Why Enterprises Feel Stuck on Their Path to AI Adoption

Every enterprise wants to unlock value from their proprietary data using AI — from search and assistants to automation and compliance monitoring – the benefits of AI are huge and the impetus to move quickly grows by the day. Yet many are hitting the same wall: privacy, security, and regulatory blockers. Unlocking value depends less on model choice and more on data being private, accessible, and ready for use.

The fallback solution, self-hosting large language models (LLMs), sounds appealing. But in practice it’s costly, complex, and risky. Teams get bogged down in infrastructure, policy hurdles, and orchestration headaches. Compliance teams raise red flags. Projects stall.

As one enterprise stakeholder put it: “We know the potential is huge, but we feel stuck between Public APIs we can’t trust and self-builds we can’t sustain.”

This tension isn’t going away. Sovereign AI is top of mind across finance, life sciences, energy, and government — but the gap between ambition and reality keeps widening.

That’s where our innovation team is focusing: helping enterprises find a middle path. One that balances control, compliance, and capability without forcing impossible trade-offs. If you’re facing these issues we’d like to connect – reach out to our team and let’s talk. As we explore enterprise solutions to AI adoption woes stay updated with our insights.