How to build an AI-ready enterprise architecture

Why today’s enterprise architectures can’t keep up

Enterprise architecture has come a long way in a relatively short time. Just a couple of decades ago, monolithic systems and applications were its primary building blocks. Systems were tightly coupled, documentation was heavy, and governance was all about control.

Now, thanks to the rapid pace of change, and successive waves of technological innovation, best practice has moved on.

Instead of being built around specific applications, modern reference architectures seek to build towards business goals, while supporting continuous transformation. They take advantage of hybrid cloud architecture, modular design, and distributed governance, and therefore cast architects as enablers – not guards and gatekeepers.

But for most, enterprise architecture modernization remains a work-in-progress. Enterprise architects may have imagined a more agile future, but in large enterprises everywhere, application-centric architecture and siloed operations are still very much the norm.

• Read our beginner’s guide to digital transformation

The monolithic systems of the 1980s, 1990s, and 2000s were followed by 20 years in which enterprises grew to depend on a multiplicity of systems. Now, Enterprise AI is demanding contextual data from across them all – to fuel AI agents that are fluent in, and able to effectively transform, enterprise operations.

What we mean by Enterprise AI

When we talk about Enterprise AI, we’re not only simply talking about generative AI, or even agentic AI. Enterprise AI isn’t a single technology. It’s the strategic practice of infusing intelligence throughout your operations to power everything from predictions and recommendations, to copilots, agents, workflows, and apps.

For Enterprise AI to work for your business, people like you need to answer at least three questions:

• What does AI need to know about my business?
• Where should I strategically deploy AI?
• How can I make AI work well with my existing investments?

The answers to those three questions come from Enterprise AI having access to the right data, drawn from across all your systems, sequenced and enriched. And access to your business context, policies, rules, KPIs, etc.

An overwhelming 89% of business leaders agree this context is crucial for the effective deployment of AI, according to the Celonis 2026 Process Optimization Report. Almost as many (82%) say AI solutions can’t deliver ROI without it.

From system migrations to intelligent modernization

Migrating core systems to more flexible, consolidated platforms may move the needle on IT agility and cost, but that alone won’t provide the data or the business context that’s vital to Enterprise AI’s success.

Instead, enterprises must actively rethink their architecture – and evolve it to support intelligent modernization. The first step? Building the data foundation.

Building the foundation: The right data integration platform

Enterprise data has many homes. Systems of record, like CRMs and ERPs. Digital workspaces and document repositories, like Google Workspace and Microsoft 365. Data lakes, data warehouses, and their offspring, the data lakehouse.

The first step to delivering Enterprise AI is bringing all relevant data together within a single data integration platform, and creating a cleansed, enriched, system-agnostic foundation for your Enterprise AI solutions.

In the Celonis Platform, this role is performed by the Celonis Data Core.

Celonis Data Core allows businesses to extract data from any source, providing rapid extraction, transformation, load (ETL) and query times. It also uses Task Mining to capture desktop actions like clicks and copy-pastes. While these offline actions aren’t recorded in your systems, they are key steps in your business processes, and as such, key information for Enterprise AI.

• Discover Enterprise AI-ready data: Meet the Celonis Data Core

AI readiness: PI & DI help AI understand your business

As we’ve discussed, having all the right data isn’t enough. Enterprise AI also needs to understand your data in the context of your business.

The information AI needs to complete any given task is, in many ways, the same information a new member of staff would need. Enterprise AI needs to know which systems to look at, where one process hands over to another, and why exceptions happen. It also needs to understand the business rules that apply, how KPIs are defined, and who’s involved.

To grasp how important this context is, imagine trying to navigate a busy city with only a list of street names and landmarks. Before you can find your way anywhere, you also need a map that reveals how the streets and landmarks relate to each other.

This is where the Celonis Context Model (CCM) comes in. The CCM is the heart of the Celonis Platform – it takes your abstracted, harmonized, system-agnostic data, and enriches it with business context: your business rules and KPIs, benchmarks and process models, even your enterprise architecture diagrams. The result is a living digital twin of your end-to-end business operations, capturing the who, what, when, where, and why.

The Context Model provides hindsight and insight into how your business runs, and enables Decision Intelligence – the foresight capabilities you need for predictive and generative AI. This is the foundation that Enterprise AI needs to succeed.

• Meet the Celonis Context Model

The Context ModelProcess Intelligence helps AI to understand the relationships between your business’s documents, materials, and people. As such, it’s an essential layer in the Enterprise AI stack. In your AI reference architecture, this context layer should sit above the data ingestion/transformation layer, and below the servicing layer – where users interact with AI solutions, from copilots and agentic platforms, to orchestration and automation tools.

Modernizing enterprise systems on your own terms

If you have to start from scratch every time you want to develop a new AI capability, solution, or agent – or if your business gets locked into offerings from a specific vendor – you’ll seriously limit the scalability and ROI of your Enterprise AI projects.

The smart move is to make composability and openness guiding principles of your enterprise architecture, and modernize on your own terms.

How composable architecture supports AI integration

Composable enterprise architecture uses modular, self-contained building blocks to create systems, solutions, and processes. Each building block connects with others through standardised interfaces (e.g. APIs) and can be reused again and again.

