Why Artificial Intelligence Is Becoming the Greatest Operational Force Multiplier and the Most Underestimated Governance Risk in 2026
Industrial and manufacturing firms entered 2025 under pressure to modernize.
Things were getting tough. Labor shortages were still a problem, and the supply chains were still fragile. Our customers were demanding more reliable, traceable and faster transportation services. And then AI came along. The magic solution to ensure predictive maintenance, autonomous planning, higher quality and lower costs across the board.
By the end of 2025, in most businesses in most industries, AI was no longer being developed and fine-tuned as a trial or an experiment. It was just there. Built-in to every aspect of the enterprise: the forecasting and planning software that automatically decided, say, how many cars to make and when to make them; the sensors that determined whether a product or subassembly was flawed; the thermostats, light switches, and circuit breakers that managed the buildings’ environment; the databases of vendors and automated purchasing systems that figured out when and what to buy;
What followed was not a technology failure.
It was an execution and governance failure.
Whitepaper 2026: The Unintended Consequences of Autonomous Decision-Making at the Edge - Summary We predict with certainty that in 2026 execution risk from the increasingly pervasive use of “Artificial Intelligence” (AI) will become one of the dominant and poorly controlled risk factors to on-going factory and production performance. The risks will not arise from occasional failures due to bugs or poorly developed systems. Rather they will arise from the fact that current operational practices were not suitably modified in anticipation of the use of probabilistic, autonomous control logic.
There is a common tendency to think of an “AI risk assessment” as a standard product that can be applied to all organizations. We disagree strongly: The AI risk in an industrial context is completely different from the AI risk in a digital or service company.
Automation challenges in manufacturing and process control environments are different. A manufacturing system is a physical entity and errors propagate physically. The direct and indirect impacts are many and varied, not only on product quality, but also on other plant performance measures such as operator and population safety, plant productivity, inventory, quality and customer confidence as well as statutory compliance. Any plant mis-operation resulting from control or automation issues has the potential to cause significant and potentially irreversible damage, both to people and plant, and therefore must be protected against by robust and failsafe control systems and effective procedures.
In 2025, all major industrial plants had long since automated and automated using the latest in AI technology to maximize production and efficiency. Responsibility, escalation protocols and the need for human intervention were all considered secondary to the bottom line. When the system failed to operate within the parameters that had been set it proved to be impossible to regain control.
In industrial contexts, latency equals loss.
AI EXECUTION RISK IN 2026 WILL HAVE A MUCH GREATER IMPACT AS BY THEN MANY AUTONOMOUS INDUSTRIAL PROCESSES WILL BE UNREVERSIBLE.
In the early days of industrial AI, the main application was decision support systems that provided information to planners, engineers and department supervisors. The workers on the production lines would still make the operational decisions.
That boundary eroded rapidly in 2025.
AI systems increasingly:
In many plants, humans monitored dashboards while AI systems acted continuously.
This transition changed the nature of operational risk.
As more tasks are performed by autonomous AI in a more automated workplace, what was once an isolated single point of failure can become a systemic issue. Errors made by autonomous systems can accumulate undetected between batches, between different vendors and between interactions with different customers.
By 2026, we will stop arguing about whether an AI system can really carry out the commands it is given and start to worry about whether we have any influence over its actions once it has begun.
Industrial firms are rich in metrics.
Every day, operations leaders in all types of production environments are focused on one or more of the following metrics: OEE, scrap rates, downtime, yield, throughput, energy intensity, inventory turns—dashboards are a dime a dozen these days. So, how could it be that the production lines for which performance is being measured are not being controlled? The answer is: measuring performance metrics is the first step only. The tougher question is: what do you do with the metrics you have if they don’t tell you and others what to do next?
In 2025, this assumption proved false.
KPIs are lagging indicators. By the time you become aware of a material deviation in a key performance indicator (KPI), the fact that an AI system has already guided the processes in the organization means that the effects of the AI-driven decisions have already occurred. In addition, using KPIs to control an organization can hide individual problems if the overall performance is in the target zone.
In 2026, metric visibility does not equal execution control.
Accountability diffusion was one of the most striking examples of failure of governance we saw in 2025.
When something went wrong as a result of a decision made by an AI system and by the time this became apparent, the delivery might already have been missed, the product could have slipped through detection, the energy consumption could have already blown through budget, or the close call on the production line could be long over, no one was sure quite where to look. Was the problem in the decision the system made? The data used to train the system? The way the system was designed? The information input to the system by an operator or technician? The operator or technician who activated the system? Its designer? The vendor that made the system?
In many organizations, no single role owned AI-driven outcomes end to end.
This type of ambiguity is a serious hazard in an industrial setting where accountability is a key to preventing accidents and ensuring operational reliability.
