Where does AI land as a priority for manufacturers during the second half of 2026 and beyond?
By Emory Lane, Senior Industry Principal, Manufacturing at UKG
Manufacturers should make workforce agility a top priority during the second half of 2026. As demand shifts, labor shortages continue and operations become more complex, organizations that can quickly align people, skills, schedules and production will be better positioned to protect margins and maintain performance. Practical AI, stronger frontline communication and better workforce visibility are helping manufacturers respond faster when conditions change.
Halfway through 2026, workforce agility has become critical for manufacturers as volatility becomes part of everyday operations. Demand patterns shift, supply chain conditions remain difficult to predict, and workforce constraints put pressure on production schedules, margins, and plant-level execution. In this environment, the ability to adjust labor quickly and intelligently is one of the defining capabilities that separates resilient manufacturers from those struggling to keep pace.
For years, manufacturers have focused on hiring challenges, retention concerns, absenteeism, overtime, and skills gaps as workforce issues. Those challenges remain, but the conversation has shifted. Workforce agility allows manufacturers to align labor, skills, schedules, and communication with changing production needs, which has become an operating requirement.
Leading manufacturers recognize that their ability to adapt to changing production needs depends on how effectively they connect workforce planning, labor visibility, scheduling, compliance, and frontline communication. Those organizations gaining ground are building more responsive workforce strategies that help them align people, production, and data in real time.
In the second half of the year and beyond, moving from reactive labor management to proactive operational decision-making will increasingly separate high-performing manufacturers from the competition.
Manufacturing leaders are still navigating a complex mix of demand variability, labor shortages, absenteeism, and overtime pressure. When these issues are managed manually or in disconnected systems, small disruptions can quickly affect throughput.
A missed shift, delayed coverage decision, or unexpected absence may seem like a workforce issue on the surface. In reality, these occurrences can create production delays, increase overtime costs, strain supervisors, and put additional pressure on employees already working in demanding environments. UKG research found that 90% of manufacturers report that skilled labor shortages have a negative impact on their bottom line. This finding highlights why workforce agility has become increasingly important. Manufacturers need better visibility into available labor, a clearer understanding of skills coverage across lines and shifts, and tools that help managers respond quickly when conditions change.
The goal is not simply to fill schedules. Manufacturers are working to make labor decisions with the same discipline they apply to production planning, inventory, and quality. When workforce data is connected more closely to operational needs, leaders can anticipate coverage gaps, manage overtime more effectively, and make better decisions before disruptions affect performance.
AI continues to generate significant interest across manufacturing, but many leaders are now focused on practical applications that improve daily operations. The most valuable applications are the ones that help supervisors, plant managers, HR teams, and operations leaders make faster, more informed decisions.
Forward-thinking manufacturers are looking at practical AI use cases tied directly to operational outcomes: identifying coverage gaps, forecasting labor needs, improving schedule quality, reducing administrative burden, and surfacing workforce risks before they affect production, all of which are tied directly to operational outcomes.
For example, a supervisor may need to understand whether enough qualified employees are available for a particular line, whether overtime is trending toward a threshold, or whether absenteeism patterns are creating risk for an upcoming shift. AI can bring attention to those signals faster, reducing the time managers spend searching for information or reacting after a problem has already appeared.
Successful AI adoption in manufacturing will depend on trust and usability. Leaders do not want tools that add complexity to already demanding environments. Instead, managers need recommendations they can act on. Employees need clarity around how AI supports better scheduling, communication, and decision-making without replacing the human judgment required on the plant floor. AI delivers the greatest value when it helps people solve real operational problems quickly and confidently.
The frontline employee experience remains a competitive differentiator in manufacturing. Organizations are competing not only on wages but also on predictability, flexibility, safety, development, and trust.
For employees, the workplace experience is often defined by practical moments: clearly communicated schedules, accurate time recording, easy-to-understand policies, and access to support when needed. These everyday touchpoints directly influence engagement, retention, and productivity, yet according to UKG, only 4 in 10 frontline employees have access to communication tools today.
