Beyond Pilots: Building an AI Practice That Delivers Real ROI

AI Pilots Are Everywhere. Success Stories Are Not.

Too many organizations stop at isolated pilots that never scale.
The difference? Treating AI as a core application-development capability, not a side experiment.

Just like APIs, cloud, and DevOps became standard in software delivery, AI must now be embedded directly into business applications and workflows.

Why an AI Practice Matters

An AI practice provides the structure to:

• Infuse AI into the applications where business actually happens.

• Scale wins across divisions instead of reinventing the wheel.

• Balance innovation with governance and security.

• Tie AI investments to ROI and strategy, not hype.

Without this foundation, even the best models stay stuck in notebooks.

Core Components of a Modern AI Practice

1. Vision & Strategy

AI cannot live in labs; it must be directly tied to how existing applications and services create value.
Prioritize use cases where feasibility, ROI, and workflow integration intersect most strongly.

2. Governance

Security and Responsible AI need to be built-in by design, not bolted on afterward.
Guardrails for compliance and ethics are essential—but they should enable, not slow down, delivery teams.

3. Operating Model

AI features should be treated like app features—deployed, monitored, and rolled back through CI/CD pipelines.
That requires clear ownership of data pipelines, model APIs, and integration points inside enterprise applications.

4. Talent & Culture

Many organizations begin with Data Scientists, but lasting success depends on more.
AI engineers, app developers, MLOps/LLMOps specialists, and domain experts must collaborate as one team.

Upskill development teams so AI becomes part of their everyday toolkit, not a separate island.
The strongest practices build a culture where AI capability = software delivery capability.

Centralized vs. Embedded Models

When setting up an AI practice, structure matters—for delivery, funding, and ROI management.

AI Center of Excellence (CoE)

Best for enterprise-level initiatives with major ROI potential: global engagement platforms, supply-chain optimization, or enterprise knowledge search.
The CoE provides governance, reusable assets, and platform capabilities to de-risk and scale these efforts.

Embedded / Federated Teams

Best for division-level initiatives with localized ROI—such as a scheduling optimizer for a plant or a forecasting app for a regional sales team.
These teams operate close to the business while leveraging shared infrastructure, security, and compliance support.

In practice, most organizations succeed with a hybrid model:
CoEs back the big bets, while embedded teams deliver local wins.

Closing Thought

AI success isn’t about chasing the next model release.
It’s about building the organizational muscle to deliver intelligent applications—repeatedly and responsibly.

Originally published on LinkedIn: Beyond Pilots: Building an AI Practice That Delivers Real ROI
© Stravoris — AI Engineering & Strategy Practice
Innovate. Integrate. Elevate.