Two female founders over 50, one from corporate communications and the other from institutional investment consulting, have launched a startup that doesn’t build AI models but teaches people how to use them. Louise Ballard and Mackenzie Howe founded Atheni AI with a mission: to prevent a two-tier society where only those with expensive tools or specialist training benefit from AI, and to ensure that massive license investments don’t go to waste.

The UK-based company tackles a paradox that many organizations now know too well: access does not equal adoption. A global study by CambrianEdge.ai, which surveyed 775 users across 104 organizations, found that 55% of professionals see isolated individual use and the lack of structured human-AI workflows as the biggest barrier to adoption. It’s not just a feeling: 25% of organizations still lack basic collaboration infrastructure like shared prompt libraries, training, and quality standards, and 18% have already scaled back AI initiatives due to poor adoption and inconsistent results.

Ballard explains with a concrete example: “Anyone can open ChatGPT and ask a question, but do they know if the answer is good? Can they provide the right context? Can they challenge the output? These are skills you learn only through guided practice and confidence.” Atheni AI’s solution flips the traditional approach: instead of generic workshops or one-off demos, the team sits alongside people while they’re working, analyzing prompt quality, workflows being built, and missed opportunities—moving users from a “curious” stage up to “pathfinder.”

Why AI in business is like a Ferrari in the supermarket

“Giving someone a Copilot license doesn’t automatically change how they work,” Ballard says, comparing shallow AI usage to driving a Ferrari only to go grocery shopping. Real change happens when processes are redesigned, not just automated. One corporate finance client, for example, redesigned a monthly spreadsheet process rather than simply speeding it up.

Atheni’s approach starts from daily work and offers continuous coaching via a browser-based assistant, with a dashboard that gives organizations transparent visibility into internal AI skills. In one case, after three months a company reached 90% adoption and a third of employees advanced to the highest capability tier. It wasn’t about writing more prompts, but about learning to stress-test ideas, analyze scenarios, and tackle problems that were previously out of reach.

The infrastructure knot: when on-premise amplifies the problem

Those choosing on-premise or self-hosted deployments for data sovereignty and control often overlook a critical dimension: without a simple interface and an accompaniment path, LLMs remain the domain of a few technical experts. The risk of “phantom due diligence”—expensive GPU servers churning out inferences but with very low actual usage—is high. Atheni AI’s lesson, even without mentioning hardware or specific frameworks, shows that adoption maturity demands contextual coaching and knowledge-sharing structures—exactly the same bottlenecks that emerge in on-premise environments.

The CambrianEdge.ai research confirms this: organizations with comprehensive AI infrastructure—shared prompt libraries, training, governance—are dramatically more likely to report significant business impact. Those that just hand out licenses (or worse, self-built models without guidance) end up with AI-written emails that don’t communicate anything particularly well.

Human at the center, even in an agentic future

Atheni AI closed a £350,000 round last May, not without difficulty. “As a female founder in my fifties, I faced a credibility gap, not just a funding gap,” Ballard admits. The startup first invested in consulting to validate the problem, then built the software platform. That path now allows them to look ahead: as AI agents become more autonomous, the real challenge will remain orchestrating them with human judgment.

Generative AI is pushing companies to rethink not just technology but the entire way of working. Whether cloud models or on-premise LLMs, the watershed isn’t owning the technology but embedding it into mental and organizational processes. As Ballard puts it: “Success is measured by how deeply AI becomes integrated into the way people think and work.” For anyone evaluating local architectures today, the message is clear: hardware alone won’t save you.