Making forecasting accessible with GenAI

Turn your operational data into useful forecasts with generative AI, without complexity or technical barriers.

Forecasting sits at the heart of proactive decision making, but for many organizations, it has long felt out of reach. Traditional methods often require deep technical expertise, expensive tools, and complex infrastructure. Meanwhile, companies generate endless streams of sensor, production, and operational data that remain largely untapped, simply because turning numbers into insight is hard.

In this three hour master class, we change that. You will discover how anyone, from maintenance managers to data owners, can transform raw time series data into dependable forecasts using modern generative AI, or GenAI.

By harnessing free foundation models and today’s coding assistants, we dramatically lower the barrier to entry. You will learn how to build a high quality forecasting solution in a single afternoon, without writing code and without paying for costly licenses.

For whom?

• Maintenance managers and engineers looking to move from reactive to predictive maintenance
• Production planners and operations managers who need reliable demand and capacity forecasts
• Data owners and technical profiles wanting to apply GenAI without complex ML pipelines
• Anyone who collects operational or sensor data and wants to extract more value from it
• Professionals in energy, utilities, logistics or any data-intensive sector looking to apply forecasting without a specialist team 

Key takeaways

• Understand how time series forecasting works in practice and which metrics matter most
• Learn when classical machine learning is sufficient and when GenAI adds value
• Gain insight into modern foundation models such as Chronos-2, TimesFM and PatchTST
• Discover how coding assistants can support forecasting workflows
• Get a clear starting point to explore forecasting in your own organisation

About Mihail Mihaylov

Mihail Mihaylov is a GenAI expert and entrepreneur with 18 years of experience in artificial intelligence, combining a decade of work in start-ups and scale-ups with eight years of applied (gen)AI and machine-learning research.

He has led the development of AI-powered SaaS products in the energy sector and introduced new concepts and algorithms in decentralized agent-based systems. His work bridges deep technical insight with practical industrial applications, helping organisations adopt cutting-edge AI technologies with confidence.