The real AI opportunity: Reinvent how your organization operates

Don't automate the old process - reimagine it

When you think about introducing AI into a process, a starting point is often human augmentation: How can AI tool help someone do their existing job faster? There's nothing inherently wrong with that, but it leaves a lot of value on the table.

The bigger opportunity comes when you ask a different question: If we designed this process today, knowing what AI can do, would it look the same?

Often, the answer is no. Actual value comes from rethinking the process itself and replacing many of its steps with AI. Humans may still be involved - and frequently should be - but what they do, when they intervene and where their judgment adds value can look very different.

To address this, I encourage clients to move away from scattered point solutions. So, instead of deploying an AI use case here and another one there, they look at a process from end to end and transform a meaningful portion of it - for example, 10 or 20 interconnected use cases working together within one part of the business.

The principle is simple: Don't layer AI on top of broken operations. Redesign your operations around what is now possible. This will give you a much better chance of seeing an impact that moves the bottom line.

Not everything needs AI

There's another misconception worth addressing: More AI doesn't automatically mean better operations. Sometimes good old-fashioned automation is exactly what you need.

If a process step is highly deterministic, with clear rules, little variation and limited need for human judgment, a rules-based technology such as robotic process automation (RPA) may be perfectly adequate: If A happens, do X; if B happens, do Y. You don't need an AI model to solve that problem.

AI becomes much more interesting when the process is probabilistic - when there is variation, ambiguity or a need for the kind of judgment that traditionally required a human.

And with agents, we can take that further. Agents give us an autonomous form of AI that can execute more of a process with less human intervention. That's where automation can move to another level and where material value starts to accrue.

The technology is only one part of the equation

This is where many AI programs get into trouble. You can create a brilliant model and deploy it successfully, but that doesn't mean value will magically appear. You have to deal with the process, the people, the data, the operating model and the governance around it.

For me, five things need to be in place:

1. End-to-end process transformation. You need to rethink the whole workflow, not just make isolated technical interventions.
2. Sponsorship - and I mean real sponsorship. In the AI era, this cannot simply be the CIO's agenda or a CFO's initiative. The CEO needs to be behind it, and the C-suite needs to be aligned. Without that intent from the top to create an enterprise AI strategy and drive adoption, transformation is going to be slow.
3. Change management. If you reinvent a process, people need to understand how work will now get done and, importantly, what their role is in that new process.
4. You need to modernize the digital core underneath all of this. AI places new demands on data platforms and infrastructure. The data needs the right latency, granularity and metadata so AI can use it as context. In some cases, for every dollar you spend activating AI, you might spend four dollars modernizing the data platform that makes it possible.
5. And finally, governance cannot be something you bolt on afterwards. You need responsible AI practices, security, privacy, access controls and, increasingly, FinOps disciplines around AI consumption. As AI scales, executives need to understand what value they are creating as well as what it's costing them to create it.

Autonomy doesn't make people irrelevant

I also think we need to be straightforward about what this means for people.

There will be situations where jobs are eliminated - particularly in routine, repeatable processes that can be replicated relatively easily through RPA, AI or a combination of the two - but that isn't the whole story.

What I hear from executives is much more often about freeing capacity. If AI can take on repetitive work, people can move toward higher-order, more strategic activities where their judgment is more valuable.

That requires deliberate redesign. You're not simply inserting technology and hoping everyone figures out where they fit afterwards. You are redesigning roles alongside the process.

It starts with one leadership decision

Right now, there is real urgency in the market. Organizations want to get out of "POC land". They've invested in AI and now need to show that the technology can deliver value at scale. At the same time, they're becoming much more conscious of the cost of running AI at scale.

So, where do you start? For me, it comes down to a deceptively simple leadership decision: Are you serious about this or not?

Executives need to be clear that what they are doing with AI contributes to their corporate strategy. Then somebody - ultimately the CEO and leadership team - has to make the decision to sponsor the change.

After that, there are many ways to go about it. You can move incrementally, experiment or build out the operating model over time. But the starting point is the same.

AI-driven autonomy isn't really about deploying more AI. It's about having the conviction to rethink how the business operates - end to end - and then doing all the hard, less glamorous work required to make that new model real.

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