Agentic AI
How to buy Agentic AI in finance without vendor lock-in
Learn the signs of vendor lock-in, how Agentic AI offers flexibility and transparency, and what to ask before committing to a new AI solution.
Every finance team is under pressure to “get AI in the door.” What keeps decision makers awake is not the proof-of-concept but the five-year contract that could block a justifiable exit. Vendor lock-in is one of the quietest risks in finance transformation. The unspoken fear is waking up to find that rules, data and workflows now live inside one black box you cannot swap or scale.
Many organizations and businesses, especially in the financial sector, face these challenges when adopting AI solutions, as they must balance innovation with flexibility and control. Artificial intelligence is transforming financial services by enabling advanced systems that enhance decision-making, efficiency, and strategic planning.
Agentic AI is setting a new standard for financial operations by enabling systems that can make autonomous decisions and execute complex tasks with minimal human intervention. Unlike traditional AI systems, which often rely on fixed rules and require frequent human input, agentic AI systems leverage advanced machine learning algorithms and large language models to analyze vast amounts of data in real time.
This empowers financial services organizations to improve risk management, streamline operational efficiency, and significantly reduce human error. By automating complex workflows, agentic AI allows financial institutions to make faster, more accurate decisions and respond dynamically to changing market conditions.
The result is a more agile, resilient, and innovative financial services sector, where organizations can focus on strategic growth while AI systems handle the heavy lifting of data analysis and task execution.
Modern AI tools look different from legacy OCR or RPA, yet the risk pattern is the same. Traditional systems often rely on predefined rules, limiting flexibility and adaptability, whereas agentic AI can go beyond these constraints to handle more complex and dynamic scenarios.
These traps rarely show in the pilot. They appear when you need to adjust scope, change providers or comply with new regulation. At these threshold moments hard dependencies like retrainable models, proprietary data structures, or tightly coupled workflows are not what a CFO, or anyone for that matter, is eager to deal with.
The good news: It doesn’t have to be this way. New architectural patterns in enterprise AI are making it possible to get the benefits of autonomous execution without committing to rigid platforms or black-box setups. An AI agent can provide explainable processes and enhance trust within financial institutions by making operations more transparent and compliant.
Traditional SaaS still ships as one logical block, even when it spans dozens of microservices. All data passes through a single orchestration layer, which is owned by the vendor. As a buyer you can tweak surface settings, but the core business logic is locked in microservices that drive the automation. These microservices depend on pre-defined, rigid data models.
Example: you can raise a tolerance or confidence threshold from 5 % to 7%, but to respond to an unexpected VAT rule change with newly introduced tax rates and make sure it’s handled correctly before it causes downstream chaos, you would have to deploy a new microservice, something that requires access to the source code, which only the vendor has.
With an agentic setup the “control tower” disappears. Each action, checking VAT, matching a PO, posting a journal runs inside its own small agent that carries clear, editable instructions. These agents are best described as AI-driven agents: autonomous, decision-making software embedded directly within financial workflows, such as treasury management, reconciliation, and financial reporting.
Autonomous AI agents can independently execute and manage financial tasks, detect threats, and adapt to changing data without human instructions. Those instructions live in human readable prompts. Your finance team can read or change it the same way they edit a policy document, version control included.
If the agent requires access to your data, they communicate with a MCP server which has access to this data.
Any modern agentic solution enables the use of such MCP servers for data access. Agents then hand results to one another through lightweight agent-to-agent (A2A) messages, not through a vendor owned orchestration layer.
Autonomous decision making allows these agents to analyze data, execute actions, and manage complex tasks without human intervention, improving speed, accuracy, and compliance in financial operations.
Agentic AI supports the finance function by enabling flexible, auditable processes that improves finance planning and performance management, allowing organizations to adapt quickly to change while maintaining oversight.
Example: If a mid-year VAT rate is introduced, you adjust the prompt of the VAT-check agent, add the new tax rate to your internal data models, slot the updated agent into the flow, and it starts enforcing the new rule and tax rate on the very next run. No waiting, no hidden code, no extra licence or service hours. Agents can quickly adapt to new data, ensuring that changes in regulations or business requirements are immediately reflected in operations.
