By Meena Athinathan, VP & Head of Strategic Business Unit for Banking, Capital Markets Infrastructure and Indexes at Cognizant
Many businesses are yet to see the full productivity benefits promised by AI. Only 32% of senior executives can point to tangible gains in business productivity, while 41% do not expect AI to move the needle for years.
For banks, one barrier is ‘people debt’. This is the accumulated knowledge, judgement and informal steps that employees use to keep operations moving – much of which sits outside formal systems and processes. Unless banks uncover and account for that hidden context, AI risks automating existing complexity rather than transforming it.
The hidden knowledge AI agents cannot see
Financial institutions run on formal policies, but they also rely on thousands of informal decisions made by experienced employees. These workers know how to move work between systems that were built at different times, hold different information and often do not communicate with each other. They know when an exception needs escalating, when a client request requires human review and when a workaround is masking a deeper process issue. Much of this knowledge is not captured in systems or standard operating procedures.
This reliance on undocumented judgement becomes a problem when AI agents are expected to take on work that has traditionally relied on human judgement. To an AI agent following the documented process, a pause or extra step may appear unnecessary. In practice, that intervention may prevent an incorrect decision from affecting a client, creating a regulatory issue or exposing the bank to financial loss. Financial services firms therefore need to understand where human judgement protects the business and where employees are compensating for outdated or poorly designed processes.
For example, when a customer makes an unusual payment, an experienced employee may draw on the customer’s history, the circumstances surrounding the transaction and previous cases to decide whether it is likely to be routine or needs further investigation. An AI agent may understand what step usually comes next, but it will not necessarily understand the context behind that decision unless the relevant knowledge has been captured. Without this understanding, AI risks reproducing existing weaknesses and workarounds, rather than removing them.
Turning employee judgement into context for AI
AI can help firms close the people debt gap, but only if they use it to understand their work before they automate it. That starts with a simple question applied to every manual judgement in a process: is this step protecting the business, or is it patching a broken process? The first kind of knowledge needs to be captured and built into how an agent works; the second needs to be fixed, not automated.
Answering the question means finding the decision points in the first place, and the challenge is that most of it is invisible and resides outside standard operating procedures. Emerging “work graph” platforms tackle this directly: rather than relying on documented processes, they passively map how work actually flows across communication channels, ticketing systems and enterprise records, then extract the unwritten knowledge employees apply along the way. That turns judgement which has lived in employees’ heads into a living context model the wider organisation and AI agents can build on.
For example, take customer onboarding. Experienced employees know how to check documents, validate information and resolve unusual cases. Some of these judgements may never have been written down because they were developed through years of handling similar cases. Mapping the cases they escalated, the ones they cleared, and the reasoning between the two makes that judgement visible. The bank must still define when the AI agent can proceed, when it should ask for review and when a human needs to make the final decision.
The same exercise separates necessary safeguards from compensating workarounds. An extra check that catches genuine risk should be built into how the agent works; one that exists only because two systems don’t talk to each other should be designed out before anything is automated. By building a clearer picture of how work actually gets done, firms give AI the context to handle tasks reliably, while keeping people involved where experience and judgement remain necessary.
Creating the right controls for AI
Incorrect or fabricated outputs can have serious consequences, especially in a regulated environment. Because agents can access information and take action across systems, implementing reliable data and effective guardrails before they begin making decisions is essential. This means institutions need to know where the data comes from, who is responsible for it and what information AI is permitted to access.
To achieve this, banks should route AI agents through a central point of control that records how they are used, rather than allowing them to operate freely across the organisation. This provides a consistent view across the enterprise and makes it easier to investigate problems when they arise.
The employees who understand how work actually gets done also have an important role to play in setting these controls. Their expertise can help define the boundaries of an agent’s role, identify when human judgement is still required, and ensure AI is used to rethink and improve how problems are solved, rather than simply being bolted onto existing processes.
From automating processes to redesigning them
AI will only deliver lasting value if banks address the complexity behind their existing processes. By making employee knowledge visible, firms can preserve valuable judgement, distinguish necessary safeguards from inefficient workarounds and redesign processes before automating them.
With clear governance, the right technology foundations and employees involved in shaping how agents operate, banks can use AI to improve how work gets done, rather than carrying old inefficiencies forward.