Industrial manufacturing’s race to 2030

PwC’s survey of industrial manufacturing executives reveals the vital US$16-trillion sector sits at a historic inflection point. Just ahead is a decisive shift in how value is created, how work gets done, and how leading companies turn technology into advantage.

Our survey presents a snapshot of momentum and risk. Manufacturers expect levels of tech enablement and automation to more than double by 2030, even as blind spots in skills, data infrastructure, and other areas threaten progress. Moreover, manufacturers are racing towards new growth opportunities through AI-native operations and tighter integration across products, services, and ecosystems. But they aren't starting from the same place. In fact, a divide is widening between ‘future-fit’ companies—those that are agile, innovative, and fast—and those still hindered by fragmented systems and capability gaps in areas such as data quality, digital skills, and decision intelligence. The gap could grow as technology, capability, and operating-model advantages reinforce one another. The question for manufacturers isn’t just how fast the sector will change but whether they can do so fast enough to compete in it.

According to a recent PwC US publication “The future of energy and manufacturing,” 93% of US industrial and energy leaders believe we’re on the brink of the next industrial revolution. Our latest global findings, drawn from a survey of 443 industrial manufacturing executives around the world, suggest it’s already begun. 

Why is that? The median share of respondents who indicate their company’s activities are largely reliant on advanced technologies is poised to more than double by 2030, rising from 26% to 68%. In two areas that will lead the way—production and operations, and product design and development—the proportion of respondents saying they’ll make heavy use of advanced technology reaches 76% and 72%, respectively. Even areas where tech use is relatively low today are poised to catch up. Respondents say the extensive use of advanced tech in business support functions, for example, will nearly quadruple by 2030.
 

Meanwhile, the automation of key business processes—including data capture and analytics, physical production, and after-sales support—is also set to boom. By 2030, the proportion of manufacturers that will have highly automated processes is expected to nearly triple. As with tech enablement, rising automation would extend into, and across, all parts of the value chain—from front office to back office, and from R&D to the shop floor.
 

Though the goals behind this investment boom vary by technology, they centre on growth and productivity. Risk management and resilience rank meaningfully lower as goals. Automation and robotics are viewed primarily as productivity levers, whereas sustainable technologies are seen largely as growth plays.
 

AI narrowly edges automation as the most important technology manufacturers choose for achieving their strategic goals, yet they’re split on what they want it to deliver. Near-equal shares of respondents see AI as supporting growth and improving efficiency. That balance may reassure investors who, in PwC’s Global Investor Survey 2025, said they want to see AI unlock both growth and productivity—though not simply through workforce reductions.

In the race to tech-enable and automate broad swaths of industrial manufacturing, not everyone will begin at the same starting line. Future-fit companies—the fastest, most agile, and most innovative companies our survey identifies (see “Is your company ‘future fit’?” below)—have a clear edge. These companies already use advanced tech more intensively than others in both product design (46% versus 34%) and production and operations (37% versus 28%), with even wider gaps expected in the years ahead. Future-fit companies also report much higher levels of automation today than other organisations (with a median of 29% versus 15%), with plans to reach 65% by 2030 (versus just 45% for other manufacturers).

What it means for manufacturers

Tech enablement and automation will surge across the value chain. Yet the most meaningful performance differentiation will come from how coherently those technologies work together. Companies that deploy advanced tools in isolation—robotics in production, analytics in supply chain, AI pilot projects in engineering—should expect limited gains. Indeed, without shared data models and synchronised workflows, these investments could become tech islands where robotics cells don’t talk to planning systems, analytics don't inform commercial decisions, and engineering insights don’t reach service teams. The result would be new tools layered onto old bottlenecks.

We believe that winning manufacturers, by contrast, will treat AI and other advanced technologies as a system, not a set of projects. Integration has two meanings for them. First, they integrate across the value chain, ensuring that design, production, supply chain, and other functions operate on shared data and connected workflows—reducing cycle times and improving decisions. Second, they integrate across technologies, as AI, automation, analytics, and engineering systems draw from the same data, follow the same decision rules, and together help maintain a single operational view of the business. In sum, their systems compound value rather than fragment it.

As tech adoption and automation become ubiquitous, advantage will shift from who has tools to who can orchestrate them. Integration could become the prerequisite for unlocking system-wide productivity, achieving AI-native operations, and scaling automation beyond local efficiency gains. Companies that build—or retrofit—for interoperability across functions and technologies will be positioned to capture compounding benefits.

