How the Next Wave in AI Will Transform Market Infrastructure

Why It Matters

AI‑driven market redesign will dictate which incumbents retain competitive advantage and how risk is managed across emerging energy and data‑intensive assets. Effective governance and sector expertise are critical to unlocking liquidity while containing systemic exposure.

Key Takeaways

• AI benefits incumbents but risks complacency, need sector expertise.
• Energy markets require real‑time data, strong governance before AI adoption.
• FMIs must address credit and equity exposures with transparency.
• Direct market access and open connectivity boost liquidity in power markets.
• Tailored mid‑term risk instruments improve design for renewable markets.

Pulse Analysis

Artificial intelligence is no longer a peripheral add‑on for financial market infrastructure; it is becoming a core engine that can accelerate product innovation and operational efficiency. Yet the panel warned that AI’s greatest value emerges when combined with deep sector expertise. Large incumbents that rely solely on generic software risk complacency, while firms that embed AI within specialized trading models and risk analytics are better positioned to capture new revenue streams in futures, data services, and energy‑linked contracts.

Energy markets present a unique set of challenges that amplify the AI equation. Physical delivery constraints, near‑real‑time grid data, and the rapid rise of renewables create timing gaps between market data and contract settlement cycles. Without clean, auditable data and strong governance frameworks, generative AI or probabilistic pricing models could produce misleading signals. The panel urged financial market infrastructures to invest in rigorous data pipelines, transparent audit trails, and mid‑term risk instruments that reflect the physical realities of power and commodity markets.

The systemic implications of these shifts are profound. Increased intraday volatility from renewable integration and rising power demand from data centers elevate credit and equity exposures across the ecosystem. FMIs that champion open connectivity and direct market access can foster deeper liquidity pools while mitigating concentration risk. By applying modern quantitative techniques and lessons from traditional market design, infrastructure providers can create resilient, transparent platforms that support both legacy participants and emerging players, ensuring the next wave of AI delivers sustainable market growth.

Original Description

The latest episode of the Innovators' Exchange, recorded live in Boca Raton, features a lively panel discussing how market infrastructure is evolving beyond traditional futures into software, data, and energy/real-assets markets. In a debate about expansion versus retrenchment, Hiten Patel talks with Carsten Kengeter, CEO of 7RIDGE, Andrea Stone, CEO of Zema Global, and Sam Tegel, CEO of ElectronX, about trading, data analytics, and market structure.  

Key themes include: technology and AI are powerful tools but can’t replace deep industry knowledge; energy markets bring unique physical, timing, and speed challenges that need new market designs and stronger data governance; and while these changes offer big opportunities — new products, liquidity pools, and participant types — they also bring systemic risks (credit and equity exposures) that financial market infrastructures (FMIs) must address with transparency and strong competitive advantages. 

Key talking points:  

• Carsten Kengeter on AI limits: Technology and AI now central and tend to benefit large incumbents, but these incumbents risk becoming complacent. His advice to large institutions is to focus deeper on sector expertise rather than broader areas and avoid investing in generic, leveraged software businesses.  

• Andrea Stone on market design: Energy markets add physical complexity, making the progression from OTC to forwards to futures more complicated. There is a big timing gap: grid and operational data are almost real-time, but many contracts and risk models are slow and fixed. This means clean data, clear audit trails, and reproducibility are essential before using generative AI or probabilistic methods. Andrea suggests FMIs invest in deep sector knowledge, strong data governance, and tailored instruments (especially for mid-term risk), while focusing on transparency and liquidity.  

• Sam Tegel on systematic risks: Rising renewable energy use causes significant intraday volatility, while growing AI and data-center demand increases overall power use, affecting both supply and demand. Market design should support direct market access and open connectivity so all participants can trade, building liquidity first on trusted hubs, then expanding to other grids. Sam shares his vision of applying modern quantitative models and market-structure lessons to power markets, unlocking finer detail, deeper liquidity, and clear value across the system. 

This episode is part of Innovators’ Exchange (https://www.oliverwyman.com/our-expertise/podcasts/innovators-exchange.html) , a series exploring financial infrastructure and technology. Tune in for an engaging look at key themes and opportunities for professionals and retail investors, including AI’s transformative role in financial markets.