AI is being used everywhere, from fraud detection and customer service to regulatory compliance and credit risk analysis, with more than 70% of organisations now using AI in at least one business function.
Still, many organisations are still asking the same question: why aren’t we seeing greater return on AI investment?
The answer rarely lies in the technology itself.
Effective AI strategies begin with clearly defined business challenges to understand operational pain points before identifying where AI can add value.
Here are five hallmarks that set banking organisations apart in successfully turning AI investment into measurable business outcomes.
1. Align AI with business priorities
A common mistake that organisations make is treating AI as an innovation project rather than a business transformation programme.
Banks should begin by identifying the business challenges they need to solve before deciding where AI can create the biggest impact for them.
This means moving beyond proofs of concept and asking strategic questions such as:
- Which customer journeys create the greatest issues?
- Which functions could improve productivity without increasing operational risk?
- How can AI support revenue growth, customer retention or regulatory compliance?
When AI initiatives are tied to measurable business outcomes, organisations can better prioritise investment and demonstrate a return on investment.
FDM Consultant Shayan Mahinfar works as an AI Engineer for a financial institution. His team actively collaborates with other departments to understand where AI can have the greatest impact. He says, “The sales team would tell us what their pain points are, and we would try to figure out how we can use the tools that we’ve developed.”
This approach helps ensure AI delivers measurable value rather than becoming technology in search of a use case.
For FDM Consultant Joshua Ji, who also works for a financial institution as an AI Engineer, the success of AI is measured through real outcomes. One of the projects he is supporting is expected to deliver annual savings of between $800,000 and $1 million by streamlining the research publishing process and reducing manual review time. It demonstrates that effective AI strategies are judged by business impact rather than the technology itself.
2. Build responsible AI governance
Financial institutions are one of the most highly regulated industries in the world, where decisions can directly impact customers, markets and financial stability. US payment gateway provider BridgePay Network Solutions faced a serious ransomware attack earlier this year, which caused service outages that affected merchants and payments across its network.
This is why strong governance frameworks are essential in ensuring those systems are reliable, transparent and accountable, especially in areas such as lending, fraud prevention, risk assessment and customer service.
Governance needs to be built into the entire AI lifecycle, from identifying use cases and managing data quality to monitoring performance and ensuring human oversight.
An AI governance model should cover key areas including:
Data quality and ownership: Banks need clear processes to manage data accuracy and ensure AI outputs are reliable.
Transparency and explainability: AI-driven decisions must be understandable to customers and regulators.
Continuous monitoring: AI models evolve as new data becomes available. To help organisations identify risks, improve performance and maintain compliance, they need to implement ongoing testing and monitoring.
Human oversight: Humans need to be involved in critical decisions as it helps organisations balance efficiency with judgement and expertise.
For example, an AI agent supporting compliance teams may be able to review large volumes of regulatory documents and identify potential risks. However, human experts are essential for validating decisions, managing exceptions and ensuring regulatory requirements are met.
3. Build AI around targeted use cases
Many organisations may feel tempted to launch multiple pilots and explore every new capability. However, without clear priorities, experimenting with AI can create fragmented solutions that fail to deliver meaningful business impact.
Organisations should focus on outcomes that directly support strategic goals.
This means asking themselves:
- Does this use case solve a meaningful business challenge?
- Can impact be measured through clear performance indicators?
- Does the organisation have the data, skills and governance required to scale AI?
- Will AI improve customer outcomes, operational efficiency or risk management?
For example, applying AI to automate a low-value administrative task may improve efficiency. However, using AI to improve fraud detection, optimise lending decisions or enhance customer personalisation could create greater strategic value.
In banking, some of the highest-value opportunities include:
Risk management: AI can analyse enormous volumes of transactions in real time, identifying unusual patterns and helping financial institutions respond faster to emerging threats.
Customer experience: AI-powered assistants can provide faster, more personalised support while enabling employees to spend more time on complex customer needs.
Operational efficiency: AI can reduce manual processing, improve accuracy and free employees to focus on higher-value activities.
Organisations need to identify the right opportunities in order for AI to deliver measurable business outcomes.
A leading global bank needing support to modernise its payment platforms, migrate to the cloud and achieve ISO 20022 compliance, whilst accelerating its AI adoption. They approached FDM for a solution that would build their in-house talent pipeline while reducing their reliance on contractor-heavy models. Within five months, 129 FDM Consultants supported their needs with teams pre-trained in critical skills including Agile delivery, DevOps, YAML and AI code validation. This resulted in reduced processing times through automated workflows and improved operational efficiency.
4. Build an AI-fluent workforce
Our research shows that 32% of businesses point out a shortage of specialist skills as a top barrier to tech adoption.
Many organisations are investing heavily in AI platforms and tools, but without the right skills, these investments risk producing limited business outcomes.
Banks need to consider skills development as part of their AI strategy from the beginning, not as a later training initiative.
This includes:
Building AI literacy across the workforce: Employees should understand how to use AI responsibly, including its capabilities, limitations and potential risks.
Developing specialist expertise: Organisations need professionals who can manage AI systems, oversee governance, analyse data and design effective AI-enabled workflows.
Creating continuous learning opportunities: As AI capabilities evolve, skills development cannot be a one-off project.
5. Keep humans in the loop
AI technology will struggle to deliver value without the help of humans. AI transformation requires clear communication from leadership, practical support for teams and a culture that encourages responsible experimentation.
Employees need to understand:
- Why AI is being introduced
- How it will support their work
- What responsibilities remain with people
- How they can develop the skills needed for the future
Without having this clarity, organisations risk creating resistance, inconsistent adoption and missed opportunities.
For banking leaders, the goal should be to build an AI-enabled workforce that understands how to combine technology and human expertise to achieve better outcomes.
Summary
AI has the potential to reshape banking, from improving operational efficiency and strengthening risk management to creating more personalised customer experiences.
However, achieving this potential requires a shift in mindset.
The banks that generate the greatest value from AI will not necessarily be those with the largest technology investments. They will be the organisations that successfully combine strategic vision, responsible governance, strong data foundations and a workforce prepared for change.
AI is becoming a fundamental business capability. For banking leaders, the priority is no longer deciding whether to adopt AI, but determining how to scale it effectively, responsibly and sustainably.
By focusing on outcomes, empowering people and building adaptable operating models, banks can move beyond AI experimentation and unlock meaningful transformation across the organisation.
How FDM can support
FDM supports organisations through a multi-tiered approach to building resilient defences against AI-powered threats.
Consultants from our IT Operations and Risk, Regulation & Compliance Practices possess the latest cyber defence tools and skills that matter to our customers and know what can affect them and what’s changing in the industry. The foundation for this is training and ongoing support.
Businesses that invest in people-powered, forward-looking strategies will not only stay ahead of evolving fraud tactics but also protect their organisations from financial and reputational damage.