The AI vendor landscape in finance is both dynamic and complex, reflecting the evolving landscape in which financial institutions must adapt to rapid advancements in AI and agentic technology. For finance teams, selecting the right partner requires careful consideration of several key factors. Vendor expertise in financial services, the scalability and flexibility of their technology, and the quality of ongoing support are all critical to long term success. Agentic AI is transforming how the modern financial institution delivers core services, impacting everything from fraud detection to customer experience.
Equally important is the vendor’s approach to security, data privacy, and regulatory compliance, areas that are non-negotiable in the financial services industry. Conducting a thorough market analysis, including evaluating references and case studies, helps finance teams identify vendors that align with their strategic goals and risk appetite. As financial institutions operate within this AI ecosystem, they must integrate and oversee AI systems to improve decision-making, compliance, and efficiency.
If your organization is rolling out automation in shared services, multi-ERP environments, or regulated jurisdictions, then agentic to you would mean that automation can be auditable and adaptive, not just efficient. Agentic AI transforms financial workflows by automating complex processes, improving efficiency, and enabling real-time decision-making across financial operations.
Next time into an RFP, make it easy for your teams to check if they are headed for paying for a sales pitch or an actual true agentic solution, by having them ask:
A “no” on any of those questions would be a lock-in signal.
AI agents are at the heart of agentic AI’s transformative impact on the financial sector. These intelligent systems enable financial institutions to act independently, making autonomous decisions based on real-time analysis of vast amounts of data.
AI agents are designed to identify potential risks, adapt to evolving market trends, and execute actions that improve risk management, all without the need for constant human intervention. By automating repetitive tasks such as data entry, reconciliation, and processing, AI agents free up valuable human resources to focus on different strategic initiatives. This not only improves operational efficiency but also allows financial institutions to respond proactively to new opportunities and challenges, ensuring they remain competitive in a rapidly changing environment.
An agentic, protocol based approach keeps control in the hands of finance, risk and process owners from day one, even as your systems evolve. You get:
Adopting agentic AI includes increased efficiency, adaptability, transparency, and cost-effectiveness in process automation.
Agentic AI can handle large amounts of data and extract key insights, improving finance planning and performance management by enabling more accurate forecasting and better resource allocation, while adapting to market changes in real time. Through continuous learning, agentic AI systems refine their capabilities and decision accuracy over time.
Reinforcement learning allows these systems to improve by interacting with their environment, making them more effective in complex and dynamic settings. As self learning systems, agentic AI can adapt to new threats and data, enhancing their ability to detect anomalies and respond to emerging risks.
AI adoption is no longer about if but how safely. Systems that bury your policies inside proprietary models look like a good deal on day one, but turn costly on day three-hundred, because they assume your rules and data will not change. Agentic design assumes the opposite, and makes change easy.
The future of agentic AI in finance is both promising and dynamic, with rapid advancements poised to reshape the industry.
As agentic AI systems become more sophisticated, financial institutions will be able to analyze even greater volumes of data, make more nuanced decisions, and execute increasingly complex tasks with minimal human oversight.
This evolution will enable the delivery of highly tailored solutions, improved operational efficiency, and enhanced risk management across the financial sector. However, as the AI landscape evolves, it is crucial for financial institutions to prioritize ethical considerations and maintain transparent, responsible practices. By adopting clear governance frameworks and staying attuned to emerging trends, financial services organizations can harness the full potential of agentic AI; driving innovation, supporting business growth, and maintaining a competitive edge in an ever-changing market.
Stay tuned for the next article in our mini-series: The Possible AI – Auditable, Governable, and Process-Friendly.
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Give them a try, and see how agentic puts the numbers, and the control, back in your hands.
Finance teams can avoid vendor lock-in by choosing agentic AI platforms that separate business logic from code, use open protocols like MCP, and allow teams to edit prompts without retraining models or relying on proprietary orchestration layers.
Explainability ensures that every AI-driven decision, such as tax calculations or reconciliation, is auditable, traceable, and transparent, enabling compliance and reducing the need for manual control workarounds.
AI agents are modular and reusable across systems, allowing finance teams to scale processes like VAT checks or journal posting across multiple ERPs or regions without duplicating development or sacrificing control.
Key features include:
This will ensure flexibility, governance, and reversibility of automation decisions.
Book a demo with us and explore how Agentic AI can drive real transformation.
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