Advancing AI's Potential for Climate Innovation: Adoption Insights from the Frontline of UK Climate Tech (Sustainable Ventures)

Summary

• Funding into AI-related UK climate tech businesses grew at a 67% CAGR over the past five years, reaching £1.45 billion in 2024.p.4
• As of June 2025, 9.7% of UK climate tech startups, 326 organisations from a cohort of 3,345 companies, were identified as AI-relevant.p.8
• AI-relevant climate tech startups command an average pre-money valuation of £22.5 million, versus £13.9 million for non-AI peers.p.8
• AI-relevant startups raised £12.9 million in total funding compared with £7.1 million for non-AI startups, an 81% uplift.p.8
• Hardware's share of total climate tech investment has declined from 60% to 37% in 2024, with absolute investment falling by £355 million since 2022.p.11
• Over 80% of global emissions come from sectors that require hardware-based decarbonisation.p.5
• Global funding into climate tech dropped over 40% year-on-year from 2023 to 2024.p.6

Key insights

1. Modern data centres consume an estimated 415 terawatt-hours of electricity annually, about 1.5% of global consumption.p.6
2. One sector's share of AI-related investment fell from 17% in 2023 to just 7% in the first three quarters of 2024, despite contributing 34% of global emissions, the largest single sectoral source.p.11
3. Agriculture accounts for 22% of emissions but received only 8% of funding.p.11
4. In climate tech, digital solutions have returned nearly three times the capital invested, while hardware and materials science investments have on average lost 80–95%.p.12
5. The report's AI Adoption Assessment Tool, developed with Cambridge Judge Business School, categorises companies from Level 0 to Level 3 by AI integration.p.12
6. 84% of adopters report improved operational efficiency, and 75% say AI has accelerated product development.p.15
7. Predictive maintenance has been shown to cut costs by 10–30% while increasing asset availability.p.15
8. Hardware ventures typically require 20–50% more equity capital than their software peers.p.15
9. Batch.Works used AI-driven design tools to cut material use by 20% and AI-enabled monitoring to reduce defects and waste by 30%.p.16