How Intelligent Will the Future Be?

Predictions are always tricky—especially when they concern the future. Alan Kay, the visionary computer scientist, said: “The best way to predict the future is to create it.”

So how are we at Celonis helping to create the future? And what drives us in this endeavor? It comes down to what we deliver: Value.

Our mission is to empower our customers to optimize their business processes. Because only when our customers create value, do we create value ourselves. And this value isn’t just financial—it can take the form of increased market share, higher customer or employee satisfaction, or a reduced environmental footprint. All of these outcomes depend on one thing: executing processes the right way.

That’s where our technology comes in. It reveals where inefficiencies and risks lie hidden within processes. It uncovers opportunities for improvement. And most importantly, it generates the insights that serve as the foundation for informed decisions. Without these insights, meaningful action—and ultimately, value creation—would be impossible.

We believe in a simple formula:

Insights × Decisions × Actions = Value

If any part of this equation is missing, the result is zero.

From Insight to Action

Actions come in three flavors: through people, through processes, or through technology. You can improve a process by training people, redesigning the process itself, or enabling smoother execution with better technology. Among these, technology plays a key role in increasing speed, minimizing risk, and ensuring scalability.

Throughout history, technology has been used to make work more efficient. Automation was a turning point—it decoupled work from human labor and made it scalable. But while machines brought power and precision, they lacked something fundamental: intelligence. Decision-making remained a uniquely human domain.

That’s now changing. Artificial Intelligence (AI) is transforming what used to be a human-exclusive capability into a scalable technological asset.

ChatGPT showed the world that AI can generate impressive content by training on vast amounts of data. But when it comes to a company’s internal processes, generic AI models fall short—they lack access to relevant business-specific knowledge. So what happens if we train an internal AI with data from Process Intelligence?

We unlock something powerful: an AI that understands the company’s processes—and can help make better decisions. Just as automation scaled physical labor, we now have the ability to scale intelligence.

Toward Autonomous Operations

What does this mean for the future of business?

The vision of autonomous operations may still sound futuristic. But we can learn a lot by comparing it to autonomous driving.

Today’s cars already automate many tasks: adaptive cruise control, lane assistance, automatic braking. But true autonomy? We’re not there yet. A truly autonomous vehicle needs sensors to perceive its surroundings, maps and rules to navigate, intelligence to make split-second decisions, and access to operational systems like steering and braking.

The same principles apply to an enterprise. It needs:

• Situational awareness, enabled by Process Intelligence
• Rules and goals, defined by Process Management
• Intelligence, powered by AI to detect deviations, recommend actions, and even take them
• Execution, managed by Process Automation and Orchestration tools interfacing with ERP and CRM systems

At the center of it all? Decisions.

Decision Intelligence: A New Frontier

Research shows that humans can juggle only 5 to 9 facts when making decisions. Beyond that, we become overwhelmed. Stress and complexity further reduce our capacity. But experience helps—experts see patterns and quickly filter what matters.

AI is a natural partner here. It can process thousands of variables at once and spot patterns far beyond human ability. That’s why the future lies in Hybrid Intelligence—the synergy of human and artificial intelligence. And because this transformation centers around how decisions are made, we call it Decision Intelligence.

Let’s break it down:

Assisted Decision: Humans remain the decision-makers. AI supports them by organizing data, identifying patterns, providing analysis and providing suggestions.

• Healthcare Example: A doctor reviewing a rare condition might use AI to gather the latest research, similar case studies, and treatment outcomes. The AI acts like a super-researcher—surfacing relevant information quickly—but the doctor applies empathy, ethics, and experience to make the final call.
• Business Example: Think of a finance team preparing a quarterly forecast. AI consolidates past performance, market trends, and real-time KPIs to suggest potential risks or opportunities. The CFO reviews the insights, challenges assumptions, and finalizes the forecast—using AI to inform, not to decide.

Augmented Decision: Humans and AI collaborate equally in the decision-making process. AI analyzes data, presents treatment options or recommendations, and the human makes the final call—or vice versa.

• Healthcare Example: An AI system processes thousands of similar patient cases, scans the latest clinical studies, and identifies three possible treatment paths for a complex diagnosis. It presents these to a human doctor, including predicted outcomes and risks. The doctor then brings in their experience, empathy, and contextual knowledge about the patient—such as personal preferences or comorbidities—to jointly decide on the best treatment. It’s a dialogue between machine intelligence and human judgment.
• Business Example: Take a procurement scenario: An AI analyzes supplier performance, pricing trends, delivery reliability, and risk indicators over the past two years. It proposes three sourcing options—one low-cost supplier with moderate reliability, one local supplier with faster delivery but higher cost, and one innovative newcomer offering long-term strategic potential. The AI presents the expected impact on cost, sustainability, and supply chain resilience.

Autonomous Decision: AI takes the lead—making decisions within human-defined boundaries. Humans intervene only in exceptional cases.

• Healthcare Example: In a smart hospital, AI monitors patient vitals 24/7. When it detects signs of sepsis in a patient, it autonomously triggers an alert, administers initial treatment protocols, and notifies medical staff—without waiting for human input, because every second counts.
• Business Example: In a business context, imagine a logistics platform where AI reroutes shipments in real time based on weather conditions, fuel costs, or warehouse capacity. It doesn’t ask for approval—it acts, staying within rules defined by humans. Managers step in only when something unexpected happens.

The more decisions become intelligent, the more intelligent the future becomes.

This evolution won’t happen overnight. It will unfold gradually, across all industries and aspects of life. But it’s already underway. In business, AI is becoming a trusted partner—supporting people at every level, and helping them make smarter, faster, and more impactful decisions.

As long as we give AI the right information, the future will be not just digital or automated—but truly intelligent.

There is no AI without PI

To make intelligent decisions, AI needs context, clarity, and relevance. That’s exactly what Process Intelligence (PI) provides. It delivers the insights AI depends on to understand what’s actually happening inside a business—how processes run, where they stall, and what outcomes they produce.

Without Process Intelligence, AI is just guessing.

That’s why we believe: There is no AI without PI.

Only when AI is grounded in process reality can it truly deliver intelligent, impactful decisions that drive value. And that’s exactly the future we’re building—one decision at a time.