Finance firms prioritise AI but struggle to scale it

Most financial services and insurance firms see artificial intelligence as a priority but fewer than half have embedded it across their operations, according to new research from technology services company HTEC.

The study, which draws on responses from 250 C-suite leaders in financial services and insurance across six countries, found that 41.6% say AI is fully embedded across multiple functions. A similar proportion, 42.8%, report that AI is deployed only in limited areas, often as isolated pilots or departmental tools.

Not a single respondent said AI was not a priority for their organisation. The findings indicate that interest in AI no longer sits at the edge of strategy discussions. It is now part of mainstream planning for banks, insurers and other financial institutions.

However, the research highlights a wide gap between intent and execution. More than half of organisations, 52%, say they are still learning and experimenting with AI with limited ability to capture value or are struggling to keep pace with the speed of change. Only 23% feel equipped to adopt and scale AI rapidly.

Legacy integration

Integration with existing technology environments emerged as the primary obstacle. Some 40% of executives cite difficulty embedding AI into existing processes and legacy systems as the top barrier to scale. Many institutions run critical workloads on long-established platforms. These systems often need extensive modification before they can support data-intensive AI tools.

Leadership alignment is another significant constraint. The report finds that 36% of organisations report a lack of strategic alignment across leadership teams. That misalignment slows organisation-wide transformation and keeps many AI efforts confined to individual functions or business units.

AI literacy at senior levels also remains uneven. Only 38% of executive teams are rated as having high AI literacy. This limits the ability of boards and leadership teams to evaluate AI proposals, set priorities and govern risk.

Skills shortages

The study points to acute shortages in technical and security skills. Some 38% of respondents say they lack data engineering and analytics skills. A further 33% cite gaps in cybersecurity and data privacy, while 32% report shortages in DevOps, automation and edge skills. AI and machine learning expertise is missing in 31% of organisations.

These talent gaps have direct commercial effects. Executives say they drive higher costs in 41% of firms, reduce innovation in 38%, and slow time to market for new products in 34%. Many institutions rely more heavily on external partners or face delays in delivering AI-based services.

Focus use cases

The report identifies clear areas where leaders expect AI to add value. Automating underwriting ranks highest, with 42% of respondents highlighting its potential to support faster and more accurate risk assessment. Fraud detection and broader risk management follow closely at 39%, reflecting regulatory pressures and the growth of digital fraud.

Personalisation and customer engagement are priorities for 38% of executives. They point to predictive and context-aware digital interactions as a way to differentiate in competitive retail and SME banking markets. AI-powered research and advisory services are also cited by 38% of respondents. These tools support real-time, context-rich insights for decision-making by both customers and internal teams.

Claims automation appears as another major area of interest, especially for insurers. Some 34% of leaders note the potential for faster and more reliable outcomes for customers. This focus reflects efforts to shorten settlement times and reduce manual processing.

Execution gap

The report concludes that the industry's challenge now lies less in identifying AI use cases and more in scaling them consistently. Many organisations have built proof-of-concept systems or deployed AI in narrow areas such as chatbots, credit scoring or transaction monitoring. Fewer have connected these initiatives into their core systems and workflows.

HTEC notes that this uneven readiness, combined with leadership alignment gaps and technical constraints, is pushing institutions to reconsider their operating models. Many are revisiting engineering foundations, reviewing data architectures and exploring partnerships where internal capacity is weak. Workforce training and broader AI literacy are also moving higher up the agenda.

Jamie Allsop, Managing Partner for Financial Services & Insurance at HTEC and a co-author of the report, said the institutions that progress fastest share a distinctive internal dynamic.

"Firms that move fastest are often those where technology and business leadership work as one team, united around measurable outcomes rather than experimenting for its own sake. Partnering across functions and with external experts is essential to accelerate value creation and realise the full potential of AI in financial services."