Anthropic has a $100 million plan to help enterprises get more from AI. No, it doesn’t involve agents. It involves people. Lots of people. How retro. On October 2, the company announced Claude Frontier Academy, with a goal of training 10,000 frontier deployed engineers by the end of 2027.
Anthropic has a $100 million plan to help enterprises get more from AI. No, it doesn’t involve agents. It involves people. Lots of people. How retro. On October 2, the company announced Claude Frontier Academy, with a goal of training 10,000 frontier deployed engineers by the end of 2027.
For a business selling increasingly capable models, it’s telling that Anthropic recognizes some of the thorniest problems in AI don’t involve AI at all. This, to me, is indicative of real progress, because the heart of any technological shift is always people.
Which people? Well, that’s where this idea becomes particularly interesting. As critical as developers are to the success of AI in any particular organization, we need those developers working alongside the people who understand the business, with authority to turn that understanding into systems that other employees can use. That’s the key to AI success.
It has always been thus, however much we try to pretend that this time it’s different. Back in 2013, I quoted Gartner analyst Svetlana Sicular discoursing on the rise of big data technologies like Hadoop: “Organizations already have people who know their own data better than mystical data scientists.” She continued, “Learning Hadoop is easier than learning the company’s business.” Data becomes useful when paired with industry expertise—from experts who understand which data actually matters.
This topic is perpetually relevant. I covered the argument in 2023 and again in 2025. We’ve always needed people who can translate business requirements into software.
AI expands the kinds of knowledge that software can work with. Language models extend that work to material that often sat outside our applications: research notes, meeting transcripts, internal debates, and explanations of why a decision was made. An employee can ask questions across that material without someone first designing a report for every possible question. That opens up useful work that would have been difficult or expensive to automate before.
In context
- Topic: Inteligencia Artificial — IA aplicada al negocio: casos, límites, costos y gobierno.
- Source: InfoWorld
- Published: 20/10/2026
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Why it matters
The conversation about artificial intelligence has become noisy, and that makes it harder to see what matters. What matters is rarely the model. It is which concrete process ends up cheaper, faster or more reliable, and who is accountable when the answer comes out wrong.
The first thing I look at is not model capability but the data feeding it. Almost every project I have seen fail did not fail on the algorithm. It failed because the data lived in three systems with three different definitions and nobody wanted to be the one to fix that.
What usually goes wrong
The point where it usually breaks is the handover to operations. The team that built the pilot knows how to read the results; the team receiving it does not. Without training and written criteria, people start accepting everything the model says or ignoring it entirely, and both are equally expensive.
What to watch
- Whether the organisation can change provider without rebuilding everything, which is the proof it did not get locked in.
- Which concrete process is touched and how it is measured before and after; with no baseline there is no result, only opinion.
- Who reviews the model outputs and how often — the point at which AI becomes auditable.
How I read this entry
I would use it for a talent conversation rather than a purchasing one. The capability that needs installing is not operating a tool, which changes every six months, but framing the problem well and judging the answer. That one stays in the house.
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Living through this in your own team?
Open the chat and tell me how you're handling it. I'm interested in comparing notes.
