Five crucial steps to scale Enterprise AI in 2026
As a Customer Transformation Lead at Celonis, I’ve guided dozens of companies through successful AI transformations. Here is our blueprint that proved to deliver >370% ROI (and full payback in less than 4 months).
1. Get your house in order
Five years ago, RPA was all the rage. We learned a tough lesson then: putting automation on top of a broken process just produces bad results faster. The same applies to AI. So before you invest, you must understand how your processes run today — not how you think they run, but how they actually do in the wild.
This is how: You need to map the hidden dependencies and local ‘customs’ that aren't in your SOPs. A digital twin is the only way to get this done at scale. (Find out all about digital twins here).
Bonus tip: Even the most sophisticated AI will become shelfware if your teams don't trust it. You have to map out a step-by-step adoption journey that answers the what, why, and how, for the people who will be using it every day.
2. Measure AI ROI, relentlessly
Measuring ROI on AI is still one of the biggest issues companies have to overcome in 2026. Many teams I’ve talked to early-on in their journey would struggle to put real numbers to their AI initiative, resorting to unprovable platitudes like ‘higher customer satisfaction’ or ‘better insights.’ But in the eyes of the C-suite, ambiguous benefits don't pay the bills. Proving AI’s impact on the P&L (beyond cutting headcount) will be top priority for most enterprises going forward.
This is how: Focus on value from the start. Don’t select AI use cases based on “crowdsourced ideas”. Identify exactly where an AI agent should be inserted into a workflow for maximum ROI (and for that, again, you need to map your processes first). More importantly, you must be able to measure value attribution — tracking the downstream results of every AI action to prove it actually worked.
Back in my days as Digital Transformation Lead, our goal was to ensure that any action we took — whether taken by an RPA bot or an AI agent – was tracked and loaded back into our digital twin. If something wasn't sticking or saving us time, we fixed it.
At Celonis, we call this ‘Agent Mining.’ So when an AI agent takes an action, you don’t only need to see the logical reasoning and the downstream result — but give the agent the ability to learn from patterns, past decisions, exceptions, and so on. If you can’t measure the effectiveness of the change (or course-correct agents that drift), you shouldn’t be spending the budget.
3. Feed Enterprise AI operational context
An AI model is only as good as the data it’s fed. If your AI is not aware of how your systems, processes, and teams interact, how can it reason correctly, let alone act accordingly? An example: Your AI might tell you to reorder material to avoid a stockout — so it’s technically solving your problem. But if it doesn't know it’s cheaper and faster to transfer stock from your own plant three towns over, it’s also draining your working capital.
To make Enterprise AI work, context is king. AI needs the full context of your business. (And 89% of the 1,400+ business leaders we’ve asked agree.) That context includes unique business knowledge such as business rules, KPI calculations, process models, benchmarks, governance policies — as well as critical dependencies between processes or manual activities that happen outside of systems of record that aren't captured anywhere. In the best case, this context layer is accompanied by decision intelligence to run simulations and what-if scenarios, effectively helping you to solve problems before they happen.
Real-life example: I recently worked with a customer to build a digital twin of their Procure-to-Pay process. We discovered that 30% of ‘stuck’ POs were waiting for a manual goods receipt people forgot to document. Because the AI agent had access to the digital twin of their end-to-end P2P process, it didn’t just flag the problem, it reasoned and acted accordingly.
It found the shipping confirmation in the Logistics system, matched it to the warehouse’s entry, and suggested the receipt be created automatically. That’s the difference between AI being a simple chatbot and an operational partner.
And we didn’t stop there: With the digital twin, we also noticed that the agent was stalling when trying to approve the supplier invoice for payment. Celonis revealed that this wasn’t the agent’s fault. The model was running into a systemic mismatch between the actual contracts and outdated ERP master data (which, by the way, opened the door for the next use case: using AI to automatically audit and clean up master data against active contracts.)
4. Orchestrate agents, don’t just integrate
The biggest drain on your budget isn’t the cost of adding new AI tools to your stack. It’s the cost of isolation. AI can’t scale when it’s trapped in a chat window. Only if you orchestrate agents across systems, to work hand-in-hand with your people, other agents, and the systems they run on, will you see enterprise-level impact. (Spoiler: take the time to define your hand-off points!)
This is how: This requires a system-independent layer — call it an orchestration or intelligence layer, a digital brain — that decouples your strategy from your legacy tech. We often see projects stall because teams try to build AI directly into a single, rigid legacy system.
The problem: These legacy systems aren’t designed to work in sync with other platforms. Even if you manage to build a clean API into your ERP, that system remains blind to your CRM, your logistics portal, or your contract database. You simply can’t run a multi-system workflow from inside a single-vendor silo. (A side note on SAP: I still struggle to wrap my head around their recent API policy change.) From what I see, the companies who are winning at AI are moving away from tightly controlled, closed platforms and toward open, agile ‘intelligence layers’ that enable cross-system orchestration.
There’s a huge temptation right now to force Agentic AI into every workflow just because it’s the ‘new shiny thing.’ I’ve learned this the hard way before, when I worked as a Digital Transformation Lead: after months of testing a new AI agent , my team and I realized that RPA could have done the same thing, but better and cheaper.
My advice? Save your tokens, and don't over-engineer when simple automation can do the job just as well. If a task doesn’t actually need complex, real-time reasoning, RPA is still the way to go.
