Companies Blindly Pour Over $10M into Agentic AI Without Understanding What They’re Buying

Corporate AI spending hits reckless new heights as businesses throw millions at technology they barely comprehend.

The Black Box Boom

Boardrooms keep greenlighting eight-figure checks for autonomous AI systems while admitting they can't explain how the algorithms actually work. We're talking $10 million+ deployments where executives couldn't distinguish between machine learning and magic if their bonuses depended on it.

Budget Blindspots

Departments compete for AI funding by slapping 'agentic' labels on everything from customer service chatbots to spreadsheet macros. The result? More wasted capital than a crypto hedge fund's 'algorithmic trading' experiment.

The Accountability Gap

Nobody gets fired for buying IBM's 2025 equivalent—even when these systems make decisions that would get junior analysts escorted from the building. It's the ultimate CYA strategy: blame the algorithm.

Until shareholders realize they're funding nine-figure sci-fi experiments with zero understanding of ROI, this theater will keep running. Maybe they should try reading the whitepaper before writing the check.

Leaders spend more, adopt less

Despite the surge in spending, implementation is crawling. Only 14% of surveyed leaders said their company had fully rolled out agentic AI. Everyone else is stuck in pilot purgatory. Dan said the gap is because companies aren’t ready for the demands.

“This includes having organized, high-quality knowledge to guide these systems and a clear understanding of how companies navigate the massive change between the current and future states.” Translation: no foundation, no rollout.

Even with returns from earlier AI tools, most firms are hesitant to MOVE forward. Dan said it’s the mix of technical weakness and change resistance that’s slowing things down. “While this combination creates a climate of uncertainty, it provides a clear roadmap for organizations,” he said. That roadmap? Fix the internal mess first. Otherwise, it’s just more expensive pilots going nowhere.

Deepankar Mathur, associate director at Searce, said the whole idea of full-scale adoption is kind of useless now. “It’s like trying to hit a constantly moving target,” Deepankar said.

The way agentic AI evolves, there’s no single launch moment. Instead, it’s about constant upgrades. Identify what needs automating, decide what matters most, use the best tools available, and then improve them again, immediately. “This cycle of improvement isn’t a temporary project; it’s an ‘always-on’ operational imperative,” he said.

Executives define roles, secure systems, and unlock teams

Dan said the way to avoid fear and confusion is to treat the AI-human mix like a real partnership. Spell out who does what. “This means crafting a strategy that outlines what tasks AI will handle and what roles humans will play,” he said. That removes doubt and gives employees room to work with the tools instead of against them.

But that only works if the AI has something to work with. “Jobs are performed through know-how and experience, which is information that may exist only in workers’ heads,” Dan said. That kind of knowledge doesn’t sit in a database. It has to be captured and turned into structured material. Agentic systems need that to make smart decisions. No input, no output.

And then there’s cybersecurity. Dan said more agents in production means more vulnerabilities. “We’re starting to get more news of the cyber implications in many agents,” he said. That means companies need to build AI-focused cyber plans from day one. Set rules around data use, privacy, ethics, and when a human has to step in. “By proactively addressing these governance questions, leaders can build a trustworthy and transparent system,” he said.

Deepankar also pushed for giving teams direct access to AI tools. He said being an engineer isn’t required anymore to build something useful. “The barrier to AI implementation has significantly lowered,” he said. But relying on steering committees or centralized AI boards just slows things down. “True progress requires leaders to actively champion and enable this widespread adoption.”

He said the most forward-leaning companies are setting up internal AI centers of excellence. These aren’t giant departments, just tight teams of experts who embed into different business units, train them, and get them building their own agentic workflows. “The most successful enterprises are building small, elite teams of ‘AI blackbelt’ specialists,” he said.

Sign up to Bybit and start trading with $30,050 in welcome gifts