Why Your Bank’s AI Strategy Is Failing to Deliver Real Value

Banks are rushing to adopt artificial intelligence, but simply layering AI onto old workflows is leaving significant productivity and profit potential on the table. To unlock true value, institutions must fundamentally rethink how they operate.

According to research by Accenture, while 84% of banking leaders report seeing at least moderate business value from AI, a mere 20% say their investments are delivering broad and sustained returns. Michael Abbott, Accenture’s global banking lead, points to a critical issue: much of the industry’s AI push is driven by a “fear of missing out,” leading them to apply new tools to outdated processes rather than reinventing those processes.

The Core Problem: Overlaying AI on Legacy Workflows

Most banks are taking a familiar path—training staff on AI tools and accelerating adoption—but are stopping short of redesigning their core operations. Abbott notes that this often means applying powerful large language models (LLMs) to tasks that simpler, specialized AI could handle better. Shockingly, experiments have shown that this piecemeal approach can even decrease overall productivity.

From Linear to Parallel: The Required Paradigm Shift

Traditional bank processes are linear and sequential, mirroring human step-by-step thinking. Abbott argues that AI demands a shift to parallel workflows. He draws an analogy to factory evolution: moving from a single water wheel powering everything to individual electric motors enabling simultaneous, independent tasks at each workstation.

“AI is forcing the industry to think about everything in parallel,” says Abbott. This requires a dramatic change in organizational structure, where multiple parts of a complex process can be addressed at once by specialized AI agents.

Real-World Examples of Process Reinvention

Mortgage Processing Reimagined: Instead of a loan application moving step-by-step, AI “parallelizes” the process. Different AI agents can simultaneously handle income verification, credit checks, and property appraisal, communicating in real-time to fill gaps later, drastically cutting timelines.

Customer Service by Intent: A Latin American bank discovered that phone calls are a series of distinct customer intents. By building AI to handle each intent separately—like “report lost card”—they could deploy that solution across all channels (call center, app, online banking), creating a unified and efficient system.

Choosing the Right AI Tool for the Job

Headlines focus on massive “frontier models,” but Abbott stresses this is not always the answer. A critical distinction exists:

• Deterministic Algorithms: Always produce the same result (e.g., 2+2=4). Essential for banking’s regulated, predictable environment.
• Stochastic Algorithms (like LLMs): Contain randomness and can “hallucinate” or make errors.

For specific tasks like ingesting a tax return, using a huge LLM is wasteful and risky. Smaller, purpose-built models deliver deterministic, accurate results, which is why many banks are replacing some LLMs with specialized models.

The Evolving Role of Humans in an AI-Driven Bank

As teams of AI agents handle tasks, human managers will focus on connective intelligence, critical thinking, and oversight. The ability to question an AI agent’s output is becoming a vital skill. Some firms even test candidates by giving them a deliberately flawed AI model; success depends on spotting the errors and crafting better prompts.

“That’s the logical mindset that’s going to be needed,” says Abbott. “Otherwise, you will think it’s right all the time. And that’s not going to work.”

The banks that succeed will be those that view AI not as a tool to augment old work, but as a catalyst to completely reconfigure how they operate, unlocking the true productivity gains waiting to be claimed.

Source: Thefinancialbrand.com