The Core Challenge
AI technology evolves faster than any previous enterprise technology. Foundation models become outdated within months. Organisations must adopt new capabilities without destabilising operations or accumulating unmanageable technical debt.
Key Concepts
| Technology radar | Systematic tracking and evaluation of emerging AI capabilities. |
| Model-agnostic architecture | System design that allows underlying AI models to be swapped without rebuilding applications. |
| Technical debt | The accumulated cost of past shortcuts and outdated technology that constrains future adaptation. |
| Abstraction layers | Design patterns that separate components so changes in one area don't cascade throughout the system. |
| Continuous learning | Infrastructure enabling AI systems to incorporate new data and adapt without complete retraining. |
Warning Signs
Watch for these indicators of technology adaptation problems:
- No systematic process for tracking emerging AI capabilities
- Systems are tightly coupled to specific models or providers
- Technical debt is accumulating faster than it's being addressed
- Workforce skills are falling behind technology evolution
- New capabilities take months or years to evaluate and adopt
- Vendor relationships provide no visibility into technology roadmaps
Questions to Ask in AI Project Reviews
- "How easily could this system incorporate a significantly better model?"
- "What technical debt does this create, and how will it be managed?"
- "What abstraction exists between the AI components and the rest of the system?"
Questions to Ask in Governance Discussions
- "What's our process for evaluating emerging AI technologies? Who decides?"
- "What visibility do we have into AI technical debt across the organisation?"
- "Are workforce capabilities keeping pace with technology demands?"
Questions to Ask in Strategy Sessions
- "What vendor relationships provide roadmap visibility and early access?"
- "What's our technology refresh cycle for AI? Is it adequate?"
- "What capabilities are we not adopting, and why?"
Reflection Prompts
- Your awareness: How current is your understanding of AI capabilities? What's changed in the last 6 months that you should know about?
- Your organisation's currency: How old are the AI systems in your area? Are they state-of-the-art, adequate, or falling behind?
- Your role: What could you do to ensure technology currency is appropriately prioritised?
Good Practice Checklist
- Systematic technology radar tracks and evaluates emerging capabilities
- Architecture enables model swapping without system rebuilding
- Technical debt is visible, tracked, and actively managed
- Regular refactoring cycles prevent debt accumulation
- Workforce skills development is aligned with technology evolution
- Vendor relationships provide roadmap visibility
Quick Reference
| Element | Question to Ask | Red Flag |
|---|---|---|
| Tracking | How do we evaluate new capabilities? | No systematic process |
| Architecture | Can we swap models easily? | Tightly coupled systems |
| Debt | What technical debt exists? | Unknown or unmanaged |
| Skills | Are capabilities keeping pace? | Growing gaps |
| Vendors | What roadmap visibility exists? | None or limited |
Technical Concepts for Non-Technical Leaders
Modular architecture means systems built as loosely connected components. If someone says "we'd have to rebuild the whole thing" when asked about adopting new capabilities, that's a red flag.
Technical debt is like financial debt—sometimes justified, but it compounds. Ask: "What debt are we taking on, and what's the repayment plan?"
Abstraction layers are like electrical outlets—you can plug in different devices without rewiring the house. Ask: "What can we change easily, and what requires major work?"