All News & Insights

As experienced workers exit the industry, manufacturers must embed product and commercial expertise into scalable, digital processes.

TAKEAWAYS:
● The loss of experienced manufacturing talent creates a hidden commercial risk that impacts sales efficiency, accuracy, and growth.
● Manufacturers can reduce dependency on tribal knowledge by embedding product and pricing expertise into standardized digital processes.
● Guided, system-driven selling approaches help both sellers and buyers navigate complexity as workforce demographics continue to shift.

Manufacturers are undergoing a pivotal workforce shift.

By 2030, 1 in 6 people in the world will be aged 60 years or over, according to the World Health Organization. In the U.S. alone, 10,000 people turn 65 each day. As the workforce ages and industry turnover continues, skilled labor and institutional knowledge are lost across technical roles, especially engineering.

While manufacturers have focused heavily on resilient production and supply chains, there’s an equally critical challenge: commercial and delivery processes still rely on the deep expertise of experienced engineering and product teams. As seasoned technical talent, including engineering and product specialists, retire or transition out of the workforce, manufacturers face knowledge gaps that slow product rollout, increase engineering interruptions, and limit the organization’s ability to scale consistently.

Addressing this challenge means codifying technical and product expertise into scalable, digital processes and rethinking how complex products move from design intent to commercial execution without increasing engineering workload. Manufacturers are navigating a major workforce transition at a time when innovation, customer expectations, and market pressures demand greater productivity to remain competitive.

The Workforce Shift Puts Profitability at Risk

Even as labor markets stabilize, manufacturers continue to struggle with workforce challenges. A survey of over 200 global manufacturers found that 30 percent of companies expect 16 percent or more of their sales and engineering talent to retire in the next five years. Only 32 percent are digitizing internal knowledge—the majority are simply rehiring or mentoring. But failing to capture and easily transfer knowledge across the business is a performance risk.

This can have an impact on both sales and engineering teams. A knowledge gap often results in:

Slower onboarding and longer time to productivity for new hires
More errors and costly rework in proposed solutions
Hours lost waiting for technical validation
Customers disengaging due to slow and inconsistent quoting processes

Lack of knowledge transfer can cost large businesses millions of dollars per year due to wasted time and missed opportunities.

Competitors that can scale and systematize knowledge are better positioned to capture opportunities with more agility. At the same time, buyer expectations are shifting. Today’s customers want conversations focused less on technical minutiae and more on how solutions support their broader business objectives.

Redefining How Manufacturers Sell Complex Products

For manufacturers with vast product portfolios and highly configurable products, sales has traditionally required deep technical expertise and heavy reliance on engineering for every configuration. This model is nearly impossible to sustain in the speed of today’s market.

New hires take longer to ramp up, engineering teams can get bogged down in creating imperfect solutions, and expecting every salesperson to be a technical expert slows the sales cycle.

By redefining how products are sold, manufacturers can shift from a knowledge-dependent model to one that emphasizes solution building. This shift includes:

Emphasizing consultative skills over technical memorization
Moving from feature-based selling to solution-oriented conversations
Equipping sales teams with structured workflows that guide decision-making
Driving value-based discussions that connect offerings to business outcomes

To make this shift scalable, manufacturers must embed product and pricing expertise directly into their sales processes, allowing teams to focus less on technical validation and more on customer engagement.

Using a Single Source of Truth in Commercial Processes

With traditional sales models heavily reliant on individual expertise, manufacturers must fundamentally rethink how knowledge flows through the organization.

Manufacturers are shifting from expert-dependent translation of engineering specifications to embedding that knowledge in standardized digital processes, like Configure, Price, Quote (CPQ) technology.

This transformation requires restructuring how product information is organized and accessed. A unified product definition helps bridge technical specifications and commercial logic, enabling teams to understand complex offerings without extensive specialized training—while still supporting millions of valid configurations.

For pricing, the challenge is similar. Highly individualized products make pricing difficult, particularly when new variants are introduced. Embedding pricing rules, margin logic, and cost considerations directly into commercial workflows enables more consistent and confident pricing decisions without requiring constant expert intervention.

Creating Standardized Components for Mass Customization at Scale

Engineer-to-order models have long provided flexibility, but at the cost of scalability. Shifting toward a hybrid configure-to-order approach allows manufacturers to standardize common configurations while still accommodating unique customer requirements.

