日本語
한국어
Tiếng Việt
简体中文
The AI Investment Boom Is Real. The Returns Are Another Story.
Banking’s AI journey has reached a pivotal turning point. Pilots have moved into production, employees have embraced the tools, and spending continues to climb at a record pace. Yet one stubborn question lingers across boardrooms worldwide: where is the measurable value?
According to KPMG’s Q1 2026 U.S. banking survey, average projected AI investment over the next 12 months stands at USD 177 million. Meanwhile, research from Cambridge reveals that 76% of large financial institutions struggle to measure the value their AI initiatives actually deliver.
Key insight: The biggest risk facing banks today is not that AI fails to work. It is that AI succeeds — but the value never makes it to the business.
The Core Challenges Banks Must Tackle
- AI activity is not AI value. Pilots launched, user counts, and hours saved do not prove that a genuine business outcome has shifted.
- Workflow change matters, but it is not the finish line. A smoother process can still generate capacity that never reaches the bottom line.
- Freed-up capacity needs a clear destination. Leaders must decide whether it fuels growth, absorbs volume, prevents unnecessary hiring, or cuts costs.
- Every material initiative needs a value realization gate. The next investment decision must depend on evidence — not momentum.
From Experimentation to Economic Accountability
Banks are now putting hard numbers behind their AI ambitions. RBC is targeting between CAD 700 million and CAD 1 billion in incremental enterprise value from AI by 2027, net of investment. DBS reported approximately SGD 1 billion in economic value generated by its data analytics and AI/ML programs in 2025.
JPMorgan Chase has also shifted its AI messaging toward tangible outcomes. In its 2026 Company Update, the bank reported benefits spanning both revenue and expense categories while doubling the number of AI use cases in production. On its Q1 2026 earnings call, CEO Jamie Dimon issued a cautionary note: AI-driven productivity gains do not automatically translate into a structurally better efficiency ratio, especially as competitors deploy the same technologies.
These figures are no longer innovation aspirations — they are business-value commitments.
Why this matters: The dialogue across financial services is shifting from “What can we deploy?” to “What value did we actually realize?”
- Separate experimentation metrics from genuine value metrics.
- Track which use cases scale successfully and what changes as a result in the business.
- Make the economic or customer outcome visible from day one.
Start With the Business Problem, Not the Technology
Technology investments lose their punch when organizations treat them as technology programs first and business transformations second.
The most effective approach begins with the operating problem. Business leaders understand where workflows are slow, fragmented, or bogged down in manual effort. Technology should remove those obstacles to achieve a clearly defined outcome. The strongest model is business-led and technology-driven.
Deployment alone is not sufficient. Employees can adopt a new platform while quietly preserving the spreadsheet workaround, legacy approval process, or manual handoff it was designed to replace. In that scenario, the organization has simply built an expensive filing cabinet.
Consider an AI assistant that reduces customer-issue research time from 12 minutes to 8 minutes. If routing, escalation rules, and staffing assumptions remain unchanged, the bank may record productivity gains without capturing meaningful economic value. The technology worked. The operating model did not.
Why this matters: Workflow redesign is a bridge to value — not the finish line.
- Define the business problem and establish a baseline before selecting or scaling the solution.
- Design the future-state workflow as an integral part of the investment case.
- Remove the workaround or duplicated activity the technology is meant to replace.
Productivity Is Not Value Until Someone Captures It
If AI transforms a 60-minute task into a 20-minute task, what happens to the remaining 40 minutes?
That is precisely where theoretical productivity becomes stranded capacity.
Citi reported that AI-assisted coding tools are generating approximately 100,000 hours of capacity each week, freeing developers to focus on higher-value innovation. The broader lesson is clear: capacity needs a destination.
The preference leans toward redeployment and growth where the economics justify it — more client time, greater volume, or higher-value work. In a mature business, taking cost out may represent the more disciplined path. Cost avoidance can create real value, but it is fundamentally different from direct expense reduction.
Why this matters: Time saved is an operating benefit. The management decision that follows determines whether it becomes enterprise value.
- Decide whether released capacity will support growth, absorb volume, avoid hiring, or reduce cost.
- Track hard savings, cost avoidance, and redeployed capacity as separate categories.
- Do not count a financial benefit until there is evidence that the capacity was actually captured.
Make the Business Own the Benefit
Many programs have an executive sponsor. That is not the same thing as having a benefit owner.
If an AI initiative promises lower cost-to-serve, increased sales, faster throughput, or a better customer experience, the business executive accountable for that outcome should also own its realization.
Technology should own enablement, architecture, and controls. The business should own the outcome and value realization. Finance can help distinguish forecast benefits, avoided costs, and realized impact.
Why this matters: Delivery accountability gets the capability live. Benefit accountability turns it into a business result.
- Assign a named business owner to each material benefit.
- Define how the benefit will be evidenced and validated.
- Review realized value through the same cadence used to review business performance.
Put a Value Realization Gate Before the Next Dollar
Banks need a deliberate checkpoint where experimentation becomes a genuine investment decision. A “value realization gate” asks whether the original business hypothesis is being proven before additional capital and talent are committed. Timing should reflect the use case and its complexity, but the gate should be established before the pilot even begins.
The chain looks like this: Business problem → workflow change → adoption → capacity decision → operating outcome → financial or customer value.
If leaders cannot trace the benefit through that chain, they should be cautious about calling it realized value.
Why this matters: Value realization should be designed into the initiative from the start — not calculated after the fact.
Before approving the next investment, leaders should be ready to answer four critical questions:
- What outcome are we buying? What revenue, cost, risk, or customer result should change?
- What changes operationally? Which work disappears or improves, and what happens to the capacity created?
- Who owns realization? Which business executive is accountable for converting the improvement into a measurable result?
- What evidence earns the next dollar? What would justify scaling, extending, redesigning, or decommissioning the initiative?
The Bottom Line for Banking’s AI Future
Banks have proven they are willing to spend on AI. Increasingly, they are attaching billion-dollar expectations to the returns.
The challenge now sits in the gap between the two.
Technology has to change the workflow. Workflow improvement has to create an operating benefit. Capacity has to be deliberately captured. That benefit then has to reach a financial or customer outcome the business can defend.
The winners in banking’s next phase of AI will not be defined by how much technology they deploy.
They will be defined by how much value they actually realize.
Matthew Maunder is Founder and Principal of Valent Advisory, with more than 17 years of experience spanning RBC, PwC Strategy& and Colliers International, focused on enterprise strategy, transformation, performance improvement, and value realization.
Source: thefinancialbrand.com