ML Lifecycle: Engineering from Test to Production

As an engineering leader with decades of industry experience, you've likely faced increasing pressure to implement AI across your organisation. Yet most AI initiatives fail to deliver meaningful business value, with studies showing that 70% of AI projects never make it to production. The problem isn't the technology itself—it's the lack of systematic foundation. 

This guide presents a comprehensive 7-phase framework developed from over 100 engineering AI implementations across automotive, aerospace, and manufacturing sectors.

This systematic approach works particularly well for organisations prioritising risk management, regulatory compliance, and comprehensive planning.  

Rather than generic AI advice, this methodology specifically addresses the unique challenges engineering organisations face when integrating machine learning into established test workflows and design processes. 

What You'll Learn: 

• How to systematically align AI strategy with engineering business objectives 

• A structured approach to project selection and prioritisation 

• Comprehensive frameworks for data preparation and model development 

• Risk-managed deployment strategies that ensure production success 

• Change management approaches that drive organisational adoption 

Phase 1: Business Alignment & Strategic Foundation 

The Challenge: Most engineering teams jump straight into technical implementation without establishing clear business alignment. This leads to impressive demos that never translate into operational value. 

The Solution: Start with strategy, not technology. 

Key Objectives: 

• Map AI initiatives to existing engineering strategy (not separate "AI strategy") 

• Quantify value in engineering terms: test reduction percentages, time-to-market improvements, defect reduction rates 

• Assess current AI maturity across five critical dimensions: data, processes, skills, tools, governance 

• Establish clear stakeholder alignment from executive level to engineering teams 
  

Practical Framework: 

Create a North Star document that links: 

• Executive strategic goals 

• Engineering operational objectives 

• Specific AI project outcomes 

• Measurable success metrics 

Key Success Metric: Before any technical work begins, ensure all stakeholders can articulate how the AI project directly supports existing engineering priorities. 

Phase 2: Engineering Use Case Prioritisation 

The Challenge: Engineering teams often struggle to identify which AI projects will deliver the highest chance of success and business impact. 

The Solution: Apply a systematic evaluation framework that balances opportunity with practical constraints. 

Now that we understand how anomaly detection could work, let’s compare these two options:  

The 4-Factor Evaluation Matrix: 

This matrix helps engineering leaders systematically evaluate potential AI projects rather than relying on intuition or technical excitement alone. The goal is identifying projects where multiple factors align favourably, significantly increasing your probability of success. 

Best Practice Approach: 

Engineering teams should focus on specific pain points rather than broad "AI applications." The most successful projects typically target areas where traditional simulation struggles, such as complex material behaviour or multi-physics interactions.

Resist the temptation to "boil the ocean"—instead, target clear, measurable improvements to existing workflows that your teams already understand and value. 

Phase 3: Engineering Data Preparation & Assessment 

The Reality Check: This phase isn't glamorous. You won't be developing cutting-edge algorithms or implementing the latest neural network architectures. But data preparation often determines 80% of your project's success. 

The Good News: Even though historical test data wasn't captured with machine learning in mind, you can extract significant value from existing engineering campaigns and build upon decades of testing insights.

Battery testing data from thermal cycling, performance degradation studies, and safety validation campaigns often contains rich patterns that ML can exploit, even when originally collected for different purposes. 

Critical Success Factors: 

Engage Domain Experts Early

The most successful data preparation efforts involve materials engineers who understand critical missing variables, test specialists who know data quality limitations, and design teams who recognise important edge cases.

In battery testing environments, this includes electrochemical engineers who understand degradation mechanisms, test engineers familiar with measurement instrument limitations, and safety specialists who know critical failure modes. These conversations often reveal crucial insights that can save weeks of frustration during model training. 

Apply Practical Data Guidelines

As a starting point, aim for 10-20 data points per input variable for initial feasibility assessment. More importantly, evaluate your design space coverage carefully.

Having thousands of test results means nothing if they all represent similar operating conditions, leaving large areas of your design space unexplored. 

Plan for Significant Preparation Investment

Data preparation typically consumes 40-60% of your total project timeline. However, this investment creates reusable organisational assets that support multiple future ML projects, delivering compounding returns on your preparation efforts. 

Phase 4: Physics-Informed Model Development 

The Key Insight: Traditional ML metrics like R-squared or mean absolute error aren't what engineering teams care about. Domain experts build better models than data scientists working in isolation. 

Engineering-Relevant Metrics: 

Rather than focusing solely on statistical accuracy, successful engineering AI projects develop metrics that directly support engineering decision-making and workflow integration. 

Hybrid Development Approach: 

Successful engineering AI projects combine detailed upfront scoping and stakeholder alignment with rapid MVP iterations that incorporate continuous engineering team feedback.

