Boards are not stepping away from AI, but they are tightening the rules, demanding clearer business cases and shorter payback windows. CFO scrutiny is rising, and proof of value is becoming the standard for continued funding. That proof is less about promise and more about repeatable outcomes in daily operations.
The problem is that many companies still treat AI initiatives as standalone tools. In practice, AI is a system with dependencies across data, cloud services, identity, applications, and networks. When outcomes fall short, the failure is typically assigned to the model, even when the real cause sits elsewhere in the technology stack, most frequently, in connectivity and performance.
Recent industry research highlights the tension behind this shift. Technology leaders report that their boards hold unrealistic expectations about how quickly new technology should translate into business performance. That gap between expectation and delivery is exactly where misdiagnosis begins.
Many “AI failures” will be performance failures in disguise
In a production setting, the model is only one component of the user experience. An AI assistant that takes too long to respond or behaves inconsistently across regions is rejected regardless of model quality. ROI then disappears due to lower adoption, longer cycle times, and a growing sense that the technology is unreliable, eroding the business case before the model itself is ever properly tested.
The dependency is already visible in enterprise environments. Organisations consistently report that their networks limit their ability to run large data and AI projects. The biggest constraints are not obscure technical issues, but fundamentals such as networks that cannot scale capacity on demand, and inconsistent application responsiveness caused by latency and performance variation.
The pattern is already clear: as AI expands beyond isolated pilots, the question shifts from whether a model works to whether the service performs consistently everywhere it is needed. In that reality, latency and jitter stop being technical concerns and become business issues, shaping whether AI accelerates a workflow or quietly slows it down.
To prove value under CFO scrutiny, leaders must separate three questions: is the use case valid, is the model performing as expected, and is the operating environment capable of delivering the benefit. Without that distinction, investments hinge on anecdotes, and projects stall for reasons unrelated to AI performance itself.
A practical response is to define performance budgets for each AI-enabled workflow. This means agreeing upfront on acceptable end-to-end response times, the amount of variation that can be tolerated, and how performance should hold across regions and sites. It also means deciding what will be measured and who owns remediation when the system drifts.
Many organisations are already prioritising networking and connectivity investments ahead of other technology categories because they can see where bottlenecks are emerging. In 2026, the ROI gap will widen between those that address these constraints now and those that wait until performance problems become visible in board metrics.
Resilience moves to the boardroom
Resilience, similarly, now carries clear financial consequences and needs to be treated as board-level material. Connectivity has long been viewed as operational plumbing, but that framing breaks down once AI is embedded into customer journeys, supply chains, and internal decision-making. When performance degrades, business impact is immediate. Research shows organisations reporting financial losses ranging from hundreds of thousands to millions annually from network-related downtime and performance degradation; these costs compound when AI systems are affected. That level of exposure belongs in board risk discussions, not buried in operational reviews.
Network readiness also shapes the probability of harm. Fewer than half of organisations believe their networks are fully ready to support new technology initiatives, and scaling AI on top of stretched infrastructure increases the risk of chronic underperformance - slow enough to erode value, but not dramatic enough to trigger fast intervention.
To address this, governance needs to link performance metrics directly to business outcomes. Traditional uptime targets are no longer enough because users can experience “availability” alongside delays or inconsistencies. Boards should push for controls that capture the full transaction path, not just the health of isolated components. That begins with observability, the ability to see how an AI-driven transaction behaves across applications, cloud services, and network routes. Without it, incidents become slow and political, with each party defending its own metrics. With it, teams can quickly identify whether issues stem from model throughput, data access, cloud congestion, routing, or local conditions.
Testing needs to change. AI workloads are spiky and often unpredictable, especially when tied to automation. Resilience testing in 2026 should routinely include degraded routing, regional congestion, cloud-dependency disruptions, and sudden demand surges, as these are the conditions that undermine user trust and ROI.
Accountability must follow the transaction
As deployments span sites, clouds, and partners, accountability frequently fragments. Each supplier may meet its own targets while the overall user experience still fails. With networking skills scarce and many organisations relying heavily on partners, clear accountability models that follow the transaction end-to-end are essential. Shared objectives, aligned measurement approaches, and defined escalation paths across organisational boundaries are no longer optional.
The test is simple: can the organisation trust AI in production? ROI will depend on delivery discipline, not model sophistication. Leaders should define performance budgets, build end-to-end observability, make resilience testing a standard gate for scale, and align accountability across teams and partners. In a period of CFO scrutiny, the advantage will go to organisations that treat network resilience as a governance priority rather than an engineering afterthought. The winners will not be those deploying the most AI, but those making it perform reliably wherever the business depends on it.
