There’s a specific moment in any technology’s life when it stops being a conversation topic and starts being a utility. Nobody at a dinner party exclaims, “Have you heard about spreadsheets?” Nobody headlines a conference with “The Revolutionary Power of Email.” The technology just… works. Quietly. In the background.
AI is heading there. And honestly? It’s about time.
The Novelty Tax
Every genuinely transformative technology carries what might be called a novelty tax — a phase where a disproportionate amount of energy goes into debating the technology rather than using it. Should we? Could we? What if it goes wrong? What if we’re left behind?
For AI, that tax has been steep. Enterprises spent the better part of two years in committees, pilots, and very expensive proof-of-concepts. Gartner tracked the average company spending $1.9 million on GenAI initiatives in 2024 — and fewer than three in ten CEOs felt good about what they got back. That’s not a technology problem. That’s a novelty-tax problem. Too much energy on the question, not enough on the answer.
The shift happening now is subtle but significant. The question in the room has changed. Not “should we use AI?” — that’s settled. The interesting question becoming “which AI bets are worth the operational complexity of scaling them?” That’s a much more productive place to be arguing from.
When Boring Is a Feature
Here’s the thing about boring technologies: they’re the ones that actually change how organisations work.
The spreadsheet didn’t transform finance by being exciting. It transformed finance by being reliable enough that analysts stopped worrying about the tool and started worrying about the analysis. Email didn’t revolutionise communication because it was thrilling — it did so because it was predictable. The magic happened when the technology faded into the background and the work moved to the foreground.
The early signals of this happening with AI are visible if you look in the right places. It’s in the companies that stopped running AI pilots and started running AI operations — with SLAs, escalation protocols, and performance reviews. It’s in the job descriptions that no longer say “AI enthusiast” but instead list specific tools as assumed competencies. It’s in the meetings where the AI question isn’t whether to use it, but which model, at what cost, governed how.
That’s the transition worth watching. Not the headline announcements. The quiet operationalisation.
The New Competitive Terrain
The implication for the competitive landscape is worth sitting with.
When everyone has access to the same foundation models, basic AI capability stops being a differentiator. What separates organisations is no longer that they’re using AI — it’s how well their people, processes, and data infrastructure actually support it. The McKinsey 2024 State of AI survey confirmed that the biggest barriers to scaling AI weren’t the models themselves. They were data quality, integration complexity, skills gaps, and governance. All deeply unsexy problems. All completely solvable with patient, unglamorous effort.
This connects the execution gap between organisations that adopted AI broadly and those that scaled it meaningfully. Gartner estimated only around 9% of enterprises had meaningfully scaled AI by the end of 2024. The organisations closing that gap in 2025 aren’t likely to do it with a breakthrough model. They’re going to do it with better change management, cleaner data pipelines, and clearer ROI measurement. The pattern worth noting: the competitive advantage is shifting from access to execution.
What Actually Gets Exciting
None of this means the interesting problems are disappearing. They’re just changing shape.
As AI becomes infrastructure rather than initiative, the layer above it gets more interesting. How do you govern AI systems that are making decisions faster than humans can review them? How do you build organisational literacy so that the benefits are distributed across functions, not concentrated in a data science team? How do you measure AI’s contribution to outcomes — not just activity?
And then there’s the agentic layer — AI that doesn’t wait to be asked, but pursues goals autonomously. As explored in the agentic AI post from last month, enterprises are only beginning to understand what oversight and control mean in that context. That’s not boring at all. That’s the genuinely hard frontier.
The hype cycle has a name for this transition. Gartner calls it the Slope of Enlightenment — the phase after disillusionment where early adopters start seeing real benefits, and the technology begins its journey toward mainstream productivity. It’s not as dramatic as the peak. But it’s where the actual value tends to accumulate.
The Founder’s Recalibration
There’s a related shift happening for the builders.
The market that rewarded “we use AI” as a differentiator is shrinking. The market asking “does your AI actually work, reliably, at scale, for this specific problem?” is growing. That’s a harder pitch to make — and a more honest one. Products that were surfing the novelty wave are now being asked to demonstrate consistent value delivery. Some will. Many will struggle.
The boring-sounding virtues — reliability, integration depth, measurable ROI, low total cost of ownership — are becoming the product criteria that matter. Which is, when you think about it, how every maturing technology market eventually settles.
The most exciting thing about AI getting boring isn’t that the interesting problems are solved. It’s that the interesting problems are finally getting the focus they deserve — underneath the noise.
What’s the AI conversation that’s shifted most noticeably in your organisation heading into this year?
Let’s keep learning — together.
harish.balasubramanian 
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