Integration with existing test infrastructure—including IoT sensor networks, data acquisition systems, and engineering data monitoring platforms—should begin from day one, not as an afterthought.

In battery testing environments, this means connecting with battery management systems, thermal monitoring equipment, and test chamber control software early in development.

Most importantly, maintain engineering interpretability throughout development—black box models simply don't work in regulated industries where decision-making transparency is essential. 

Success Indicator: Your engineering teams should understand and trust the model's decision-making process, not just its outputs. 

Phase 5: Production Integration & Workflow Embedding

The Strategic Principle: Design backwards from the desired engineering process change, not forwards from model capability. 

Integration Framework: 

Begin by defining specific workflow integration points such as design reviews, test planning, and validation protocols. Establish evaluation criteria tied to concrete engineering outcomes—for example, "achieve 25% test reduction whilst maintaining validation confidence"—rather than focusing purely on model performance statistics.

Your deployment roadmap must account for industry-specific regulatory and compliance requirements whilst respecting existing engineering governance processes. 

Critical Considerations: 

Modern engineering organisations require seamless integration with existing test infrastructure including IoT sensors, data acquisition systems, and engineering data monitoring platforms.  Battery testing environments, for example, need integration with battery management systems, thermal monitoring equipment, and test chamber control software.

Establish validated engineering sign-off protocols, comprehensive regulatory compliance documentation, and robust backup procedures for potential model failures. These aren't afterthoughts—they're fundamental requirements that should shape your entire implementation approach. 

Phase 6: Organisational Adoption & Change Management 

The Reality: Technical success doesn't guarantee organisational adoption. Change management requires as much investment as technical development. 

Proven Adoption Strategies:

Build Engineering Credibility

Success depends on using respected engineering team members as internal champions rather than external consultants. Demonstrate value through engineering language and concrete results, not AI evangelism or theoretical possibilities.

Provide hands-on training using real engineering examples from your organisation, not generic case studies from other industries. 

Implement Parallel Running with Realistic Expectations

Parallel running represents a significant investment that requires running both old and new processes simultaneously for meaningful comparison.

This approach allows you to validate benefits, understand new process dynamics, and reduce adoption risk. However, the cost is necessary and substantial—plan for this investment in your project timeline and resource allocation from the beginning. 

Establish Clear Governance

Develop explicit procedures for model updates and validation, clear engineering sign-off processes, and robust performance monitoring protocols. These governance frameworks provide the confidence engineering teams need to rely on AI-driven insights for critical decisions. 

Phase 7: Monitoring, Validation & Continuous Improvement

Focus on Engineering Outcomes, Not Just Model Statistics 

Key Monitoring Areas: 

Focus on engineering process improvements such as faster design cycles, reduced physical testing requirements, and measurable product quality enhancements. Actively gather stakeholder feedback from test engineers and design teams who interact with the models daily.

Measure business impact using metrics relevant to engineering leadership, and continuously assess model performance under real-world engineering conditions rather than just laboratory settings. 

Long-term Success Framework: 

Plan for regular model updates as your engineering processes and requirements evolve. Establish feedback loops with engineering teams to capture insights and improvement opportunities.

Build institutional knowledge systematically to support scaling AI capabilities to additional applications across your organisation. Most importantly, maintain clear ROI measurement and reporting to stakeholders using engineering and business metrics they understand and value. 

Key Success Factors for Systematic Engineering AI Implementation 

Engineering organisations typically achieve sustainable AI success when they treat ML as engineering tool enhancement rather than replacement.

Maintaining focus on engineering value delivery throughout the project lifecycle, combined with equal investment in change management and technical development, creates the foundation for long-term adoption.

Building on existing engineering domain expertise whilst planning for organisational scaling from project inception ensures that AI initiatives integrate naturally into established workflows. 

Common Implementation Challenges 

Organisations often struggle when they begin with technical implementation before establishing business alignment, or when they underestimate the complexity of data preparation in engineering contexts.

Focusing exclusively on model accuracy metrics instead of engineering process improvements can derail projects, as can insufficient involvement of domain experts and end users. Inadequate change management for production deployment represents another frequent challenge that systematic approaches help address. 

The Critical Question: Are You Building the Right Project? 

This framework covers AI project execution, but the most critical decision is solving the right problem first. 

Success depends on identifying where AI delivers maximum impact. The difference between transformative projects and expensive experiments usually comes down to project selection, not execution. 

We've refined our discovery methodology across hundreds of projects to help leaders identify high-impact opportunities. 

We'll explore this selection approach in our upcoming blog—because choosing the right project determines whether execution efforts matter. 

 Frequently Asked Questions