Evolving your FI operating model in the age of AI
Financial institutions cannot capture the full value of AI using operating models designed for a pre-AI world. Realizing value requires rethinking what the work is, how to organize around it, and how to bring it to life.
Key takeaways
- Integrating AI into an operating model is not about adding tools to existing processes. It requires rethinking work, the organization around that work, and how teams execute and learn.
- As AI moves from experimentation into day-to-day operations, organizations that redesign both workflows and operating models will be better positioned to adapt, scale, and compete.
- The end state is an integrated human-agentic workforce: AI handles routine synthesis, monitoring, workflow acceleration, and exception detection; people focus on judgment, oversight, risk trade-offs, relationships, and accountability.
- Operating model redesign should start with high-volume, judgment-rich workflows, then scale through outcome-led teams, embedded controls, agile execution, and continuous change management.
AI is changing how financial institutions (FIs) work, organize, and compete. But new operating models cannot be built by adding AI to old structures. They must be designed for faster decisions, smarter execution, and clearer accountability.
Operating models integrated with AI can improve decision quality, strengthen trust, and help organizations move faster while managing risk. Still, according to Deloitte’s State of AI in the Enterprise, 84% of companies have not redesigned jobs or the nature of work itself around AI capabilities.
For financial services leaders, the practical challenge is not whether AI can create value. It is how to redesign the model so value is captured safely, repeatedly, and at scale. That requires three shifts: rethinking the work, rethinking the structures around the work, and rethinking the ways of working that bring the model to life.
1. Rethinking the work
AI is forcing organizations to rethink how work gets done and where humans create value. Many roles in financial services were designed around activities such as gathering information, analyzing data, coordinating stakeholders, and processing transactions. As AI assumes more of these activities, organizations have an opportunity to redesign work around where humans create the greatest value. The challenge is no longer simply automating tasks. It is determining how work should be distributed between humans and AI to improve outcomes, manage risk, and increase capacity.
Decompose work into tasks, decisions, and judgment
Historically, many jobs combined information gathering, analysis, coordination, and decision-making into a single role. AI changes that assumption. Leaders should examine the work itself and determine which activities can be automated, which require human oversight, and where judgment remains essential. This becomes the foundation for redesigning roles and responsibilities.
Redesign end-to-end workflows
The goal is not simply to make individual tasks faster. It is to rethink how work moves through the organization. AI can reduce the effort required to gather information, validate data, coordinate stakeholders, and move work through approvals. Organizations that redesign workflows around these capabilities can simplify handoffs, reduce delays, and improve decision speed and consistency.
Elevate human contribution
As AI assumes more routine analysis and coordination activities, employees spend less time preparing information and more time applying judgment. Human effort increasingly shifts toward managing exceptions, making complex decisions, navigating risk trade-offs, strengthening client relationships, and maintaining accountability for outcomes. The objective is not to remove people from the process, but to ensure their time is spent where it creates the most value.
2. Rethinking the structures around the work
As work changes, organizational structures must evolve with it. Most FIs were built around functions that optimize individual activities such as operations, technology, risk, compliance, and customer service. While these capabilities remain essential, customers don’t care how work is organized within the enterprise—they care how quickly and effectively their need is resolved. AI creates an opportunity to organize more directly around outcomes, bringing together the expertise required to deliver results end-to-end.
Organize around outcomes, not functions
Traditional operating models often prioritize functional efficiency. Future operating models will increasingly emphasize customer, employee, and business outcomes. Rather than optimizing individual parts of the process, organizations can align teams around the end-to-end journeys that create value.
Build multidisciplinary teams
Delivering outcomes requires expertise from across the enterprise. Successful organizations bring together business, operations, technology, data, risk, compliance, legal, and change capabilities within the same team. By embedding expertise directly where decisions are being made, organizations reduce handoffs, improve collaboration, and accelerate execution.
Clarify accountability and decision rights
As AI becomes embedded into workflows, accountability becomes even more important. Leaders need to be clear about which decisions can be delegated, where AI can operate within defined guardrails, and when human intervention is required.
This is particularly important in financial services, where risk management and control frameworks have historically been designed around human-led decision making. As AI assumes a greater role in day-to-day operations, banks and insurers will need to evolve their risk appetite, governance approaches, and decision-making frameworks to support a human-agentic workforce while maintaining appropriate oversight, regulatory compliance, and customer trust. Governance, funding, performance management, and risk oversight should reinforce ownership of outcomes rather than functional priorities.
