In Andalusia, water arguments rarely begin as climate arguments.
They begin with a well.
A field.
A failed crop.
A neighbour asking why strawberries, irrigation, tourism, politics, aquifers, and Europe’s most important wetlands now seem to be fighting over the same shrinking resource. Around Doñana, this is no abstraction. In 2021, the European Court of Justice ruled that Spain had breached EU law because excessive groundwater extraction was damaging the protected area, and WWF has estimated that around 1,000 illegal wells and 3,000 hectares of illegal farms have contributed to unsustainable water use.
That is what resource constraint looks like when it stops being theory.
It becomes local.
It becomes political.
It becomes permission.
And that is precisely where artificial intelligence is heading.
AI is still spoken about in strangely weightless language. Models. Agents. Workflows. Productivity. Acceleration. The vocabulary is digital, frictionless, almost airborne. Yet the reality is anything but. AI sits in data centres. Data centres sit on land. They draw electricity from grids, water from local systems, hardware from strained supply chains, and legitimacy from communities, customers, regulators, investors, and employees.
That was the most important thread in my Climate Confident conversation with Sophia Mendelsohn, who leads SAP’s Global Sustainability Platform. Her argument was clear: AI needs sustainability. A company cannot achieve its AI objectives – or the growth those objectives are meant to deliver – without the discipline, data, and stakeholder experience that sustainability leaders hold.
That is not a moral flourish.
It is a business argument.
Because in 2026, AI is no longer merely a technology decision. It is an infrastructure decision. A procurement decision. A governance decision. A social licence decision. And, increasingly, a board decision.
The data says AI is becoming an industrial load
The International Energy Agency’s 2025 Energy and AI analysis estimates that global data centre electricity consumption will more than double to around 945 TWh by 2030 – slightly more than Japan’s current total electricity use. The same analysis projects data centre electricity demand to grow by around 15% per year between 2024 and 2030, more than four times faster than electricity demand growth from all other sectors combined.
The 2026 update sharpened the picture. The IEA reported that global data centre electricity demand grew 17% in 2025, while AI-focused data centres grew even faster, with electricity use from that subset rising 50%.
This is not a rounding error inside an IT budget.
It is a new industrial load arriving at speed, often in places where grids are already being asked to electrify transport, heat, buildings, and industry. The bottlenecks are physical: transformers, substations, switchgear, circuit breakers, land, permitting, water, grid queues, and skilled labour. Reuters reported in July 2026 that surging AI data centre demand is worsening shortages of critical electrical equipment in the US, with some lead times reaching up to 160 weeks. Wood Mackenzie expects US data centre capacity to rise from roughly 24 GW today to 110 GW by 2030.
That is the point many boardrooms still miss.
AI does not scale because a strategy deck says it should. It scales where power, water, hardware, permits, public consent, and procurement discipline allow it to scale.
Water is the most visceral example. The Uptime Institute rightly cautions against lazy comparisons between data centres and cities. In its 2024 Cooling System survey, only 14% of respondents with water-cooled data centres used more than 16 million US gallons, or around 60,000 cubic metres, per year.
But water is not a global average.
Water is local.
A litre used in a wet, cool region is not the same as a litre drawn from a stressed aquifer in a Mediterranean heatwave. That distinction matters because data centre impacts are felt at the level of specific grids, specific watersheds, specific communities, and specific planning authorities.
The question, therefore, is not “Are data centres efficient on average?”
The sharper question is: whose constraint does this facility worsen?
The implications are bigger than carbon
The easy version of this conversation is to say AI may increase emissions. True. But too narrow.
The real issue is that AI can collide with energy security, customer commitments, water politics, community trust, employee acceptance, industrial competitiveness, and long-term affordability. That makes sustainability a strategic discipline, not a reporting function.
For CEOs, the risk is not simply that AI adds tonnes of carbon to a Scope 3 inventory. The risk is that poorly governed AI creates hidden exposure across the business.
An AI contract may look clean on paper while masking grid congestion, water scarcity, weak supplier disclosure, volatile power prices, hardware replacement cycles, and reputational risk. A workload may appear cheap because the vendor has absorbed some costs for now. A procurement decision may look efficient because the sustainability questions were never asked.
That is not innovation.
That is deferred due diligence.
Sophia’s point is powerful because it reverses the usual caricature. Sustainability teams are often portrayed as the people who slow things down. In the AI infrastructure buildout, they may be the people who prevent programmes from stalling later – under the weight of energy constraints, community opposition, regulatory scrutiny, or customer pushback.
AI scales only where it is permitted to scale.
That permission comes from three constituencies.
First, the communities asked to host the infrastructure.
Second, the customers whose own public commitments may be contradicted by the technology they buy.
Third, the employees asked to adopt AI systems in their daily work.
That is social licence to operate. Mining, energy, chemicals, agriculture, and manufacturing have had to earn it for decades. Technology is now learning the lesson at uncomfortable speed.
The C-suite question, then, is no longer “How fast can we deploy AI?”
