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As artificial intelligence moves from experimentation to execution, manufacturers face a leadership test on the factory floor. AI’s potential to help improve safety, quality, productivity, and decision-making is clear. Its success, however, will depend on how effectively frontline leaders introduce, explain, and integrate AI into daily work. Their role extends beyond supporting adoption. They can help teams move from skepticism to excitement, from early use to integration, and ultimately to reimagine how work is performed across the frontline.
The timing makes this leadership test especially consequential. In 2025, manufacturing averaged approximately 420,000 job openings, and it may need as many as 3.8 million new workers by 2033. In response, manufacturers are accelerating investment in AI and automation, with 86% of high-growth companies doing so. These investments are reshaping how work is performed more than they’re reducing labor demand. Rather than easing pressure on the frontline, AI often introduces new complexity by changing how decisions are made, how performance is measured, and how work is executed day to day.
Leading through this shift is fundamentally different from managing prior waves of automation. Traditional automation targeted discrete tasks or efficiency gains. AI influences judgment, decision-making, and daily workflows, requiring leaders to guide not only process changes but mindset shifts. AI adoption depends on leaders who can guide teams along an adoption curve that moves from skepticism to excitement, from experimentation to integration, and ultimately to reimagining work. Frontline leaders should communicate AI’s purpose and value, address concerns about disruption and trust, and sustain operational performance as new tools become part of daily routines. For manufacturers, the question isn’t how quickly AI can be deployed but how effectively frontline leaders are prepared to guide their teams through this transition.
To examine these dynamics, PwC and the Manufacturing Institute surveyed manufacturing leaders across operations, human resources, and executive roles for this 2026 report. This marks the third year of our research series focused on the manufacturing frontline. In 2024, manufacturers emphasized that addressing attrition and talent shortages required intentionally prioritizing the frontline employee experience. In 2025, respondents reinforced that strengthening frontline leadership capability was essential to improving that experience. Now, as AI influences work, the next step is clear. Manufacturers should expand frontline leadership capabilities further to help guide AI-driven change.
Let’s look at five findings that highlight a widening gap between frontline leader influence and readiness to lead AI-driven change, uneven adoption across leadership levels, limitations in traditional training approaches, interconnected human and system constraints, and the underuse of frontline leader input in shaping AI initiatives.
Together, these insights illustrate how companies are moving from early use to integration. For manufacturers, the implication is clear. Strengthening frontline leadership is not only essential for AI adoption but central to realizing its operational and workforce impact.
Finding 1: Frontline leaders’ impact is growing, but readiness to lead AI lags behind
Despite growing investment in AI, manufacturers have limited confidence in how prepared their frontline leaders are to lead through it. When asked to rate their readiness to lead AI-driven change, 54% of respondents reported low or very low confidence, and none reported high or very high confidence.
A widening gap between frontline leadership and AI readiness
Q: How would you rate your frontline leaders’ overall competence in leading AI-driven change?
Source: PwC and MI Frontline Leadership and AI Survey, Q3 2025; base 102
This stands in sharp contrast to how they view the broader influence of frontline leaders. Nearly half (48%) rate their frontline leaders as very or extremely effective in shaping the overall employee experience of hourly frontline workers
That gap matters because the frontline leader’s role in stabilizing the workforce has only grown. In 2024, 66% of manufacturers reported that positive employee experience significantly reduces attrition. In 2025, that figure climbed to 82%. The same pattern appears in absenteeism, rising from 40% in 2024 to 70% in 2025. Whether employees stay and whether they show up are increasingly tied to their daily experience of work, placing frontline leaders at the center of workforce stability. As AI influences frontline operations, worker willingness to adopt and sustain new ways of working depends largely on how change is introduced, reinforced, and supported each day by frontline leaders.
The contrast points to a growing capability gap that may reflect not only individual readiness, but whether organizations have provided frontline leaders with the tools, training and structural support required to lead through AI adoption effectively. Expectations are evolving faster than their preparation, widening the distance between responsibility and readiness.
Treating AI leadership as a secondary extension of existing responsibilities rather than a core capability risks slowing AI adoption at the point of execution. Moving on from experimentation means more than deploying new tools. Manufacturers can strengthen frontline leaders’ capacity for AI-related communication, coaching, and change leadership so they are better positioned to translate AI advances into confident daily practice. The impact of AI will depend less on the technology itself and more on what happens on the factory floor between frontline leaders and their teams.
Finding 2: Uneven AI adoption slows integration into daily operations
Across manufacturers, AI adoption remains largely exploratory. Most report limited use of AI at the executive level, and both frontline leaders and frontline workers cluster in early stages of skepticism or initial excitement. Very few describe AI as meaningfully integrated into daily operations, and fewer still are redesigning work because of it.
The data shows curiosity without confidence. Nearly half (45%) of frontline leaders are described as skeptical of AI, even as 50% express excitement about its potential. Frontline workers are earlier on the curve, with 62% viewed as skeptical and just 24% described as excited. That divergence has implications for execution. Frontline leaders may be positioned to drive implementation, but sustained adoption depends on the conviction of the workers responsible for day-to-day execution.
