The hardest part of enterprise AI isn't the AI

I've been off for a couple of weeks, enjoying vacation, but still keeping one eye on tech news, and it's a little unbelievable how much happened in two weeks in July, a time when news is supposed to slow down. 

Consider that over the last couple of weeks:

• We had major model releases from OpenAI and Anthropic. 
• Microsoft launched the Microsoft Frontier Company to embed engineers inside customers.
• Meta increased its data center investment in Louisiana to $50 billion.

Meanwhile, Mira Murati's company, Thinking Machines, released Inkling, a customizable model aimed at enterprises that continue to struggle to adapt general-purpose models from the big AI labs to their own needs.

According to Axios reporting, the startup hopes that by developing a more flexible model, customers can get better results at a lower cost. It's certainly a creative approach, but it's not yet clear whether it will solve the fundamental organizational problems that appear to be at the root of companies' AI challenges.

It ain't easy

When you look at Thinking Machines in the context of these other major announcements, a story begins to unfold. Microsoft, for example, has chosen to deploy (expensive) engineers inside customer companies to help them find more success implementing AI. OpenAI announced a similar approach in May. Both launches represent a tacit admission that, surprise, surprise, AI is hard to do well.

Perhaps the reason Microsoft decided to launch a new organization to help customers implement AI is because research shows that most companies continue to have problems taking advantage of AI, even as the models become more capable. 

A smarter model doesn't help you get good data to feed the model. It doesn't help you rethink workflows or change the way you work. All of these things require understanding most companies don't have, and consultants in theory should be able to point them in the right direction -- for a price of course. 

Thinking Machines is trying a different approach. Instead of building the most powerful model, which is great for bragging rights, but not necessarily practical, it's shooting for something that enterprises can work with. Whether Inkling is the solution or not, it's an acknowledgement that companies are having a hard time and they need a model that helps them work smarter, not necessarily the smartest model.

The struggle is real

The Thinking Machines approach might work better if employees weren't using AI. But the research suggests that they actually are. The problem is they don't appear to be getting the most out of it from an organizational perspective, an important distinction. 

After interviewing 750 employees and leaders across industries, a July 2026 McKinsey Report found that "Most organizations are still early in their AI transformation journey—and that employees are more ready to use AI than their organizations are to change around it." The fact that we are almost four years from the launch of ChatGPT, and companies continue to struggle in spite of internal usage is telling.

And executives are getting impatient. A Bain report released this month that asked 100 CEOs about their company's AI efforts found that "roughly 80% said they are dissatisfied with the progress of their AI programs, despite the heavy investment and high expectations." That's probably because they aren't getting the promised efficiency gains, while spending increasingly large sums of money trying to get there. That's not a combination that is going to make many CEOs happy.

Both reports suggest there is a fundamental organizational disconnect between AI usage and getting the most out of the technology. The Thinking Machines solution is to solve this with a more flexible model, but that still doesn't fix the basic organizational problems.

As we have learned with other major technological shifts, you have to change more than the software to get the most out of the transformation. That usually requires additional investment and a long-term and focused commitment companies have a hard time sticking to, especially when the cost of doing business keeps going up without good results.

~Ron