Following the advent of the personal computer, the rise of Web 2.0, or the boom in social networks, artificial intelligence is arguably the greatest revolution of our century. As AI becomes a core part of corporate branding for every business, digital responsibility is a must for leaders; it becomes a major strategic challenge. The reactive approach—waiting for government regulations to catch up—is risky. Instead of being subjected to emerging regulatory frameworks, leaders should take proactive steps, or risk being penalized by stakeholders—employees, clients, investors—who are often more watchful and skeptical than true AI enthusiasts. With the rise of 'brand sustainability' at the forefront of the corporate world, could new leadership lie in a leader's ability to give technological innovation meaning?
The Roots of Demanding Responsible Leadership
Endowed with an undeniable sociological aspect, corporate branding must now provide meaning and evolve to maintain team engagement and public commitment amidst significant changes. This necessity is particularly pressing with AI, which has become essential in the competitive equation of any organization but arrives with its set of tensions and questions.
As Plato reminded us in Protagoras through the myth of Prometheus, humanity has always seen each technological progress as both a promise and a threat. The rapid rise of AI has sparked a cultural battle over digital issues, fueled by concerns about the ecological impact of digital infrastructures, algorithmic system transparency, and the deviations feared early on by writer Philip K. Dick.
This is why, despite a diverse ecosystem, the paths of digital innovation are now undeniably directed towards responsibility. Just as CSR once did, digital responsibility is now a firm item on leaders' agendas, propelled by a once-watchful civil society that now mobilizes and takes action.
New Demands, New Mobilizations
According to the 2025 Responsible AI Observatory, 74% of French employees want AI regulation, and nearly half demand internal oversight of its usage in their companies. However, only 9% currently have an ethical charter or a dedicated committee. Such a gap is cause for concern. For example, during social negotiations, unions now seek clear guarantees on training, skill recognition, and the implementation of human oversight mechanisms. AI has become a distinct governance issue.
In this context, we see a cross-industry mobilization emerging in France, Germany, the United States, and Canada, where NGOs, citizen groups, unions, and researchers are calling for better-managed AI. The Hiatus coalition, launched in February 2025 at the AI Summit in Paris to highlight opposition to AI excesses, embodies this momentum. This public pressure is accompanied by more direct actions: in October 2024, Extinction Rebellion protested in front of Google's offices to denounce the ecological footprint of its data centers. Authorities are also stepping up their efforts. A case in point is the Commission Nationale de l'Informatique et des Libertés (CNIL), which sanctioned Clearview AI for illegal data collection, while the NGO NOYB (None Of Your Business) sued OpenAI for non-compliance with the General Data Protection Regulation (GDPR).
These subtle signals demonstrate that stakeholders are willing to use legal action or public mobilization to pressure businesses, including demanding internal adjustments to digital systems if needed. Yet, in reality, this wave of protest, which feels like pressure, is an opportunity for leaders to strengthen their corporate brand in exchange for boldness and commitment.
Brand IA Sustainability in Action: Three Strategic Challenges
Businesses already understand that digital responsibility (which today includes responsible AI) is more than an apparent constraint—it is an opportunity. An opportunity to build a trusted brand in a world seeking direction, where AI will likely never be removed. It is also a chance to embrace brand sustainability, a corporate attribute that has crucially become essential for reinforcing their enterprise’s brand leadership and differentiation. However, this opportunity also presents a challenge, as assuming strong leadership on this topic requires navigating three major paradoxes.
↳ First Challenge: Balancing Efficiency with Environmental Impact
The primary challenge for leaders is balancing AI efficiency with its environmental impact. Touted for its efficiency gains, AI operations, however, rely on highly energy-intensive architectures, powered by powerful GPUs (Graphics Processing Units) hosted in data centers whose consumption rivals that of entire cities. The most advanced models, especially those based on deep learning, consume megawatts at each training and inferencing phase. For instance, a data center with a 100-megawatt capacity has an electrical consumption equivalent to 100,000 households. Those being built today will use 20 times more energy, matching the consumption of 2 million households. Even giants like Google or Microsoft, despite their carbon neutrality commitments, saw their emissions jump by 48% and 29% in a year due to AI. Leaders must navigate this contradiction and consider AI’s energy consumption as a vital strategic indicator, alongside ROI and operational performance.
↳ Second Challenge: Avoiding Over-Engineering
In a climate of perpetual innovation, tempted by the corporate attribute of being an 'innovative company,' there’s a strong appeal to opt for the newest, most powerful models, even when a real need doesn't justify it. Models that would consume more data, energy, and computation time, often for tasks solvable with more streamlined, targeted architectures. By following this logic, companies increase their ecological impact, multiply technical costs, frequently without a real strategic gain. The true leadership today, especially when 'responsibility' is a clear or implicit value, would be choosing the right solution. But are we ready to pay the price? As highlighted by Landier and Thesmar (2022)*, this represents another dilemma for leaders to arbitrate, potentially calling for managerial decisions favoring sobriety, though not free as it would entail financial and performance costs.
↳ Third Challenge: Managing Rebound Effects
The last major challenge is anticipating and managing rebound effects. Improving the energy efficiency of models and making them faster and less resource-intensive is beneficial. But if this leads to broader deployment, integration into more services, and availability to more employees or clients, overall resource consumption will undoubtedly rise. Just as the steam engine did in the 19th century, whose efficiency led to an explosion in usage and usage numbers, increasing rather than decreasing coal consumption, envisioned by James Watt. For leaders, the challenge here is ensuring savvy oversight. They must set priorities, prohibit certain uses, and decide on volume strategies to place safeguards between what’s possible and what’s acceptable. Could having a brand IA sustainability also mean knowing when to hit the brakes or even to forgo?
Govern, Communicate, Co-Create: Building a Strong Brand
As AI becomes integrated into business models, the nature of leadership evolves. No longer able to forgo this technology, a company’s corporate strength might soon be measured by its ability to clarify its technological choices, define their scope, and align its decisions with a clear direction, shared with employees and other stakeholders. As Géraldine Michel* explains, brand identity, as a core corporate object, materializes only if the entire organization translates it into action. Ultimately, for leadership to engage, it must evolve into co-creation.
In conclusion, to effectively govern AI, communicate your choices clearly, and co-create with your employees and partners, it becomes essential to establish a strong brand. Even more so now, as AI touches everything: how we produce, recruit, analyze, recommend, decide... It becomes undeniably a reflection of your corporate brand’s maturity. Tomorrow belongs to those who dared to gain a head start by proactively managing AI-related challenges that, if not addressed now, might eventually be imposed on everyone. Boldness wins!
Landier, Augustin and David Thesmar. The Price of Our Values: When Our Ideals Clash with Our Material Desires. Paris, Éditions Flammarion, January 2022.
Michel, Géraldine. At the Heart of the Brand: Keys to Brand Management. 4th edition. Paris, Dunod, 2022.
Plato, Protagoras, translated from Greek by Jean Prévost, introduction and notes by Luc Brisson, in Complete Works, vol. I, Paris, Gallimard, in “Bibliothèque de la Pléiade,” 1989.
