Let's be honest for a second. Everyone in the C-suite is talking about AI. Your board is asking for an AI strategy. You're probably already running pilots, experimenting with new tools, and feeling the pressure to show that all this investment is actually doing something for the bottom line.
But for a lot of companies, it’s just not clicking. The flashy demos are great, but the real-world results are… underwhelming.
So, what’s going wrong? Is the AI not smart enough? Is the tech just hype?
I’ve been covering this space for a long time, and I can tell you the issue usually isn't the AI itself. According to Alex Rinke, the co-CEO of process intelligence leader Celonis, the problem is that we’re giving AI a job without any context. It’s like hiring a genius-level intern and telling them to "improve the business" without explaining how anything actually works.
As Rinke puts it, without understanding the nitty-gritty of your business processes, AI risks becoming "just an internal social experiment." Ouch. But he’s got a point. For AI to be more than a cool toy, it needs to understand the messy, complex, and unique reality of how your company operates.
The Billion-Dollar Question: Where's the AI Payback?
This whole AI ROI challenge is the main event at Celosphere, the big process intelligence conference happening next week. It’s a hot topic because the numbers are pretty stark.
Gartner reports that a whopping 64% of board members see AI as a top priority. You’d think with that much focus, the money would be rolling in. But here’s the kicker: only 10% of organizations say they’re seeing any meaningful financial returns from their AI efforts. That’s a massive gap between expectation and reality.
This is where things get interesting. Some companies are bucking this trend, and the common thread seems to be this idea of process intelligence (PI). A Forrester study on Celonis customers found they achieved an incredible 383% ROI over three years, with the investment paying for itself in just six months.
Think about that. One company went from automating 33% of its sales orders to 86%, saving a cool $24.5 million. The study estimated a total benefit of $44.1 million over three years. These aren't just marginal gains; they’re the kind of results that get you promoted. The pattern is clear: when you first understand and optimize your processes, the AI you layer on top actually has a chance to succeed.
Okay, But Does This Actually Work in the Real World?
It's easy to talk in hypotheticals, but what does this look like on the ground?
Celosphere is highlighting some fascinating case studies. We're talking about global giants like Mercedes-Benz and Vinmar Group building what they call "future-fit" operations.
Here are a few examples that really stood out to me:
- AstraZeneca: The pharma giant had a classic problem—how to reduce excess inventory without risking a shortage of critical medicines. By using Celonis to understand their supply chain processes, they created a foundation to work with OpenAI, keeping medicine flowing while cutting waste.
- The State of Oklahoma: Imagine the sheer volume of procurement questions a government body gets. They're now using AI to answer those questions at scale, unlocking over $10 million in value. That’s taxpayer money being put to better use.
- Cosentino: You know those annoying "blocked sales orders" that grind everything to a halt? They’re using an AI-powered assistant to clear them up to five times faster. That’s real speed and real revenue.
These aren't just "AI for AI's sake." They are specific, painful business problems being solved because the AI was given the context it needed to be effective.
When AI Gets a Promotion: From Advisor to Actor
Here’s where things get really wild. We're moving from an era where AI gives you suggestions (AI-as-advisor) to one where it takes action on its own (AI-as-actor). Rinke says this shift "changes everything."
Think about it. An AI agent that can independently trigger a purchase order, reroute a shipment, or approve a credit exception. The upside is massive efficiency. The downside? If that agent has bad context or misunderstands a process, it can cause catastrophic problems at an incredible scale.
This is why Rinke insists that "process intelligence provides those rails." It’s the playbook that tells the AI agent not just what to do, but how your specific business actually does it, what the rules are, and what guardrails to stay within.
At Celosphere, they’ll be showing how companies are using an "Orchestration Engine" to coordinate these AI agents alongside human employees and existing systems. It's about preventing chaos—making sure agents aren't duplicating work, stepping on each other's toes, or letting important tasks fall through the cracks.
Trying to Use AI in a World of Constant Chaos
If running a business wasn't hard enough, we're also dealing with insane global volatility. New tariffs, supply chain shocks, you name it. As Rinke notes, this isn't just a headline—it's an operational nightmare.
A single policy change can ripple across thousands of products, forcing you to find new suppliers, reroute shipments, and rebalance inventory on the fly.
Your standard AI, trained on historical data from a more stable time, is completely lost. It can't predict this kind of chaos. But this is where process intelligence shines. It gives you a real-time, living map of your operations, so you can see exactly how these disruptions are rippling through your business and react intelligently.
Companies like the packaging giant Smurfit Westrock are using PI to manage their inventory and costs amid all this tariff uncertainty. The fashion retailer ASOS is using it to make its supply chain more efficient and resilient. They’re turning disruption into a competitive advantage.
Why a "Band-Aid" AI Solution Won't Cut It
So, how does Celonis actually do this? Rinke argues that their edge comes from treating process intelligence as the foundational layer of the entire company, not just another tool you bolt on.
Instead of a simple optimization app, the platform creates what they call a "digital twin" of your business operations. It’s a continuously updated model that shows how work really gets done, across all your different systems and even offline tasks. This living map is what gives AI the context it needs to move from simple analysis to smart, autonomous action.
"What sets Celonis apart is visibility across systems and offline tasks, which is critical for true intelligent automation," Rinke says. It’s about having a single source of truth for your processes, from start to finish.
This is all part of their "Free the Process" philosophy, which is about giving companies ownership of their own process data and avoiding getting locked into a single vendor's ecosystem. By promoting open APIs and a growing partner network, they're building the connective tissue for a more interoperable and automated future.
Ultimately, it all comes back to a simple, powerful idea. AI is just a tool. A powerful one, yes, but still just a tool. To make it truly work for your business, you have to give it a deep understanding of how you operate.
As Rinke says, process intelligence creates a "flywheel." Better understanding of your processes leads to better optimization. Better optimization enables smarter AI. And that smarter AI drives even greater understanding.
It’s a virtuous cycle. And it’s why, when you really dig into it, you realize there’s no such thing as effective enterprise AI without process intelligence.
