AI Governance: Clear Rules for Better Innovation

Develop an AI Framework Without Slowing Innovation
AI Governance: Clear Rules for Better Innovation

There’s a good chance that your employees already use artificial intelligence (AI) to summarize reports, draft presentations, analyze data and handle repetitive tasks. The risk doesn’t lie in AI use itself, but in environments with a lack of clear rules, unsuitable tools and the transmission of data that should remain within the company.

Without a framework, business leaders quickly lose track of how their teams are using AI. On the one hand, leaders face more difficulty identifying risks, determining which initiatives are effective and disseminating best practices. On the other hand, overly restrictive rules can lead certain employees to circumvent them by choosing external tools that are entirely uncontrolled.

The challenge that leaders face is building a framework that is clear enough to ensure safe AI use without hindering experimentation. An effective AI governance process does not prohibit tools. Instead, it equips teams with specific guidelines for confidently using technology and creating tangible gains for the organization.

Define business needs

The first step involves determining what your organization would like to accomplish with AI. Which issues would you like to resolve? Which tasks are eating into your teams’ time? Where would you like to boost efficiency or better serve your clients?

For example, a construction company may wish to speed up the tenders preparation process if this step slows down the operational teams. Another organization might be interested in further tailoring their marketing or reducing the time spent on certain administrative tasks.

In other words, the goal is not to integrate AI at any cost, but to pinpoint the needs that it can actually address.

From this starting point, your governance framework will take shape. You can determine where AI will be used, which tools will be authorized, who will approve new use cases, which rules will apply and how you can assess the results. Simply choosing a tool that works isn’t enough. It must deliver the expected benefits.

Put initial safeguards in place

If your company is just beginning to explore AI, there’s no pressing need to build a complex structure. A handful of measures can serve to guide first-time users.

Start out by designating who is responsible for leading the project and ensuring follow-up. Then draft a simple policy that lists the authorized tools and permitted and prohibited uses, specifies which data must never be transmitted, and outlines situations that require human validation.

Offer a short training session to help your employees understand the capabilities, limitations and risks of these tools. You could also create a simple forum for asking questions or reporting errors, concerns and incidents. Lastly, maintain a log of the primary initiatives. This will allow you to track where AI is used, what has been tested and what each project has delivered.

The objective is not to ensure perfect governance right from the start, but to create enough reference points to allow your teams to experiment without making up rules as they go.

However, the level of supervision must reflect the potential risk. If your organization handles personal information or other sensitive data, leadership must introduce more stringent procedures than a company whose initial projects involve little confidential information.

The two-minute test

How can you tell if your rules are clear enough? Ask an employee to explain in two minutes what they should and should not do with AI. If they cannot do this, the rules are likely too complex, not effectively communicated or too far removed from their day-to-day tasks.
A detailed policy is not truly effective where only the IT, legal affairs or risk management teams can understand it. Managers, professionals and employees should all be able to quickly determine which tool to use depending on the situation, what precautions they must take and who they can turn to when in doubt.

Essentially, communication is an integral part of AI governance and change management should be taken into account when regulating its usage. Rules must not only be defined, but also understood and easy to apply on a daily basis.

Share what you learn

Your framework should be reviewed regularly. As your organization’s AI maturity grows, you can refine the framework with a view to better structuring data management, formalizing risk assessment, defining responsibilities or adding control mechanisms for more sensitive use cases.

Take note of how your employees are actually using AI. A community of practice can help by providing a space where employees can share how they use the tools and which prompts are effective, in addition to sharing their successes and less conclusive attempts. This allows best practices to spread more quickly, and helps prevent teams from repeating the same mistakes.

With an increasing use of AI agents, such exchanges have become even more important. When an AI agent is found to be useful in performing a task, making it available to other teams is better than letting individuals come up with their own solutions. This approach prevents employees from working in silos and offers a better overview.

When it helps you to identify and document what works in practice, governance becomes a tangible driver of innovation. Your teams can then experiment more quickly and with greater confidence while your organization retains the capacity to measure the impacts.