Smart data, smarter business – AI and data as the foundation of strategic change

The client aimed to integrate artificial intelligence (AI) into its operational processes but lacked a clear strategy and understanding of potential areas of use across the company. Support was needed to identify and prioritize AI initiatives that could bring significant business value.

We chose a comprehensive and collaborative approach, starting with a series of workshops with business unit owners. The goal was to understand their challenges and opportunities and identify relevant AI use cases. These scenarios were then prioritized based on impact, feasibility, and alignment with the client’s strategic goals. We proposed a structure for AI Operations, IT delivery, and governance that supports scaling AI solutions and ensures regulatory compliance. Three candidate projects in the field of generative AI were selected and underwent technical assessment. The outcome was a three-year AI strategy with a clear roadmap for operational transformation and achieving business objectives.

Company leadership and division heads

-EY.ai Maturity Model: Strategically plan to close GenAI capability gaps, create an effective roadmap, and responsibly leverage new capabilities.
-EY.ai Confidence Index: Test the entire AI model lifecycle in your organization according to principles of responsible AI usage.
-EY.ai Value Accelerator: Identify opportunities to create business value through AI, leading to measurable growth.

C-level executives

Complete realization

The project brought several key benefits to the client:

• A clearly defined and feasible AI strategy tailored to the client’s specific needs and industry context.

• A prioritized list of AI use cases, strategically focused on those with the highest potential business impact.

• A foundational structure for AI Ops, IT delivery, and governance, enabling effective management and scaling of AI initiatives.

• Identification and technical assessment of three PoC projects as a starting point for practical AI adoption and value realization.

• A strategic three-year vision enabling the client to make informed decisions and investments in AI technologies.

Overall, the project positioned the client at the forefront of AI adoption in the energy sector and equipped them with the tools and knowledge to support innovation and maintain competitive advantage.

• Cross-team collaboration: Active involvement of business unit owners allowed deep understanding of operational needs and identification of relevant AI scenarios.

• Structured and methodical approach: A clearly defined framework for identifying, prioritizing, and technically assessing AI initiatives ensured consistent decision-making.

• Data quality and client openness: Availability of high-quality data and willingness to embrace innovation significantly accelerated analysis and solution design.

• Start with business, not technology: Understand the needs of business units first—AI should solve concrete problems, not be the goal itself.

• Create a structured framework: For identifying, evaluating, and prioritizing AI initiatives, a clear methodological approach is key.

• Ensure leadership support and stakeholder involvement: The success of an AI strategy depends on organization-wide collaboration.

• Don’t forget governance and scalability: From the outset, think about how AI solutions will be governed, operated, and scaled.

• Test small, plan big: PoC projects help validate benefits and prepare the ground for broader adoption.