The problem is no longer having apps, but connecting them well
A few years ago, the main question of digitalization was simple. Move from Excel and email to a proper system. Get an ERP, roll out a CRM, launch an e-shop, or have a custom app built. Most companies have that behind them now.
The number of systems grew, but each one lives a life of its own. Sales logs a new opportunity in the CRM, finance issues an invoice in the ERP, service keeps documents somewhere on SharePoint, and management pulls numbers into a spreadsheet. The same data sits in several systems, and every department trusts it a little differently.
The numbers back this up. According to the Czech Statistical Office, in 2025 already 49.7 percent of Czech companies with ten or more employees used an ERP, and 36.2 percent a CRM. Among mid-sized and large firms those figures are higher still. So the market no longer asks whether to have a system. It asks how to connect it to everything else.
And this is where the integration layer comes in. Something the end user never sees, yet it decides whether your individual apps become one working whole or just an expensive collection of separate islands. At eMan we see this with clients all the time. Often the client doesn’t need another app. They need the ones they already have to finally start talking to each other.
“Our systems don’t talk to each other.” What’s really behind it
We hear this sentence over and over, and it hides several problems at once.
First, manual re-typing. Someone takes data from one system and keys it into another. Boring, lost time, typos. Then duplicates, because the same customer exists in three versions and nobody knows which one is valid. Add slow approvals, errors in orders, and reports management doesn’t trust, because every department calculates them from different input numbers.
In practice it looks like this. A manufacturing company has a CRM, a document system, and an ERP. Each one works great on its own. Sales enters data into the CRM, a technician digs for documents, accounting copies numbers into the ERP. The same piece of information travels through the company, from person to person. And with every re-entry, the odds of an error somewhere go up.
Without connection, you pay for the same data several times over. Once in the system license, again in people’s manual work, a third time in the errors you then have to firefight, and a fourth time in the fact that every later change takes forever. Integration debt turns from a technical detail into a bill that genuinely hurts.
What an integration layer actually is
An integration layer is a set of tools and rules that let applications, data, and processes communicate securely, reliably, and repeatably. It isn’t one specific product. It’s more a family of approaches you pick from depending on the situation.
With a handful of stable systems, sometimes it’s enough to connect them directly, point to point. It’s cheap, fast, and it works. The trouble usually comes once the number of systems grows. Figuratively speaking, a few connectors turn into a tangle of cables where one change knocks out five other interfaces. It’s called spaghetti architecture, and it’s the nightmare of anyone responsible for operations.
That’s why the integration logic gets pulled out into its own layer. In older, on-premise environments this is usually middleware or an ESB (Enterprise Service Bus), a central layer that translates, routes, and polices the data. In the cloud and SaaS world, an iPaaS often makes sense today, an integration platform as a service you rent complete with ready-made connectors to common systems. And when you need to react in real time, an event-driven architecture joins in.
API management plays an important part too. It turns individual connections into governed, versioned, reusable interfaces. A connection to the ERP, once built, can be safely offered to other apps without writing it again from scratch.
When a direct connection is enough and when it isn’t
A practical question may come to mind. When can we still manage without a truly robust layer, and when can’t we?
A simple rule of thumb. As long as you have few systems and they change rarely, a direct connection serves just fine. But once the number of apps grows, the integrations start changing often, or every tweak in one system raises worries about what it will break elsewhere, it pays to separate the integration out and manage it on its own. That cost comes back the moment you stop asking, with every change, what else is going to fall apart.
The second rule of thumb is a business one. Wherever connecting things removes manual work or shortens time to the customer, start there. Where it’s a marginal process, it can wait.
Why AI doesn’t scale without connected data
Now to the topic that currently stirs up just about every leadership meeting: deploying AI.
Many AI pilots don’t hit the limits of the model. The problem comes only when the AI has to reach safely into real company systems and finds the data scattered across silos, without interfaces and without control. Connecting AI to an ERP, to SAP, or to a fifteen-year-old application is not the same as running a demo in an isolated environment.
