Singapore, Feb. 4, 2026 — Dyna.Ai released an executive insights report with GXS Partners and Smartkarma that argues most banks are still struggling to turn AI spending into measurable revenue, while a smaller group is doing so by tying deployments to specific commercial outcomes and operating them at production scale.
The report points to AI-driven personalization as one area where some institutions are seeing measurable impact, citing up to a 6% revenue uplift in parts of the banking, financial services and insurance (BFSI) sector. It also argues that overall BFSI AI spending is expected to rise sharply over the next decade, but that competitive advantage will depend less on the number of pilots and more on execution discipline—governance, data readiness, and clear accountability for outcomes.
The research describes a persistent “pilot trap,” noting that many organizations report early ROI signals but struggle to translate them into enterprise-wide results. It claims the differentiator among faster adopters is operating model design: assigning ownership from deployment through business performance, defining outcome metrics up front, and structuring external partnerships so responsibility for results is explicit.
Tomas Skoumal said the report finds a wide gap between perceived progress and measurable returns, and frames “accountability” as the key constraint in moving from experimentation to scaled impact.
Regional snapshots highlighted in the report
Southeast Asia: The report links revenue impact to mobile-first customer channels and cites DBS as an example of a bank reporting material value from multiple AI use cases. It also references an estimated lending gap for ASEAN MSMEs as a potential growth area where improved underwriting and servicing could matter.
Middle East: The report points to sovereign AI strategies and fintech momentum, and cites PwC estimates on the macroeconomic upside of AI, with early use cases concentrated in wealth management and cross-border payments.
Latin America: It emphasizes credit decisioning using alternative data and fraud prevention as priorities amid high exclusion and risk, and references BBVA Mexico as an example of AI-enabled decisioning applied alongside risk controls.
The report also promotes “results-as-a-service” contracting as an emerging approach, describing models where vendors are compensated based on measurable business outcomes rather than tool delivery. Dyna.Ai said it positions its offering around end-to-end execution—from domain models and agentic systems to operational delivery—aligned to outcome measurement.
