Recently, this is a recurring discussion topic with my peers.

We are seeing a massive wave of "pilot fatigue" across the industry. C-Suites have poured significant capital into "Agentic" workflows over the past 12 months, expecting autonomous systems that can reason and execute on their own.

Instead, they bought excitement first, and chaos followed.

If you give an LLM a goal, a set of tools, and tell it to "figure it out," it will work beautifully in a demo. But in a production environment with strict SLAs, a bit of more work (actually a lot) is required.

The transition from pilot to production requires a shift from open-ended generation to a orchestrated, professional execution.

To build systems that survive contact with the real world, the architecture must evolve:
- a deterministic backbone: application logic must be driven by state-aware orchestration, not an unpredictable prompt chain.
- LLM and reusability: Use the LLM strictly for what it excels at—intent extraction and unstructured data processing—not for deciding the next critical operational step.
- state machine, security, safety embedded: The agent navigates dynamically, but only within pre-approved, safe, secured, financially sound business transitions.

At Dunewind, we design this framework. We're helping you build a sustainable enterprise asset, and not just subsidize a permanent science project.

Where are you seeing the biggest friction in moving AI pilots out of R&D and into full production?

Last week, I was deep in a roadmap session with a Tier-1 European Telco.

The whiteboard was full of the "unsexy" reality of enterprise scale: OSS/BSS integration, complex billing cycles, strict 50ms latency targets, and a hard requirement to handle multi-billion transactions per year without a blink.

When we finally got to the topic of AI, the mood shifted. A stakeholder waved a hand and said: "Well, surely your team can just use Claude to write the code faster and shrink the timeline?"

It struck me. This is the trap.

They viewed AI as a productivity hack—a way to type code faster. But in an environment with that much inertia and massive architectural constraints, "faster code" is just a faster way to break things.

At the end of the meeting I asked for a separate session on Dunewind.

I told them: "We aren't just going to use AI to write the code. We are going to use AI to monitor its execution and improve it automatically, while delivering a completely new experience to the user. Dunewind doesn't just execute, but governs the logic when you hit an edge case."

The conversation pivoted instantly: from delivery velocity to architectural improvements, new operational advantages and improved business plans.

The lesson for me: if you are pitching AI to a large enterprise solely as "speed," you are solving an R&D problem.

To solve a CTO's problem, you must pitch Control, Security and Resilience. With hashtag#MWC26 around the corner, this is the only conversation that matters.

The feed is loud today. New models, new capabilities, and the predictable scramble to integrate "the next big thing."

Reality remains unchanged: Intelligence is becoming a commodity, but Control is becoming a luxury.

We are witnessing a critical shift. It is no longer enough to be a Builder—simply wiring models to interfaces. The industry is demanding an evolution toward the Architect—designing the constraints, the intent, and the economic bounds of the system.

We see SMEs rushing to plug these massive new models into their existing SaaS workflows. The result isn't "magic." It's just a faster way to burn cash.

If you don't have:
1. Deterministic State Machines to guardrail the output.
2. Intelligent Templates to stop paying for the same answer twice.
3. Modular Unit Economics that track ROI per interaction.
...then a more powerful model is just a more expensive way to be inefficient.

We are helping enterprises solve this exact two-driver dilemma: aiming for the velocity of models like Claude while demanding 100% control over code sovereignty, security, and legal compliance. Our approach decouples the speed from the risk—allowing you to consume code generation as a raw commodity, while ensuring you remain the sole architect of your quality standards and transactional norms.

This is the reality for major enterprises—from Telcos to Industrials like ABB, Siemens, Bosch, ... They are solving a two-driver dilemma: leveraging the velocity of models like Claude, while enforcing 100% sovereignty over code, security, and legal compliance.

The value isn't in the model anymore. It's in the structure you build around it.

The question isn't "What can the model do?" The question is "What do you allow it to do?"

Following my post yesterday on the "SaaS Illusion," several of you reached out to ask about a specific point: Reusability.

I’ve spent the last year diving deep into this concept with my teams at ATES, specifically within the Dunewind project. Here is the reality: most companies are building AI solutions with disposable outcomes.

When you use AI in your SME to build or deliver a service, you cannot afford to "throw it away" and start from scratch every time. If you do, you’re trapped in a cycle of:
- Constant re-generation of results (solving the same problem twice).
- Endless token consumption.
- Skyrocketing GPU costs that eat your ROI alive.

The solution? Start compounding with Intelligent templates.

This shift toward reusability aligns directly with better business plans and long-term sustainability; it transforms AI from a volatile expense into a predictable asset.

At Dunewind, we have implemented reusability through:
- Building modularly: Create intelligence units that solve specific problems, then re-use them dynamically.
- Stopping the token leak: If a task has been solved once, the logic should be distilled into a smaller, cheaper model.
- Questioning the LLM: Just because you can use an LLM for every step doesn't mean you should. Reusability often means moving from a high-cost model to a low-cost, dedicated module.

What’s your strategy for keeping AI costs under control? Contact us if you'd like to know more about Dunewind and intelligent templates.

I’ve been working with my teams on AI since 2017. Now that the real challenges of moving LLM-based systems into production are surfacing, I wanted to share a few thoughts on why this transition is anything but automatic.

While the "demo" feels like magic, the reality of 24/7 operations requires a shift in mindset: Business first, technology second. Without an updated business plan and sound architecture, costs can spiral quickly on a monthly basis, even while the delivered value remains flat.

The move to production is far from automatic for two critical reasons:
a) The SaaS Illusion: unlike traditional software, AI doesn’t follow a flat-fee model. Costs can spiral monthly, with significant incremental costs per user that can eat margins if not carefully architected.
b) The Complexity Gap: building a "cool" prototype is fast; maintaining a robust, reliable, and secure AI system 24/7 requires a specialized skill set that goes way beyond the initial prompt engineering.

For a PME, the goal isn't just to have AI—it's to have a sustainable, profitable business fueled by it. To bridge this gap, the focus must shift to re-engineering the business model for a premium, sustainable experience.

Success in production requires a sound architecture that prioritizes efficiency over hype:
a) Solve the "Tough Situations". Deploy AI where it tackles high-value, complex problems that humans find bottlenecked or repetitive.
b) Focus on reusability – build modular systems with AI first, then ask yourself if each module must be done with AI or not.
c) Own the Intelligence – training your own models can drastically reduce per-user costs.
d) Look for alternatives – keep an eye on evolving partners; telcos, for example, are quickly becoming key AI actors (probably this will be one of the key annoucements @ MWC).

The shift from pilot to production is a business transformation. Start from here.

What’s your view on this? Are you seeing the same "SaaS illusion" in your own projects

?