Executives hear “data is the new oil” every week. AI proves the opposite. Data is more like plumbing. Nobody celebrates plumbing until it breaks, then everything stops.
The AI Maturity framework highlights Infrastructure and Integration as a central barrier. The Technology portion of the 10 20 70 model is where scaling gets real. Leaders should focus on building a platform that supports reuse, reliability, and speed.
What “Minimal Viable Infrastructure” Means in Practice
Minimal viable infrastructure is not a grand redesign. It is a set of capabilities that allow repeatable delivery:
Accessible data with clear ownership and quality rules
Secure identity and access management
Standard pipelines and feature stores where relevant
Model deployment tooling and monitoring
Integration patterns into systems of record
Cost management and capacity planning
Leaders should avoid platform perfection projects. Leaders should insist on platform increments tied to lighthouse programs. Platform work should ride alongside value delivery.
Common Platform Failure Modes
Fragmented tooling across functions that blocks reuse
Unclear data definitions that create conflicting metrics
Legacy integration bottlenecks that slow deployment
Security controls bolted on late, causing delays and risk
No monitoring, leading to silent drift and degraded outcomes
Each failure mode maps to a maturity gap. Fixing them is less about a single technology choice and more about an Operating Model that enforces standards.
Applying This to a Modern Trend: Real Time Decisioning
Marketing, pricing, fraud, and supply chain increasingly require near real time decisioning. AI can support it, but only if data pipelines and integration patterns can meet latency requirements.
Leaders should specify latency and reliability requirements as part of use case selection. A use case that needs sub second decisions cannot rely on batch data refreshed weekly. This sounds obvious. Teams ignore it constantly.
A mature platform approach sets tiers for data freshness, then aligns use cases to the tiers. The result is less disappointment, fewer rewrites, and faster scaling.
Executive Questions That Reveal Platform Maturity
“Which data products are used by multiple functions today?”
“How long does it take to deploy a model change safely?”
“Which systems of record are integrated into AI workflows?”
“What percent of models have monitoring and alerting in production?”
Clear answers indicate maturity. Vague answers indicate theater.
Interested in learning more about the AI Maturity Model? You can download an editable PowerPoint presentation on the AI Maturity Model hereon the Flevy documents marketplace.
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