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Artificial intelligence is becoming embedded in how financial institutions operate. The point of differentiation is increasingly defined by how these capabilities are integrated into workflows, data environments, and decision-making processes.
Building on the discussion in “AI in Balance Sheet Management: How Agents Enable Autonomous ALM Workflows”, this article examines the progression of AI adoption within financial institutions and how it evolves from individual usage to organizational capability.
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In practice, the adoption of AI does not occur as a single step. It follows a progression that reflects both the expansion of capability and the depth of integration within the organization. What begins as individual use of general-purpose tools evolves into systems that are connected to institutional knowledge and operational processes, and ultimately into workflows that operate with increasing continuity.
This progression provides a structured way to understand how AI moves from isolated usage to organizational capability.
Phase 1: Tools and Individual Productivity
The initial phase of AI adoption is characterized by individual usage. Employees begin to use general-purpose AI systems to support tasks such as drafting documents, summarizing information, or conducting research. These tools are typically accessed through conversational interfaces and operate without direct integration into the organization’s systems.
The impact at this stage is immediate. Tasks that previously required manual effort can be completed more quickly, and individuals are able to increase their productivity across a range of activities. The barrier to entry is low, and adoption often spreads organically within teams.
Despite these gains, the scope of impact remains limited:
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The AI operates without access to institutional context, including internal data, regulatory frameworks, or historical decisions
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Each interaction requires the user to provide the relevant information, and the output is constrained by what is included in the prompt.
This creates a model of usage that is inherently fragmented. Different individuals may use AI in different ways, leading to variability in output and a lack of consistency across the organization. The benefits are real, although they remain localized and difficult to scale.
Phase 1 is characterized by productivity at the individual level, where AI is used as a tool to support tasks. At this stage, it operates outside the organization’s systems and processes, rather than as an embedded capability.
Phase 2: Agents with Knowledge and Action
The second phase introduces a different model of interaction. AI systems are extended with access to domain knowledge and operational tools, allowing them to operate within the context of the organization.
At this stage, agents combine two elements: knowledge and action. Domain knowledge provides the interpretative layer, including regulatory frameworks, internal policies, and historical data. Operational access enables interaction with systems, allowing the agent to retrieve data, perform calculations, and generate outputs.
These capabilities can be understood through two complementary roles:
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Informational agents
These agents provide responses grounded in institutional knowledge. They can explain regulatory metrics, interpret internal frameworks, and answer questions based on the organization’s own data and documentation. -
Operational agents
These agents interact with systems to perform tasks. They can retrieve data from internal platforms, run calculations, and generate outputs such as reports or analyses.
The combination of these roles creates a model in which AI is no longer limited to responding to prompts. It becomes part of the analytical infrastructure, capable of both understanding context and acting on it.
This integration changes how work is produced. Analysis can be generated directly from institutional data, without requiring manual preparation or consolidation, and questions can be answered in a way that reflects the organization’s actual position, rather than a generic interpretation.
The interaction model also becomes more consistent. Instead of each user providing context manually, the system operates with a shared understanding of the organization’s data and frameworks. This reduces variability and improves the reliability of outputs.
The effect of combining knowledge and action is not additive. When both elements are present, agents are able to perform tasks that neither capability would support independently. Context enables interpretation, and system access enables execution. Together, they expand the range of tasks that can be performed within the organization.
As it is, Phase 2 is defined by integration. AI becomes part of how analysis is produced and how systems are used, rather than an external tool applied to individual tasks.
In practice, this stage is where specialized solutions begin to emerge. Platforms such as Mirai AI layer are designed to embed agents directly within balance sheet management environments, combining access to institutional data, regulatory frameworks, and analytical models. This enables financial institutions to move beyond generic AI usage and toward systems that produce analysis grounded in their own operational and regulatory context.
Phase 3: Automation and Coordinated Workflows
The third phase extends this model into automation. Instead of responding to individual requests, AI systems begin to execute workflows based on predefined conditions.
Tasks are triggered by events such as data updates, scheduled processes, or changes in key indicators. Once triggered, workflows can proceed through multiple steps without requiring manual initiation. These workflows can include data retrieval, analysis, validation, and the generation of outputs.
Two types of structures are typically involved:
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Deterministic workflows
These workflows follow predefined sequences of steps. They are suited to structured processes such as reporting, data validation, or routine analysis. -
Coordinated agent systems
In more dynamic scenarios, multiple agents can interact to complete a task. Different agents may be responsible for data retrieval, analysis, and output generation, working together as part of a coordinated system.
