AI starts with visibility: you cannot reshape, automate, or augment a workflow you cannot see. Whether a firm is deploying GenAI, agentic AI, predictive models, or deterministic automation, each requires explicit steps, clear decision points, and a stable foundation. Without that, the technology will infer, guess, or execute the wrong pattern at speed.

Many wealth and asset management firms underestimate how much operational precision AI requires. Teams will often operate on informal habits. Managers assume a neat happy path that rarely exists. AI applied to undocumented workflows magnifies inconsistencies, through inference (in GenAI), rigid execution (in agentic AI), or replication of historic patterns (in predictive models). Mapping workflows reveals bottlenecks, hidden handoffs, exceptions and allows firms to identify where AI fits and (critically) where workflows should be redesigned instead of just enhanced.

Beyond workflow documentation, task-level detail provides the context and explicit instructions AI needs. Decision boundaries matter too. AI cannot guess where human judgement starts and stops, and missing boundaries create risk.

Lastly, establish operational baseline metrics before applying AI systems. If you don’t know how long tasks take, how often they need correcting, or how many get completed, improvement from AI becomes a matter of opinion. Baselines make it possible to quantify whether AI is actually helping, where adjustments are needed, and when a use case is ready to scale.

Client Experience: Where the Return Shows

Client Experience (CX) is where AI proves it was worth the investment.

The research supports this. AI-enabled engagement tools that reduce effort, increase personalisation, and improve clarity significantly lift customer-perceived value (Hollebeek et al., Psychology & Marketing, 2024). Service efficiency improvements driven by AI correlate strongly with higher satisfaction and loyalty intentions (Singh & Singh, Cogent Business & Management, 2024). For wealth and asset managers this means that AI earns its return when clients see a difference.

Many firms focus on back-office gains and treat CX as secondary. You end up with AI that improves processing but not client outcomes – no visible uplift in quality of service, and weak commercial justification for continued investment. The result is efficient but mediocre: smoother operations delivering the same client experience as before.

Better client experience should be the test of whether AI is working. Start by considering client outcomes when designing AI solutions. Whether serving individuals, advisers, or institutional allocators, AI should enhance clarity, speed, and relevance for clients, and do so transparently.  Also, equip client-facing teams with AI that elevates interactions. Advisers and RMs need summarisation and contextualisation; Distribution and Investment teams need tools that synthesise information and improve reporting quality.

Measure CX impact directly. Track turnaround times, issue resolution, communication relevance, and client engagement. These metrics strengthen the business case and demonstrate value outside the firm – turning AI from an efficiency initiative into something that has a noticeable (positive) impact on clients.

Before Part 3

Operations hands AI the structure to work safely and consistently at scale. Client Experience is where that capability reaches clients and earns its return. Together, they turn AI strategy into impact (and separate firms genuinely transforming from those running experiments that fail to scale).

In the final part of this series, Part 3 ,we cover Talent and Controls – the human and governance dimensions – and provide a consolidated view of how all six pillars work together. This article will be published in the May edition of the PIMFA Journal