In 2026, all manufacturing companies will be structurally exposed if they cannot answer to the question “who is responsible for the results of the execution of the AI in charge”?
Industrial operating models evolved for human-paced decision-making.
All these variables were considered so that the handover of the shift could be carried out smoothly, together with the daily production meetings, weekly planning and the escalation ladders.
AI collapses these cycles.
In 2025 a bunch of plants were upset that an AI had to make hundreds of small decisions about individual items between each human inspection. It wasn’t until people realized something was off that it was too late.
The result was a widening gap between decision velocity and governance velocity.
By 2026, the Future of Maintenance Report 2023 by BearingPoint, Sinvent and Mekonovo predicts that this will be one of the largest execution risks in manufacturing.
Most legacy systems were not replaced by more modern ones. Instead, AI was merely “bolted on”.
We’re standing in an operations room, surrounded by rows of screens plastered with all sorts of information. Every system in the production environment is connected to these screens: MES, ERP, SCADA, PLM and quality systems. In between and above them, the many AI engines that look for anomalies and correlations are parked. Connecting all these systems requires interfaces and links and a good understanding of dependencies and potential points of failure.
As of 2025, almost no companies had an end-to-end understanding of how their systems made decisions with the aid of AI and machine learning.
This layering amplified complexity, not efficiency.
By 2026, almost all the industrial AI risk we experience will not be the result of forecast errors but rather the emergence of uncharacteristic behavior when these systems are connected.
Unlike digital businesses, industrial firms cannot tolerate “acceptable error rates.”
Safety incidents, non-compliances and product quality issues can have an instant impact on people, litigation and reputation. Introducing risk into business operations, particularly where AI-driven process optimization is delivered without safe and compliant rules in place, is simply not acceptable.
In 2025 several large industrial organizations, after several near misses, scaled back and prohibited the use of the AI technology because it was causing too much trouble and expense.
In 2026, governance must precede deployment, not follow incidents.
Most industrial boards had the Artificial Intelligence topics included in the regular innovation or competitiveness update. None had an execution risk briefing.
Most companies believed that AI presented an opportunity to expand their business. None anticipated that AI could represent a significant threat to their control of operations. Instead, most trusted local site leaders to handle any challenges that arose at the individual sites where autonomous systems were deployed.
This created a governance gap.
The technology issues faced by IT and Ops teams are generally well understood, with many dealing with what essentially cyber-attacks and associated risks to the business are arising from the increasing use of technologies such as AI and Machine Learning in their own environments. But the risks to others are not so obvious. In high value to people risk situations where there is also a risk of regulatory fines, reputational damage, business interruption etc., CxO and boards have a fiduciary duty to ensure they are managing Execution risks effectively.
In 2026 people look back and are shocked and horrified by realization that boards that didn’t discuss the dangers of industrial AI have not done their job and are therefore complicit.
This risk is often called “execution risk”. However, just because we have a name for a risk does not mean that the only way to manage that risk is to abandon the underlying technology. Clearly there is more to be said on this subject than can be covered in a short article, but to begin we should at least acknowledge that what is generally called “execution risk” is, in fact, two closely interlinked risks: The first is that of an innocent person being found liable for actions for which they were not responsible. The second is that the person found liable may not have the means to pay what the court has decided is
This includes:
Organizations that implemented these principles in late 2025 saw materially better outcomes.
In 2026, these practices become non-negotiable.
Many industrial firms lack in-house experience governing AI at scale.
In 2025, the companies that didn't suffer a catastrophic failure of their systems would be bringing in interim or fractional chief operating officers to modernize their internal workings.
This approach avoided permanent structural disruption while restoring control.
By 2026, the future of work will demand that all employees lead flexibly to reduce risk from the impact of AI.
In 2026 #ArtificialIntelligence will determine the competitiveness of each sector. And consequently, the failure of those who have not implemented good governance in each of their sectors.
The greatest risk is not AI malfunction.
It is uncontrolled autonomy inside complex physical systems.
The way operational work is governed by industrial organizations will undergo a profound shift in response to the differing time horizons and rhythms that are created by the advent of AI. On the one hand, some risks will be exposed and can be controlled in advance as the value created through the acceleration of operational processes is made available. On the other, other risks are opaquer and the effects build up gradually until they suddenly explode into a crisis when they are finally noticed.
In manufacturing, efficiency is optional.
Control is not.
About International Executive Consulting
International Executive Consulting helps industrial and manufacturing Boards, CEOs and Investors mitigate and manage the execution risk associated with deploying more complex and higher risk AI technologies, that transform the way businesses operate and help secure the future of companies exposed to the rapid pace of technological change and digital disruption. IEC will enable trusted at scale deployment of AI in 2026 and beyond.
Author: Cyril Moreau
At International Executive Consulting, we excel in driving business transformation and organizational change - enhancing corporate performance while optimizing efficiency.