PRESENTED BY
Cyber AI Chronicle
By Simon Ganiere · 26th August 2025
Welcome back!
📓 Editor's Note
"Despite $30-40 billion in enterprise AI investment, 95% of organizations achieve zero measurable return" - State of AI Business 2025 Report
Does that mean we need to stop investing in AI? Is the technology significantly worse than what we thought? What if this isn't a technology problem?
What if this is the result of organizational design failure? What if AI, despite all of its promises, is still subject to the same organizational rules?
The research demonstrates a classic information barrier. The top of the organization sees lagging indicators—pilot counts, deployment metrics, demo success stories. The bottom experiences leading indicators—actual usability, workflow integration, daily friction. Organizations celebrate pilots and PoCs while employees quietly abandon expensive enterprise AI tools for $20 ChatGPT subscriptions. This creates a dangerous feedback loop where investment decisions get made on outdated success signals.
This information gap isn't unique to AI and we've observed it across transformation initiatives. That barrier has killed more initiatives than any technical limitation.
Build vs. Buy. Nothing new here and this decision has impacted countless projects. The research reveals something telling: external partnerships succeed at twice the rate of internal builds, partly because vendors force risk decisions upward in the organization. When you build internally, departments can incrementally commit resources without triggering enterprise-wide risk review. Decentralized management works, but only with proper escalation frameworks. Without them, that gap between top and bottom leads to risk management decisions being taken down the line without senior awareness.
The hype is real. The research shows 50% of investments flow to sales and marketing despite back-office automation delivering superior documented returns. We're still in the phase where senior management chases the "shiny thing" rather than deep diving into the more difficult but ultimately rewarding work of fixing actual business processes. Back-office failures remain invisible to boards while sales tool problems generate immediate executive attention. Organizations optimize for visibility rather than impact.
Here's what makes this worse: most enterprises procure AI like traditional software, expecting it to work out-of-the-box. But successful AI tools learn and adapt over time while failing ones remain static. The complexity of making systems that actually evolve with usage patterns conflicts with standard enterprise procurement and governance approaches.
Unless companies take the hard road and realize this requires fundamentally different organizational approaches—not just deploying chatbots—we'll continue seeing minimal enterprise AI impact while shadow AI usage explodes around official systems.
The real transformation is happening despite enterprise systems, not through them