Accelerated Innovation

Building Shared AI Services and APIs

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Building Shared AI Services and APIs

Description

Building Shared AI Services and APIs enables reusable capabilities that accelerate GenAI adoption across the enterprise. These services provide standardized interfaces for common functions-such as model access, inference routing, logging, and monitoring-so that teams don’t have to rebuild them from scratch. This capability allows teams to focus on their unique value while relying on shared foundations for speed, scale, and consistency.

Why it's Important

As GenAI demand expands, so does the duplication of effort-leading to inconsistent tooling, fragmented architecture, and rising technical debt. Without shared AI services, each team reinvents basic components, slowing delivery and increasing risk. Establishing reusable APIs and services avoids this trap by giving teams easy access to secure, enterprise-approved capabilities. Done right, this reduces development time, improves governance, and ensures performance and reliability at scale. It also makes GenAI adoption more inclusive-enabling business units without deep technical expertise to tap into powerful AI capabilities.

Why it's Challenging @ Scale

  • Fragmented development efforts: Without shared services, teams often build overlapping functionality in isolation, wasting resources and reducing cohesion.
  • Inconsistent quality and security standards: Independently built APIs can vary widely in performance, reliability, and compliance readiness.
  • High initial investment in foundational infrastructure: Building robust, scalable services and APIs takes time, cross-team coordination, and upfront commitment.
  • Difficulty balancing standardization with team autonomy: Central services can be seen as bottlenecks or too generic if not designed with extensibility in mind.
  • Lack of enterprise-wide discovery and reuse mechanisms: Even when services exist, teams may not know about them or trust them due to limited visibility or support.

Complexity

High: Maturing this capability requires deep architectural alignment, strong platform engineering, cross-team coordination, and robust governance to ensure quality and reuse at scale.

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Taking Action

Though most organizations begin their GenAI journey with significant knowledge gaps, there are targeted actions that can be taken to accelerate the process. Select your group’s current maturity, based on your assessment results, and act today.