Enthusiasm hasn’t yet translated into operational scale. Integration rates remain in the single digits, 2% for frontline leaders and 8% for frontline workers. For most manufacturers, AI is still something employees are hearing about or piloting in limited cases, not something embedded into standard work. In this early phase, perceptions are forming while norms, routines, and trust remain unsettled.
Recent industry research reinforces this exploratory pattern. According to the Manufacturing Leadership Council’s report, Shaping the AI-Powered Factory of the Future, companies mainly apply AI to targeted use cases such as predictive maintenance, quality inspection through computer vision, supply chain optimization, process automation, and production scheduling. These applications are often deployed as discrete solutions within specific functions rather than as fully integrated, enterprise-wide systems. While they generate measurable gains in uptime, yield, and efficiency, they often remain confined to pilot environments or isolated workflows, limiting their influence on how work is structured and led.
Executive adoption patterns help explain the limited integration. Fifty-eight percent of respondents report that AI use among executive leadership remains limited, and another 19% say executives are still exploring rather than consistently applying AI. At the same time, 74% identify leadership as the defining factor in the success of major initiatives. When senior manufacturing leaders haven’t operationalized AI in their own decision-making and storytelling, it becomes more difficult to set clear expectations, reinforce priorities across plants, or model how AI should inform safety, quality, and production decisions.
Executive AI adoption lags despite its influence on outcomes
Q: How would you describe the current stage of AI adoption among executive leadership in your organization?
Source: PwC and MI Frontline Leadership and AI Survey, Q3 2025; base 102
This creates a fragile stage of adoption for many manufacturers. Without clear use cases and guardrails, early curiosity can give way to resistance. Leadership sponsorship should be visible. Executives who model AI use in their own decisions, communicate its purpose clearly, and reinforce expectations consistently across operations help establish credibility. Otherwise, AI tools risk appearing disconnected from production realities or worker priorities.
Moving decisively can alter that trajectory. By linking AI to outcomes that matter on the factory floor, such as safety, quality, and ease of execution, executives establish direction and relevance. Frontline leaders can then reinforce that direction by integrating AI into real work processes, helping workers see practical value rather than abstract promise. Providing hands-on exposure to AI within real production tasks allows its relevance to be experienced rather than explained.
Finding 3: Advancing along the adoption curve requires training and experiential capability building
While many manufacturers approach AI readiness primarily as a training issue, current training and development practices for frontline leaders haven’t kept pace with technological change. More than half (52%) provide role-specific development for frontline leaders only once a year, and nearly one in five (17%) never provide it. When it comes to AI specifically, just 19% offer any AI-related training. In an environment where tools, workflows, and expectations evolve rapidly, episodic instruction leaves frontline leaders underprepared to move their teams along the adoption curve. The same dynamic often extends to onboarding, where AI expectations are not yet consistently embedded into how new frontline leaders and workers are introduced to their roles.
Confidence data reflects that limitation. Nearly half (49%) of respondents report that existing programs don’t prepare frontline employees for AI-driven role changes at all, and only 2% express high confidence that their teams are ready for how AI will reshape daily work. These findings don’t suggest that training lacks value. Rather, they indicate that training is most effective when it’s contextual and reinforced through application.
The format of development further shapes outcomes. Self-paced e-learning is cited far less often as effective than more applied approaches. Half of our respondents identify in-person workshops as the most effective way to build frontline leadership skills, and 35% point to learning on the job. Trust in AI grows when it is experienced in real production scenarios rather than introduced in abstraction, making understanding more concrete and strengthening confidence.
Within the adoption framework, frontline leaders are already more likely to express excitement about AI’s potential, yet integration into daily routines remains limited. Frontline workers, by contrast, remain concentrated in skepticism. Moving workers from skepticism to excitement often involves more than instruction. It involves visible problem-solving. Excitement grows when teams see AI applied to real production challenges that matter to them. Integration accelerates when frontline leaders are not only trained on AI but actively engaged in shaping how it improves the safety, quality, and performance of the work they manage.
For manufacturers seeking to advance from exploration to integration, capability building should combine formal instruction with structured experimentation. Foundational training, including embedding AI into onboarding, creates shared language and baseline confidence, while applied use cases and protected time for frontline leaders and workers to test AI tools within real workflows can help translate exposure into ownership. Progress along the curve from skepticism to excitement and ultimately to integration is more sustainable when education is reinforced through experimentation.
Finding 4: Human and system readiness gaps constrain AI adoption
When manufacturing leaders assess their biggest barriers to major technological change, the obstacles appear overwhelmingly human. Seventy-two percent cite resistance from employees comfortable with existing systems, and 57% identify lack of training and readiness. Yet the data suggests that AI adoption depends on more than individual preparedness alone. Technology integration and data maturity also shape outcomes, particularly at the frontline where AI tools must operate within established workflows and data environments.
