1980s

The Monolithic Era

https://delivery-p141552-e1488202.adobeaemcloud.com/adobe/assets/urn:aaid:aem:8dd561a6-a342-45d9-a4b9-c2061f3d0d26/original/as/260426_ComposabilityHub_LandingPage_Module3-Timeline01-1620x1078px.jpg

Before monoliths, enterprises ran on fragmented, bespoke islands of automation that couldn't talk to each other. The monolithic ERP changed everything. SAP. Oracle. Microsoft. One system to rule them all — a single source of truth that could span global supply chains.

For the C-suite, this was a revelation. One contract. One account manager. One screen showing everything. Acquisitions became straightforward: Install the same system, instant integration.

But the ceiling arrived fast. Business-critical operations ground to a halt whenever change was needed. New features meant waiting two years for the next major release. Although systems could be heavily customized for the business’ needs, this came with the downside of multi-year implementation projects. Oh, and if you changed one module, you risked breaking everything... Essentially, instead of centralizing the business, the monolith held it hostage, killing agility. For CEOs navigating fast-moving markets, this posed an existential problem.

Go back to timeline

2000s

The Best-of-Breed Era

https://delivery-p141552-e1488202.adobeaemcloud.com/adobe/assets/urn:aaid:aem:9902af83-c1eb-41fd-9eee-e92ae77caec8/original/as/260426_ComposabilityHub_LandingPage_Module3-Timeline02-1620x1078px.jpg

The monolith's grip loosened, and enterprises…went on a shopping spree. For the first time, each department could choose the best tool for a specific job. Best-in-class CRM. Best-in-class HR. Best-in-class Finance.

The C-suite loved it because choice felt like power — like buying your competitive edge off the shelf. Finally, vendor lock-in was dead.

… Until it wasn't. Every new best-of-breed tool created a new data silo. And every silo needed a custom-coded bridge to the next. The enterprise stack became a Frankenstein architecture — expensive to maintain, almost impossible to evolve, and opaque from the top. CFOs watching integration costs balloon knew something had gone wrong. The dream of meritocracy had produced a data fragmentation crisis. Business-critical operations now ran across disconnected systems that could barely communicate. Best-of-breed had delivered better parts — just not a better whole.

Go back to timeline

2010s

The SOA & API Era

https://delivery-p141552-e1488202.adobeaemcloud.com/adobe/assets/urn:aaid:aem:4337631f-81d6-4c88-8e6b-bbfdc700e82e/original/as/260504_ComposabilityHub_LandingPage_Module3-Timeline03-1620x1078px.jpg

APIs were the breakthrough enterprises had been waiting for. Service-Oriented Architecture promised a connected enterprise, with a, “Write once, use everywhere” philosophy. Build a login service, a payment service, a notification service — then call them from anything. CFOs saw development costs falling. CIOs saw the data silos of the best-of-breed era finally being bridged.

And the gains were real. The efficiency was genuine. For the first time, systems could exchange data without needing to understand each other's internals. This was the first genuine step toward modularity.

But there was a ceiling. APIs could move data, sure. But they couldn't move intelligence. Business logic stayed buried inside applications, invisible and immovable. To free up that logic from their siloed systems and put it to use, enterprises would need something more than connectivity. They needed orchestration. And that chapter was still to come.

Go back to timeline

2020s

The Cloud-Native Era

https://delivery-p141552-e1488202.adobeaemcloud.com/adobe/assets/urn:aaid:aem:fbede1a0-a27e-421a-81bf-076ec07de330/original/as/260426_ComposabilityHub_LandingPage_Module3-Timeline04-1620x1078px.jpg

The cloud-native era felt like invincibility. Microservices. Docker. Kubernetes. Enterprises stopped worrying about servers melting under traffic surges and started thinking purely about growth. CapEx became OpEx. Scale became — theoretically — limitless. For CEOs, it was a new kind of confidence.

The resilience was transformative. Customization was easy and could be done while keeping the core clean. Deployments accelerated while downtime shrank. Business-critical operations finally had infrastructure to match their ambition.

BUT. Agility at the infrastructure layer didn't translate to agility at the business layer. Cloud-native architectures required a standing army of DevOps engineers to manage them. And for business leaders, although the cloud felt powerful, it also felt opaque.

Go back to timeline

Today

The Composable Era

https://delivery-p141552-e1488202.adobeaemcloud.com/adobe/assets/urn:aaid:aem:da0fd8bd-d0fc-450d-ae87-2c4f0ce59f6d/original/as/260426_ComposabilityHub_LandingPage_Module3-Timeline05-1620x1078px.jpg

This is the era enterprises were always building toward. In a composable, AI-driven enterprise, business capabilities become interchangeable building blocks, each component performing one function brilliantly, connected through standardised interfaces.

Want to swap your checkout provider? Keen to automate, agentify, and bring AI into your manual operations without breaking a critical process? Looking to launch a subscription model in Japan next week? Compose it. Deploy it. Done.

For the C-suite, the composable era has revealed a key reason why AI pilots fail to scale: They were built as monoliths in disguise — isolated experiments that couldn't connect to the broader business. Composability breaks that pattern, freeing up the end-to-end business context that gives AI tools the ability to make accurate and reliable decisions.

Finally, enterprises can industrialize Enterprise AI across business-critical operations, not just proof-of-concept sandboxes.

But (and there’s always a but) — composability without visibility is just a new kind of chaos. To succeed in the composable era you need real-time insight into how change propagates across systems, teams, and value chains, and crucially, the foresight to take the right actions based on what’s going to happen next That’s what the Celonis Context Model enables.

Go back to timeline