On-Demand Workshop
August 28th, 2025 @ 15:00 BST / 10:00 ET
What Engineering Leaders Must Know About AI Data Strategy
Strategy Workshop for R&D and Tech Executives
Most engineering teams have a data strategy—few have a good one.
The cost? Retests, delays, and costly mistakes.
If you lead R&D or testing, this workshop will challenge how you think about AI and test data, and share proven best practices from top engineering teams.
Your data strategy is crucial; it impacts your team's innovation speed and the value of your testing infrastructure. Many OEMs, however, struggle with fragmented data ecosystems that reduce performance and incur significant costs.
The Framework We Built Across Hundreds of AI Engineering Projects
In this executive session, Monolith’s Arnaud Doko shares the AI framework that top OEMs use to audit test workflows and turn data into a strategic asset. You’ll see anonymised case studies, from battery labs to full vehicle programs, showing where others found hidden inefficiencies and how they fixed them.
This is a framework that we walk through all the time with tech leaders, and in this workshop, we will walk through the process with our experts.
Benchmark Yourself and Use Our AI Use Case Matrix
Join us to benchmark against industry leaders, estimate the cost of bad data, and get a downloadable matrix to spot AI opportunities in your lab. Challenge assumptions, uncover blind spots, and take the first step toward a modern data strategy.
Watch Monolith's Exclusive Workshop
Who Should Watch?
CTOs, VPs, and Directors driving digital transformation and AI readiness in R&D and testing.
OEM & Tier-1 executives benchmarking AI-enabled test strategies against industry leaders.
Heads of R&D, Validation, and Testing focused on reducing costs and accelerating timelines.
Digital transformation and data leaders unifying lab data to maximise business value.
Webinar Highlights
- The True Cost of Data Waste – A high-level walkthrough of a cost framework for inefficient test strategies.
- How the Best Operate – Breakdown of how industry leaders like CATL build AI-powered test ecosystems.
- Real Stories, Real Gaps – Anonymised case studies from major OEMs (battery, vehicle, and component systems).
- The Strategic Framework – Our framework to improve how your team approaches data strategy
- The Use Case Matrix – A downloadable tool to identify where AI delivers ROI in your engineering lab.
If you are unable to attend the live session, we would still encourage you to register to receive the webinar recording.
Meet our Speakers
Arnaud Doko
Solutions Engineer
Arnaud Doko works in our Solutions Engineering team with experience at CERN, Metaview, and in OEM-focused automotive design. An RAE Scholar, he holds an MEng in Mechanical Engineering from the University of Bath and brings deep technical expertise with a strong focus on real-world engineering challenges.
At Monolith, Arnaud regularly leads workshops with engineering leaders worldwide, advising on AI strategy, data quality, and implementation. He’s also the core developer behind several of Monolith’s key frameworks used to guide tier-1 OEMs on applying AI effectively across their development cycles.
Simon Daigneault
Product Marketing Engineer
Simon holds an MEng in Mechanical Engineering from Imperial College London, specialising in battery testing. He also spent time at a battery energy storage startup, supporting pack design, testing, and system integration.
At Monolith, Simon interfaces with product, engineering, and commercial teams to translate advances in AI into practical insights for battery development.