How process frameworks enhance AI accountability

Last month I wrote about the importance of treating process excellence as a renovation project, not a new construction. It’s vital that we keep operations functioning effectively while still striving to improve them. To continue the analogy, it’s worth considering the value of an efficient contractor.

Imagine if you had a highly skilled tradesperson who could take in the breadth of the job and pull together the work quickly and effectively. It seems like a perfect solution, but how can you be sure that what they’re building is what you really need? It’s a matter of governance, and even the best builder can only work with the resources and blueprints you give them.

The importance of governance in AI

This analogy is helpful when we consider the proliferation of artificial intelligence (AI) tools for business. They have immense potential, able to provide quick and far-ranging analytics, and combined with automation solutions, can eliminate much of the drudgery of data processing, accelerating business processes and reducing costly errors.

AI is efficient and relatively easy to apply to just about any organization at some level. According to PWC’s AI Business Predictions report for 2025, 73 percent of companies have already adopted AI in some form for an area of their business. These new technologies are very much the highly skilled contractor you might have been hoping for to renovate your operations. 

Governance is one of the big question marks hovering over many AI process implementations though. As data management increasingly comes under the spotlight, it’s vital that we recognize the limitations of these tools, and what we can do to make them more robust, reducing our risks and increasing their effectiveness.

Any AI is only as good as its training. The tool needs to learn from data that reflects the environment it’s working in. In general, an AI takes the massive amount of information it’s exposed to and makes assumptions based on patterns and structures it finds there. When it’s asked to make decisions about a process, it relies on what it’s learned. Unfortunately, if what it’s learned is poorly structured, unclear or missing vital elements, the AI will make a guess to fill in the blanks, and that could have significant repercussions for any business. It’s very difficult to apply controls to a system that doesn’t recognize their significance, so the solution is clear: make sure those checks and balances are in your processes before you unleash an AI on them.

The power of well managed processes

Well managed processes are an unbelievable governance structure for AI because they create a solid responsible, accountable, consulted and informed (RACI) framework for the AI to build upon. Your business processes need to recognize more than just the executive signoffs. They should identify all the significant inputs and outputs, from the business teams doing the work to the stakeholders in the systems they use.

Think about all the human touchpoints in a process, the various systems that are employed and the products of each step. Well defined processes define and describe these, and account for the big picture, identifying how they participate in wider business activities and all those who contribute to their successful execution. 

Like a builder working on an established house, the AI needs to know what connects where and why, so it doesn’t inadvertently break something important. It needs to know who should be kept in the loop so there’s no disruption to the flow of decisions and data. Documenting these elements provides context for an AI to grasp just how an action is woven into essential operations. By first constructing robust process frameworks, any AI tool that gets integrated will be scaffolded with all the relevant information it needs to make effective decisions.

Aiding AI deployment

Those kinds of processes also help with the eventual deployment of AI tooling. Controlling who has access to the AI data, and how it’s employed, is as important as the way it’s utilized. This is where effective business processes can protect your organization, ensuring that there are adequate checks and balances employed when data is being accessed or employed. Risk management for AI deployments should emphasize clear limitations on what data is implemented, how it’s used and by whom. It’s about the RACI matrix again – setting clear limits and lines of responsibility to ensure data security.

The more we want to leverage AI tools, the more we need to recognize the importance of robust and comprehensive processes beneath the surface. They provide the blueprint for digital intelligence to build upon, and clarify both the avenues and boundaries of process flows. Like clear plans for a tradesperson, your process framework will create a robust dataset for AI applications to work from, ensuring data is handled appropriately and risk is minimized wherever possible. As AI tools continue to develop, make sure your processes create an environment where they can really shine.