AI governance roadmap: Best practices for responsible AI innovation, efficiency and AI Act compliance

Now it’s time to act on the EU AI Act. The world’s first comprehensive AI regulation is officially in force and its transition periods are approaching. Ensuring AI literacy is one of EU AI Act’s requirements that came to force 1 August 2024 with transition period ending on 2 February 2025, covering requirements for AI literacy and prohibited AI systems.

Getting ready early makes your journey to compliance much smoother. Getting ready at the eleventh hour, on the other hand, can result in operational disruptions, if your systems aren’t compliant before the rules apply.

And besides, if you design and adopt AI systems with the Act’s requirements in mind from the start, you can avoid costly rebuilds and revisions down the line.

However, AI governance is more than just compliance: It’s about developing safe, transparent and high-quality AI products that produce the results you’re hoping for. The target of AI governance is to reduce uncertainty related to risks and legal compliance, improve AI time-to-market and thus enable responsible AI innovations.

So, how can you confidently prepare for the AI Act, future-proof your AI systems and enable efficiency?

In this blog, you’ll get a walkthrough of the responsible AI governance roadmap and learn some best practice to help you build:

AI literacy: Equip your team with the skills and knowledge to use AI safely.

AI governance framework: Establish robust AI governance practices and ensure AI is used safely in your organisation.

AI inventory: Maintain a clear, transparent and comprehensive documentation of your AI systems, their intended purpose, context of use, their risk level and how you’re managing the risks and enabling the compliance of those systems.

Ready to dive in? Let’s get started.

1. Establish AI literacy – Equip your employees with essential AI knowledge and skills

AI has evolved rapidly over the last few years, and it has impacted many different aspects of our lives, such as business models, how our kids learn at school, and how governments design and deliver essential public services, just to name a few.

Ultimately, responsible AI development and use should be centred around humans because we should be in control of AI.

To ensure safe use of AI in your organisation, you need to equip your employees with the necessary skills and knowledge about AI risks, regulatory requirements for using AI, how AI models work, what you can and cannot use them for, how they perform technically, and what kinds of performance issues and risks they might have.

On top of knowing and understanding AI, AI literacy also involves its underlying concepts and the ethical concerns to ensure responsible AI use and development.
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‍Watch our webinar with AI Governance Specialist Bruna and learn:

Why is AI literacy necessary for your organisation?
What is the AI Act's AI literacy obligation?
What is essential in ensuring AI literacy?
How can Saidot help you ensure AI literacy in your organisation?

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Best practices for establishing AI literacy

Organise trainings to start improving AI literacy

AI literacy training is just one (read: not the only way) to educate your organisation about AI and its considerations. You should organise training sessions for your AI developers, employees using AI, and other stakeholders.

Tailor the training sessions to their needs, considering their technical knowledge, experience, education, the context in which the AI systems will be used, and the people these AI systems affect. Also, remember to hold role-based training sessions for product owners, technical, compliance and risk specialists.

Without training, it’s hard for them, especially for your data scientists and business stakeholders, to understand AI’s capabilities, limitations, risks, benefits, key concepts and regulative requirements and how to develop and use AI responsibly.

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Saidot's AI literacy training package

This three-hour AI literacy training provides your teams with foundational knowledge on the use of AI, its benefits and risks, and how to apply AI governance to keep AI under control.

‍Learning objectives:
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1. Become familiar with AI: Explore how it is used in the real world, which problems it aims to solve, and which opportunities it leverages
2. Understand the capabilities and benefits of AI, and its limitations and risks
3. Learn about the concept of trustworthy AI and its fundamental components
4. Discover how and why AI is being regulated in the EU, and what AI governance is

Contact us for more information

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Provide access to curated AI knowledge

People in your organisation want to use AI safely and they need more information on the benefits and risks of AI, how different AI models perform and what risks they pose. Therefore, you should give them that knowledge.

However, keeping up with AI keeps getting more challenging as the AI value chain is getting more complex and the amount and variety of information needed for AI governance can be quite extensive. This can make AI compliance and safety feel overwhelming, understandably so.

