The conversation about AI governance often starts in the wrong place. It starts with models, vendors, copilots, or regulation. It should start with operating conditions. In 2026, one of the most important operating conditions is energy instability. The conflict in the Middle East has already disrupted oil flows, pushed prices higher, and increased pressure on fuel affordability. The US Energy Information Administration reported in March 2026 that Brent crude had risen sharply, settling at $94 per barrel on March 9, with shipments through the Strait of Hormuz reduced and some Middle East production shut in. The IEA has gone further, describing the current disruption to oil and gas flows and attacks on energy infrastructure as a major threat to global energy security and affordability.
For business leaders, that matters well beyond transport and procurement. Fuel disruption changes workforce behaviour. It makes commuting more expensive, increases the appeal of home working, and reinforces a pattern already visible in the data. The UK Office for National Statistics previously found that half of homeworkers reported spending less on fuel and parking, while the CIPD has pointed directly to rising travel costs as a factor that can accelerate home working. That does not mean every organisation will suddenly become remote-first. It does mean that when fuel becomes more expensive or less reliable, the commercial logic of working from home becomes stronger for both employees and employers. That is a reasonable operational inference from the available labour and cost data.
This is where AI risk changes shape. A more distributed workforce does not automatically create bad governance, but it does create more unmanaged decision points. Employees working from home are more likely to solve problems locally. They need to summarise documents, draft client emails, interpret spreadsheets, analyse contracts, prepare presentations, and speed up everyday administration. If the official enterprise AI tools are slow to approve, hard to access, or too limited, people will route around them. That is the classic origin story of shadow IT, and the UK’s National Cyber Security Centre is explicit that shadow IT consists of unknown and unmanaged assets, services, and devices that the organisation does not properly control. Shadow AI is simply the 2026 expression of the same problem, but with higher stakes because the asset involved is not only software, but decision support, content generation, and data handling.
The scale of the issue is no longer hypothetical. Microsoft reported in 2024 that 75% of global knowledge workers were already using generative AI at work, and that employees were bringing their own AI tools into the workplace. In 2026, Microsoft’s security reporting added a more pointed metric: 29% of employees were using unsanctioned AI agents for work tasks, while only 47% of organisations had established AI usage policies. Even allowing for methodology and sample limitations, the signal is obvious. Adoption is running ahead of governance. That gap is dangerous in any environment. It becomes more dangerous when external pressures, such as fuel costs and operational disruption, make informal remote workarounds more attractive.
Many organisations still treat AI GRC as a compliance wrapper that can be added later. That is the wrong model. In practice, governance has to be an enabling control system installed early, before usage patterns harden into undocumented workarounds. Once shadow AI is embedded in workflows, the business is no longer dealing with a clean deployment question. It is dealing with unrecorded data flows, unreviewed prompts, inconsistent outputs, unmanaged third-party exposure, and decisions being influenced by tools that have never passed risk, security, legal, procurement, or records-management review. The later GRC arrives, the more it acts as a disruptive clean-up exercise rather than a scalable operating model.
The remote work dimension intensifies several specific risks. First, data leakage risk rises because employees are more likely to paste commercially sensitive content into consumer AI services when working outside tightly managed office environments. Second, identity and access risk rises when approved tools are mixed with personal accounts, browser sessions, plugins, and unsanctioned agents. Third, model risk rises because outputs are reused across client, legal, finance, HR, and operational tasks without clear validation rules. Fourth, accountability risk rises because nobody can easily reconstruct which tool generated which recommendation using which source data. Microsoft’s Digital Defense reporting has highlighted risks such as sensitive data exposure, prompt injection, insecure plugin design, and platform-level weaknesses. Those are not abstract security concepts. They are direct governance failures when they occur inside a business process.
This is why robust AI GRC should now be treated as an operational resilience capability, not merely an AI policy exercise. Boards already understand the concept in other domains. When supply chains are fragile, treasury tightens oversight. When cyber threats rise, controls are strengthened. When energy markets become unstable and workforce patterns shift in response, the same principle should apply to AI. The question is not whether staff will use AI. The question is whether leadership has created a governed path that is easier, faster, and safer than the unmanaged alternatives. If the answer is no, shadow AI becomes the default operating model.
A mature response starts with visibility. Most organisations still do not have an accurate inventory of where AI is already being used, by whom, for what tasks, and with what data classes. Without that baseline, risk committees are discussing AI in theory while it is already being used in practice. Visibility means traffic analysis, approved-tool inventories, data-flow mapping, process-level interviews, and functional risk reviews across departments such as sales, operations, HR, finance, legal, and customer service. The purpose is not to punish experimentation. It is to separate acceptable local optimisation from material enterprise risk. The NCSC’s shadow IT guidance is highly relevant here: unknown assets become unmanaged assets, and unmanaged assets become governance blind spots.
The second step is to define decision rights early. Businesses need explicit answers to simple questions: which AI tools are approved, which use cases are prohibited, what data may never be entered into external models, when human review is mandatory, which outputs can be customer-facing, who signs off on new AI use cases, and which control evidence must be retained. This is the difference between AI enthusiasm and AI management. Employees do not need a 40-page policy document. They need a clear operating model they can actually follow under time pressure. In a fuel-stressed, hybrid, convenience-driven environment, the governed route must be friction-light or it will be bypassed. That is not a culture problem. It is a design problem.
The third step is to align AI GRC with resilience, procurement, and value. Too much AI governance still sits in a silo, usually with risk, legal, or IT security. That is insufficient. The present environment demands cross-functional governance because the trigger conditions are cross-functional. Energy shocks affect labour patterns. Labour patterns affect tool usage. Tool usage affects cyber exposure, data protection, compliance, and output quality. Strong AI GRC therefore needs procurement controls on vendors, technical controls on access and data movement, policy controls on acceptable use, assurance controls on testing and monitoring, and management information that shows whether AI is delivering measurable value. Governance that only slows usage will be resisted. Governance that enables safe productivity will be adopted.
The strategic lesson is straightforward. External instability changes internal behaviour. As the Middle East conflict continues to affect fuel markets and affordability, more organisations will find that home working and hybrid flexibility remain economically attractive. As work disperses, unofficial AI use will expand unless the enterprise provides a controlled alternative. That makes early, robust AI GRC a commercial necessity. Not because regulation says so, although regulation does matter. Not because cyber teams prefer central control, although they do. But because unmanaged AI adoption is what happens when business pressure meets friction. In 2026, that friction is being increased by geopolitics, energy risk, and cost pressure at exactly the moment AI tools are becoming normal work infrastructure.
The organisations that handle this well will not be the ones that ban AI or flood the business with policy. They will be the ones that recognise the chain reaction early: fuel disruption changes commuting economics, commuting economics strengthen remote-work incentives, remote work increases local tool choice, and local tool choice accelerates shadow AI unless governance is already in place. The correct response is disciplined enablement. Build the approved path. Make it usable. Put controls where risk actually sits. Monitor adoption. Treat AI governance as part of business continuity, not as a post-deployment compliance task. That is what robust GRC now means in practice.