A composable architecture offers many advantages over one based around buying or building a monolithic application, especially when it comes to Enterprise AI:

• Reduced time to RoAI. Because you’re using prebuilt, pretested components, and can rapidly assemble and iterate on new AI solutions.
• Reduced cost and complexity. Because reusing components saves AI development time and money.
• Improved collaboration and business-centric innovation. Because business stakeholders are able to contribute more directly to AI tool creation, within a clear data governance framework.

The benefits of enterprise interoperability and open IT

Composable enterprise architecture also supports enterprise interoperability and open IT, by prioritizing loose coupling and abstracted infrastructure, without compromising on governance.

This is another boon for Enterprise AI which, at its most successful, seeks to use the best technology and/or partner for the task at hand, regardless of existing vendor relationships.

From composable technology to composable processes

Celonis facilitates composability at every level. As discussed, the Celonis Data Core abstracts data from underlying systems. It also makes expanding or changing your data sources easy, so you can rapidly adapt to the needs of each AI initiative.

The Celonis Context Model is accessible through our Intelligence API and MCP server. You’ve the freedom to build each new AI solution inside Celonis, or through the AI application building platform of your choice.

Importantly, Celonis also enables composability at a process level. Our Platform empowers enterprises to analyze, design, and operate AI-driven processes in a modular, flexible way, through our flexible Build Experience:

• Customizable process analysis. Deep process analysis helps organizations to find and prioritize their highest value use cases for Enterprise AI. The Celonis Platform puts you in full control, with the ability to build custom analytical and operational applications. (Though you’ll want to check the hundreds of apps on Celonis Marketplace first. There’s every chance someone’s already built the dashboard you need.) Find out more about analyzing processes.
• Flexible process design. To design AI-enabled processes, you need an intuitive view of end-to-end process journeys. That’s what the Celonis platform provides, making it easy to define process guardrails and outcomes. Find out more about designing AI-ready processes.
• Orchestrated process operation. The success of Enterprise AI depends on AI agents, humans, and systems working together seamlessly. Celonis lets you coordinate and automate on your own terms, using low-code/no-code Action Flows to trigger alerts and actions, and to power automation inside and outside our Platform. Find out more about operating AI-enabled processes.

All this adds up to power to modernize systems, and embrace Enterprise AI, in the way that’s right for your organization. You can build AI solutions in the platform of your choice, and plug in the Process Intelligence they need. Then you can manage your AI-enabled processes in Celonis – orchestrating people, new and existing AI agents, RPA bots, and workflows – while driving adherence and continuous improvement.

How Celonis uses AI to simplify change

The journey from knowing what an AI-ready enterprise architecture looks like, to making it an RoAI-unlocking reality can, like any other IT transformation, feel daunting.

Celonis uses AI to shorten the journey and simplify the change. Here are just a few ways we use AI technologies to support your Enterprise AI implementation:

• AI-driven process insights. Our platform uses AI to surface patterns in your processes – the kind that are impacting your KPIs. This helps organizations to quickly and effectively identify and evaluate their potential Enterprise AI use cases.

• Process modelling with generative AI. Modeling a newly AI-enhanced process can take time. Our platform uses generative AI to give you a headstart on documentation creation, and in turn, process adherence and regulatory compliance.

• Unlocking automation with AI Annotation Builder. It’s relatively easy to apply AI-driven automation to structured data. But what about unstructured data? Using Gen AI, our AI Annotation Builder lets you define business rules in natural language before applying them to natural language – producing a clear data signal that can be used to trigger automated next steps.

The digital twin of your end-to-end business operations provided by the Celonis Context Model is, as we’ve seen, crucial from a technological point of view. Why? Well, it mirrors the reality of your operations, and provides the missing operational context enterprise AI needs to succeed.

But it’s also invaluable when it comes to driving, managing, and scaling change.

The Celonis Context Model gives organizations a single source of truth for business processes, and a shared language – the language of process improvement – for collaborating on Enterprise AI.

This means anyone in the business and beyond (IT stakeholders, business stakeholders, partner organizations) can become an effective change agent, driving AI-enabled innovation regardless of their own field of expertise.

The power of a shared view of the business to focus and accelerate transformation shouldn’t be underestimated. Deutsche Telekom Services Europe uses Celonis to deliver an objective view of process flows, allowing teams across the company to get behind a single goal.

• Read Deutsche Telekom Services Europe’s Celonis story

Other forward-thinking companies are using Celonis to develop highly impactful Enterprise AI solutions. Cosentino, a leading design and architectural surface manufacturer, has implemented an AI assistant for credit block management. The assistant analyzes blocked orders within seconds, helping credit managers to process up to 5x more orders per day.

• Learn more about Cosentino’s AI deployment

Build the backbone for AI and continuous innovation

AI isn’t a single technology. And it isn’t standing still. The Celonis 2026 Process Optimization Report reveals that, while just 19% of businesses are currently using multi-agent systems, 85% aim to become ‘agentic enterprises’ within the next two-to-three years.

To make the most of today’s and tomorrow’s AI opportunities, enterprises need an architecture designed to simplify AI integration, and empower AI systems and humans alike.

They need an enterprise architecture that’s composable and open, leverages reusable components, supports rapid AI experimentation, and drives enterprise interoperability. And they need an enterprise architecture that has the full context.

• Speak to Celonis experts about AI-ready IT modernization