With that in mind, we created Saidot Library, your go-to place for all the information you need to succeed with AI governance.

As a part of Saidot AI Governance Platform, Saidot Library offers constantly curated details on numerous AI models, evaluations, risks and regulations so that you don’t need to dig deep into various sources to make well-informed decisions.

It’s critical for your organisation to use this kind of information to increase confidence in using AI and ensure compliant and safe AI that produces high-quality outputs.

Use this knowledge in everyday governance

The purpose of AI governance is not to slow down or stop innovation but to boost and speed up AI time-to-market responsibly.

Analysing the risks of AI models, products and systems in the early stages of AI solution design process, or even when initiating the idea, helps you focus development resources on the most promising ideas. This enables you to incorporate potential risk mitigations to the solution design and test out new innovative solutions safely.

The power of Saidot Library as an AI literacy tool is that it gives you access to connected information about AI risks, legal requirements, models and performance evaluations to the actual AI systems you are designing, developing and deploying. For example, it helps you understand the kinds of risks related to the models you are using.

With that confidence, when your organisation understands how to use AI responsibly to gain a competitive advantage, you can create a safe space for responsible AI innovation.

2. Build an AI governance framework – Define responsibilities, processes, and tools

Contrary to this being the number two point of this blog, building an AI governance framework is actually the first step for many companies getting started with AI governance. AI governance framework clarifies working practices and collaboration models between AI and data teams, legal, procurement and business and ensures efficiency.

It provides structure and processes for the responsible development, deployment and use of AI within your organisation and helps you align and embed AI governance practices into your existing corporate governance model and processes.

We’ve seen AI spread increasingly across organisations as generative AI tools have democratised access to it. In such a wide-spread AI setup, it’s even more critical to support your stakeholders and maintain consistency of governance with the help of solid AI governance framework.

Building on our hands-on experience and leading standards, we've put together an AI governance framework so that you don't have to. Using Saidot, our customers can jump right onto implementation of a leading AI governance framework bypassing months of process and methodology development.

Best practices for building an AI governance framework

Create an AI policy or guidance

AI policy is a document that states your organisation’s formal direction and commitment to responsible AI. More than just principles, AI policy sets the principles, objectives and measures for developing and using AI responsibly within your organisation.

The best practice is to develop a set of clear AI principles and measures aligned with your corporate strategy and values, ethical considerations and legal requirements. You should also align your AI policy with other corporate guidelines and policies, such as cyber security.
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How to craft a generative AI policy (+ free template)

To help you get started, we've put together a free template for crafting your GenAI policy, including questions on each of the 12 recommended themes.

Download free template
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Put AI lifecycle management processes in place

AI governance is not just a one-time project. The journey starts already when you initiate and design an AI system, but it does not end once you’ve checked all the boxes. It’s an ongoing effort that must adapt to evolving regulations and your organisation’s growing AI portfolio and use cases.

The AI system lifecycle model provides a standardised approach for defining how your AI systems evolve from initiation to retirement. It helps build and govern AI systems more effectively and efficiently, maintaining consistency across your entire AI portfolio.

By introducing AI governance practices, such as data management, risk management, compliance management, and technical performance evaluations along your AI lifecycle, you will enable compliance with the AI Act and other AI regulations and ensure that the AI systems are working technically as they are expected to work.

You should align your existing AI development phases with industry standards and best practices in governing AI in every lifecycle phase. Also, set success metrics for AI governance to ensure quality, coverage and speed in AI time-to-market.

Promote AI governance collaboration, ownership and effectiveness

It is important to note, however, for AI governance processes to work, you should not build it into a separate way of doing things but align it with your organisation’s existing processes instead.

And since this effort requires cross-functional collaboration, you should also promote effective AI governance across your organisation and support consistent implementation of best practices and compliance with regulations.

AI governance is also not just a manual effort of gathering AI-related documentation. Effective AI governance is built on intelligent automation and integration features.

