The green transition will not be won by intent alone. It will be won by organisations that can think, adapt, and operate faster — and that now means AI.
ARTIFICIAL intelligence has moved beyond hype cycles and pilot projects.
In 2026, it is reshaping how organisations think, operate and compete, including across the green economy.
This feature draws on the recent 2026 predictions of AI commentator Andreas Horn to examine what this next phase of AI actually means for clean energy, climate technology, and sustainability-led businesses. (You can subscribe to his excellent ‘Human In The Loop’ newsletter here). The focus here is not tools nor trends, but capability: where AI is already delivering real advantage, where risk is being misunderstood, and why excluding AI from strategy is no longer a neutral position for leaders navigating an increasingly complex world.
There is a widening gap opening up across the green sector, and it has nothing to do with intent. Most founders, executives and policymakers I’ve talked to in recent times genuinely want to decarbonise faster, build resilient systems, and deliver long-term impact. The gap is about ‘operational capability’. In an environment defined by complexity, tight margins, technical debt and accelerating timelines, good intentions are no longer enough.
In 2026, the organisations that succeed will not simply be those with the best climate narrative or the most elegant technology. They will be the ones that can reason faster, operate more reliably, and adapt continuously. That capability is now inseparable from how AI is embedded into the core of the business; not as a bolt-on, but as infrastructure.
A common objection I hear (particularly from experienced founders and operators) is that AI amplifies cybersecurity and intellectual property risk. That concern certainly isn’t wrong but it is often misdirected. The real risk is not AI itself, but undisciplined AI adoption.
There is a growing maturity around how sensitive data is protected: private and on-prem models, strict data-segmentation, zero-trust architectures, encryption in transit and at rest, role-based access, air-gapped systems for critical IP, and clear rules around what data never touches public models. These controls are already standard in sectors like finance, defence, and critical infrastructure. In other words, the choice in 2026 is not between AI and security — it is between secure AI by design or unmanaged complexity without it.
From AI Hype to AI Reliability
The first lesson of the past two years has been sobering. Despite loud claims about autonomy and AI “agents,” most systems proved fragile when exposed to real-world complexity. Tasks with multiple variables, long time horizons and genuine trade-offs quickly revealed the limits of automation without judgment.
For green infrastructure — grids, generation assets, supply chains, water systems — fragility is unacceptable. These are environments where small errors compound and recovery matters as much as prediction. What emerges in response is not abandonment of AI, but a recalibration. The focus shifts from autonomy to reliability.
As Horn says, the next generation of systems behave less like impulsive executors and more like careful operators — pausing, reasoning, checking assumptions before acting. This shift is foundational for the green economy. It’s the difference between AI as a risk multiplier and AI as a stabilising force.
Scientific Acceleration Moves Into the Real World
Where AI’s impact becomes unmistakable is in scientific and engineering acceleration. Systems are now supporting tasks once reserved for deep human expertise: evaluating competing hypotheses, proposing experiments, interpreting results, and iterating rapidly with human oversight.
In climate and clean-tech, this compresses timelines that were previously governed by human bandwidth alone. Materials discovery for batteries and electrolysers, grid optimisation, carbon capture efficiency, and climate modelling all benefit from faster learning loops.
This is not marginal productivity. It is ‘time compression’ — fewer dead ends, more shots on goal and faster translation from idea to deployment. In sectors racing against physics and planetary limits, that acceleration is decisive.
Intelligence Is No Longer Just About Bigger Models
A critical but underappreciated shift is that intelligence gains are no longer tied exclusively to larger models or more data. Increasingly, they come from how AI systems allocate thinking at the moment decisions are made.
Inference-time compute — allowing systems to reason deeply when problems are complex and act quickly when they’re not — turns intelligence into a strategic lever. For energy markets, infrastructure planning, and climate forecasting, this enables better outcomes without constant retraining or ballooning costs.
The implication is clear: smarter systems emerge not just from scale, but from judgment embedded in execution.
The Quiet Power of Very Large Models
Very large language models (operating at frontier scale) will not dominate every workload, nor should they. Their real value lies in setting the upper ceiling of reasoning and synthesis.
In the green economy, these models underpin cross-domain intelligence: policy meets engineering, climate science meets economics, human behaviour meets systems modelling. They serve as the capability backbone — feeding insight to agents, copilots, and decision-support systems — while smaller models handle execution closer to the edge.
Their impact will be largely invisible. But like energy grids or cloud infrastructure, invisibility is the mark of foundational systems.
Why Small Models Win on the Ground
At the same time, very small, domain-specific models are quietly becoming indispensable. These systems are fast, cheap, predictable, and capable of running locally, think laptops, control systems, and edge devices.
In renewables operations, smart buildings, EV infrastructure, and environmental monitoring, small models do the work. They integrate tightly into workflows, respect data boundaries, and deliver consistent results without the overhead of frontier systems.
