In 2026, beyond the demo:
Bringing AI to Production
In 2026, beyond the demo:
From Playground to Profit
How many AI projects did you launch last year? And how many of them are generating revenue today?
If the answer is "few" or "none," don't worry: you're in good company. 2025, now feeling so distant, was definitely the year of enthusiasm: chatbots built in an afternoon and demos that left jaws on the floor (followed by a stall). And let's be honest: the hype is over—or nearly so. Decision-makers have stopped applauding demos and have started asking for results, metrics, and receipts.
2026 takes no prisoners. The question has shifted from "What can this AI do?" to "How much does it save/make us?".
Welcome to "PoC Purgatory"
There’s a limbo where good ideas go to die. Let's call it "PoC Purgatory", just to be clear.
Have you ever had a prototype that looked like magic on a developer's laptop, but crumbled under the weight of costs and accuracy issues the moment it hit the real world?
Here’s the thing: the problem isn't the technology. The problem is we've been treating AI like a magic toy instead of a software component (how many of you measure predictive metrics?). To break out of this stall and turn costs into revenue, you need three things. And none of them involves just a bigger, more powerful model, though if you have one, it doesn't hurt.
The three levers to get serious
1. Governance: the brakes that let you drive much faster
Sounds like a paradox? It isn't. In Formula 1, powerful brakes are exactly what allow drivers to brake later and go faster.
Bringing AI into production without guardrails is corporate suicide. You cannot afford your virtual assistant inventing discounts that don't exist (look at the egregious cases that have already happened) or snapping at a VIP client, or worse, answering inappropriately to an employee asking about anti-discrimination policies.
Governance wasn't just boring red tape: it's the only insurance policy that lets you sleep at night while your Agents work 24/7.
2. Dirty Integration
An AI living in an isolated chat window is just a style exercise. Real value explodes when AI "gets its hands dirty" with your legacy systems or analyzes data continuously for you, highlighting critical issues and opportunities you wouldn't see otherwise.
Imagine an Agent that doesn't just read a complaint email, but logs into your ERP, checks the shipment status on SAP, verifies the return policy in your CRM, and drafts the refund wire transfer autonomously. Sci-fi?
This is the difference between a chatbot and a "quasi" digital colleague. And to do this, you need software engineering, not just prompt engineering or a nice demo.
3. Stop burning tokens: ROI matters
The biggest, most expensive model isn't always the right choice. Often, a smaller, agile model, well-trained on your data, provides a better result at a tenth of the cost.
In 2026, every token spent must have a measurable ROI. Monitoring costs in real-time isn't optional; it's survival. And even if they tell you that hosting the model on-premise cuts costs, act carefully: hardware has maintenance costs (OpEx) and SPOF (single point of failure) that often exceed cloud estimates.
Not only Solutions but Processes and Evolutionary Paths
At Volcanic Minds, we occasionally receive requests (actually, more often than we should) from companies looking for miracle software, a "turnkey" AI agent that does everything alone and will never need help or supervision. The truth? For now, it doesn't exist. And even if it arrives tomorrow, today doesn't count: every technological leap brings new adoption times and costs. It's useless to wait for the "Car of 2030" if we have to compete in 2026: let's choose the best one today and aim for the finish line.
Bringing AI into production is bespoke work. It means taking your unique processes, taking them apart, and figuring out where artificial intelligence removes friction and adds margin.
We don't just install a model for you. We build the infrastructure (often hybrid and complex) that keeps it standing, secure, and productive. We use frameworks like Volcano SDK or Mastra to orchestrate agents that actually work on complex workflows, not just chat. And we won't always say it's possible; we are innovators, but we know reality well and what ROI means.
It’s time to invoice
So the question "How many AI projects are worth launching?" can be answered with a healthy "It depends" on how it's approached. If we continue to treat it like an R&D experiment, it will remain a cost-fun and rewarding, but a cost nonetheless. If we start treating it like an industrial asset, it will become a competitive advantage.
Well, are you ready to escape purgatory? Let's schedule a no-obligation chat to see if we can help you resolve your doubts.
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Tag: AI, Management
Data di pubblicazione: 29 gennaio 2026