Manufacturers are investing in technology that gives employees easier access to schedules, time information, communications, career opportunities, and support. The most effective tools simplify everyday work for employees while reducing administrative demands on managers.
This is especially important in environments where employees may work across shifts, roles, lines, facilities, or union and nonunion settings. Communication needs to be timely, consistent, and relevant. Employees need confidence that they have accurate information and that their concerns can be addressed quickly.
When manufacturers reduce friction in the frontline experience, they strengthen trust, which supports higher retention, stronger safety outcomes, and better overall performance.
Compliance and labor complexity continue to intensify for manufacturers, particularly those operating across multiple sites, regions, or countries. Leaders are managing a growing mix of labor rules, union considerations, overtime requirements, leave policies, safety requirements, and local regulations that directly impact scheduling, staffing, and plant-level execution.
The challenge is balancing consistency with flexibility. Organizations need enough control to reduce risk and create standard workforce practices, while allowing each facility to address its own operational needs.
A policy or process that works well in one plant may need to be adapted for another based on local agreements, staffing models, production requirements, or regulatory obligations. Without clear visibility and consistent governance, complexity can create compliance risk and uneven employee experiences.
Manufacturers are increasingly looking for workforce systems that help standardize core practices while allowing appropriate local variation. This approach gives leaders a clearer view of labor activity across the organization while helping plant-level teams operate within the realities of their environment. Greater consistency helps manufacturers meet compliance requirements while giving leaders better visibility into workforce operations across every location.
Skills and retention remain major constraints for growth. Manufacturers continue to compete for experienced workers while preparing employees to take on new responsibilities. As workforce demands change, organizations are placing greater emphasis on building long-term workforce stability.
Manufacturers are focused on retaining experienced employees, accelerating onboarding, cross-training workers, and understanding where turnover, burnout, and productivity risks are emerging. This is especially important as experienced employees retire, new workers enter the industry, and production environments require more technical and adaptable skill sets.
Cross training is becoming a critical part of workforce agility. When employees can move across roles or lines safely and effectively, manufacturers have more flexibility to respond to demand changes or unexpected absences. Employees also gain new skills and career opportunities that can improve engagement and encourage them to stay with the organization.
Workforce data plays an important role in this shift. Leaders need to understand where skills gaps exist, which teams are under pressure, where turnover is highest, and where onboarding may take too long. These insights can help organizations move beyond short-term staffing fixes and develop workforce plans that better support future business needs.
The manufacturers best positioned for the second half of 2026 and the years ahead are the ones that treat workforce agility and workforce technology as operational decision-making capabilities, not just administrative functions.
That means using better labor visibility to anticipate risk, improving scheduling flexibility to adapt faster, supporting managers with usable insights, and giving frontline employees clearer access to the information and support they need.
Manufacturing will continue to face demand shifts, supply chain disruption, labor complexity, and skills pressure. Organizations that connect people, production, and workforce data will be better prepared to respond quickly, strengthen employee engagement, and maintain operational performance as conditions continue to evolve.
Manufacturers face ongoing labor shortages, changing customer demand and unpredictable operating conditions. Workforce agility helps organizations adjust staffing, schedules and skills more quickly while minimizing disruptions to production.
Many manufacturers use AI to forecast labor needs, identify scheduling gaps, monitor overtime trends and provide managers with information that supports faster operational decisions.
Employees who have access to clear schedules, timely communication and simple workforce tools are more likely to remain engaged, productive and committed to their employer.
Organizations should focus on workforce agility, practical AI applications, stronger frontline communication, workforce visibility and skills development to improve operational performance and prepare for continued uncertainty.
About the Author:
Emory Lane is Senior Industry Principal for Manufacturing at UKG, where he works with manufacturers to improve workforce performance, operational efficiency and frontline employee engagement. With more than 35 years of manufacturing leadership experience, including executive roles at Mohawk Industries, he brings practical expertise in workforce strategy, lean manufacturing and digital operations.
Read more from the author:
Research: AI Adoption Reduces Burnout, Builds Trust | Industry Today, October 27, 2025.
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