A remarkable characteristic of this phase is continuity. Workflows operate on an ongoing basis, rather than being initiated manually for each task. This allows analysis and reporting processes to be maintained in a more consistent and timely manner.
The coordination between agents also introduces a different operational model: tasks can be distributed across specialized components, each performing a specific function. The system as a whole produces outputs that reflect the combined activity of these components.
This does not remove the role of human involvement. Instead, it changes how it is applied. Human input shifts toward defining workflows, setting parameters, and reviewing outputs, while the execution of tasks is increasingly handled by the system.
Phase 3 centers on automation, with AI integrated into continuous workflows that support processes extending beyond individual interactions.
How to Move from AI Tools to Autonomous ALM Workflows
Discover how agents and automation are reshaping balance sheet management.
The Progression of AI Adoption in Financial Institutions
The three phases represent a progression in both capability and integration. Each phase builds on the previous one, expanding the range of tasks that can be performed and the degree to which AI is embedded within the organization.
This progression can be summarized as follows:
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Phase 1: Individual productivity through general-purpose tools
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Phase 2: Agents integrated with knowledge and systems
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Phase 3: Automated workflows operating continuously
The transition between phases is gradual, with organizations often operating across multiple stages at the same time, as different functions or teams adopt AI at varying levels of maturity.
This progression reflects a broader move toward embedded capability, where AI becomes part of the systems and processes that support the organization’s operations, rather than remaining a tool used at the individual level.
Implications of the AI Adoption for Financial Institutions
Understanding this progression provides a framework for assessing where an organization stands and how it may evolve. In financial institutions, this is reflected in how analysis is produced, how reporting is structured, and how decisions are supported. Functions that depend on integrated data, regulatory context, and ongoing monitoring are particularly affected.
As organizations move beyond Phase 1, Phase 2 introduces a model based on integration. Agents connect domain knowledge with access to systems, allowing analysis to be generated directly from institutional data. Work becomes more consistent across teams, as processes are supported by shared context and system interaction. This stage also brings greater focus on governance and the reliability of outputs.
Phase 3 extends this model through automation. Workflows operate based on data changes, schedules, or predefined conditions, with coordinated agents supporting the execution of tasks across systems. This places greater emphasis on architecture, as systems, data, and analytical processes need to interact in a continuous and structured way.
In this context, the role of specialized platforms becomes increasingly relevant. Software such as Mirai AI capabilities is designed to operate within governed environments, where access to data, models, and regulatory frameworks is controlled and auditable. This supports not only the production of analysis but also its consistency, traceability, and alignment with internal policies and supervisory expectations.
Adoption varies across institutions depending on infrastructure, access to data, and broader strategic priorities, and over time, these differences are likely to be reflected in operational performance.
AI Adoption as Organizational Capability
AI adoption in financial institutions follows a structured progression, where tools support individual productivity, agents integrate knowledge with system interaction, and automation enables continuous workflows.
This framework provides a way to understand how AI evolves within the organization, moving from isolated usage toward embedded capability. As integration deepens, AI becomes part of how work is structured and executed across functions.
The relevance of this progression is reflected in its application. The integration of AI into workflows, systems, and analytical processes is becoming part of the operational model of financial institutions, with its impact already visible across functions.
As AI adoption progresses, financial institutions are increasingly evaluating how to embed these capabilities within their existing architecture, often through platforms such as Mirai AI that are designed to integrate data, models, and workflows into a unified analytical environment.
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3 Phases of AI adoption that Will Redefine How ALM Teams Work
FAQs: AI Adoption in Financial Institutions
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What are the phases of AI adoption in financial institutions?
AI adoption typically evolves through three phases: individual tool usage, agents integrated with data and systems, and automated workflows operating continuously. -
What is the difference between AI tools and AI Agents?
AI tools support individual tasks without system integration. AI agents combine institutional knowledge with system access to perform analysis and actions.
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Why are AI agents important for financial institutions?
They enable analysis based on internal data and regulatory context, improving consistency, scalability, and operational efficiency. -
What does automation mean in AI adoption?
Automation refers to workflows triggered by events or schedules, where AI executes tasks continuously without manual initiation. -
How does AI impact ALM and balance sheet management?
AI improves how analysis is produced, enabling real-time insights, consistent reporting, and integration across systems and data sources.
