Beyond automation: rethinking business processes with AI

Making a process faster is not the same as making it better. This is an important distinction, especially today, as AI makes it possible to automate tasks that, until recently, required significant time, expertise, and human intervention.

The real question is how we introduce AI into business processes, and whether we're using it simply to automate existing tasks or to rethink how those processes work.

This is where the distinction between AI process automation and AI process transformation becomes essential.

We can apply AI to an existing process, automating certain steps and improving efficiency. Or we can start with the desired outcome and redesign the process, recognizing that people, data, and AI can play different roles than they did in the past.

These may seem like similar ways of introducing the same technology, but they lead to very different results: in one scenario, we improve what already exists; in the other, we question whether that process should continue to exist in the same form.

AI process automation is not the same as transformation

AI can speed up repetitive and time-consuming tasks, such as classifying documents, extracting information, generating content, summarizing data, or supporting decision-making. The resulting improvements can be quantified by measuring the time spent (less!), the manual tasks performed (less!), and operational capacity (increased!).

These are valuable gains. But they don't necessarily mean the underlying process has improved.

The overall workflow may remain fragmented, information may still be scattered across different systems, and, above all, the logic behind the process's original design may remain unchanged.

In some cases, AI process automation can even amplify existing inefficiencies: generating more outputs that add little value, multiplying information that is difficult to manage, or introducing new tools that simply sit alongside existing ones instead of replacing them.

This is the automation paradox: we risk making an inefficient process faster when what we really need is to question why it works that way in the first place.

We've already explored how AI can support more efficient, human-centered workflows in our article on business process optimization with AI, but efficiency is only one part of the equation.

The bigger opportunity lies in understanding when optimization is enough and when a process needs to be redesigned.

AI business process redesign starts with outcomes

AI-driven business process redesign requires a different approach. Rather than starting with the technology, we start with the outcome the process is expected to deliver.

This means understanding the problem before defining the solution, identifying who uses the process and what they actually need, making assumptions explicit, and designing an end-to-end experience rather than optimizing individual steps.

It also means measuring outcomes instead of focusing exclusively on outputs.

When applied to process transformation, this approach changes the role of AI. The goal is no longer simply to introduce new technology into the organization, but to determine which combination of processes, people, data, and technology can deliver the best result.

In traditional automation projects, the most immediate metric is often time: hours saved, manual tasks eliminated, or the volume of operations completed within a given period. These indicators tell us something important about efficiency, but they don't necessarily tell us whether the process is delivering greater value.

The question should not be limited to how much time we've saved, but what we can now do better than before.

Can we make more informed decisions? Improve the quality of a service? Reduce errors? Respond more effectively to customers' needs? Enable activities that previously weren't possible? These are the questions that help distinguish a faster process from a better one.

Of course, redesigning a process also requires understanding the organization's starting point. Its data, technology, ways of working, and existing capabilities all influence what can realistically be transformed.

This is why a Digital Maturity Assessment can provide a valuable foundation: not to assign an overall score, but to identify where the organization stands and which changes should be prioritized.

From AI process automation to business transformation

AI makes it possible to automate an increasing number of tasks. This makes it even more important to decide which processes should be maintained, which should be simplified or eliminated, and which should be completely rethought.

This is where the difference between automation and transformation becomes clear.

Automation applies new technological capabilities within an existing framework. Transformation uses those capabilities to challenge and redesign the framework itself.

The second approach is more complex. It requires organizations to work simultaneously on technology, processes, data, and business objectives. But it is also where AI can move beyond operational efficiency and become a driver of structural change.

Data plays a particularly important role in this transition. Before introducing AI into a redesigned process, organizations need to understand whether the information they collect is actually aligned with the decisions they want to improve. As we discuss in The Data Strategy Sequencing Error, investing in infrastructure or tools before clarifying the decisions they need to support can create another layer of complexity rather than solving the underlying problem.

The same principle applies to AI adoption. Introducing artificial intelligence into an organization isn't simply a matter of selecting the right tools. It requires understanding the context in which those tools will operate, the people involved, and the outcomes they are expected to generate.

In our article on AI Adoption, we explore how organizations can move from experimentation to meaningful impact by aligning technology, processes, data, and people.

Because AI process transformation isn't about automating everything that can be automated. It's about making deliberate choices about what should change and why.

Rethinking processes to create real value

At 20tab, we approach AI process transformation by first understanding when automation can improve an existing workflow and when business process redesign is needed to create greater value. From strategy and process analysis to the design and development of AI solutions, we help organizations identify the real problem, rethink how work gets done, and measure success through the outcomes achieved.

Sometimes this means introducing automation into an existing process. Other times, it means questioning the process itself and designing a different way to achieve the same goal.