Banks cancel more AI projects before deployment as scrutiny of returns increases: Infosys

Pre-deployment AI project cancellations rose 33% between late 2024 and late 2025, even as 59% of deployed AI initiatives are delivering tangible business value, Dennis Gada, Executive Vice President and Global Head of Banking & Financial Services at Infosys, informed TNIE.

Key points

• According to the Infosys Bank Tech Index, 59% of deployed AI initiatives are already delivering tangible business value.
• "This surge in early-stage pruning is healthy; it demonstrates that institutions are failing fast and cheap rather than funding multi-million-dollar science experiments, " Gada remarked.
• "The sustainable differentiator is not the base model; it is the bank's proprietary data estate, the orchestration middleware, and the governance guardrails governing execution, " Gada remarked.
• Areas involving unsupervised customer-facing financial advice, algorithmic credit decisions and autonomous capital allocation remained restricted since of regulatory and explainability requirements, Gada stated.
• The initial wave of spending was dominated by model access, experimentation licenses, and standalone front-end tools.

Gada remarked the rise in cancellations reflected stricter scrutiny of projects before they were deployed, with banks placing greater emphasis on whether AI initiatives could demonstrate measurable business outcomes.

He remarked the gap in banking was no longer simply between institutions experimenting with AI and those putting it into production.

"The real divide across global banking is no longer experimentation versus production-it is between institutions that can make AI work inside a controlled sandbox and those equipped with the enterprise data architectures, governance frameworks, and operational discipline to run it dependably at scale, " Gada remarked.

Gada remarked projects that fail to reach production often face three difficulties – the absence of measurable unit economics, fragmented data and treating AI as an IT project rather than changing the underlying workflow. "Models cannot compensate for siloed, uncurated, or stale data, " he remarked.

Meanwhile, Gada remarked the nature of banks' AI spending was changing, with investment moving away from model access, experimentation licences and standalone tools towards the systems needed to backing AI deployment at scale.

"We are seeing a decisive capital rotation. The initial wave of spending was dominated by model access, experimentation licenses, and standalone front-end tools. Today, capital is flowing into the underlying plumbing required to create AI enterprise-grade: unified data platforms, hybrid cloud environments, zero-trust cybersecurity, and legacy core modernization, " Gada stated.

Banks had additionally recognised that foundation models were becoming less of a differentiator, he stated.

In practice, the projects moving fastest into production are largely those involving high-volume and rules-based work, including transaction surveillance, anti-money laundering triage, contract parsing and software testing, Gada remarked.

"These environments offer deterministic ground truths, continuous audit logging, and measurable operational savings without exposing the bank to black-box decision risk, " he remarked.

Banks, nevertheless, were taking a cautious approach to systems that carry out multi-step tasks throughout different systems. Such systems were being applied for internal workflow triage, with human checks retained before financial commitments or ledger postings were completed.

While software engineering and cybersecurity were among the areas producing the largest operational cost savings, customer service represented the highest single source of value creation from AI in banking at 22%, according to Gada.

For now, banks cancel more AI projects before deployment as scrutiny of returns increases: remains the part of the story worth watching, and further updates are likely as more details are confirmed.