When asked to rate data readiness to support AI at the frontline level, 35% report having usable but inconsistent or partially siloed data, and 28% say they’re still in the early stages of organizing and preparing their data. Many manufacturers are attempting to scale AI on foundations that are not yet fully connected or standardized.
Broader industry data underscores the structural nature of this challenge. The Manufacturing Leadership Council reports that only 13% of manufacturing operations have formally integrated their data and AI strategies. In most companies, data governance, infrastructure modernization, and AI deployment are advancing on parallel tracks rather than as a unified effort. When that alignment is missing, frontline leaders are often left with unclear processes, inconsistent data and shifting expectations.
Anticipated challenges frontline leaders face
Q: If you expect your organization to undergo a major technological change, what challenges do you anticipate your frontline leaders to experience?
Source: PwC and MI Frontline Leadership and AI Survey, Q3 2025; base 102
When initiatives fall short, the barriers are often organizational. Sixty-one percent point to delayed or ineffective decision-making, and 52% cite inadequate allocation of resources and training. While executives may anticipate resistance from employees, the data indicates that manufacturers frequently under-support the very people expected to operationalize new technologies.
Frontline concerns reinforce this pattern. Respondents report that the most common issues raised by frontline leaders relate to insufficient training (40%) and lack of clarity regarding purpose and return on investment (38%). Fewer cite fear of job displacement (25%). In short, it’s hard to adopt a tool you don’t understand.
Human and technical readiness are interconnected. Inconsistent data environments, siloed systems, and unclear governance can make it difficult to trust AI outputs or reinforce their use with confidence. When data quality is uneven or system logic unclear, skepticism can intensify across roles. Without clarity of purpose, reliable data foundations, and consistent leadership alignment, even well-designed AI solutions will probably struggle to gain sustained traction on the factory floor.
Training and clarity rank above fear in frontline AI concerns
Q: Which of the following are the most common concerns from frontline leaders regarding AI adoption?
Source: PwC and MI Frontline Leadership and AI Survey, Q3 2025; base 102
Trust becomes especially fragile in applications such as computer vision and performance monitoring systems. For frontline workers, technology designed to flag defects or improve quality may also be perceived as surveillance. Addressing this tension calls for transparency regarding what data is collected, who has access to it, and how it is used. Without that clarity, AI is more likely to be viewed as a threat to autonomy rather than as a tool to enhance safety, quality, and productivity.
Finding 5: Limited frontline leader input constrains adoption and execution
Frontline leaders play a meaningful role in how AI reaches the factory floor, but their influence is often limited by when and how they are engaged. Half of respondents (50%) report that frontline leaders have moderate to high influence over how AI tools are introduced and explained to workers. That influence, however, rarely extends upstream into solution design and strategic decision-making.
The data highlights a disconnect. Forty-five percent of respondents indicate that unsuccessful AI initiatives failed in part because frontline leaders were not sufficiently included in design or rollout. At the same time, only 3% report that most frontline AI suggestions are implemented. In many organizations, engagement with frontline leaders begins after key technology decisions have already been made, limiting their ability to shape use cases, surface operational constraints, or address workforce concerns before deployment.
Frontline leaders have limited influence over AI solution adoption
Q: To what extent does your organization implement suggestions for the use of AI tools from frontline leaders?
Source: PwC and MI Frontline Leadership and AI Survey, Q3 2025; base 102
Conversely, when asked what drives successful AI initiatives, nearly three quarters of respondents (74%) cite leadership as the defining factor. Yet leadership influence is often concentrated at senior levels, while frontline leaders are positioned as downstream communicators rather than contributors to solution design. Excluding frontline leaders from shaping AI initiatives weakens execution quality and undermines credibility with the very teams expected to adopt new tools.
Treating frontline leaders as messengers, rather than active contributors, limits insight into how AI performs under real operating conditions. When frontline leaders help identify where technology delivers tangible operational and workforce value, they become credible advocates rather than interpreters of decisions made elsewhere. That credibility strengthens execution, accelerates adoption, and improves the likelihood that AI investments deliver sustained operational impact.
Leading AI where it matters: A leadership framework for frontline execution
The findings point to a consistent conclusion. AI adoption on the factory floor is constrained less by technology than by how effectively leaders at every level enable people to experience, use, and trust it in daily work. Manufacturers that move from experimentation to sustained adoption do so by strengthening three reinforcing conditions—leading curiosity, delivering value, and earning trust.
Each requires deliberate leadership across executive, plant, and frontline levels.
About the study
In Q3 2025, PwC and the Manufacturing Institute conducted a survey examining the needs and experiences of the manufacturing frontline and the actions organizations are taking to help improve them. A frontline leader is defined as a salaried employee in an operational leadership role who oversees production line activities and manages hourly workers, either directly or indirectly. Examples include production supervisors, line leaders, and manufacturing team leaders. The survey included responses from 102 human resources and operations leaders across the manufacturing industry.
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