The most important part of any journey is starting… To move from “Exploring” to “Experimenting”, focus on the following key actions:
  • Explore Key Concepts & Best Practices: Complete the GenAI Center of Enablement (CoE) Best Practices workshop (2 hrs.) to understand foundational key concepts and explore applied best practices.
  • Defining the vision and mission of a GenAI CoE.
  • Establishing governance and ownership structures.
  • Cataloging core services and support functions.
  • Communicating value and success metrics.
  • Planning the evolution and scaling of the CoE.
  • Define Your Action Plan: Outline concrete, prioritized steps your organization will take to implement GenAI Strategy.
  • Align on your Current State and define your Target State.
  • Create an actionable enablement plan.
  • Define target timeline and measures of success.
  • Deliver Quick Wins: Small, high-impact GenAI projects that can demonstrate tangible value in a short time frame.
  • Stand up a shared inference API: Create a lightweight internal service that allows teams to access and test models via a common interface.
  • Launch a service catalog pilot: Document and publish an initial set of reusable GenAI functions available for internal use.
  • Pilot an authentication and logging wrapper: Develop a basic security and observability layer that can be reused across model endpoints.
To move from Experimentation to “Lifting-Off”, prioritize the following actions:
  • Complete one or more of our Deep Dive Courses: Begin exploring key concepts and best practices, including:
  • GenAI Use Case Discovery & Prioritization Best Practices.
  • GenAI R&D Acceleration & Applied Innovation Best Practices.
  • GenAI R&D Acceleration & Applied Innovation Best Practices.
  • Enterprise GenAI Architecture & Tooling Best Practices.
  • GenAI Development Best Practices & Support.
  • Nail It Before You Scale It: Assess and optimize your solution or process before adopting it at scale.
  • Assess Your Proposed Solution or Process: Review service usage patterns and identify gaps in functionality or performance.
  • Define in-scope Processes and Guardrails: Clarify which services are reusable, which teams own them, and how updates are managed.
  • Close any Data or Measurement Gaps: Ensure service usage, uptime, and latency are tracked and reported through unified dashboards.
  • Define Your Adoption & Scaling Plan: Create a structured roadmap for how GenAI solutions will be rolled out across teams, workflows, or business units.
  • Define Your Phased Implementation Plan: Prioritize APIs by impact and usage potential, and roll out in stages to avoid disruption.
  • Build Awareness and Finalize Enablers: Develop onboarding materials, developer documentation, and support processes.
  • Operationalize Your Comms Plan: Establish a communication rhythm to inform stakeholders of new capabilities and changes to shared services.
To move from Lifting-Off to “Accelerating”, prioritize the following actions:
  • Formalize Your Best Practices: Document and standardize what’s working to ensure consistent, scalable success across teams and use cases
  • Publish a shared service design playbook: Capture architecture, security patterns, and versioning strategies in a reference guide.
  • Standardize API governance protocols: Define standards for API documentation, change control, and lifecycle management.
  • Embed observability requirements: Ensure every service includes default logging, monitoring, and alerting capabilities.
  • Accelerate Your Adoption: Intensify efforts to embed GenAI across your organization by expanding use cases, increasing user engagement, and removing adoption barriers
  • Promote services through internal showcases: Host demos or office hours to raise awareness and increase usage.
  • Decentralize service integration support: Empower platform champions within each domain to assist with onboarding.
  • Expand library of plug-and-play components: Create wrappers, SDKs, and UI modules that reduce the effort to consume APIs.
  • Celebrate Your Wins: Publicly acknowledge team accomplishments to build and sustain adoption momentum
  • Spotlight successful reuse stories: Highlight how shared services accelerated delivery for specific GenAI projects.
  • Recognize cross-functional contributors: Acknowledge those who helped design or scale APIs across departments.
  • Share performance and impact metrics: Visualize reductions in duplication, delivery time, or support tickets.
The “Accelerating” stage represents “Target State” for many capabilities. “Breaking Away”, on the other hand, suggests that the specific Capability represents a clear competitive advantage for your business.
  • Streamline & Embed: Integrate GenAI into core workflows while eliminating friction points to make usage seamless and routine
  • Integrate APIs into enterprise developer portals: Make it easy for product teams to discover and adopt services in their workflows.
  • Ensure consistency through service blueprints: Define reusable service templates that encode standard functionality and guardrails.
  • Automate onboarding and access provisioning: Use workflows to provision access and credentials without manual review.
  • Leverage Automation: Use GenAI-powered tools and workflows to streamline repetitive tasks, enhance operational efficiency, and reduce manual effort
  • Auto-generate API usage summaries and alerts: Provide real-time insights on usage anomalies, errors, and optimization opportunities.
  • Implement automated version deprecation flows: Proactively guide consumers through upgrade paths with automated alerts and support.
  • Continuously test and validate service health: Use bots or pipelines to run diagnostics across services and flag regressions early.
  • Evolve & Further Accelerate: Continuously refine GenAI strategies based on insights and outcomes, while expanding into more complex or high-impact use cases
  • Incorporate feedback loops from service consumers: Use surveys or forums to gather ideas for new features and improvements.
  • Expand services to support emerging GenAI modalities: Enable capabilities like multimodal inputs or agent frameworks through shared APIs.
  • Benchmark against best-in-class internal platforms: Evaluate your shared AI platform using maturity rubrics and internal performance data.

Key "Watchouts"

As you take action you’ll want to avoid:

  • Over-engineering your initial service layer: Trying to build a comprehensive platform too early can delay delivery and overwhelm teams.
  • Neglecting real developer needs: Services designed without ongoing input from consumers may be underused or abandoned.
  • Lack of clear ownership and support models: Shared services without accountable teams can create confusion and reliability issues.
  • Forgetting to market your services internally: If teams don’t know the services exist-or how to access them-they won’t be adopted.
  • Inconsistent standards across services: Without alignment, APIs can vary in format, security, and integration effort, eroding trust.

Targeted Benefits

While Building Shared AI Services and APIs can be challenging, its benefits are clear and compelling, including:

  • Accelerated delivery of GenAI solutions: Reusable services allow teams to focus on differentiation, not infrastructure.
  • Improved quality and reliability at scale: Standardized services reduce bugs, improve monitoring, and ensure consistent performance.
  • Stronger governance and compliance alignment: Shared APIs make it easier to embed controls for access, usage, and audit.
  • Faster onboarding for new teams and products: Prebuilt services lower the barrier to entry for less technical users and new adopters.
  • Reduced duplication and platform sprawl: Centralized APIs streamline enterprise architecture and cut unnecessary development costs.

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Eddie
Accelerated Innovation

Hi, I'm Eddie 👋

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