The gap between the availability of industrial AI tools and the ability to use them consistently is becoming the biggest constraint in manufacturing. For years, most manufacturers have managed the costs of reactive maintenance—like unplanned shutdowns, overtime, and lost customer trust—without much choice. However, the rapid deployment of AI in maintenance has changed the game, shifting from theoretical potential to real, deployable solutions that make the investment case clear. Despite strong industry commitment to AI, progress is slower than anticipated. Research shows that about 78% of the barriers to AI adoption are related to the workforce. While AI tools are being introduced more quickly, the ability to use them consistently has not kept pace. Predictive maintenance, which uses AI to anticipate equipment failures, has grown significantly, but reactive maintenance—fixing problems after they occur—has remained steady. Proactive maintenance, which aims to prevent issues before they arise, has even declined. This doesn’t signal failure but rather a practical shift, as early AI projects have provided useful proof of concept. The first wave of AI implementation proved the tools themselves, not just the models. While technology can move quickly into the budget, changing work habits, building trust, and adjusting frontline confidence take longer. AI investment is now shifting from exploratory projects to more practical operational priorities, such as cybersecurity, data management, and generative AI. This reflects a more realistic view of digital maturity, where leaders are focusing on areas where the cost of delays, downtime, and poor data is immediate. Expectations around Industry 5.0—where human expertise and AI work together—are also evolving. As industrial technology moves from the automation-focused Industry 4.0 model to a more human-in-the-loop approach, leaders now expect a one- to four-year journey. The 78% workforce barrier isn’t just about a labor shortage but highlights a lack of expertise, knowledge gaps, and broader capability deficits. This is about an organization's ability to absorb and apply new knowledge effectively, known as absorptive capacity. In maintenance, this includes how quickly a business can recognize new insights and convert them into practical actions, like a night-shift supervisor deciding whether an anomaly requires immediate attention. The UK Government’s AI Skills for the UK Workforce report highlights the need for more than just technical skills like training AI models. It also emphasizes the ability to interpret AI outputs, adapt workflows, and communicate changes to frontline teams. Addressing these workforce issues can't be treated as a separate HR task but must be integrated into the system that decides whether AI investments lead to better execution. The success of Industry 5.0 will depend on the capabilities of the people on the plant floor. The commercial impact of the lag between AI spending and returns is clear. While companies are investing in data-driven execution, many still lose significant time to avoidable issues. According to Siemens’ 2024 True Cost of Downtime research, unplanned downtime costs the world’s 500 largest companies $1.4 trillion annually—11% of their total revenues. This reflects the incomplete shift from reactive to predictive maintenance. When this gap persists, the return on technology investment comes slowly and unevenly, leading to extended timelines and weakened confidence in delivery. The same discipline applied to tools and platforms must now be applied to people and routines. Leaders should start by identifying the weakest links in their operations. Which assets still depend on a few experienced technicians for interpretation? Which work histories are too thin to support the next diagnosis? Which alerts lead to immediate action, and which are ignored until the right person is on shift? These questions reveal whether predictive maintenance has become part of daily operations or if it still sits on top of reactive habits. Capturing the knowledge that remains in people’s minds before it leaves the company, ensuring work histories are complete, and training teams to understand AI alerts are all critical steps. Connected reliability—where asset data, maintenance history, and frontline judgment work together—supports practical execution when it stays close to the work. Research shows nearly half of respondents plan to advance connected reliability initiatives in the next year, treating reliability as the bridge between immediate operational needs and long-term goals. Manufacturing leaders must now audit their capabilities with the same seriousness as their technology investments. They should ask not only what AI has been deployed but also who can act on it, where decisions slow down, what knowledge is missing, and which workflows still pull teams back into reactive work. Only then can the full potential of AI be realized.