[NEW WEBSITE] - Blogs

AI-Assisted COBOL Modernization: Why Understanding the System Matters More Than Rewriting It

Benny Vranckx — Tue, 23 Jun 2026 06:49:32 GMT

What Leaders Often Get Wrong About Legacy Modernization

A common assumption is that legacy modernization is primarily a technical challenge. Replace COBOL, migrate code, move to a new platform. That’s the narrative. In reality, the bigger issue is organizational memory loss.

From Idea to Application: Four Principles for the Reliable Use of AI in Applications

Bertje Bensaïd — Fri, 03 Apr 2026 13:30:00 GMT

This shift affects the entire software application, from backend to user interface. Designing software is no longer just about strict correctness, but about building resilient systems in which humans and AI work together.

Four design principles help make this transition manageable in practice.

Why AI-Driven Speed Requires Human Intelligence

stijn.lambrechts@cegeka.com (Stijn Lambrechts) — Wed, 04 Mar 2026 23:00:00 GMT

Human intelligence remains essential

Delivering software has never been just about writing code. Even with AI, customers rarely provide requirements that are complete or detailed enough to realize the intended business outcome. So we continue to sit down with customers, clarify the real business problem, define the right goals, and make ethical and pragmatic decisions together.

From Cost Control to Value-First Software Delivery

Isabelle D'heedene — Tue, 17 Feb 2026 09:12:14 GMT

*�� Application Services emphasizes value-first delivery. What does this mean in practice?

At its core, value-first delivery means being very deliberate about why we build something, not just what we build. The starting point is always the business outcome we want to achieve, and only then do we decide what software capabilities are truly needed to support that outcome.

That doesn’t mean cost suddenly becomes irrelevant. Budgets are always a reality. But instead of letting cost alone drive decisions, we help organizations prioritize what genuinely contributes to their strategic goals. The conversation shifts from “How cheaply can we deliver?” to “What will move the needle for the business, and how do we deliver that efficiently?”

In practice, this requires a clear shift from output metrics to outcome metrics. Output is about what you produce: delivery speed, cost per story point, through put. Those are useful indicators, but they don’t tell you whether the business is actually better off. Outcomes, on the other hand, are about impact, for example revenue growth, customer satisfaction, faster time-to-market, or improved compliance. Those outcomes give direction and meaning to delivery.

“Value-first delivery means shifting the focus from output — what we deliver — to business outcome: the impact that delivery has on the organization.”

Is this approach new?

The underlying principle isn’t new. Value-driven thinking has long been part of agile and product-centric ways of working. What has changed is the pace and scale at which software is developed and evolved today, especially with the rise of AI.

AI amplifies the need for value-based prioritization. If your application landscape is fragmented, weighed down by technical debt, or poorly integrated, you simply can’t take responsible advantage of AI. Modernization becomes a prerequisite: clean interfaces, reliable data, solid security, and a product-centric way of working that allows teams to iterate in a controlled, meaningful way.

In that context, being crystal-clear about value is no longer optional. When delivery accelerates, the cost of building the wrong thing, or building too much, increases dramatically.

So how do you demonstrate the business value of IT investments and decide which metrics to use?

It starts with creating a direct and explicit link between business priorities and IT activities. Together with the customer, we first define the desired business outcome of an initiative in qualitative terms, for example growth, efficiency, or improved customer experience. We then translate that ambition into a concrete, time‑bound business objective.

From there, we look at which business processes need to change to support that objective. That analysis might reveal, for instance, that reducing a fulfillment cycle from several days to a much shorter timeframe would unlock measurable value. Only then do we translate those process improvements into software capabilities and delivery priorities.

For every feature we build, we deliberately ask: Which business outcome does this support? That discipline makes value visible and measurable, and it reframes IT from a cost center into a tangible value creator for the organization.

Does this approach also help reduce investment waste?

Absolutely. A value-first approach naturally reduces waste, because it forces tough prioritization. We typically distinguish between two main types of waste: scope waste and technical waste.

