How mature are your engineering AI capabilities?

Many organisations are committed to adopting AI in their engineering teams, but they struggle to know how to do it at scale. To truly benefit from AI, it's essential to understand the technical, process, data management, and business issues that must come together. Without a clear strategy, it's easy to waste time and effort without seeing any significant results. 

Monolith 

At Monolith, we work with hundreds of engineering teams implementing various AI projects across industries and engineering domains. Through our extensive experience, we've witnessed both best practices and failed efforts. Many customers who struggle with AI adoption turn to us for guidance. 

Over time, we've developed a structured process for running projects that includes strategy alignment, value creation, data engineering, integration, change management, analysis, implementation, and training. From this work, we've created the Engineering AI Maturity Matrix to formalize the different aspects crucial for success. Within each aspect, we define levels of maturity, from the initial stages of Initiating to Mastering and Optimizing. This tool helps our customers understand their current capabilities and, more importantly, where they can go and how we can help them get there. 

Maturity Matrix 

Using the matrix, we can have a more structured conversation with our customers to help them understand: 

• Where they are relative to the market (similar companies) 
• The possibilities for improving their maturity within each capability area 
• Prioritizing which areas are most important to them right now
• Creating an improvement plan to make progress 

The Engineering AI Maturity Matrix covers five capability areas, with five levels of maturity for each. The capabilities are: 

Business Alignment

• Is the business making the right investments? 
• Is there alignment across departments? 
• Is there a unified vision? 
• Is the business ready and able to change key processes and tools? 

Test Strategy 

• How sophisticated are your test plans? 
• How do you know you’re testing enough? 
• Do you have feedback mechanisms to improve your testing on the fly? 

Infrastructure and Data Readiness 

• Do you have clear data governance roles and processes in place? 
• How and where are you storing test plans, conditions, and results?
• Do you have the IT support required for centralized and aggregated data lakes? 

Operations 

• Are you reusing AI models across teams, projects, or departments? 
• Are you integrating models within your current engineering workflow? 
• Are you optimizing the results based on clear business ROI metrics? 

People 

• How are you introducing AI tools and capabilities to your teams?
• Are there structured training and adoption plans in place? 

Conclusion