Nearly 32% entities expect AI to drive re-skilling over job cuts: Survey

Nearly one-third of entities surveyed anticipate that Artificial Intelligence will primarily fuel workforce re-skilling and transformation, rather than resulting in substantial job cuts, according to the International Financial Services Centres Authority (IFSC) AI Survey 2026 report. Concurrently, about ten percent foresee AI fostering the creation of new roles through novel product lines across various sectors, including fund managers, banking units, insurers, capital-market intermediaries, finance, leasing, and fintech firms.

Operational efficiency is identified as the primary catalyst for integrating AI technology, with its importance surging from 64% in 2025 to 82% in 2026. Process automation and productivity enhancements in areas such as internal operations, risk, and compliance are the main drivers behind this shift. Cost reduction, customer experience enhancement, and regulatory compliance follow closely behind, making up 35%, 33%, and 28% of secondary drivers, respectively.

Moreover, employee engagement has risen, with 57% of surveyed entities reporting that their staff utilize AI tools, thanks to the establishment of internal usage policies.

The major hurdles to AI adoption are data quality and regulatory clarity, while lack of executive buy-in is the least cited barrier, suggesting that leadership intent is not a significant impediment. Financially, 60% of entities are actively planning, scaling, or maintaining investments in AI capabilities, leaving 40% to reassess their business models.

Investments range from 20% that have already been made, 11% scaling up investments, and 29% planning future commitments, with an allocation of 1% to 5% of IT budgets being the most common. Generative AI is currently being explored or implemented by 65% of entities, while Agentic AI is emerging as the next frontier for early adopters.

Regulatory direction is a key requirement, with 48% of entities seeking clarity on existing rules and 46% requesting principles-based guidance. Key obstacles remain regulatory uncertainty and data quality, each cited by 41% of entities, followed by the lack of responsible-AI tools at 34% and talent shortages at 27%. Data privacy is the primary risk concern, prompting institutions to establish governance frameworks with human-in-the-loop oversight as the primary production safeguard.