Bridging the AI Delivery Gap: Practical Recommendations for Platform Teams
Five actionable steps your platform team can take today to move beyond experimentation and deliver AI at enterprise scale.
In my latest blog post on The CTO Advisor, I explored a crucial challenge that's stifling enterprise AI adoption:
AI platforms consistently fail to empower platform teams to deliver AI to developers at scale.
👉 Read the full analysis here: The AI Delivery Gap: Why Enterprise Platforms Are Failing Platform Teams
In this extended discussion, I want to dig deeper into the practical steps your organization can take to overcome these challenges.
What's the Real Problem?
Today’s AI platforms are heavily optimized for experimentation, training, and research—but not for delivery. Platform teams are left manually stitching together brittle workflows, leading to scalability issues, poor governance, and operational complexity.
How Can Your Organization Bridge This Gap?
Drawing on real-world examples and industry best practices, here are five actionable strategies:
Adopt a ModelOps Framework
Standardize your ML deployment pipelines using tools like MLflow, Kubeflow, or Seldon Core alongside your existing CI/CD processes.
Example: GoDaddy leverages MLflow integrated with AWS SageMaker for streamlined, compliant model lifecycle management.
Invest in Self-Service AI Capabilities
Empower developers with simple-to-use AI APIs from AWS, Google Cloud, OpenAI, or internal developer portals.
Example: Morgan Stanley integrated OpenAI APIs directly into its advisor workflows, eliminating ML complexity for developers.
Integrate AI with Internal Developer Platforms (IDPs)
Connect AI tooling seamlessly to IDPs like Backstage, Crossplane, or Terraform for governance, policy enforcement, and self-service.
Example: VMware (Broadcom)’s exploration of integrating Generative AI within Backstage through projects like "Back Chat."
Strengthen Observability, Governance, and Cost Transparency
Implement robust usage metering, cost allocation, and policy-as-code frameworks (e.g., Open Policy Agent) tailored specifically for AI workloads.
Example: Enterprises increasingly leverage FinOps principles to manage AI-specific expenditures and enforce governance.
Evaluate Emerging AI-Native Solutions
Explore specialized platforms such as Hugging Face Enterprise Hub, Run:AI, or OctoML designed explicitly for AI operationalization.
Example: Companies like Prophia use Hugging Face integrated with AWS SageMaker Pipelines, simplifying enterprise model deployments.
The Strategic Imperative
To move beyond AI experimentation to true enterprise-scale delivery, platform teams must become the central enablers of innovation. Companies that master AI enablement today will set the pace for the next decade.
Is your organization taking these steps? What’s your biggest roadblock?
Let’s continue the conversation—leave a comment below or join me over at The CTO Advisor.
🔗 Read the full CTO Advisor post here.
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