Industrialize & Scale
Building scalable architecture and pervasive test-and-learn culture to expand AI capabilities across the enterprise.
From Pilots to Enterprise Scale
The Scaling stage marks a significant step up in an organization's AI journey. The focus is on industrializing AI capabilities and scaling them across the enterprise. This requires significant investment in building a scalable enterprise architecture and fostering a pervasive test-and-learn culture.
Organizations at this stage make significant use of foundation models and small language models, applying them to proprietary data to create and capture new value on secure platforms.
Key Activities
Build Scalable Architecture
Invest in building a modern, scalable enterprise architecture that can support the development and deployment of AI models at scale.
Develop Test-and-Learn Culture
Foster a culture of continuous experimentation and learning, where teams are encouraged to test new ideas, learn from failures, and share insights.
Expand Automation
Broaden the scope of business process automation, moving from simple task automation to more complex, end-to-end process orchestration.
Utilize Foundation Models
Begin to leverage large-scale foundation models and small language models, fine-tuning them with proprietary data to create unique applications.
The Holy Trinity of AI
Companies in Stage 3 are developing proprietary models, which leads to the "holy trinity" of AI: architecture, reuse, and agents. These are the really hard parts of this stage.
Architecture
Building a robust, scalable architecture that can support AI models across the enterprise. This includes data pipelines, model deployment infrastructure, monitoring systems, and governance frameworks.
Reuse
Creating reusable AI components, models, and patterns that can be leveraged across multiple use cases and business units. This accelerates development and ensures consistency.
Agents
Developing AI agents that can autonomously perform tasks, make decisions within defined parameters, and collaborate with humans and other agents to achieve complex goals.
Making Data and Outcomes Transparent
A critical component of Stage 3 is making data and outcomes transparent via business dashboards. This enables:
Leaders and teams can see AI performance and business impact in real-time
Decisions are based on actual performance data, not assumptions
Teams can quickly identify what's working and what needs adjustment
Transparency builds trust in AI systems and their outputs
Developing a Pervasive Test-and-Learn Culture
At this stage, experimentation and learning must become embedded in how the organization operates, not just special projects.
Encourage Rapid Experimentation
Make it easy for teams to test new AI applications and approaches. Reduce barriers to experimentation while maintaining appropriate governance.
Celebrate Learning, Not Just Success
Reward teams for generating valuable insights, whether experiments succeed or fail. Document and share learnings across the organization.
Build Feedback Loops
Create systematic ways to capture insights from AI deployments and feed them back into development and improvement cycles.
Success Indicators
You'll know you're ready to move to Stage 4 when you've achieved: