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. AI-powered product features

These initiatives integrate AI directly into product features for the purpose of customer facing value.

• Natural language interfaces
• Customer support chatbots
• Content moderation
• Intelligent search

2. Developer 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:

1. Start with “Adopt” opportunities in high-impact categories - These are your quick wins
2. Design controlled experiments for “Trial” items - Set clear success criteria
3. Allocate small resources to monitor “Assess” opportunities - Stay informed without overcommitting
4. 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 that 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 that then accelerated future projects.

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.