Artificial Intelligence is reshaping every industry, including the boardroom. Yet, embracing AI without proper safeguards can be a risky endeavor. Beyond the excitement, directors must also consider the environmental impact, algorithmic bias, and opaque decision-making processes associated with AI.
This tension has given rise to a new idea: AI veganism. The term might sound provocative, even quirky. But at its heart, it is a practical governance lens, a way of asking: Do we really need this model? At what cost? And can we choose a safer, leaner, or more ethical alternative?
For top management, embracing this lens is less about ideology and more about stewardship. It provides boards with a disciplined method to balance growth with responsibility, ensuring innovation is purposeful rather than reckless.
The Definition of AI Veganism and Why It Matters to Boards
AI veganism works on two related dimensions:
- Personal or Institutional Restraint: The conscious choice of individuals or institutions to fully abstain from using AI, particularly where it involves opaque applications, high energy usage, or morally questionable uses.
- A governance framework is a formal process to determine potential harms, intensity in terms of resources, and reputational risks before obtaining approval.
Boards should take notice for three main reasons:
1) Moral Leadership: How companies utilize technology is under increasing scrutiny by investors, consumers, and employees. Here, credibility is enhanced by taking a moral stance.
2) Risk Mitigation: Boards minimize their risk of government fines, climate risks, and damage to their reputation by challenging the necessity and impact of AI projects.
3) Strategic Clarity: By distinguishing actual innovation from hype, AI veganism forces companies to spend resources only in those instances when value is equal to cost.
The Evolving AI Risk Landscape
In the past few years, there has been a substantial shift in the context of AI adoption:
1) Looking at the Environmental Impact: It takes a lot of water and electricity to train and run large models. A recent forecast by the International Energy Agency (IEA) estimated that by the end of the decade, AI-driven data center demand could double the global electricity use for ICT.
2) Investor Expectations and ESG: More disclosure regarding the social and environmental impacts of AI is being demanded by asset managers.
3) Employee and Customer Pressure: By staging technology walkouts and consumer boycotts, stakeholders are protesting against the abuse of AI. Boards stand to lose their reputation if they disregard this.
4) Legislative Regulations: Legislators are now looking to pass legislation that not only makes AI systems transparent, considers their impacts and complies with ethical standards.
Boards need to comply with the directives as set out in the EU AI Act and other similar legislation. It is evident from such stresses that the regulation of AI cannot be waived off.
The Business Situation: When showcasing Self-Control
Having a more vegan strategy for AI is about steering it in the right direction, where the business benefits from using the tool:
1) Environmental Responsibility: Businesses need to save energy costs, minimize their carbon footprint, and improve their ESG levels by avoiding any model that requires higher resources. It also keeps net-zero commitments on track.
2) Regulatory Readiness: Companies that conduct impact tests and demand transparency from their AI partners will be in a strong position to meet the upcoming laws. Proactive measures minimize the possibility of fines, business disruptions, or loss of reputation.
3) Reputation & Trust: Clients and staff trust those firms that use technology responsibly. Already, it has been proven that consumer commitment and employer recognition increase through public commitments to responsibly use AI.
4) Operational Efficiency: Learner AI systems tend to be less expensive in terms of infrastructure, less prone to vendor lock-in, and easier maintenance cycles. So, responsible decisions can be cost-effective as well as risk-minimizing.
The message to the board is clear: restraint is not anti-innovation. It is the smarter way to innovate.
Implementing Ideas for AI veganism
Shifting from idea to implementation is a challenge for top leaders. Three easy steps can be followed to put AI veganism into practice:
- Adopt the “Abstain–Reduce–Replace” policy
Reject projects that threaten your reputation, ethics, or the planet significantly. Some examples are large generalist models with unviable footprints or surveillance uses with poor oversight.
So the best way is to use alternatives such as domain-specific models, edge computing, or classic analytics.
- Have a clear system of Accountability
A C-level executive or board subcommittee should be designated as the Responsible AI Lead by the board. All significant projects must be subjected to AI impact assessments considering bias, privacy, society, and the environment.
In addition to financial and ESG metrics, ensure AI governance is included in quarterly board reporting.
- Track Measurable KPIs
Quantifiable metrics such as carbon emissions (tCO₂e) per project or per million inferences must be mandated by boards. A portion of the AI workload is powered by renewable energy sources.
These KPIs not only help transform intangible problems into measurable numbers that can be monitored, contrasted, and improved.
Some Challenges that Boards will encounter:
When taking an ‘AI vegan’ system, the Boards have to navigate the following challenges:
1) Lack of Data: Many suppliers are not yet offering full lifecycle information on the utilization of resources and emissions. Directors need to encourage transparency and make it mandatory in procurement.
2) Cultural Pushback: Product teams may assume that limits are reducing. This constraint must be framed by management as “smarter innovation” and the prioritization of long-term value over velocity.
3) Dependency of Vendors: Companies can become trapped by proprietary AI platforms. The Board members need to have a diverse response to this and use a mix of open-source tools when required.
4) Changes in Regulations: The laws are changing at a fast pace in such a way that organizations need to stay flexible. Especially when it comes to dealing with AI platforms.
By addressing these challenges head-on, it is obvious that being an AI vegan is about controlling wisely instead of stopping the progression.
Conclusion — a pragmatic ethical signal from the top
“AI veganism” should be treated less as a binary ideology and more as an executive heuristic: a way for boards to say, “We will only adopt AI that demonstrably advances value while meeting our ethical and sustainability thresholds.” Using this lens, boards can preserve innovation, reduce systemic risk, strengthen stakeholder trust, and meet ESG commitments. Lead with measurable thresholds, clear ownership, and transparent reporting — and you’ll turn a cultural trend into a strategic advantage.
