Agentic AI in financial services: Why success depends on better delivery decisions

Agentic AI is a delivery maturity test, where success depends on two delivery foundations. The first is decision alignment: ensuring work is connected to strategic intent, and tooling is built for the people using it. The second is information integrity: ensuring decisions are supported by a connected tooling ecosystem and there is trusted data and clear sources of truth.

Foundation 1: Decision alignment

Ask most delivery leaders whether the initiatives their teams are running actually connect to strategic priorities and you’ll get a pause before the answer. Objectives get set at the top, initiatives get stood up in the middle, and by the time work reaches a team, the line back to strategic intent has usually gone thin or disappeared. Leaders end up managing on status updates that describe activity, not progress against outcomes.

In financial services, the challenge extends beyond delivery. Leaders need to demonstrate why investment was approved, how a change aligns to risk appetite, which controls or models it impacts, and who remains accountable for the outcome. When agents begin making recommendations or initiating work, that traceability becomes significantly more important.

Fixing this means creating a clear thread from strategy through to execution, linking initiatives and individual pieces of work back to measurable outcomes. Many delivery tools now offer this capability out of the box, making it tempting to treat the challenge as a tooling exercise but that approach can often backfire. For example, a recent client adopted Objectives and Key Results (OKRs) to measure outcomes and align work to strategic priorities, but the objectives and key results themselves did not meet basic OKR standards. Success was poorly defined, work was not clearly linked to outcomes, and the organisation was unable to fully leverage the capabilities of its delivery tooling. Without the underlying discipline, a tool simply provides a cleaner view of the same disconnected picture and agentic AI raises the stakes further by executing and making recommendations based on it.

Get this right, however, and the metrics change too as it becomes less important to track activity. What matters more is whether work remains traceable to strategy, how quickly it moves from idea to outcome, and whether it achieves the results it was intended to deliver. For regulated organisations, that traceability is also exactly the kind of decision trail regulators are increasingly expecting as they monitor the transition to more autonomous models.

Tooling must be built for the people using it

The patterns we see most often are delivery tools configured for process compliance or configured team-by-team with no thought to who else needs what out of them. A C-suite executive and a product owner are asking completely different questions of the same portfolio, and most tooling solutions rarely answer both well.

Three groups drive most of the value here: executives who need portfolio-level visibility to make funding calls, team-of-teams leads who need to see dependencies and flow across big features of work, and product owners who need to plan with minimum friction at team level. Optimise entirely for the third, the common pitfall is that you get empowered teams but no trustworthy view above them, plus a mountain of manual reconciliation to produce one. Over-correct the other way and you get rigid, top-down tooling that slows the teams down without giving leadership better decisions.

In financial services, there is also a fourth audience that is often overlooked: the control functions. Risk, compliance, audit, model governance, and operational resilience teams all need visibility into the delivery ecosystem, not to manage delivery itself, but to understand how decisions are being made, which controls are affected, and where accountability sits. As agents take on a greater role in delivery planning, prioritisation, and execution, that visibility becomes increasingly important. An agent can only operate confidently when the people responsible for oversight can do the same.

At one of PA’s major banking clients redesigning delivery tooling around the specific questions each persona needed, rather than one generic view, created a connected ecosystem that executives, product owners, and control functions all trusted. But it must be designed deliberately for all personas so that organisations can shift effort away from manual reconciliation and towards better investment, prioritisation, and delivery decisions.

Foundation 2: Information integrity

Most financial services organisations spend too much time debating which platform to buy and not enough time defining how their delivery ecosystem should work. The result is often a landscape of overlapping tools, inconsistent configurations, and duplicated data that makes decision-making difficult.

Every delivery ecosystem needs a clear understanding of where strategic priorities are managed, where delivery execution and risk occurs, where financial performance is measured, and where the operating model and delivery structures are hosted. This challenge is particularly acute in financial services, where a single initiative may cross business units, legal entities, legacy platforms, and multiple control functions while being represented differently across portfolio, finance, risk and delivery reporting. Agentic AI amplifies this challenge because it needs authoritative sources, system relationships, decision rights, and escalation paths to be explicit.

Organisations that succeed with agentic delivery establish a coherent tooling architecture, clear system ownership, and explicit data flows before deploying agents at scale.

At one PA client, a UK asset manager, years of inconsistent project structures in a single delivery tool made meaningful portfolio reporting impossible because of the absence of one enforced way to use it. Working with the client, PA introduced common standards, governance and ownership across the delivery tooling ecosystem, creating a trusted foundation for portfolio decision-making that agents will optimise once deployed.

Data has to be trustworthy

Financial services organisations rarely lack data. The challenge is delivery, investment, risk, and financial information are often represented differently across systems. That becomes critical when agents are being asked to recommend priorities, identify emerging delivery risks, forecast investment outcomes, or generate information for executive and regulatory oversight. Modern tools have largely solved data completeness issues through mandatory fields, prompts at point of entry, and automated quality checks. However, they haven’t solved for whether the same initiative means the same thing across different systems, whether delivery data can be traced back to strategic objectives, or whether teams are working from a common version of the truth.

This doesn’t mean organisations need to fix all of their data before adopting AI. Instead, they need to identify the critical decisions, workflows, and data domains where agents will operate, then establish clear ownership and governance around those areas first.

A delivery environment with clean, connected, trustworthy data is one an organisation can start to model, not just report on. Instead of discovering three months into an initiative that a dependency will cause delays, teams can test the scenario before committing resources. Leaders can understand how re-prioritisation affects delivery dates or what a capacity shortfall in one value stream means for others downstream; moving from reactive status reporting to forecasting-enabled delivery.

Four questions to assess agentic foundations

Leadership teams investing in agentic delivery should assess the strength of both foundations by asking four questions:

For decision alignment:

• Can we consistently trace work back to strategic objectives?
• Are our delivery tools designed around the decisions different personas need to make?

For information integrity:

• Do we have a clear tooling strategy and defined sources of truth?
• Would we trust an agent to make decisions using our delivery data today?

If the answer to any of these questions is no, that’s where the transformation should start. Agentic AI will expose weaknesses in delivery environments much faster than existing tools and processes do. Strengthening decision alignment and information integrity now will give financial services leaders the confidence to deploy agents where they can deliver meaningful value.