AI costs are all over the news right now, with Anthropic forced to change how they’re billing companies who are surging as power users.
But so many companies I talk to aren’t even at that stage.
And it’s not because they’re struggling to build solutions, because building AI is no longer the hard part.
The issue I keep seeing companies run into now is scaling.
Most teams I talk with already have their use cases down. They may have a working solution, and even some early traction.
But their momentum is shot—the project stuck in the mud.
And some of these solutions are very strong, too, but adoption is proving much more challenging than expected.
Many didn’t expect when they started that this is where their initiative would go to die.
Don’t Be Fooled: The Adoption Climb Can Be Steep
It’s sometimes not about a lack of solutions as it is about having too many.
Every department has its own tools, with its own preferences, and ways of working. Every executive has their backlog of “high-priority” initiatives.
Say you’ve got a really valuable AI solution. You still need to fight for:
- Budget
- Attention
- Integration resources
At Peterson Technology Partners (PTP), I’m seeing this play out across clients managing millions of interactions every month. Still, their problem isn’t value which can be clearly shown.
It’s about getting it into production fast enough to actually get that value before it’s too late. But once you enter the enterprise buying cycle, you can find your momentum being ground to a halt.
Where Is Your Momentum Dying?
I’ve written before about the startup mentality vs “manager mode.” The difference between these mentalities plays out here, with many AI companies underestimating the problems real businesses face.
Because while AI moves lightning fast, enterprises typically don’t.
I see pilots that have everyone excited run headlong into:
- Lengthy procurement reviews
- Uncertain security approvals
- Risk-averse legal cycles
- General IT integration queues
It’s not unusual for AI pilots to blow minds in their first week and then take three to four more months just to really deploy.
By the time everything gets approved, the urgency is long gone and internal priorities have shifted onto something else.
For many enterprises, this can be the traditional, prudent system. But it’s proving a very challenging fit for AI.
Slow Deployment Is Likely Far More Costly than You Think
Time is money, and the real risk in AI may not be losing the deal as much as missing the window.
When deployment slows down too much:
- Executive sponsors lose leverage
- Internal champions get pulled away
- Use cases evolve or disappear
- Feedback loops break
We have seen this firsthand with large enterprise programs where early traction was incredibly strong, but as delays in rollout piled up, they struggled to get to scale.
You see it time and again: the winners in AI are pulling ahead and leaving the rest even further behind.
With AI’s exponential scaling, speed is a critical advantage.
Scaling AI for Real
Okay, so for enterprises, how are some managing to realize their transformative AI projects at scale while others are still stuck in the swamp?
It’s usually not about building better products as much as treating adoption itself as a product problem, not just a sales challenge.
Here I mean:
- Deployment in days, not months
- Security and compliance that are built in and proven from the start
- Integrations light enough to not require heavy IT lift
- Clear ROI that’s hype-proof and resonates across stakeholders
At PTP, we have learned that success does not come from winning just a single champion. It comes from aligning your entire ecosystem. Meaning:
- End users who rely on the solution daily
- The IT team that must support it
- The finance team that must justify it
Because if any one of these is not in, your scaling is not going to happen.
The Shifts I Recommend to Most Teams
Once you have your first 10 customers, you’ve got your production milestone. The next 100 is the adoption milestone.
That requires a mindset shift: focus less on features and more on time-to-value; and focus less on demos and more on deployment.
This is a recurring, and avoidable, problem: teams continuously improving their AI solutions while adoption stays flat.
Yes, your product must work and work well. But the path to production cannot be too slow.
Final Thoughts
For many companies of size, the hardest part of AI right now may not be the product itself so much as what happens after the demo ends.
The teams that win will not be the ones with the most advanced technology.
They will be the ones who make it the easiest to adopt.
Schedule a short conversation with us to see how we help make this a reality.
