Most teams start their AI journey with momentum. Investments are growing, executives are aligned, and early pilots often show quick wins. But right after those first proofs of concept, something familiar happens: progress slows. Not because the team isn’t capable or the models aren’t good, but because the real work of operationalizing AI begins.
That slowdown is normal. In fact, it’s predictable. And it has far less to do with model performance than with an organization’s ability to get the right data, in the right shape, to the right systems, at the right time.
A Clear Look at Enterprise AI Readiness
Gartner’s AI Maturity Model offers a helpful framing: most companies aren’t beginners anymore, but they’re not truly scaling either. They sit in the middle: somewhere between “we’ve run successful experiments” and “we can run this across the business.”
That middle zone is where things tend to stall. The jump from experimentation to broader expansion breaks when AI systems need reliable, real-time access to enterprise data at the moment of use.
Why AI Progress Slows Across Industries
You’ll hear the same story from leaders in finance, healthcare, retail, energy, and beyond. The barriers aren’t unique…they’re shared.
- Data is spread across dozens of systems
- Each team relies on different tools and access patterns
- Custom pipelines and manual workarounds stack up over time
- Quality and reliability drift as new use cases emerge
These challenges surface most clearly when AI systems need to retrieve data dynamically, across systems, without creating new copies or over-permissioned access.
What’s Missing in Most AI Strategies
The missing layer isn’t another model. It’s a foundation the models can depend on:
- Consistent, secure access to enterprise data
- Repeatable patterns for how data enters AI workflows
- Clear control over what data AI systems can retrieve, at query time
Organizations that solve these challenges don’t just scale AI, they accelerate it. And this sets us up for the next part of the series, where we dig into what reliable, real-time data access for AI actually looks like and why it changes the maturity curve entirely.
AI doesn’t stall because teams lack talent or vision. It stalls because the data foundation underneath the work can’t keep pace with the growing number of models, decisions, and workflows. The real AI maturity curve bends not at the point of innovation, but at the point of data readiness. When organizations can deliver secure, reliable, real-time access to the information their AI depends on, the slowdown disappears, and the curve shoots upward again.