For enterprises managing industrial digitalisation, the adoption of converged AI and IoT (AIoT) offers key operational efficiency gains. While combining these technologies creates measurable revenue opportunities, moving beyond initial pilots remains a primary obstacle for global decision-makers.
According to a November 2025 InfoBrief by IDC, sponsored by SAS, 62 percent of organisations worldwide have adopted a combination of AI and IoT, with another 31 percent planning to do so. Yet the depth of this integration varies. Despite widespread interest, over half of these organisations (57%) report being stuck in limited deployments or proof-of-concept stages.
For CIOs and COOs, this data highlights an operational risk: the potential for “pilot purgatory” where investments fail to reach the scale necessary for genuine ROI. By contrast, the 43 percent of firms that have achieved widespread or fully integrated deployments are reaping rewards that outpace their competitors.
The ROI of deep industrial AIoT adoption
The distinction between tentative experimentation and full-scale commitment is measurable. Research indicates that organisations classifying themselves as “heavy users” of AI in IoT are twice as likely to report benefits that greatly exceed their initial expectations compared to those with lighter usage.
The returns compound as the technology becomes more embedded in the core business. Under three percent of industrial executives surveyed stated that the value of AIoT did not meet expectations.
Kathy Lange, IDC Research Director for AI Software, commented: “The takeaway is clear: AIoT is fueling innovation, streamlining operations, and driving smarter, faster decisions.”
Predictive maintenance currently drives the highest adoption. Approximately 71 percent of organisations now utilise AIoT for this purpose, making it the most widely adopted use case. By analysing real-time data to anticipate asset failure, companies can reduce unplanned downtime and lower operational costs. IT automation follows as the second most cited use case at 53 percent, with supply and logistics at 47 percent.
Factory automation and grid resilience
Beyond maintenance, practical applications are altering specific verticals. In the manufacturing sector, AIoT facilitates factory automation, allowing firms to automate complex decisions rather than just simple tasks. This capability optimises processes and improves product quality in an environment facing labour shortages and supply chain disruptions.
In the energy sector, industrial AIoT adoption strengthens grid resilience. By analysing data from sensors across generators, power plants, and wind turbines, AIoT assists operators in managing costs, predicting demand, and optimising operations.
Jason Mann, Vice President of IoT at SAS, explained: “This IDC InfoBrief confirms what manufacturing and energy customers are telling us worldwide: AIoT has evolved from a buzzword to a potent technology and business imperative.
“Whether enhancing the predictive maintenance of critical equipment or improving operations across factories and electric grids, AIoT drives major cost savings, quality improvements, and efficiency gains.”
The ongoing skills shortage
While technological capability has advanced, the human infrastructure required to support it continues to be under strain. Shifting from previous trends, skills-related challenges have risen to become the number one obstacle for industrial AIoT adoption in 2025, a sharp rise from fifth place in 2019.
This talent shortage threatens deployment schedules. Operational Technology (OT) personnel, traditionally focused on physical processes and industrial systems, must now collaborate closely with IT teams focused on analytics and digital systems. The disparity in expertise between these groups can stall projects.
The technology itself may offer a solution to the problem it highlights. Modern AI technologies enable more employees, including those with varying skill levels and job roles, to interact with data effectively. This democratisation of data allows personnel working on the plant floor or creating corporate strategy to make data-driven decisions using generative and traditional machine learning tools.
While technical skills have become scarcer, cultural resistance has waned. Organisational pushback, which was the top challenge in 2019, has fallen to the sixth position. The workforce appears psychologically ready for AI tools, even if they lack the technical proficiency to wield them effectively.
Regional nuances in global operations
For multinational enterprises, understanding regional adoption curves is vital for allocating resources. North America has historically led in the heavy usage of AI within IoT, but the landscape is evening out.
The APAC region currently leads in moderate adoption, while EMEA remains optimistic across all levels of investment. Both regions are actively investing to close the gap with North American leaders. Looking ahead to the next 12- 24 months, 64 percent of organisations globally expect growth in their AIoT adoption.
Dez Tsai, Global Senior Director of AI, Data, and Vendor Transformation at TD SYNNEX, commented: “AIoT drives business value, and the more industrial companies use it, the greater benefits they see. We anticipate the adoption of AIoT solutions will accelerate as companies experience greater efficiency, productivity, and cost savings.”
Overcoming barriers to industrial AIoT adoption
To move from pilot to production, leadership must address persistent infrastructural and procedural roadblocks. Aside from the skills shortage, high implementation costs and legacy system integration are cited as major impediments.
Data quality also remains a continuing issue, maintaining its relative importance as a challenge since 2019. Without clean, reliable data streams, complex AI models will fail to deliver accurate insights.
IDC analysts recommend a strategy focused on “workforce enablement” to counter these barriers. Upskilling teams to work with AI-driven systems and capturing legacy knowledge are essential steps to building internal literacy. Upgrading legacy systems and using edge computing can provide the necessary technical foundation for real-time capabilities.
The trajectory for industrial operations is defined by the convergence of physical and digital assets. With 79 percent of respondents viewing AIoT as essential for maintaining a competitive advantage over the next three years, success depends on more than just software procurement.
Leaders must pivot their attention from the feasibility of industrial AIoT technology, which is now proven, to the adoption readiness of their organisation. This implies a dual focus: modernising the data infrastructure to support integration and investing in the technical fluency of the workforce.
Only by addressing the skills gap and data governance can enterprises bridge the divide between a successful pilot and a modernised operation.
See also: Can one AI model run your robotic fleet?
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