As AI adoption accelerates across financial services, firms are presented with an ever-growing number of AI platforms, each designed to address specific use cases and business challenges.

It is increasingly common for financial services firms to pilot, deploy and integrate multiple AI platforms across the business. However, when a platform is no longer required, it is essential that it is properly decommissioned in a controlled and secure manner. Failure to remove integrations, revoke user access, and securely manage organisational data can leave behind unnecessary security, compliance, and operational risks, creating a layer of AI-related technical debt that may prove costly to address in the years ahead.

Technical debt

But what exactly is technical debt?

Technical debt is the cost of taking shortcuts in technology, systems, or processes to achieve a quicker outcome today, only to create additional work, complexity, or risk in the future.

For example, a business might rapidly deploy a new AI platform without implementing the appropriate governance, documentation, or security controls. While this can speed up adoption, it can also lead to technical debt that later requires remediation, such as access reviews, security improvements, integration clean-up, or even platform redesign.

Many organisations pilot multiple AI platforms before determining which solutions are best suited for the business. This can create technical debt if a robust offboarding process is not followed. Failure to properly remove integrations, revoke access, and manage organisational data can leave behind unnecessary complexity, security risks, and potential data exposure.

Over time, these residual connections, unused accounts and unmanaged dependencies can increase operational overheads, complicate governance efforts and weaken an organisation's overall security posture.

Onboarding and offboarding – One cannot exist without the other

Onboarding and offboarding are two sides of the same process. Onboarding ensures a vendor or platform is assessed, approved, and introduced securely into the organisation, while offboarding ensures it is removed in a controlled manner when it is no longer required. Together, they help organisations manage risk, protect data, and maintain control throughout the vendor lifecycle.

A strong offboarding process also plays a critical role in reducing technical debt. By removing unused integrations, revoking access, decommissioning platforms, and cleaning up dependencies, organisations can prevent the build-up of unnecessary complexity and unmanaged technology. This not only reduces long-term maintenance overheads and security risks, but it also ensures that AI pilots which are not adopted do not leave behind residual connections, data exposure risks, or operational challenges.

AI onboard and offboard process – A practical guide

A structured AI onboarding and offboarding process helps organisations adopt and remove AI platforms in a secure, controlled, and risk-managed manner. It ensures appropriate due diligence is performed before deployment and that access, integrations, and data are properly managed when a platform is no longer required.

A typical process includes:

1. Define the goal & desired outcome – Clearly identify the business problem being solved and the objectives the AI platform is expected to achieve. Establishing clear success criteria at the outset helps keep the pilot focused on delivering measurable business value and provides a benchmark against which outcomes can be assessed.
2. Platform review – Assess the platform’s security, privacy, compliance, and governance capabilities. This should include understanding how the data is protected, how access is managed, how regulatory requirements are supported and which controls are available to help govern its use within the organisation.
3. Permissions & access assessment – Determine which data, systems, and users the platform will require access to. This assessment should identify the level of access needed, the permissions being requested, and any potential risks associated with connecting the platform to business data and services.
4. Define guardrails – Establish the security controls, acceptable use policies and governance requirements that will govern the use of AI within the organisation. These guardrails help ensure the platform operates within the organisation’s risk appetite while protecting sensitive data, supporting compliance obligations and promoting responsible use of AI.
5. Pilot testing – Conduct a controlled proof of concept with clearly defined success criteria. This allows the organisation to validate the platform’s business value, usability and effectiveness while assessing any security, compliance, or operational risks before wider adoption.
6. Evaluate findings – Review the outcomes of the pilot against the original objectives, success criteria, and business requirements. This stage should assess the overall value delivered, identify any security or operational risks, and determine whether the platform is suitable for wider adoption.
7. Offboarding process – If the platform is not adopted, a structured offboarding process should be followed to ensure it is securely removed from the organisation. This includes revoking access, removing integrations, managing organisational data appropriately, and verifying that no residual connections, permissions or dependencies remain.

This lifecycle approach helps organisations align AI initiatives with clearly defined business objectives while maintaining appropriate security, governance, and operational controls throughout the platform lifecycle. By defining desired outcomes, assessing risks, implementing guardrails and formally evaluating results, organisations can make informed decisions about AI adoption.

Equally important, a structured offboarding process helps prevent unsuccessful pilots from creating technical debt, unmanaged access risks, or unnecessary data exposure. Together, these practices enable organisations to innovate with confidence while maintaining control, compliance, and operational resilience.

The key to long-term AI success

The rapid growth of AI presents enormous opportunities for organisations, but it also creates new risks that must be actively managed. As businesses pilot and adopt an increasing number of AI platforms, the challenge is no longer simply enabling innovation, it is maintaining control, governance, and operational resilience as adoption scales.

Without a structured approach, organisations risk creating technical debt, unnecessary complexity, unmanaged access, and potential data exposure that can take years to remediate. By embedding robust onboarding and offboarding processes throughout the AI lifecycle, organisations can ensure platforms are assessed appropriately, deployed responsibly, and decommissioned cleanly when no longer required.

Ultimately, successful AI adoption is not just about selecting the right technology. It is about establishing the governance, processes, and controls needed to manage that technology throughout its lifecycle. Organisations that do this well will be better positioned to realise the benefits of AI while minimising risk, reducing technical debt, and maintaining long-term security and compliance.