If the early AI race was about who could catch the trend first and bolt it onto the business, today the key challenge is whether you can turn AI into a durable operating model. Unfortunately, most companies are still failing that test.
In PwC’s Global CEO Survey 2026, 56% of CEOs say AI has delivered neither higher revenues nor lower costs so far, while only 12% report successes in both. I think that difference comes down to these CEOs’ design priorities.
Many firms continue to treat AI as something you can plug into a product without changing the mechanism underneath it. Such an approach rarely compounds. The data remains fragmented, responsibility is vague, and the model ends up constrained by the same legacy systems it was supposed to improve.
So, what should business owners do if they want to end up on the right side of that statistic? In short, build for AI and decentralization from day one. Now, let’s unravel what that actually means.
When “AI-Powered” Stops Selling
A couple of years ago, at the dawn of AI development, just one “AI-powered” or “blockchain-based” promise could do the job. It really sounded like progress. Today, though, it doesn’t. As the technology evolves, users, partners, investors — pretty much everyone has learned how to distinguish real progress from marketing headlines.
That’s the difference between true business strategy and trend cosmetics. If there’s a genuine understanding of where AI’s job fits in the product, what stable value it creates, and how it makes the users’ lives better, it’s certainly worth building. That’s not about having a perfect system from scratch. Even a minimum working version that proves its value is enough.
However, what still makes businesses fail is the “plug-in” mindset. From my experience, when AI is treated as just a feature that can be added or removed without changing the product’s integrity, it ends up as pure decoration. Yes, the team can ship it fast, but the business doesn’t get stronger for it.
So, as more people take off the rose-tinted glasses, investors also start asking tougher questions. Where does the tech sit: inside the core workflow, or on the side? And what actually improves because of it — cost, latency, fraud loss, conversion, retention, support load?
If a business can’t answer those things, the “AI layer” remains in demo mode.
Build It In, or Pay for It Later
The point is that when AI and decentralization are built into the architecture, they don’t work like detached modules anymore. Instead, they determine how data moves, how workflows execute, how costs evolve, and, more importantly, how trust is created.
In the first 12–24 months, this is the difference between just adding a feature and making viable architecture decisions. Those decisions are what eventually affect scalability, unit economics, pricing, and the business you’ll be running five to ten years from now.
When AI capabilities are exposed at the platform level — say, through core APIs and SDKs — the compounding effect appears immediately. Take real-time voice or video translation. If it’s built as a base capability, any product that adds voice or video can switch it on without heavy custom work. That reduces integration friction, speeds up partner onboarding, and lowers the marginal cost of shipping AI features downstream.
Decentralization works the same way, given it’s built into the system from day one. When servers, incentives, and operating rules are tied to on-chain logic early, the cost curve becomes different — and so does the trust model. Processes become much more verifiable, as execution follows transparent rules. Scaling, in turn, gets cheaper, as the network is designed for distributed growth.
All of this opens the door to community-run infrastructure. Autonomous AI can operate closer to the edge, while data ownership aligns with the people who generate and use it. As a result, there’s more flexible pricing, measurable service quality, and governance that can be audited rather than merely promised.
Tech FOMO Won’t Save You
Some may wonder why so many businesses chase the latest trends like AI, but end up embedding them on the surface. The answer is what I call “Tech FOMO.” Leaders are afraid of being left behind as soon as new technology appears, so they try to catch up in a rush.
Even so, fear-driven adoption rarely brings benefits. It pushes teams into late retrofits, messy integrations, and extra layers that increase costs and weaken reliability. Moreover, it makes products harder to scale because every “quick add-on” becomes another dependency.
That’s why, in my view, the better path is calmer and more deliberate. Treat AI and decentralization as architecture choices. Build them into core workflows, data flows, and accountability early, so they can transform how the business operates and earns trust. Remember: trends fade fast — foundations don’t.
