The Case for Private AI

Most leadership teams still debate whether to adopt AI. Their employees settled that question long ago. The decision that remains open, and matters far more, is where that AI runs, and therefore who ends up holding the company's knowledge.

The case for keeping it in-house rests on three arguments.

1. Your data is already leaving

"Shadow AI" is the name for AI tools staff use without the company's knowledge or approval: a free chatbot to summarise a client file, a browser plug-in to tidy up a contract. It is rarely malicious. It is people trying to work faster with whatever is at hand. But every one of those prompts sends company data to a provider the company has no agreement with.

The scale is no longer anecdotal. Netskope's 2026 Cloud and Threat Report found that the average organisation now records 223 incidents a month of employees sending sensitive data to AI tools, double the previous year. IBM's 2026 Cost of a Data Breach study found shadow AI involved in 43 percent of breaches, up from 20 percent a year earlier. Those breaches cost $5.39 million on average, against $4.63 million for others, and 68 percent of the organisations affected had no AI governance policy at all.

The risk is not AI. It is AI that runs outside the company's control.

2. The private alternative is now good enough

Until recently, keeping AI in-house meant accepting a much weaker tool. That trade-off has largely disappeared, for two reasons.

First, open-weight models, which anyone can download and run on their own hardware, have caught up. Epoch AI, an independent research group, estimates that during 2026 the best of them have trailed the leading commercial systems by around four months on average.

Second, most business work does not need a model that knows everything. It needs one that can read the company's own documents and answer from them. That is what retrieval-augmented generation does. The technique, introduced by Lewis and colleagues in 2020, finds the relevant passages in a company's files and gives them to the model alongside the question, with a citation for each answer. The model reads rather than recalls, so compact models running on ordinary business hardware can do the job well.

For the work that involves a company's most sensitive information, its contracts, client records and internal analysis, a private system is no longer the compromise option.

3. A better tool beats a ban

The instinctive response to shadow AI is prohibition. It rarely works: people who find AI useful simply move to personal devices and accounts, where the company can see even less.

The evidence points the other way. In Netskope's data, the share of AI users relying on personal accounts fell from 78 percent to 47 percent in a year, while the share using company-managed accounts rose from 25 to 62 percent. When employees are given a sanctioned tool, most of them use it.

Private AI is the strongest version of that answer, because the sanctioned tool is also the safe one. Data, questions and answers stay inside the company, so the governance question is settled by design rather than by policy.

The choice in front of leaders

None of this means abandoning commercial AI. For the hardest reasoning problems and for work on public information, the leading cloud models still have an edge, and little is lost by using them.

But for the knowledge that actually differentiates a business, the default should now be private. The companies that get this right will be able to say, for every AI workflow, where it runs and why. Those that don't will keep discovering the answer in their breach reports.