Scope waste occurs when features are added that don’t meaningfully contribute to business goals. It’s a common pitfall in product development: good intentions lead to over‑designed solutions with increasing complexity, cost, and limited return. In a product‑centric engagement model, our teams actively challenge feature requests together with the customer. Not to slow things down, but to make sure every addition earns its place by contributing to the intended outcome.

Technical waste is different in nature. It’s not just about performance issues or instability; it’s about technical choices that increase long‑term cost and limit adaptability without delivering proportional business value. Poor architectural decisions, unnecessary complexity, or solutions that are hard to evolve all create drag over time. These are exactly the kinds of trade‑offs that a value‑first mindset brings into focus.

To wrap up: what is the first concrete step organizations should take toward value-first delivery?

Start by organizing application teams around end-to-end responsibility for business outcomes, not just delivery tasks. Give those teams ownership over clear business goals, along with the budgets needed to achieve them.

That requires continuous and explicit alignment between business owners and IT teams; clear agreements on expected results, priorities, and constraints. Just as importantly, teams must regularly validate whether what they’re building still contributes to the intended goals, especially as those goals evolve.

*��’s product-centric outsourcing model is designed precisely for that purpose: embedding value-first decision‑making and sustained business outcome ownership into how software is delivered.

“Our product-centric outsourcing model embeds value-first decisions and shared ownership of business outcomes into AI powered software delivery.”

Why AI Accelerates Software Delivery and How to Avoid Accelerating Waste

Koen Janssen — Tue, 17 Feb 2026 09:10:15 GMT

Generative AI, in the form of large language models (LLMs), is often hailed as an accelerator of software delivery. It’s especially useful for swiftly generating boilerplate code and simple, isolated functions. It’s also surprisingly good at code reviews: checking whether new code adheres to a project’s coding style and recommending changes to resolve identified issues. To a certain degree, LLMs can even debug code, interpret error messages, and suggest viable solutions. Moreover, AI assists developers navigating legacy code bases by providing valuable insights and documentation, enabling a head start. By leveraging LLMs for these tasks, software developers can improve their efficiency significantly.

TechXplorers Insights: From Hype to Hands-On – AI’s Impact on Business and Tech

Florentina Sima — Wed, 27 Aug 2025 13:38:52 GMT

After the energy of our first TechXplorers event, we knew one thing for sure: this wasn’t going to be a one-time conversation. There was too much curiosity in the room, too many meaningful connections made, and too many important questions left to explore. That’s why our second event, “From Hype to Hands-On: AI’s Impact on Business and Tech”, felt more like the natural next chapter of a shared journey.

*�� Level Up Program: Inside our QA Automation curriculum

Florentina Sima — Tue, 19 Aug 2025 13:41:37 GMT

After launching our first *�� Level Up Program, we wanted to take a moment to share not just what we covered, but why we chose this structure in the first place.

Vendor risk assessment: A strategic pillar for every business

Ludovica Gaspari — Wed, 06 Aug 2025 11:45:00 GMT

Do you really know the risks you're taking when outsourcing critical services and operations?

Many companies only realize the danger when it’s too late—when security has already been breached, operations disrupted, and financial, legal, and reputational damage has taken its toll.

What the Discovery Phase really delivers

Florentina Sima — Mon, 23 Jun 2025 09:40:22 GMT

When building a digital product, whether it's a platform, tool, or transformation initiative, the first few weeks can define the outcome of the entire project. Many teams underestimate this, jumping into design or development with vague requirements, unclear goals, and incomplete alignment. The result: delays, scope changes, unnecessary costs, and frustrated stakeholders.

*�� Level Up Program: growing together in a changing IT landscape

Florentina Sima — Fri, 06 Jun 2025 09:40:58 GMT

It’s no secret that the IT landscape has changed. The expectations are evolving, and what once felt stable now requires constant adaptation. In Romania especially, we’ve seen a wave of changes across the industry: from layoffs and restructurings to a growing need for more specialized, tech roles. For many professionals, whether in development, support, testing, or project roles, these shifts have brought some real uncertainty. And the question a lot of people are asking right now is: “What’s next?”