A burst of business and operating model innovation is primed to accelerate in manufacturing, part of a sweeping, global economic reconfiguration driven by AI, climate change, and other megatrends. PwC research shows that more and more industrial manufacturers expect growth to come from new activities beyond their historical core; fully 44% of total revenue is projected to come from outside the manufacturing of industrial and consumer products by 2030.

The top-three growth hotspots include:

• Technology, digital, and communications products and services
• Defence, governmental, and educational offerings
• Energy and fuel production and distribution
   

Moreover, manufacturers increasingly view themselves as providers of integrated solutions rather than sellers of discrete products. Indeed, the survey finds manufacturers shifting towards offerings that bundle a range of equipment, know-how, and services—such as intelligent and connected solutions, flexible equipment, extended services, and electrical and data centre equipment. For their part, future-fit manufacturers are more likely than others to prioritise intelligent and connected solutions, as well as recurring or outcome-based models, as part of their growth strategy.

The shift is also happening across customer types, as manufacturers see opportunity with OEMs, EPCs, and end users, among others.
 

To pursue these opportunities, manufacturers are focusing on a mix of internal capability development (which we’ll discuss later), ecosystem collaboration, and deal-making. Ecosystems are both broadly popular and significantly more so among future-fit manufacturers. Fully 60% rate ecosystems among their top-three growth strategies, versus 52% of other respondents.

Collaborations with technology, power and utilities, transportation and logistics, and aerospace and defence (A&D) companies are poised to increase, reflecting the capabilities manufacturers need to better compete in connected, electrified, automated, and service-rich markets. At the same time, more traditional partnerships (with oil and gas, mining and metals, and chemicals players) are seen as relatively less important, reflecting the shift towards new industrial value chains.
 

What it means for manufacturers

The scale and direction of manufacturers’ growth plans imply a level of strategic reinvention the sector hasn’t seen in decades. Shifting more than 40% of future revenue into areas outside traditional manufacturing means competing in markets where value is created through service delivery, software, data, and integration across economic domains. That requires new ways of creating, delivering, and capturing value across boundaries that historically separated manufacturing from other sectors. 

Ecosystem capabilities will become a decisive enabler. The growth areas manufacturers are targeting depend on technologies, data, and expertise that no single firm can own. But effective ecosystems don’t just happen—they require clear roles, shared incentives, trust, interoperable systems, and governance that allow multiple parties to coordinate offerings and jointly deliver coherent solutions. Successful companies will need to understand which partners are essential, how value and data should flow, and how to design solutions across firm boundaries.

Idea in motion at Eaton

The power management company Eaton’s recently announced acquisition of Boyd Corporation’s thermal business offers a clear example of using deals to play in new domains of growth. The deal would expand Eaton’s portfolio into adjacent spaces where heat, energy, and data intersect—most notably high-density data centres and the advanced electronic systems that underpin them. The move also strengthens Eaton’s ability to expand into adjacent areas such as electric mobility and aerospace and defence, where high-power electronics and thermal constraints are increasingly intertwined.

In effect, the acquisition positions Eaton to move beyond primarily discrete electrical components towards more integrated power-and-cooling subsystems that sit at the heart of next-generation electrified and digital infrastructure.

Fully 70% of executives rate “developing new capabilities internally” as their top means of accessing growth opportunities—ahead of ecosystems, M&A, or any other move. Yet the survey also suggests that key capabilities required to realise these ambitions could be underdeveloped relative to their importance. 

The exhibit highlights several potential blind spots, such as digital and data infrastructure, workforce upskilling and reskilling, and analytics and decision intelligence, all of which are prerequisites for the faster learning and smarter decision-making needed to compete in a more AI-enabled and automated industrial future.
 

Regarding organisational culture, PwC data shows a clear divide between future-fit companies and the rest. Future-fit firms have cultures that are more agile, empowered, data-driven, and tolerant of risk—traits that would help them learn and adapt faster and bring new products and services to market more quickly. 
 

Previously unpublished data from PwC’s Global Workforce Hopes and Fears Survey 2025 shows how fragile these cultural strengths are across industrial manufacturing more broadly. That study found that only 56% of industrial manufacturing workers (above entry level) felt it was safe to try new approaches, and just 55% said failures were treated as learning opportunities. Psychological safety also fell sharply with seniority: 71% of manufacturing executives felt it was safe to experiment, compared with just 46% of non-managers—the very employees who must define, adopt, and master new tools and processes.

Similar disparities appear in upskilling, where fewer than half of non-managers said they had adequate access to learning resources, versus two-thirds of managers. The Hopes and Fears research also identified an important link between upskilling and motivation: workers who felt supported to upskill were 73% more motivated than those with the least support.