The scale of the problem is well captured by the Connectivity Benchmark Report 2026 from Salesforce and MuleSoft. According to it, large enterprise organizations manage 957 applications on average, yet only 27 percent of them are connected. No wonder companies hit a ceiling the moment they want to use that data for automation or AI. Without an integration layer, deploying artificial intelligence becomes more of a headache than a real solution.
In practice it means an AI assistant answers nicely in a demo, but in live operation it has nowhere to pull current data from. So it fails the company exactly where it should help most. Integration, then, isn’t a competitor to AI. It’s the precondition for AI to do what we expect of it in the first place.
Security and control: a highway and a customs post for data
Once you connect systems, data starts to flow. But that also brings risk, because errors spread just as fast. That’s why a good integration layer works not only as a highway but also as a customs post. It checks what may pass through, and in what shape.
In business terms that means three things. The integration layer is the central place where you control who may reach into the ERP, CRM, and other systems, when, and how. It also guards quality, things like required fields, duplicates, and data formats. And finally there’s monitoring, so when an order doesn’t make it from the e-shop into the ERP, you trace why, instead of hunting through emails and the database.
And this side of things has stopped being optional. The EU NIS2 directive and its Czech implementation push companies to treat cybersecurity as risk management, not a technical detail. In regulated sectors such as energy, financial markets, transport, healthcare, or public administration, that goes double. At integration points it will matter more and more who has access, who watches them, and who is accountable for them. Scripts “off to the side somewhere” will be harder and harder to defend.
How to do it so it actually delivers
The most common mistake? A company starts with the tool instead of the process. A platform gets picked, a license bought, and only then does anyone think about what is actually supposed to be connected.
At eMan we always look at the problem first and the technology second. So we begin by mapping the flows that hurt the most and carry the most value. Typically the path from inquiry to order, from order to invoicing, customer onboarding, or document approvals. Only then do we decide which system is the source of truth for given data, where you need a live interface, and where a batch transfer will do.
Start where a new integration eliminates manual re-entry. It’s concrete, easy to grasp, quick to measure. A pilot that connects, say, the CRM with the ERP pays back sooner and earns you both the trust and the budget for the next steps.
Building something inside an existing environment is often the real challenge. Connecting two or three apps usually isn’t the hardest part. The harder part is making it work amid the dozens of systems around them, so the new solution runs and breaks nothing else. After years on client projects, we see it again and again.
And the most important thing last. Treat integration as a product, not a one-off project. It has an owner, a backlog for further development, testing, and operational care. That’s the only way it won’t end up resting on one clever person who, when they leave, takes the know-how with them in their head.
If data in your company travels from person to person and every change in one system throws three others off, get in touch. We’ll gladly look at where it grinds the most and propose a first integration step with a clear business impact.
Frequently asked questions
What is an integration layer?
It’s a layer between your IT systems that makes applications exchange data automatically, securely, and reliably. Instead of dozens of direct connections that are hard to maintain, you have one governed place through which the data flows.
When is it worth handling integration on its own?
When the number of systems is growing or changing often, and people spend time re-typing data between them. With two or three stable systems, a direct connection may be enough. Once there are more, a dedicated layer pays for itself quickly.
What’s the difference between ESB, iPaaS, and API management?
In short: an ESB is a centralized layer typical of more complex on-premise environments and legacy systems. An iPaaS is a cloud integration platform that usually offers ready-made connectors, workflows, and tools to link SaaS, cloud, and on-premise systems. API management then makes sure interfaces are secure, versioned, and reusable. In practice these approaches are often combined.
Is integration linked to the success of AI projects?
Yes, directly. A lot of AI pilots end not because of a weak model, but because the AI has no secure access to the right company data. Without connected systems and governed interfaces, AI stays at the demo stage and never reaches real operation.
Where should we start when we want to connect our systems?
With an audit of data flows, not with choosing technology. Map where data is re-typed by hand and where it hurts the most. Then pick one specific flow with a clear return, connect it, and scale further only based on the result.