In many organisations, the information and knowledge needed for AI governance is scattered across different development and governance tools, such as MLOps platforms, data catalogues, corporate risk management systems, privacy tools and agile development tools. The effectiveness and impact of AI governance should come from bringing this knowledge together and thus being able to provide a holistic view and insights on AI risks, safety and performance.

Your AI product and business teams should be in the centre of AI governance and take accountability for the impact of AI. That’s why it’s crucial for you to build the ownership of AI governance to the first line where AI is used and developed in your organisation.

As the first step of our collaboration with Microsoft, we enable Microsoft customers to integrate their Azure model registry with Saidot’s AI Governance Platform. These new features connect customer’s AI and machine learning models into holistic AI risk and compliance management capabilities on our platform.

3. Create an AI inventory – Improve visibility to your AI systems and enable risk-based governance

Having an AI inventory helps you not only understand your AI systems’ risk levels and business benefits but also enables risk-based governance through each system’s lifecycle.

To ensure compliance with relevant regulations and industry standards, create a comprehensive inventory of AI systems developed and deployed in your organisation.

Without a company-wide AI inventory, you will have a hard time understanding how AI is used in your organisation and what kinds of risks and compliance requirements you need to manage.

Best practices for creating an AI inventory

Set up a structured AI system registration process

You can improve your AI governance efficiency by having a structured way to document and govern your AI systems, and a tool that guides every system owner to do it in the same way. Doing this helps you ensure that your documentation is done according to regulatory requirements, industry standards and your company-specific documentation standard.

During the registration process, you should record all your systems – not only those you’re building yourself but also third-party AI systems, models, products and components your organisation is deploying.

Then, to adopt right-sized governance, you should classify them based on their risk level. After all, you don't need to enforce all the AI Act's requirements for all your systems.

Classify AI systems based on risk and impact

Understanding and balancing the overall risk and impact of an AI system involves thorough AI system risk classification and assessment of the business and organisational impact.

AI system risk classification refers to the perceived risk based on its specific use context, intended purpose, and the associated regulatory and business risks. Depending on your organisation's risk classification criteria, an AI system will be categorised as low risk, medium risk, high risk, or prohibited.

These criteria may arise from regulations and business decisions. Also, individual risks you have identified during the risk management process will help you iteratively determine the risk level.

AI system risk classification is a critical tool to adjust and optimise the governance effort. The lower the risk level, the less governance work you need to do. If your system is classified as high risk under the AI Act or other law, you need to complete specific compliance management actions.

Your organisation may also decide to recommend further risk management to all its AI systems categorised as high risk (also those categorised based on business criteria). This way you can also ensure the risk management of systems causing significant risk to your business, stakeholders, brand reputation, or other non-regulative areas.

How Saidot helps you align AI governance with your existing processes and practices

Saidot is an advanced AI governance tool for ensuring safe and ethical AI, founded on cutting-edge knowledge, smart recommendations and leading best practices.

Not only that, but we also help your business, technical and compliance teams to understand, pilot and operate AI governance effectively and with high quality. Our common target is to enable responsible AI innovations, prove your compliance and boost your AI time-to-market.

To get started on your AI governance journey, here’s what we can help you with:

Designing a working model: To begin with, we help you align and embed the AI governance best practices and framework to your organisation and processes and clarify AI governance related roles, responsibilities and targets for smooth deployment.
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Piloting AI systems in practice: Saidot enables effective AI governance by creating first AI systems to your AI inventory, managing risks, proving compliance and using our extensive library as a knowledge base all the way.
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‍Scaling to succeed: Based on the pilots, we’ll help you scale your tested and validated AI governance model to an organisation-wide practice to gain full coverage and compliance. We can also help you measure and optimise AI governance efficiency to boost AI innovation time-to-market.
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‍Building competence: Our AI governance experts enable effective AI governance operations through trainings and sparring sessions whenever you need a knowledge boost along your journey.

Book a meeting with our experts to learn more

AI governance roadmap: Best practices for responsible AI innovation, efficiency and AI Act compliance

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