Finally, levels of trust were low: the Hopes and Fears survey found that just half of industrial manufacturing workers above entry level said they trust their managers, and just 42% trusted top leadership—both markedly worse than global averages. This trust deficit comes at a moment when accelerated automation and AI adoption are reshaping roles and raising the stakes for companies to invest not only in technology but also in the programmes and practices that help workers navigate that change.

What it means for manufacturers

The findings point to an irony. Despite the defining role technology will play, success in the next industrial revolution will be constrained less by hardware or software than by the human and organisational systems that rely on it. This is particularly true as AI and other advanced technologies necessitate shifts to new skills, raising understandable questions for workers concerned about how their roles will evolve.

To grapple with these issues, senior manufacturing leaders can start by having candid, sometimes uncomfortable conversations with their teams. Three questions can help frame the dialogue:

• Are our technology ambitions outpacing our organisation’s capacity to absorb them? Organisations that can’t experiment, redesign workflows, or deal with problems quickly will struggle to realise the value of tech or automation investments. Deficits in skills, learning access, and psychological safety are the warning indicators. Should these signs appear, managers should be encouraged to pull the metaphorical Andon cord and alert leadership immediately. Leadership that resists, dismisses, or punishes bad news is indicative of a more serious problem.

• How far are we from future fit—and how can we get there? The future-fit firms in our survey already operate with more agile, empowered, and data-driven cultures than their peers. They appear poised to learn faster, reallocate faster, de-risk faster—and therefore adopt technology faster. Such gains can quickly become self-reinforcing. Benchmarking progress against peers—on a mix of operational and cultural measures—creates urgency and provides leaders with clearer signals about where to double down, course-correct, or rethink assumptions.

• Do we have the employee trust required to change at speed? As our experience (and the Hopes and Fears survey findings) suggests, trust is a measurable operational constraint—affecting willingness to learn, experiment, and adopt new ways of working. Scan carefully for ‘say–do’ gaps on issues of training and capability development, as discrepancies will quickly corrode trust.

Idea in motion at Bosch

Bosch’s global upskilling efforts suggest how manufacturers can better prepare their workforces for a more digital, automated, and AI-enabled future without losing sight of the human realities that accompany such a change. In 2024, the German engineering and technology group announced it had trained more than 130,000 employees worldwide in data analytics, automation, connectivity, and other future-facing technologies. Rather than positioning these programmes narrowly around tool adoption, Bosch emphasises lifelong learning and navigating the transition itself—important for developing the problem-solving skills, collaboration, and digital fluency needed as new roles and ways of working emerge. Bosch frames this not only as a technical necessity but also as a cultural imperative: employees must feel capable, supported, and part of the company’s long-term trajectory. This approach of pairing tech- and people-focused investments should help the company strengthen trust and cultural resilience as the industrial landscape continues to shift.

Industrial manufacturers aren’t converging on a single dominant strategy. Instead, PwC research finds they’re clustering around three distinct ways to play:

• Customer-centricity, built on commercial excellence and rapid responsiveness to customer needs (38% of all respondents)
• Operational excellence, anchored in efficiency, reliability, resilience, and sustainability (36% of all respondents)
• Innovation and product leadership, driven by R&D intensity, platform-based innovation, and advanced engineering (26% of all respondents)

A persistent bias in manufacturing (and business more broadly) assumes that innovation wins by default. But crucially, the data shows that future-fit manufacturers inhabit all three clusters. This suggests that each approach can succeed if supported by the right capabilities.

Operational excellence deserves particular attention. Sometimes taken for granted or cast as yesterday’s game, we believe operations will be the most demanding—and unforgiving—of the three strategies in an AI-enabled and increasingly automated industrial landscape. Why? Operational excellence depends on clean, connected data; interoperable systems; and real-time visibility across the value chain. These foundations must work end to end for advanced operations to function at all. Outcomes such as reliability, quality, resilience, and sustainability are only possible when the underlying digital and operational infrastructure is truly ready.

The same data integrity, interoperability, and digital backbone required for advanced operations also underpin the other two strategies. Customer-centricity depends on accurate, timely data to support personalisation and predictive services. Product leadership benefits from the use of digital twins, simulation environments, and shared architectures that enable rapid product innovation—all of which require consistent, integrated operational data. When these foundations are weak, companies pursuing customer-centricity or innovation capabilities struggle to achieve the speed, insight, and iteration those strategies demand.

