Generative AI (GenAI) is steadily shifting from proof-of-concept to mission-critical transformation across several industries. Last month, in its EY.ai Unplugged podcast series, Ernst & Young explored how GenAI is unlocking deep value in the financial services, renewable energy, and manufacturing/auto sectors. Here are the leading opportunities in each sector, as per EY’s experts, and critical considerations for scaling.
Financial Services: Conversational banking, smarter credit, and workflow automation
In Episode 29, Pratik Shah outlined how GenAI is accelerating the transformation of financial institutions.
One of the most visible use cases is conversational banking, which is the use of GenAI agents that understand text, voice, images, and more, capable of engaging customers in natural dialogue. These agents can not only answer queries but help users choose products, guide investments, or even execute transactions—all within the banking app interface.
On the credit and lending front, GenAI can generate draft credit memos, perform sentiment analysis, automate financial spreading, and propose recommendations. This helps to compress underwriting timelines and allows human underwriters to focus on nuanced judgment.
Relationship managers can be empowered by “sales copilots” that draft customer communications, suggest cross-sell ideas, and capture interactions. In collections and customer servicing, GenAI can automate large volumes of interactions, potentially reducing dependency on traditional call-centres.
Further, GenAI can streamline internal workflows and manual tasks, providing scale and efficiency gains across back-office systems.
These opportunities are not just theoretical. One fintech reportedly handled 2.3 million GenAI-based interactions in a single month, eliminating the need for 700 agents.
Renewable Energy / Power & Utilities: Forecasting, predictive maintenance, smart grids
In Episode 28, Vinit Mishra explored how GenAI is reshaping the power/renewables and utility landscape.
GenAI can ingest historical usage data, weather forecasts, economic indicators, and external patterns to generate superior energy demand forecasts. Better forecasts help utilities to plan power procurement, allocate reserves, and stabilise grid operations.
By processing IoT sensor data, telemetry, and real‐time signals, GenAI can anticipate failures in turbines, transformers, or grid components, allowing maintenance before breakdowns occur. This reduces downtime and maintenance costs.
One of the more complex and high-impact use cases is optimising distributed energy resources like solar, wind, storage, and microgrids. GenAI can model different scenarios, manage resource dispatch, and support grid stability under variable conditions. This plays a key role in integrating renewables smoothly into legacy grids.
By combining forecasting, predictive insights, and intelligent dispatch, GenAI helps drive resilience, efficiency, and sustainability across power systems.
Manufacturing & Automotive: From smart factories to scalable deployment
Episode 27 featured Neville Dumasia discussing how GenAI is transforming manufacturing, automotive, and heavy industries.
Rather than proof-of-concepts, leaders are pushing to embed GenAI across core processes such as demand planning, production scheduling, quality control, supply chain optimisation, and more.
GenAI extends traditional analytics by ingesting text, audio, video, and simulation data. In a smart factory, GenAI may analyse camera footage, sensor logs, maintenance records, operator voice inputs, and more to detect anomalies or inefficiencies. Digital twins enhanced by generative models can simulate “what-if” scenarios, suggest process tweaks, and help optimise operations.
GenAI can optimize procurement by modeling supply-demand volatility, suggest alternate sourcing, flag risks in the supply chain, and even draft negotiation strategies or contract summaries. In material usage, it can propose adjustments or substitutions in real time. While not always explicit in the podcast, this aligns with broader industrial AI trends discussed across EY insights.
Dumasia emphasised that success does not necessarily hinge on the model, but on the people, organisational readiness, and responsible AI governance. AI adoption must address data quality, change management, trust, security, and explainability to reach industrial scale.
Cross-Sector Themes & Implementation Imperatives
Across all sectors, the EY podcasts emphasise that GenAI’s promise is compelling, but realising it demands careful execution.
- Data readiness & integration: High-quality, well-governed data is foundational. Incomplete, inconsistent, or siloed data will hamper model performance.
- From pilot to scale: Many organisations stall after pilots. The real value emerges when GenAI becomes embedded into business processes and drives measurable ROI.
- Workforce readiness: Enabling employees with AI literacy, redefining roles, and fostering trust in AI systems is critical.
- Responsible, explainable, and secure AI: Model governance, transparency, risk controls, and cybersecurity are non-negotiable, especially in regulated industries.
- Cross-functional collaboration: AI success requires alignment across business leaders, IT, operations, legal, and compliance functions.
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