In a volatile context with strong tensions over energy, leaders must resolve a delicate equation: exploiting the potential of AI while preserving financial stability.
According to the 2025 barometer of Artificial Intelligence from consulting firm EY, more than half of organizations (56%) have already saved money or increased profits through the adoption of artificial intelligence. In a volatile context with strong tensions over energy where each investment is analyzed closely, managers must resolve a delicate equation: exploiting the potential of AI while preserving financial stability.
The key is not to rule out certain AI projects, but to define key performance indicators in order to engage them with a business-oriented vision and measure the benefits in the medium and long term.
Investing in AI with strategic precision
When budgets are constrained, investing in AI is never easy. The costs associated with recruiting talent, infrastructure and data management as well as its governance and cybersecurity require careful planning at each stage. Even the most successful companies do not launch into AI by spending lavishly, they move forward with a clear intention, especially since after the AI construction phase (build), there will be the production phase (run) where the use of language models (LLM) will be billed per incoming and/or outgoing token.
Everything therefore often starts with a simple question: what concrete problem do we want to solve?
Organizations measure their level of maturity then draw their roadmap by identifying the economic issues, the relevance of the purchase versus that of the development of AI models in order to define where it can really make a difference. And rather than immediately committing to large, heavy and costly programs, they start step by step with targeted, vertical projects, bringing visible gains, quickly proving that the value is there.
Take the French industrial sector for example, where many sites use AI-based predictive maintenance to anticipate equipment failures, thereby reducing downtime, costly repairs and industrial waste. This targeted application directly translates into improved operational efficiency and increased profits, clearly justifying the initial investment. Starting small with sensors and verticalized AI, these companies acquire data, dynamics and internal expertise, consolidating a solid foundation for the future and in the second phase more advanced AI applications such as digital twins, industrial robotics, R&D or augmented industrial engineering.
Starting with concrete use cases allows companies to immediately anchor AI in operational reality. The focus shifts from technology to the measurable benefits it produces. In this dynamic, AI is emerging as a tool to predict, optimize, automate and assist operators in production environments
Avoiding pitfalls: the cost of oversupply
Europe is at a turning point. While AI generates unprecedented enthusiasm, a silent challenge looms: oversupply. According to Gartnerby 2030, companies that fail to streamline their AI infrastructure will spend 50% more than those that have optimized their environment. This waste is not limited to budgets. It increases energy bills, impacts the environment, adds an additional layer of operational complexity and increases the attack surface for hackers.
The message is clear: only infrastructures capable of adapting to the real pace of activity make it possible to preserve margins and build a sustainable trajectory. In areas where data and energy constraints are severe, companies are now favoring pay-as-you-go billing models. By aligning their investments with real demand and continuously adjusting their resources, they preserve their capacity for adaptation while controlling costs.
More efficiency and profitability in Europe
Of all the economic arguments in favor of AI, the one that comes up the most often and, also, the most spectacular concerns the ability of AI to unlock previously unimaginable levels of efficiency for businesses.
Businesses everywhere are leveraging AI to optimize routes, manage inventory, and forecast demand with remarkable accuracy. This not only reduces operational costs, but also improves customer satisfaction by ensuring more reliable delivery times. By taking over repetitive tasks, AI gives teams time back. This freed up time becomes fertile ground for analyzing business data in depth, for cross-referencing sources, for strengthening creativity, strategic thinking and, ultimately, for encouraging innovation. According to a study by Dell Technologies and Vanson Bourne dating from the summer of 2025, 97% of global organizations believe that as powerful as it is, AI only becomes fully operational in the hands of trained women and men.
The key is to view AI as a collaborator for a Human-Machine partnership. It is a technology based on sovereign data that augments human capabilities, enabling individuals to achieve goals that were previously out of reach. Managing an AI project thoughtfully empowers employees, streamlines work processes, and advances the entire organization.
The true potential of data-driven decisions
The short-term financial benefits are real, but the most compelling economic benefit of AI is its ability to reshape an organization’s operating model and turn it into a deeply data-driven enterprise. Although businesses manipulate massive amounts of data, extracting relevant insights remains complex. AI provides the analytical capabilities needed to process this data at scale, identify patterns and highlight key trends, thereby informing and strengthening strategic decision-making.
In France, many retailers rely on AI to personalize the customer experience: purchase analysis, targeted recommendations, tailor-made journeys. This personalization boosts sales while promoting lasting loyalty, a strategic advantage in a saturated market. By understanding their customers’ needs more intimately, companies develop even more tailored products and services, which solidifies their competitive advantage.
To make AI truly profitable, everything starts with a massive and structured investment in data: collecting it, managing it, analyzing it. In other words, investing in the company’s ability to think, anticipate and detect new sources of value. At a time when the world is changing faster than ever, this ability to quickly correlate data sources to identify patterns and guarantee anticipation. It gives organizations the confidence to act quickly, decide correctly and take bold bets.
A future based on smart investments
Adopting AI requires a clear vision, strategic planning and an unwavering focus on value creation. For European business leaders, the question is not whether to invest in AI, but how to invest. Still according to the same study, 82% of global business leaders agree that AI is already generating substantial returns on investment and lastingly transforming productivity levels. By focusing on solving real-world problems, empowering people, and building a data-centric culture, organizations can reap the benefits of AI. In doing so, they not only address current economic pressures, but also lay the foundations for a more prosperous, more efficient and more innovative future.