3. Rethinking the ways of working
Redesigning work and organizing teams around outcomes are important first steps, but they are not enough on their own. To realize the full value of an AI-enabled operating model, organizations must also rethink how work is managed, governed, and continuously improved.
As organizations gain access to faster insights and near real-time operational data, agility becomes a critical organizational capability—not just a delivery methodology. FIs will need management systems that enable teams to make decisions faster, adapt to changing conditions, and learn continuously, while maintaining the governance, controls, and accountability required in highly regulated environments.
Governance: move decisions closer to the work
Traditional governance models often rely on functional handoffs, approval gates, and periodic reviews. While these controls remain important, they can slow decision-making and create friction across the customer journey.
In an AI-enabled operating model, organizations increasingly organize around outcomes rather than functions. Cross-functional teams bring together business, operations, technology, data, risk, legal, and compliance expertise from the start, supported by AI that helps monitor work, surface risks, and identify bottlenecks. Governance shifts from reviewing work after the fact to setting guardrails that allow teams to move faster while maintaining appropriate oversight.
Cadences: shorter planning and learning cycles
AI increases the pace at which organizations can learn and adapt. Annual planning cycles alone are often too slow.
Many organizations are adopting quarterly planning cycles tied to measurable business, customer, operational, and risk outcomes. Teams use regular delivery cadences to test changes, learn from results, and adjust priorities as conditions evolve. AI provides faster visibility into performance and operational signals, helping teams make more informed decisions throughout the quarter.
Leadership: from directing work to enabling outcomes
As AI becomes part of everyday work, leadership responsibilities evolve. Leaders spend less time coordinating activity and more time setting direction, establishing guardrails, clarifying priorities, and ensuring teams have the autonomy to act.
Success increasingly depends on creating an environment where human judgment and AI capabilities reinforce one another. Leaders must think beyond functional silos, empower teams around outcomes, and remain accountable for critical decisions involving risk, customer experience, and trust.
Organizations that succeed will treat AI not as a technology initiative, but as a new management system—one built around faster learning, clearer accountability, and continuous improvement.
What leaders should do now
Shift from AI use cases to re-imagining end-to-end workflows that create business outcomes
To realize the benefits of scaled AI, embed it into how the organization makes decisions, allocates capacity, governs risk, designs work, and measures performance. AI should be embedded in the workflows where the business creates value—not treated as a separate technology initiative. Business teams should have the flexibility to innovate close to the work, supported by clear enterprise guardrails for risk, governance, and technology standards.
Identify the 5–10 highest-value workflows
Start with customer journeys, value streams, and critical decisions where speed, coordination, and trust matter most. Maintain strong individual functions but reduce the distance between them by orienting execution around shared outcomes.
Design for an integrated human-agentic workforce
Be explicit about where human judgment is essential, where AI can act within guardrails, and how work moves between the two. The clearer the model, the safer and more scalable it becomes. A human first AI enabled workforce puts human skills at the forefront—requiring organizations to double down on the capabilities that will be essential in the future—relationship building, storytelling, communication, creativity, empathy and negotiation, influencing and coaching.
Plan, learn, and adapt more frequently
Build the operating model so teams can pilot, learn, and scale in shorter cycles. That means product-style ownership, clear priorities, empowered cross-functional teams, embedded controls, and regular feedback loops that turn live execution into continuous improvement.
Drive adoption through iterative change management
Communicate how employees’ work will change, what decisions will remain human-led, how AI will be governed, and how skills will evolve. Adoption should be managed through ongoing engagement, training, and feedback as the model learns and scales. Applying a Minimum Viable Change (MVC) approach can help break change into smaller, more manageable iterations.
How Deloitte can help
Deloitte can help you reimagine your firm's operating model around a human-agentic workforce—redesigning processes, roles, ways of working and governance. Applying proven frameworks to pinpoint where AI creates value and building practical roadmaps using tools like Deloitte Enterprise AI Navigator enables organizations to cut AI strategy and design time by up to 50%.
FIs that successfully rethink their operating models will be better positioned to translate AI investment into measurable business value—while maintaining the trust, governance, and accountability expected in financial services.