This bifurcation matters. Big models think. Small models act. Together, they form an intelligence stack that is both powerful and practical.
AI Moves Into the Engine Room
Perhaps the most consequential shift in 2026 is where AI is applied. After proving its value in customer experience and basic automation, AI moves up the value chain into core operations.
AIOps — the application of AI to manage and stabilise complex systems — is a clear example. With organisations already spending most of IT budgets just keeping systems running, the pressure to reduce noise, predict failure and improve resilience is intense. This kind of pressure is existential in energy and infrastructure.
To make this tangible, consider what’s already happening inside parts of the renewable energy sector. Large wind and solar operators are quietly using AI systems to monitor equipment health, weather variability, grid constraints, and market signals in real time. Not to automate decisions blindly, but to prioritise human attention. Instead of engineers reacting to hundreds of alerts, AI surfaces the few signals that actually matter: which turbine is likely to fail, which asset needs intervention first, which maintenance window reduces both downtime and cost.
The result isn’t science fiction. It’s fewer outages, longer asset life, faster response times and teams freed to focus on resilience rather than firefighting.
Why Manufacturing Without AI Is a Dead End
Nowhere is the cost of ignoring AI more visible than in manufacturing, particularly advanced manufacturing tied to clean energy, electrification, hydrogen, batteries and sustainable materials.
Traditional manufacturing business planning was built for a slower, more linear world: stable suppliers, predictable demand, fixed production schedules, and incremental optimisation. That world no longer exists.
Modern manufacturing operates inside a web of volatility: fluctuating energy prices, constrained supply chains, geopolitical risk, skills shortages, regulatory pressure and razor-thin margins. Planning production runs, inventory, maintenance, workforce allocation, energy use and logistics as separate problems is no longer viable. AI changes this by treating manufacturing as a living system, not a static process.
Already, leading manufacturers are using AI to synchronise design, production, maintenance and supply in real time.
In advanced clean-tech facilities, AI models simulate thousands of production scenarios before a single unit is built — optimising material inputs, energy consumption, yield rates and waste streams simultaneously. Predictive maintenance systems no longer just flag failures; they dynamically reschedule production around equipment health, workforce availability, and energy pricing.
In battery and electrolyser manufacturing, AI-driven quality control systems detect microscopic defects invisible to humans, reducing scrap rates and dramatically improving consistency (I witnessed some presentations outlining extraordinary advancements in new technologies at recent energy conferences in Australia and online).
The implication is stark. Manufacturing businesses that still rely on quarterly planning cycles, static spreadsheets and human-only decision loops are planning for a past that no longer exists.
Without AI embedded across design, procurement, production, quality, energy management and logistics, manufacturing strategies become brittle the moment conditions change. In the green economy — where capital intensity is high and failure is expensive — that brittleness is fatal. AI is no longer an efficiency upgrade for manufacturing. It is the coordination layer that makes modern, resilient and scalable production possible at all.
Memory Becomes a Design Question
As AI systems gain persistent memory, experiences become more personalised and continuous, but memory introduces risk alongside reward. What is remembered, for how long, and under whose control becomes a strategic decision (not technical).
In regulated, trust-sensitive sectors like energy and climate infrastructure, poorly designed memory can erode confidence fast. Done well, it creates continuity and insight. Done badly, it creates privacy risks and brittle failure modes. Memory is power, and it must be governed deliberately.
Human-in-the-Loop Becomes the Default
The most durable AI systems do not remove humans from the loop. They redesign it. Judgment, values and accountability remain human. Speed, scale and execution shift to machines.
Crucially, this no longer requires people to think like engineers.
The burden moves from prompt-crafting to clear handoffs and shared responsibility. This is where trust, adoption and impact finally converge.
What Smart Leaders Do Now
So, what does a smart response look like?
It starts with leadership recognising that AI is no longer an experiment or a side project — it is a capability layer that demands ownership.
The most effective organisations are auditing where AI is already creeping into workflows, separating deep-thinking systems from fast execution tools, and treating governance as an enabler rather than a brake. They are investing in reliability before autonomy, memory before scale and human–machine collaboration before cost-cutting.
Above all, they are embedding AI into strategy early, because waiting for certainty in a fast-moving system is not prudence, it’s abdication.
As Horn himself points out, in a year we will look back and see which of these theses held up, and which didn’t. But the direction of travel is already clear. For the green economy, the choice is no longer whether to engage with AI, but how intelligently and how fast.
And on that question, standing still is not neutral. To ignore or ban AI from business planning and strategy is fatal.
Future Now Green News is a forward-thinking media platform dedicated to spotlighting the people, projects, and innovations driving the green & blue economy across Australia, Asia and Pacific region. Our mission is to inform, inspire, and connect changemakers through thought leadership and solutions-focused storytelling in sustainability, clean energy, regenerative tourism, climate action, and future-ready industries.
