What It Is: With the advent of AI, the question is how to integrate it effectively at an enterprise level. The long-term view should be a synthesis of applications, AI, and data, working in harmony, providing integrated capabilities that maximize effectiveness and productivity for the end users of technology
Why It Matters: Much like the .com era, there are lofty expectations of what AI can deliver without a fundamental strategy for how those capabilities will be integrated and leveraged at scale. Selecting the right approach that balances tactical gains with strategic infrastructure will be critical to optimizing and delivering differentiated value rapidly and consistently in a highly competitive business environment
Key Concepts
- AI is a capability, not an end in itself. User-centered design is more important than ever
- Resist the temptation to treat AI as a one-off and integrate it with existing portfolio processes
- The end goal is to expose and harness all of an organization’s capabilities in a consistent way
- Agentic solutions will become much more mainstream, along with orchestration of processes
- The more agentic solutions become standard, the less application-specific front ends are needed
- Natural language input will become common to reduce manual entry in various processes
- We will shift from content via LLMs to optimizing processes and transactions via causal models
- AI should help personalize solutions, reduce complexity, and improve productivity
- Only a limited number of sidecar applications can be deployed before overwhelming end users
- The less standardized the environment is, the longer it will take to achieve enterprise AI benefits
- As with any transformation, don’t try to boil the ocean, have a strategy and migrate over time
Approach
- Ensure architecture governance is in place quickly to avoid accruing significant technical debt
- Design towards an enterprise architecture framework to enable rapid scaling and deployment
- Migrate towards domain-based ecosystems to facilitate evolution and rapid scaling of capability
- Enable rapid, disciplined, and governed experiments to explore tools and solution approaches
- Place heavy emphasis on integration standards as a means to deploy new AI services with speed
- Develop a conceptual “template” for how AI capabilities will be integrated to facilitate reuse
- Organize AI services into insights (inform), agents (assist), and experts (benchmark, train, act)
- Separate internal from package-provided AI services to provide agility and manage overall costs
- Evaluate internal and external solutions by their ability to integrate services and enable agents
- Reinforce data management and data governance processes to enable quality insights
- Define roles and expectations for those in the organization who develop, use, and manage AI
For Additional Information: Part 1: The Cost of Complexity, Part 2: A Framework for the Future, Part 3: Integrating Artificial Intelligence, Part 4: Evolving Applications, Part 5: Deconstructing Data-Centricity, Part 6: Managing Transition, Part 7: IT Organizational Implications
Excellence doesn’t happen by accident. Courageous leadership is essential.
Put value creation first, be disciplined, but nimble.
Want to discuss more? Please send me a message. I’m happy to explore with you.
-CJG 11/03/2025
Organized Chaos 