Ultimately, what will differentiate companies is their ability to translate technology into performance, regardless of which strategy they pursue. Customer-centric players win when digital tools enable personalised engagement and predictive service. Operational excellence players win when automation and analytics strengthen reliability, quality, and resilience. Product leaders win when AI-supported design and modular engineering shorten development cycles and support breakthrough offerings. Strategy sets out what a company competes on; the ability to convert technology into results determines how well it competes.

Each of the three pathways is a viable choice. The real risk isn’t in selecting the wrong approach but failing to support the decision with the scaffolding required to bring it to life.

What it means for manufacturers

The three ways to play aren’t just concepts; each implies a different system of capability levers that must be pulled together. For example, a customer-centric strategy requires an insight-to-action reflex rooted in deeper customer intelligence, predictive service, and commercial and service workflows that can respond rapidly. An operational strategy benefits from an automated, integrated operating system with end-to-end visibility, standardised architectures, and real-time control. And a product-leadership strategy needs platform-based innovation that allows for shared digital models, simulation environments, and tight coupling between engineering, software, and field feedback. Everyone’s list will vary, but the point is the same: strategy becomes real through the capability demands it makes, and through the signature moves required to deliver it.

The survey also suggests that bottlenecks—not intent—will ultimately determine how far a strategy advances. Skills gaps, organisational frictions, legacy systems, and slow decision cycles remain stubborn inhibitors of change. A promising customer-centric plan stalls when data is fragmented. Operational excellence moves lose momentum when systems can’t interoperate. A product-leadership strategy slows when engineering and software teams don’t share common digital architectures. Strategy may set direction, but capabilities will determine velocity.

Idea in motion at John Deere, Komatsu, and ABB

Customer-centricity. Agricultural-equipment giant John Deere is moving beyond equipment towards a digital platform that helps farmers make faster, better decisions. Deere’s Operations Center connects machine, field, and agronomic data into a unified interface, giving customers real-time insight into agricultural input use and machine health. Autonomy and precision features build on this system, enhancing guidance, seeding, and spraying with continuously updated data models.

This architecture supports Deere’s ambition to generate a meaningful share of revenue from software and subscription-based services. The platform’s value lies not in automating Deere’s own operations but in improving customer economics and outcomes. Although many manufacturers are expanding digitally, Deere’s bet centres on owning the customer interface and decision environment—reinforcing the insight-to-action engine that defines a customer-centric strategy.

Operational excellence. The heavy equipment maker Komatsu offers an illustration of an operations-led strategy entering its next phase. After pioneering autonomous haulage in mining, the company aims to extend autonomy across job sites through its Earthbrain platform, which integrates construction machines, drone-based surveying, and digital workflows into an increasingly comprehensive site model. AI helps analyse site data, enhance planning, and flag emerging maintenance risks. Similarly, Komatsu’s Komtrax Plus telemetry system feeds sensor data into predictive algorithms that support earlier failure detection and more consistent performance. Together, such moves suggest how operational excellence can evolve from discrete automation into a system of consistent, measurable improvement—one built on data integrity, interoperability, and real-time intelligence.

Innovation and product leadership. Industrial giant ABB’s recent generations of robots and controllers are designed around a shared, simulation-ready architecture to improve productivity and accelerate deployment across industries such as logistics, electronics, and machining.

At the centre of this approach is RobotStudio, ABB’s virtual commissioning environment, which helps engineers to model, test, debug, and refine robot tasks before equipment arrives on site—or to make updates without disturbing ongoing production. Together, these moves highlight how product leadership increasingly depends on shared architectures and simulation environments that let companies innovate, customise, and scale faster.

The next industrial revolution is no longer a distant prospect—it’s already reshaping how manufacturers create value, compete, and grow. PwC’s survey of global manufacturing leaders shows AI, automation, and advanced technologies moving rapidly from experimentation to scale, with tech enablement and automation set to more than double by 2030. At the same time, growth is breaking beyond the historical core, as manufacturers pursue smart, connected offerings; services; and ecosystem-led plays in adjacent markets. The result is a sector in motion—defined as much by integration and orchestration as by machines and materials.

Yet the research also reveals a widening divide. A future-fit cohort is pulling ahead by combining technology with clean data, interoperable systems, and cultures that learn and adapt quickly. Others risk being slowed by skills gaps, fragmented infrastructure, and organisational friction. As advanced tools become ubiquitous, advantage will shift from who adopts them to who can turn them into performance—aligning strategy, capability, and execution to move fast enough not just to keep up, but to lead.