AI is growing rapidly, but the electricity bill and CO2Emissions are growing accordingly. Globally, we're seeing strong demand for powerful data centers and infrastructure to enable AI applications, from generative models to real-time analytics. At the same time, pressure to control energy consumption, costs, and climate impact is increasing.
For European SMEs, this isn't a theoretical exercise: servers, cloud accounts, and energy bills are very tangible. The key question then becomes: how do you use AI intelligently without exploding your IT footprint? In this blog post, we'll explore the latest developments in AI and energy, its impact on organizations, and how you can start making practical, sustainable choices for your business today.
What exactly is going on
Companies are investing heavily in AI solutions for automation, data analysis, and new digital services. These applications require significant computing power. As a result, data center and digital infrastructure providers are seeing strong, "intense, and urgent" demand for AI-supporting systems.
At the same time, there's increasing attention to the energy consumption of this infrastructure. Therefore, players in the sector are emphasizing:
- more efficient cooling of data centers;
- better energy management solutions;
- hardware that delivers more AI computing power per watt;
- reducing operational costs and greenhouse gas emissions.
The bottom line: AI is becoming more important for business, but it cannot be viewed in isolation from energy and climate goals. Tech companies are looking for ways to develop AI capacity with the lowest possible environmental impact.
Impact on people and society
For people on the work floor, this evolution means that AI is no longer just about smart tools, but also about conscious choices. An AI project today is also an energy project. This affects:
- IT teams, who not only look at performance, but also at efficiency and lifecycle of hardware.
- Managers and directors, who must weigh ROI against operational costs, ESG objectives and reporting.
- Staff, who increasingly work with AI software and expect it to be reliable, safe and responsible.
On a societal level, we see that AI innovation and climate goals don't have to be contradictory, but they do need to be aligned. Those who invest consciously today can improve processes and simultaneously contribute to lower CO2 emissions.2-footprint. That requires sensible decisions, no hype and no doom-mongering.
Ethical and sustainable considerations
Responsible AI goes beyond "does it work?" and "is it fast enough?"; it also considers "is it necessary?" and "what is the cost – human, financial, and ecological?" Key questions for your organization:
- Energy consumption & environmentDoes your AI solution run on oversized models and hardware, or do you consciously choose more efficient alternatives? Less computing power often means less energy, less cooling, and therefore lower emissions.
- TransparencyCan you explain why you chose a particular AI model, cloud provider, or data center, and what its impact is on energy and the environment?
- Honesty & biasLarge models require a lot of data. How do you handle the origin of that data, potential biases, and the consequences for real people?
- Ethics & Safety: Are you using AI to support people or to monitor them? Does the technology help employees do better work, or does it put extra pressure on pace and monitoring?
Sustainable AI is therefore not just a technical issue, but also a moral one. It's about respect for people, their data, and their environment.
Safety and risk dimension
Expanded AI infrastructure also means a larger attack surface. Some specific risks:
- Hacking of AI infrastructurePowerful GPU servers and clusters are attractive targets. A successful hack could lead to misuse of computing power (e.g., for crypto mining) or further attacks within your network.
- Data leaksAI systems often process sensitive data (customer data, operational figures, intellectual property). Poor configuration or insufficient access control can lead to leaks.
- Privacy: Linking AI to customer interactions, production data or HR processes requires strict handling of personal data, especially in a GDPR context.
- Abuse of AIPowerful models can be used to refine phishing, automate social engineering, or create deepfakes. This is also part of the risk landscape surrounding AI.
The solution is not “no AI,” but controlled AI: clear governance, roles and processes, combined with basic technical measures such as network segmentation, logging, access control and encryption.
What does this mean for your business?
For Flemish and European SMEs wanting to get started with AI, this primarily means making a conscious choice. You don't have to become a hyperscaler to derive value from AI, but you do need to consider:
- PurposefulnessWhich processes do you really want to improve? Often, smaller, targeted models are sufficient instead of massive generic systems.
- ArchitectureShould everything be in the cloud, or is a hybrid approach better? Can you distribute workloads across less intensive components, with peak loads only where necessary?
- LifespanHow long do you want to use your solution? A robust, scalable architecture prevents you from having to start over in two years – which also offers environmental benefits.
- Compliance and reportingHow does your AI strategy fit within ESG objectives, the AI Act, and internal policies?
A down-to-earth approach means: no 'AI because it has to be', but AI that demonstrably delivers value, is safe, and respects your energy and climate goals.
3 concrete recommendations for SMEs
- Start small and measurable: start with one clearly defined use case (for example, invoice processing or quality control), set KPIs (time savings, error reduction, energy consumption) and evaluate after a few months.
- Choose conscious infrastructure: Ask suppliers explicitly about energy efficiency, data center location (preferably in Europe), certifications, and options for monitoring consumption. Avoid oversizing.
- Build governance in from day one: Define who is responsible for data, models, security, and ethics. Document choices regarding privacy, bias, logging, and access rights, and involve IT, business, and HR.
Conclusion: Technology that works for people
AI can help your company simplify processes, reduce employee workloads, and better serve customers. But for this to happen, the technology must serve people—not the other way around. By taking a realistic look at impact, energy consumption, safety, and ethics, you can build solutions you can be proud of: efficient, fair, and future-proof.
At Canyon Clan, we design and build human-centric, sustainable AI and software solutions for SMEs. We help you choose the right scale, set up your infrastructure wisely, and manage risks. Want to explore what responsible AI can do for your organization? Feel free to contact us for a no-obligation consultation.