In a previous article, I argued that businesses need to stop looking for AI-shaped holes. There is no universal “killer app,” no single use case that magically works for every organization. That remains true today.
What matters is how thoughtfully a company evaluates AI within its own strategic context – its priorities, its people, and the problems it is trying to solve. The smartest organizations are building bespoke AI innovation programs instead of chasing hype.
But something has changed. Thinking is no longer enough.
As one executive recently told me:
“Waiting has become too dangerous.”
Organizations that delay getting on the high-speed AI train risk being leapfrogged by competitors who have already started building capability. The latest data supports this shift — enterprise AI investments are climbing rapidly, and companies are moving beyond experiments into real operational use.
The conversation is no longer if you should adopt AI.
It is how fast you can do it responsibly.
Where are we on the adoption curve?
Adoption is accelerating – but not evenly.
McKinsey’s 2025 global AI survey reports that roughly 88% of organizations use AI in at least one business function, yet only about one-third have achieved scaled deployment.
In other words: most companies have dipped a toe in the water, but relatively few are swimming confidently.
This follows the classic technology adoption curve popularized in Everett Rogers’ Diffusion of Innovations: innovators move first, early adopters follow, and eventually the majority comes on board.
What makes today different is the speed at which organizations are moving through this curve.
Let’s look at where enterprise AI adoption currently sits across different categories:
Here’s the important signal many leaders miss:
When productivity and operational use cases approach majority adoption, value is no longer theoretical – it’s proven.
That should be a red flag.
Not a panic signal – but a clear indicator that the window for “wait and see” is closing.
The question is no longer Should we start?
It is: Where should we start?
When evaluating your enterprise value chain, the best entry points for AI tend to share a few characteristics:
- Repetitive and manual
- Not fully automated
- Blend structured and unstructured data
- Use information that already exists
- Create measurable business impact
Starting here helps teams build momentum. You may not launch the most disruptive AI initiative immediately but you will develop capability, confidence, and organizational muscle.
And that is what separates AI tourists from AI leaders.
Two domains consistently rise to the top.
1. Customer support and service
AI in customer service is no longer experimental – it is rapidly becoming foundational.
A growing share of customer interactions will soon be AI-assisted or AI-led, enabling faster responses, better personalization, and support organizations that scale without matching headcount growth.
The benefits extend beyond efficiency:
- Customers get answers faster
- Agents focus on complex, high-value conversations
- Resolution times shrink
- Experience improves
Platforms like Computer, DevRev’s AI-powered support assistant, help teams handle complex inquiries by surfacing relevant product information, past interactions, and proven solutions in real time.
Support leaders often see 30–50% efficiency gains – fewer handoffs, faster resolutions, and less time spent searching for context.
The impact doesn’t stop with support.
Sales teams benefit as well. When reps can instantly access customer history, contracts, and relevant insights during critical deal moments, they spend less time hunting for information and more time selling.
2. Enterprise search & knowledge work
Most knowledge workers lose a surprising portion of their day simply trying to find information – often 25–30% of their time.
It is one of the largest invisible productivity drains inside modern enterprises.
AI changes this dynamic entirely.
Instead of digging through emails, documents, tickets, and dashboards, employees can ask a natural question and receive a contextual answer immediately – drawing from institutional knowledge across the organization.
Tools like Computer make this possible by connecting support tickets, conversations, customer data, and internal documentation into a unified intelligence layer.
The result is not just efficiency but organizational clarity.
Teams surface answers that would otherwise remain buried. Decisions happen faster. And employees reclaim time for the work humans are uniquely good at – judgment, creativity, and relationship-building.
This is less about working faster. It is about working smarter.
Why this matters now
The advantage of modern AI platforms is not just capability – it is accessibility.
The best solutions integrate into workflows teams already use, minimizing disruption while bridging disconnected systems like CRMs, ticketing platforms, knowledge bases, and communication tools.
AI delivers the most value when it feels less like a new tool – and more like an intelligence layer across your business.
Conclusion
The gap between AI strategy and execution is where many organizations stall.
But the cost of hesitation is rising.
Waiting has become too dangerous – not because every company must transform overnight, but because capability takes time to build. The organizations experimenting today are developing the experience that will compound into competitive advantage tomorrow.
The real risk is not falling behind the hype cycle.
It is missing the opportunity to turn AI into measurable business impact.
Start thoughtfully. Start pragmatically.
But above all, start now.
