Authored by Nick Merritt, Executive Director of Designit 

In financial services, 2025 was powered by a sense of AI-fuelled optimism. Excitement, associated with new technology, made innovation the key growth driver: 78% of organisations using AI in at least one business function in 2025 – a marked increase from 55% a year earlier.

The defining force in 2026, however, will be accountability. 

The new year marks the start of a more disciplined era, one shaped by inflationary pressure, regulatory scrutiny, and difficult questions from board members about what innovation is actually delivering.

For everyone in financial services, success in 2026 will be a matter of how intelligently new technologies are applied, with discipline being valued over novelty.

AI’s ‘honeymoon’ period is over

In 2026, financial institutions will stop treating AI as a silver bullet and start demanding quantifiable ROI. Pressure from boards and regulators will shift investment away from experimental use cases and towards explainable, auditable applications.

In the case of retail banks, there will be a move away from flashy generative assistants and a focus on automating the unglamorous but high-volume processes, such as onboarding, KYC, compliance checks, and internal reporting. 

Insurance will follow suit. Instead of chasing “AI underwriting”, the focus will shift to data accuracy and automation of claims handling. 

This means the value of AI will depend on the quality of the plumbing, not the brilliance of the interface. 

Meanwhile, commercial banks will double down on decision support and scenario modelling, particularly for credit and risk. But those tools will have to process resilience under scrutiny. CFOs will demand the same financial discipline from AI initiatives that they expect from any capital investment. 

Inflation and debt will reshape the balance sheet – and the workforce that runs it

This financial discipline will also see a sense of caution from financial institutions – especially in the face of macro-economic factors such as persistent inflation and stubbornly high cost of capital.

Retail banks will tighten mortgage lending as affordability stress tests bite. Meanwhile, lending to small businesses will become more selective, and insurers will reprice long-term products to reflect higher claims costs and investment volatility.

For commercial banks, capital efficiency will take priority over expansion. Liquidity buffers, provisioning, and cost control will move back to the top of the agenda. It will feel like a return to pre-2008 discipline, but with digital tools to manage exposure in real time.

That caution will extend to strategy. With margin pressure rising, firms will reassess the economics of talent. Hybrid and remote models, once seen as a lifestyle perk, will increasingly be treated as financial levers. 

If trading or analytics functions can operate effectively from Leeds, Warsaw, or Bangalore, the logic of paying London premiums weakens. A quiet migration of roles north and abroad is likely.

At the top end of the market, high earners will look for exits. If enough senior talent relocates to Dubai or Singapore, firms will have to rethink retention, leadership mobility, and incentives for a borderless workforce.

Inflation will tighten more than capital flows. It will reshape how and where finance works. The institutions that adapt their workforce model as intelligently as their balance sheet will stay resilient.

When the AI hits the data wall, financial services will rethink what intelligence really means

If the growth in AI infrastructure and training data begins to level off, the sector will feel it first through a slowing of “AI-driven” innovation cycles. The so-called “data wall” – the point where useful, high-quality data becomes harder to source and more expensive to label – will reduce the rate of improvement in large models. That doesn’t kill progress, but it does reset expectations. 

For financial services, this moment could actually be healthy. 

Rather than chasing ever-larger, ever-more-generalised models, institutions will refocus on domain-specific intelligence; models trained on proprietary, high-quality financial and behavioural data. 

Banks, insurers, and asset managers will start treating their internal data as a strategic rather than a compliance headache. 

We should therefore expect a surge in interest around synthetic data, federated learning, and new forms of privacy-preserving analytics. The smartest firms will invest in internal data stewardship capabilities, tuning what was once “data hygiene” into competitive advantage. 

This shift will also alter market behaviour. Investors and analysts will pay more attention to which financial institutions are generating real productivity gains from AI, not just marketing them. 

Lenders and insurers that demonstrate clear efficiency improvements, faster risk modelling, and more accurate decision-making will attract capital. Those still talking about “innovation journeys” will lose credibility. 

The hype that has driven investment in AI is understandable. But now we need to start seeing what it’s all been for. 

In 2026, innovation will be judged less by how ambitious it sounds and more by how reliably it performs. Institutions that apply the same rigour to technology as they do to capital, risk, and regulation will be best placed to navigate the year ahead.

Photo of Designit’s London office courtesy of Designit