It is: “Can we deploy AI in a way that strengthens the business rather than quietly loading it with new resource, reputational, and regulatory risk?”
The strategies start before the contract is signed
The first move is procedural, but vital: put sustainability into AI procurement before contracts harden.
Not after deployment.
Not when the annual report is being assembled.
Not when a journalist asks where the data centre gets its water.
Before.
Every significant AI contract should ask where workloads run, how electricity is sourced, what cooling systems are used, what water risks exist, what hardware lifecycle assumptions sit behind the service, how e-waste is managed, how emissions are calculated, and whether the provider can report at a level useful for Scope 3 accounting.
The direction of travel in Europe is already clear. The EU Energy Efficiency Directive introduced monitoring and reporting obligations for data centres, and the European Commission’s database collects data relevant to energy performance and water footprint for facilities with significant energy consumption.
Second, boards need to broaden the AI ROI model.
Most AI business cases still over-index on speed, labour productivity, customer response times, automation, and margin improvement. Those are legitimate metrics. But they are incomplete.
A serious AI business case should also include energy exposure, water exposure, regulatory risk, vendor concentration, Scope 3 implications, grid delay risk, community acceptance, and operational resilience.
A narrow ROI model can make a fragile decision look precise.
Third, leaders should rank AI workloads by value density.
Not every prompt deserves expensive compute. Not every internal experiment deserves scale. Not every “AI-powered” feature creates strategic value. Some will be transformative. Some will be useful. Some will be computational theatre dressed up as digital leadership.
Token discipline is becoming financial discipline.
It is also becoming climate discipline.
Fourth, sustainability teams should use AI to change their own operating model. This is where the opportunity becomes genuinely interesting.
For years, Scope 3 work has depended on supplier questionnaires, partial disclosures, inconsistent spreadsheets, and the corporate equivalent of politely asking into the void. AI can shift that posture. Companies can model baselines, estimate product carbon footprints, flag anomalies, pressure-test assumptions, and then ask suppliers to confirm, correct, or improve the data.
That changes the power dynamic.
Sustainability moves from passive data collection to decision intelligence.
Finally, AI infrastructure should be designed around clean power, flexibility, and storage. The good news is that the clean technology stack is improving quickly. IRENA’s 2026 24/7 Renewables report estimates that four-hour utility-scale battery costs fell to around $140/kWh in 2025, close to 95% below 2010 levels.
That matters because AI loads do not have to be dumb loads. With better software, storage, grid signals, and contractual design, some workloads can become more flexible. Some can shift. Some can pair with clean power. Some can support grid stability rather than merely compete for capacity.
That is the strategic prize.
Not AI versus sustainability.
AI with sustainability built into the operating system of the business.
The signal of change is already visible
The transition is not hypothetical.
According to Ember’s Global Electricity Review 2026, solar met 75% of global electricity demand growth in 2025, while renewables supplied 33.8% of global power generation. Solar generation rose by a record 636 TWh in a single year, equivalent to roughly twice the UK’s annual electricity demand.
Clean power is scaling.
Storage is scaling.
Electrification is scaling.
And AI is now arriving as a major new claimant on that same electricity system.
This creates a fork in the road. Poorly governed, AI demand could extend grid bottlenecks, raise costs, intensify local water conflicts, and slow decarbonisation. Governed well, it could accelerate investment in renewables, storage, grid flexibility, efficiency, and better digital energy management.
That is why the sustainability function belongs in the AI conversation now.
Not as a compliance afterthought.
As a strategic partner.
The CEO should want sustainability in the room because sustainability leaders understand resource constraints. The CFO should want them there because hidden infrastructure risk becomes financial risk. The CIO should want them there because systems without trust fail adoption. The procurement leader should want them there because the leverage is greatest before the contract is signed. And the board should want them there because AI is becoming too material to treat as a siloed technology programme.
The companies that get this right will not simply have cleaner AI.
They will have more durable AI.
More investable AI.
More credible AI.
More useful AI.
And that brings us back to water.
A dry aquifer does not care about anyone’s innovation narrative. Neither does an overloaded grid. Neither does a community facing heat, rising bills, water stress, and another large infrastructure project at the edge of town.
Physical systems do not negotiate with PowerPoint.
AI’s promise is real. In medicine, science, engineering, climate modelling, materials discovery, logistics, and energy optimisation, it can be extraordinary. But the promise will only turn into durable business value if leaders treat AI as physical infrastructure, governed inside the constraints of power, water, land, materials, climate, and trust.
That is why my Climate Confident conversation with Sophia Mendelsohn matters. It is not another generic discussion about AI and sustainability. It is a board-level warning and a business opportunity.
The warning is this: treat AI as weightless software, and you will miss the risks until they become expensive.
The opportunity is better: bring sustainability into AI strategy now, and resource discipline becomes competitive advantage.
The next phase of AI will not be won by the organisations that deploy the most tools.
It will be won by the organisations that deploy the right AI, in the right places, for the right reasons, with the right infrastructure beneath it.
Listen to the full Climate Confident episode with Sophia Mendelsohn for the deeper conversation.
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