Discover why governance must be considered from the initial stage of AI adoption and how it can help you achieve your goals.

As the use of Artificial Intelligence (AI) increases, the need for ethical, legal, and governance compliances is paramount to ensure a positive impact on business decision-making. As the need for AI grows, from fortune 500 to start-ups have started to look more into safety standards and governance guidelines to manage their risks. To better understand AI governance, please refer to the AI Governance page on our page.

 From the initial stages of AI adoption to implementation, companies must ensure that their AI systems adhere to safety standards and governance guidelines to minimize risk. Implementing the correct governance measures will ensure that AI systems are used in an efficient, secure, and ethical manner. Let us discuss how to implement governance in each stage of AI adoption.


  Organizations must come together and understand the need for AI implementation in their business.  The lineage of data or models at any given time must be explained in the framework documentation as part of AI governance solutions.

 Each business needs a unique adoption process that is tailored to meet their business objectives, and each strategy should have its specific governance criteria and steps. This will guarantee that firms’ governance objectives are consistent throughout the entire organization.

The target audiences for the framework are

  1. AI Developer → any group that creates the model
  2. AI Provider → any business that provides for model management
  3. AI User → any consumer who uses the developed model or from provider
  4. AI Data Provider → any business that involves in data collection, pre-processing, or training for any of the above user


There are several important points to remember when deploying a governance framework.

  1. Identify roles and responsibilities: It’s critical to spell out each stakeholder’s obligations within the governance structure.
  2. Create a strong communication strategy: To make sure that all stakeholders are aware of updates and developments in the governance framework, a solid communication strategy should be created.
  3. Create accountability: Ensure that the governance framework creates accountability for each stakeholder. Everyone will be held accountable for their deeds and choices as a result.
  4. Document processes: Good governance requires thorough documentation. The stakeholders should have access to all documentation about processes and procedures.
  5. Monitor and assess: Consistently check in on and assess the governance framework’s performance. This can assist in identifying problem areas and necessary course corrections.
  6. Utilize technology: Technology can be used to automate and streamline governance processes. This can help reduce costs and improve efficiency.
  7. Be adaptable: Be flexible and open to change as the environment changes. This will ensure that the governance framework remains relevant and effective.


After a project or product has been finished and released, post-production governance processes and procedures are put into place. It is intended to make sure that the project or product complies with all relevant rules and legal requirements, as well as the targeted performance and quality standards.

Post-production governance also includes the monitoring of production costs, the evaluation of production performance, and the development of strategies for improving the quality of post-production activities. This helps organizations ensure that they are getting the most out of their technology investments and address any potential risks associated with their IT initiatives.


Financial services and healthcare are the most regulated industries with standard principles and policies. Non-compliance to legal and ethical guidelines may cost the reputation of the banks and the credibility of their operations.

Let us consider a use case scenario of the banking sector and governance implementation in each stage of their AI adoption


The design scope of adopting governance in the banking industry often include developing a governance framework that specifies the obligations of various parties. This includes identifying the roles and responsibilities of regulators, banks, and other financial institutions.  It also describes the steps and processes that these participants must take to achieve efficient governance. 


Organizations gather information from a variety of sources during the data-collecting phase of adopting governance in the banking industry, including customer complaints, internal and external audits, surveys, interviews, and operational data. To ensure maximum accuracy and security, we must maintain a data mapping document, use full datasets instead of sample data, and thoroughly document the data lineage.

 Additionally, customers have the right to deny the use of their data and how their data is processed. By keeping these practices in mind, we can ensure that data aggregation is done with the utmost accuracy and security.

General Data Protection Regulation (GDPR) states that Privacy Impact Assessment for  data privacy and personal information algorithm for decision-making should be assessed for fairness


Data gathered from diverse sources is utilized in the model-building stage of adopting governance in the banking sector to create models that would aid in identifying and quantifying the risks connected to banking activities. 

Before each deployment, the models, their approval process, and the data set must be completely reviewed and signed off by management. The governance team should document the review process and verify if the built algorithm is consistent with the original specifications and be able to answer internal and external auditing at any point in time


Once the AI system has been implemented and the governance structure is in place, the bank would need to monitor the system regularly to ensure that it is functioning correctly and that it is complying with the governance policies. This would involve collecting data from the AI system, analyzing it, and using the results to make adjustments to the system as necessary.

The KPI metrics and retraining metrics must be verified regularly and must be created in a feedback loop with proper explanation.


In the end, governance implementation in AI business is crucial for success. The application of governance concepts contributes to the ethical, legal, and responsible conduct of AI initiatives. By ensuring that AI initiatives are clearly defined, well-defined, and properly monitored, businesses can maximize their AI investments and achieve their desired outcomes. 

Governance further makes ensuring that AI initiatives are carried out in a manner that complies with legal standards and privacy legislation. Businesses can make sure that their AI initiatives are successful, moral, and advantageous to all stakeholders by investing the time to ensure that governance is correctly implemented.