Discovering AI opportunities with a Tech Radar approach
So how do you figure out where AI genuinely fits? The Technology Radar approach—popularized by ThoughtWorks—is a powerful framework to classify potential use cases by both their technical maturity and their strategic relevance.
This approach offers a visual way to categorize initiatives to better understand:
Strategic relevant to your business goals
Technical maturity of the underlying capabilities
The radar consists of concentric rings representing different levels of recommended action:
Adopt: Proven value, ready to implement broadly.
Trial: Potential value, but requires piloting in a controlled environment.
Assess: Emerging or uncertain; keep tabs on progress before investing heavily.
Hold: Not recommended currently (e.g., too high risk or cost, unclear ROI).
Four key categories for AI initiatives
When applying this framework specifically to AI opportunities, we’ve found these four categories especially useful for organizing your thinking:
1. Product features
These initiatives integrate AI directly into product features for the purpose of customer facing value.
Natural language interfaces
Content moderation
Intelligent search
2. Internal enablement (platform & productivity)
These use cases focus on helping your internal teams work more efficiently:
Pull-request agents that scan for code quality issues
AI-based incident triage and resolution suggestions
Knowledge management systems with natural language understanding
Document summarization and insight extraction
Real-world impact: A financial services firm implemented an AI assistant for their support desk that could understand and categorize incoming tickets. The system reduced ticket routing time by 72% and helped identify common issues that needed permanent fixes.
3. Data science & ML operations
This category focuses on managing the AI model lifecycle itself:
Experiment tracking and versioning
Automated model training and evaluation
Drift detection and model monitoring
Feature stores and data pipeline automation
Success story: By implementing comprehensive MLOps practices, a retail analytics team reduced their model deployment time from weeks to days while simultaneously improving model performance through more frequent retraining cycles.
4. Business strategy & transformation
These are the big-picture applications that can fundamentally change how your business operates:
Customer churn prediction and prevention
Demand forecasting and inventory optimization
Process automation and workflow intelligence
Strategic decision support systems
Transformation example: An insurance company implemented an AI-driven risk assessment system that not only improved underwriting accuracy by 31% but also opened entirely new market segments they previously couldn’t serve profitably.
Building your own radar
To apply this approach at your organization, follow these steps:
Step 1: Inventory potential AI opportunities
Gather key stakeholders from across your organization for a brainstorming session. Ask:
What repetitive tasks consume significant engineering time?
Where do we have data that isn’t being fully utilized?
Which customer pain points might be addressed with AI?
What competitive pressures could AI help us address?
For each idea, capture:
The business problem it solves
Required data sources
Expected impact (qualitative and quantitative)
Potential challenges or risks
Step 2: Evaluate technical feasibility
For each opportunity, assess:
Data availability and quality
Required AI capabilities (are they mature or emerging?)
Integration complexity with existing systems
Skills and resources needed for implementation
Step 3: Map to the radar
Place each opportunity on your radar based on:
Which category it belongs to (engineering, enablement, MLOps, or business)
Which ring reflects its readiness (adopt, trial, assess, or hold)
This visual mapping immediately highlights where your immediate priorities should be.
Step 4: Develop your action plan
Based on your radar, create a phased implementation plan:
Start with “Adopt” opportunities in high-impact categories - These are your quick wins
Design controlled experiments for “Trial” items - Set clear success criteria
Allocate small resources to monitor “Assess” opportunities - Stay informed without overcommitting
Document reasons for “Hold” decisions - Revisit periodically as technology evolves
Building a roadmap (and avoiding AI chaos)
Once your radar has uncovered your most promising AI opportunities, how do you move forward without creating chaos?
Start small, dream Big
Begin with a low-risk, high-value use case—something you can implement and measure quickly. This builds momentum and helps refine your processes before tackling larger AI initiatives.
A media company I advised started with a simple content tagging AI before moving to more complex recommendation engines. The initial project took just six weeks but demonstrated value and built crucial organizational capabilities.
Make smart build vs. buy decisions
For each initiative, consider:
Buy: When speed matters and the capability is commoditized
Build: When your unique data or domain expertise provides a competitive edge
Trial: For unproven technology where controlled experimentation makes sense
Wait: When the technology is still maturing or ROI remains unclear
Practical examples of AI in software development
Here are real-world examples of how organizations have successfully implemented AI in their engineering workflows:
Code generation and completion
AI-powered coding assistants for faster delivery and reduced development cost. Morgan Stanley reported a 22% increase in developer productivity after implementing code generation tools across their engineering teams.
Developer onboarding
AI systems that help new developers navigate codebases, understand architectural decisions, and learn team-specific practices faster. Spotify reported reduced time-to-productivity for new engineers by 40% using AI-assisted onboarding.
Automated code reviews
AI tools that analyze code changes to identify bugs, security issues, and maintainability concerns, before human review. This improves deployment frequency and reduces defect rates. Microsoft’s internal engineering teams reported 28% faster pull request review cycles while maintaining quality standards.
Infrastructure cost optimization
AI systems that analyze cloud resource usage patterns and recommend optimization strategies. Deutsche Bank reduced cloud infrastructure costs by 30% while maintaining performance requirements.
Technical debt identification
AI analysis of codebases to prioritize technical debt remediation based on business impact, e.g., code coverage gaps. Intuit used AI to identify high-impact technical debt, resulting in a 15% reduction in production incidents.
Context-aware documentation generation
Automatically generated and maintained technical documentation from code and commit messages. Shopify reported a 40% reduction in time spent on documentation while improving knowledge transfer.
Architectural compliance
AI tools that continuously verify system implementations against architectural standards and patterns. Capital One reported that architecture validation reduced compliance exceptions by 45% and accelerated their cloud migration.
Predictive incident management
AI systems that identify potential production issues before they impact customers. American Airlines implemented predictive monitoring that reduced customer-impacting incidents by 23% through early detection.
Your competitive advantage through AI
When implemented strategically, AI can:
Boost developer productivity through automation of routine tasks
Enhance user experiences with intelligent, personalized features
Create sustainable competitive differentiation through unique capabilities
The radar approach ensures you focus on initiatives that actually matter to your organization rather than chasing the latest AI headlines. By methodically evaluating opportunities across both technical feasibility and strategic relevance, you’ll build an AI portfolio that delivers consistent, measurable value.
Want to try it at home? Check out our Tech Radar workshop!
