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Everyone is talking about AI, but in 2026 we need to talk about it in the context of energy, conflict and industrial volatility.
Over the past few years the global system has seen many shocks. The increase in global conflicts has had a direct impact on trade routes, energy infrastructure, and of course, shipping lanes. Each of these shocks ripples through the supply chain, increasing prices for energy and goods to companies and consumers alike.
For industrial businesses, this directly affects cost structures, production schedules, supply chains and long-term investment decisions. This is the context in which AI is being deployed.
Industrial disruption is no longer an isolated incident
Volatility has become a permanent feature of global logistics. Geopolitical tension, shifting trade routes, extreme weather, capacity swings, none of it feels exceptional anymore. Most executive teams plan on disruption as an 'unexpected' event. Something to manage through and then return to normal. However, today the operating environment is proving to be structurally different.
Energy markets today are deeply shaped by geopolitical tensions, while supply chains are being reconfigured with resilience in mind rather than pure cost efficiency. At the same time, industrial companies are under growing pressure to balance decarbonization goals with cost control and uninterrupted operations, a combination that is increasingly difficult to manage using traditional approaches alone.
Systems of record remain essential, trusted structured data with safeguards and human-based controls are key, but they are no longer sufficient in isolation. What's needed now are systems that go beyond capturing past activity to actively anticipating future scenarios and enabling faster, more informed responses. The leap from transactional record-keeping to strategic, forward-looking capability is what makes this moment particularly compelling.
This is not about features
There's a lot of information and misinformation about various AI features. Copilots. Chat interfaces. Productivity tools layered into existing software. There is, again, value in these but what I am seeing with industrial leaders is a different reality. The focus is shifting from tools that help individuals to systems that orchestrate entire business workflows.
For example, what happens when:
- Energy prices move sharply in a short period;
- A key shipping route becomes constrained;
- A supplier in one region cannot deliver on time;
- Maintenance windows need to be replanned due to sudden and restrictive resource constraints.
Historically those adjustments rely on human co-ordination across multiple systems and teams.
Agentic AI has the potential to change that equation. When it's built into workflows at the heart of how the business actually operates, it can begin to model scenarios, simulate cost exposure, optimize production schedules and trigger alternative supply routes. That shift from insight to execution goes beyond the capabilities of earlier predictive analytics and automation technologies to deliver more complete assessments and, ultimately, autonomous actions.
Energy volatility is now a design constraint
AI discussions often focus on data and models. Increasingly, energy is part of that equation too.
Industrial businesses are exposed to energy availability and pricing in a very direct way. Manufacturing, utilities, mining, A&D, heavy asset operations all depend on predictable supply and cost. At the same time, AI workloads themselves consume energy. Data centers, compute intensity and digital infrastructure all sit on top of power grids that are under pressure in some regions.
The relationship between AI and energy is now two-way. Sure, AI can increase demand on infrastructure. But it can also optimize energy consumption, forecast demand, align maintenance cycles with supply constraints and reduce waste across industrial operations. For asset-intensive industries, that optimization capability is not incremental but now foundational.
Vertical AI is where resilience lives
Horizontal AI improves personal productivity. It helps people write faster, analyze faster and communicate faster. But in industrial sectors, resilience comes from people having the confidence and subsequently consistent proof, that systems, tech and operating procedures can predict, react to and adapt to change.
Vertical AI understands asset hierarchies. It understands maintenance logic. It understands regulatory requirements and operational interdependencies. It can interpret technical drawings, equipment relationships and safety constraints in context.
When conflict disrupts supply routes or energy markets tighten, those contextual relationships matter. Generic intelligence isn't enough. And when organizations get that wrong, the consequences compound.
Business decision-makers disengage, or worse, go looking for alternatives and quietly restart the innovation process on a parallel track. We've all seen what happens when a leader loses patience and pulls the plug before the work lands. All that momentum, lost. The gatekeepers, the people inside the business who genuinely understand both the jeopardy of getting this wrong and the value of getting it right, are what prevent that from happening. Strong leadership and the courage to hold the line is what makes the difference.
Leadership now means embedding intelligence into the core
What I hear consistently from industrial executives is this — volatility is becoming normal.
Persistent unpredictability is the baseline — as a lifelong Leeds United soccer fan, I can definitely relate!
That means leadership teams have to rethink how they design their operating models. It is no longer enough to optimize for steady state efficiency.
We need:
- Scenario modeling built into planning cycles.
- Autonomous decision support across Global APM.
- Maintenance optimization that factors in energy exposure.
- Digital systems that can reconfigure workflows dynamically.
We are already seeing this with our customers. Kodiak Gas, for example, is using IFS Loops' Material Replenisher Agent to generate $3 million in ROI through saved time and increased operational resilience — in a volatile energy and geopolitical environment, that kind of AI-driven adaptability becomes a competitive advantage.
The real question
The real question for industrial organizations is whether AI is being embedded deeply enough to act when the environment shifts. Because it will shift and, in all likelihood, it's already happening.
Energy markets will continue to move. Trade routes will keep on evolving. Supply chains are flexing through necessity. Costs are fluctuating at a greater rate.
In that world, AI is about operational continuity and intelligent adaptation. It means moving from systems that document the past, from classical data-models and complex multi-vendor frameworks, to systems that actively shape the future. This is within the context of real world constraints — the ability for organizations to absorb change, the technology landscape with data sovereignty, security, ethics, AI trust in focus and practical, robust integration.
If you are leading digital transformation in asset-intensive industries, I would be interested to hear how you are designing for volatility rather than hoping for stability.
The operating environment has changed. Managing this reality is a challenge, but it's one we'll have to enjoy adapting and evolving to — the industrial economy is counting on it.