Andrew Mohammed, Director of Data and AI at OVO Energy, discusses how to deliver enterprise value with AI agents in a regulated industry, sharing practical lessons on design, adoption, governance and model economics.

Andrew traces his career from commodity trading at Centrica and EDF, through data science leadership at Lloyds Bank, to leading a 90-person data and AI function at OVO Energy. He explains how agentic systems help customers understand their bills, support advisors with vulnerable customers and catch billing errors before they go out. He argues that most AI value comes from adoption rather than technology, and that regulation works best when built in from the start. He also covers earning board credibility, the economics of using a portfolio of models, how Claude Code is speeding up delivery, and his vision for personalised, greener energy choices powered by AI.

Steel has been shaped by fire and force for centuries. What happens when you add intelligence to that equation?

This episode goes inside the AI operation at SHS, a major German steel group, where a 20-person interdisciplinary team is embedding AI across production, research, and administration. Guests Michael Schaefer, Anna Volker, Ulrike Faltings, and Tobias Bettinger cover how they grew from three people in 2017, the challenges of legacy systems and messy industrial data, and why close collaboration with domain experts has been key to their success, with honest reflections on data quality, model monitoring, and the future of AI in steel.

Key Topics:

• Modern Steel Plant Explained — Today's steel facilities operate as digital ecosystems of sensors, simulation, and AI. High-quality steel, used in automotive and offshore wind, demands far greater precision than commodity alternatives.
• Building the AI Team — Starting with three people in 2017, SHS's AI function has grown to 20 specialists across three sub-teams: Specialised AI, Gen AI, and R&D — deliberately interdisciplinary across physics, maths, computer science, and engineering.
• Infrastructure and Data Complexity — SHS runs its own data centres for real-time, low-latency production control. Managing a heterogeneous landscape of legacy systems and up to 10,000 sensors creates significant integration challenges.
• Data Quality and Pipelines — Messy data — from Excel records and missing values to sensor drift — is the biggest obstacle to AI development. Robust pipelines depend on domain expert input, strict guardrails, and continuous model monitoring.
• High-Impact Production Use Cases — Standout projects include an input material cost optimisation model, a defect detection system that saved a major customer relationship, and AI-driven temperature and oxygen prediction models that outperform traditional physical approaches.
• Gen AI in Administration — LLMs are being used to automate feasibility analysis and process unstructured customer enquiries via a centralised, governed internal platform. Gen AI is kept out of live production due to hallucination risks, with human-in-the-loop built into all workflows.
• Domain Expert Collaboration — Plant engineers and operators are central to every stage of AI development — shaping pipelines, detecting model failures, and bringing unsolved problems to the team. Years of shared successes have built deep mutual trust and a genuine two-way knowledge exchange.
• Future Outlook and Advice — SHS aims to make the entire group AI-powered. Key advice for data scientists in heavy industry: understand processes and people before algorithms, embrace imperfect data, and play the long game. Smaller specialised models are an exciting near-term development; physical AI a longer-term frontier.

Michael Haft, founder of xplain Data, discusses causal discovery and causal AI, explaining how understanding cause-and-effect relationships goes beyond predictive modeling to enable truly intelligent interventions. He explores the technical foundations of object analytics, real-world applications in healthcare and manufacturing, and his vision for integrating causal AI into future intelligent systems.

Episode Summary

1. Causal Discovery vs. Prediction - Causal discovery aims to understand why things happen rather than just predicting what will happen. Unlike predictive models that rely on correlations, causal discovery identifies true cause-and-effect relationships necessary for intelligent interventions and goal achievement.
2. The Confounder Challenge - Understanding causality requires comprehensive data to identify confounders—hidden common causes that create spurious correlations. The gray hair and glasses example illustrates how age acts as a confounder, making the two correlated without a direct causal relationship between them.
3. Object Analytics Technology - Traditional machine learning requires flat tables, but real-world data (like electronic health records with 150+ tables) is inherently complex. Object analytics allows algorithms to work with comprehensive, holistic data structures, enabling deeper causal analysis without manual feature engineering.
4. Manufacturing Use Case - A cylinder head manufacturing example demonstrates how causal discovery identified the complete pathway from washing machine timing through part temperature to false negative leakage test results, enabling an intelligent process intervention that traditional predictive models couldn't provide.
5. Healthcare Applications - Projects using MIMIC hospital data analyze causes of pressure injuries in patients. The vision is to provide doctors with causal knowledge derived from millions of patient records to improve treatment decisions, discover new drug effects, and enable cost-efficient healthcare.
6. Path to Causal Maturity - Organizations need education on the difference between prediction and causality, comprehensive data availability, and engagement from both business owners (who have problems to solve) and data science teams. The shift requires iterative learning and hands-on experience with the technology.
7. Community Edition Launch - Explained Data is releasing a community edition starting with pre-configured object analytics models for the MIMIC healthcare dataset, followed by a full version for the broader data science community, with free access for universities and evaluation purposes.
8. Future of Causal AI - The next generation of AI systems will integrate causal layers with large language models, moving beyond text rephrasing to answering "why" questions based on empirical cause-and-effect relationships, particularly transforming healthcare and enabling more explainable, intelligent decision-making systems.