The financial services sector is entering a new phase of transformation, driven not just by automation but by the need for smarter, continuously improving decisions. After years of investing in AI to increase efficiency, organisations are now focusing on how those decisions perform over time and how they can be refined in real time.
Findings from the Provenir 2026 Global Decisioning Survey highlight the scale of this shift. 77% of senior decision-makers say decision intelligence will be very valuable to their strategy over the next two to three years.
At the same time, 60% of organisations plan to invest in AI or embedded intelligence for decisioning in 2026, making it their top investment priority. The momentum is clear, with 75% already collaborating on AI-driven decision intelligence initiatives and a further 18% exploring partnerships.
From Automation to Continuous Improvement
Traditional AI approaches in financial services have focused on automation and efficiency. Models are deployed, results are measured periodically, and updates are made on a scheduled basis. While this has delivered operational gains, it often lacks the responsiveness required in today’s environment.
Decision Intelligence introduces a different model. It enables organisations to execute decisions at scale, measure outcomes continuously, and optimise performance in real time. Instead of relying on quarterly updates, firms can refine strategies based on live data and evolving conditions.
Interest in this approach is growing rapidly. 66% of organisations say they are very interested in using AI for strategy implementation and optimisation. This reflects a shift from using AI as a tool for execution to using it as a driver of strategic decision-making.
What Organisations Are Prioritising
As financial institutions adopt more advanced AI capabilities, their priorities are changing. The focus is moving beyond basic automation toward features that improve accessibility, speed, and transparency.
Natural language interaction is one of the most valued capabilities. 51% of organisations highlight the ability to use generative AI for natural language queries as a key feature. Overall, 92% say it is important to interact with data quickly using conversational interfaces, with 62% describing this as very important and 30% as moderately important.
This shift allows a broader range of users to engage with AI systems. Business teams, executives, and compliance staff can all access insights without relying on technical specialists.
Real-time decisioning is another priority, with 49% of organisations highlighting its importance. The ability to respond instantly across customer touchpoints helps improve consistency and reduce operational complexity.
Transparency is also critical. 50% of respondents say explainability of AI models is a top requirement, reflecting the need to justify decisions to regulators and stakeholders. In addition, 47% emphasise the importance of integrating AI with existing systems and data sources, rather than replacing infrastructure entirely.
Measurable Business Benefits
The adoption of Decision Intelligence is delivering tangible results across multiple areas of the business.
Operational efficiency is the most widely cited benefit, with 62% of organisations reporting improvements. Automated decision-making reduces manual intervention, accelerates processes, and lowers costs while maintaining consistency.
Customer experience is also improving. 52% of organisations say faster decisions and more personalised interactions are enhancing customer journeys. In a competitive market, the ability to deliver seamless and responsive experiences is increasingly important.
Model accuracy is another key area of impact. Approximately 58% of organisations report improvements in the accuracy of their models and strategies. Continuous learning allows systems to adapt and refine predictions over time.
The speed of innovation is also increasing. 56% of respondents say they can deploy new decision strategies more quickly, enabling them to respond to market changes and competitive pressures with greater agility.
A Continuous Decisioning Cycle
Organisations begin by shaping strategy based on real performance data. Decisions are then executed in real time across customer interactions, using data, context, and historical insights. Outcomes are measured and linked directly to key business metrics such as revenue, risk, and profitability.
The system then learns from these outcomes and refines strategies accordingly. This creates a self-improving cycle where each decision contributes to better future performance.
Expanding Access Through Natural Language
The growing importance of natural language interaction is transforming how organisations use AI. With 92% of firms prioritising this capability, it is becoming a central feature of modern decisioning platforms.
Natural language querying allows business users to explore data without needing technical skills. Executives can access insights instantly, operations teams can investigate issues in real time, and compliance teams can review decisions more effectively.
This broader access also helps address concerns around explainability. When more people can interact with AI systems and understand how decisions are made, transparency improves across the organisation.
Addressing Key Challenges
Decision Intelligence is helping organisations overcome several long-standing barriers to AI adoption.
Explainability is improved by providing clear visibility into how decisions are made and how they perform. Governance becomes more manageable when decisions are directly linked to business outcomes. Integration challenges are reduced through platforms that work with existing systems rather than replacing them.
Speed is another critical factor. Continuous optimisation allows organisations to respond more quickly to changing conditions, addressing challenges such as fraud detection, where 50% of firms cite speed as a major obstacle.
A Strategic Shift in Focus
The data points to a clear trend. Around 77% of organisations see Decision Intelligence as very valuable, 75% are already implementing it, 66% are interested in using AI for strategy optimisation, and 60% are planning further investment in 2026.
This represents a shift in how financial institutions view AI. Traditional approaches focused on speed and automation. Decision intelligence focuses on outcomes and continuous improvement.
As the industry evolves, organisations that build systems capable of learning and adapting over time will be better positioned to compete. The ability to make smarter decisions consistently and at scale is becoming a defining factor in long-term success.