In the past year, many companies have made headlines for massive job cuts because of AI automation. And while these cuts have quickly reduced headcounts and employee expenses, they haven’t all been effective. Studies show that about a third of companies that have laid off employees because of AI have already hired people to fill some of those vacated roles, and about half will make similar moves by 2027, writes Forbes senior contributor Rachel Wells.

Figuring out the proper balance between employees and technology is difficult—especially at this stage in the hype cycle, when there may be a gap between what it appears AI can do and what it actually takes for AI to do it at a company. I talked to Eric Helmer, CTO at third-party enterprise software support provider Rimini Street, about how to actually do the math on this problem—and convince other executives your plan is best. An excerpt from our conversation is later in this newsletter.

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While it’s well known that AI adds cybersecurity risk, Kiteworks’ 2026 Data Security and Compliance Risk Annual Survey Report, shared exclusively with Forbes, adds stark numbers showing how vulnerable most enterprises are—and how much they have already lost. The report shows that most companies are nowhere near ready for the risks AI poses. Kiteworks evaluated companies based on their operational controls on a scale from 1 to 100. For combined data and AI governance—in a measurement called the Data Security and Compliance Readiness Index—the average score was 16.2. But even without that score, the study found incidents increasing. Four out of five organizations had at least one security or AI-related incident in the last 12 months.

“Organizations have deployed AI far faster than they’ve built the governance infrastructure to manage it,” Kiteworks Chief Strategy Officer Tim Freestone said in a statement.

The biggest problems Kiteworks found are a lack of AI governance strategy and visibility into the system. Nearly 70% of organizations have no behavioral monitoring of AI systems, and close to three-quarters cannot trace AI outputs to source data. Meanwhile, close to two-thirds discovered employees using shadow AI tools—with a third of them finding employees input sensitive data including company login credentials, personal and HR data and customer and client data.

There is a long way to go for improvement, and Kiteworks recommends several steps to get on the right track. These include classifying and tagging sensitive data, adding controls so it can be tracked in your system and cannot be moved to personal AI; automatically logging everything that happens with sensitive data; building auditable tracking of AI actions; making AI data governance someone’s full-time job; consolidating sensitive data platforms; and testing AI kill........

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