Engineering teams can break complex products into modular components and pre-validated options, allowing sales teams to assemble solutions without relying on engineering for every request. These standardized components can then be easily translated into rule-based logic for sales teams.

A key enabler of this shift is constraint-based configuration logic. Instead of rigid rules that limit flexibility, a constraint-based approach to CPQ defines how components can be combined based on shared properties, allowing new modules to be introduced without increasing complexity or sales risk. This equates for fewer rules to be maintained and updated as the portfolio grows.

The Move from Expert-Dependent Selling to Design-Governed Solution Guidance

Technical expertise will become harder to scale. Manufacturers are shifting from expert-dependent interactions to design-governed solution guidance. Guided workflows facilitate customer-focused conversations without requiring deep technical expertise in every interaction, while still enforcing engineering intent and product constraints.

Rather than relying on detailed technical knowledge or feature memorization, structured workflows guide conversations around application requirements, operating conditions, and performance outcomes. These inputs are translated into valid, manufacturable solutions based on embedded engineering logic.

Performance requirements
Operating environment
Space or regulatory constraints
Expected throughput or output

This approach not only protects engineering teams from repeated validation requests, but also improves the customer experience by making complex offerings easier to understand, evaluate, and confidently specify.

Buyers Are Losing Expertise Too

A new generation of buyers is entering decision-making committees with mixed technical backgrounds and higher expectations for transparency and guidance.

As customers lose their own internal experts, they increasingly rely on manufacturers to provide clarity earlier in the buying journey, across both traditional and self-service channels. This level of consistency and confidence depends on a single, digitized source of product knowledge that ensures every channel and interaction reflects the same validated information.

Providing intuitive, guided experiences enables buyers to explore options without waiting for human intervention. This ultimately reduces delays and accelerates buyer decision-making.

Scaling Across Products and Markets

Operational transformation takes time, but it pays off. Manufacturers build resilience by standardizing how technical and product knowledge flows from design through order fulfillment. When engineering rules, constraints, and configuration logic are embedded directly into lifecycle processes, rather than living in people’s heads or disconnected documents, organizations launch new products and variants faster, limit ongoing engineering involvement in commercial requests, and expand into new markets without redesigning core offerings. This approach shortens time to market and creates more consistent and scalable execution across operations.

Managing Your Workforce for Change

Transitioning to scalable commercial models requires thoughtful change management. Veteran experts should be positioned as champions whose knowledge is being amplified rather than replaced. Phased rollouts, cross-functional alignment, and clear communication help ensure adoption while preserving innovation and quality.

Become a Future-Ready Organization

When expertise is embedded into systems rather than tied to individuals, you create operations that can scale, adapt, and perform well beyond today’s workforce constraints. M

About the author:

Brian Cuttica is Senior Vice President of Sales in North America at Tacton.

Manufacturing leaders can improve real-time supply chain visibility by using advanced technologies, real-time data monitoring, and cross-functional governance.

TAKEAWAYS:
● Companies should integrate risk management into their daily operations, rather than treating it as a standalone process.
● Advanced technologies allow manufacturers to map their supplier ecosystems with unprecedented depth and precision.
● Data organization and clear data governance are essential for modernizing third-party risk management processes.

The risk landscape for manufacturers is evolving so rapidly that traditional, one-dimensional risk management approaches are becoming obsolete. Supply chain disruptions—whether caused by natural disasters, pandemics, cyberattacks, or supplier insolvencies—are a primary concern for industry leaders. As supply chains become more complex and interconnected, managing third-party risks has never been more challenging. As a result, companies need to integrate risk management into everyday enterprise operations, rather than treating it as a standalone, periodic review process.

The renewed consideration of nearshoring and reshoring strategies adds further complexity as companies adapt their supply networks to shifting global market and regulatory dynamics. In this environment, relying on outdated third-party risk management processes can expose organizations to hidden vulnerabilities and blind spots across their global supplier base.

For C-suite manufacturing executives, the convergence of these factors underscores the need to revisit and reassess internal third-party risk management processes. This is crucial for unlocking data-driven insights that improve supply chain visibility and make the business more adaptable.

Where to Start? Mapping the Supplier Ecosystem

Reassessing third-party risk management processes and frameworks begins with a holistic understanding of the company’s supply network. The first step is to map the entire supplier ecosystem, from strategic Tier 1 partners to smaller, potentially overlooked vendors at the edges of the network, through Tier 2, Tier 3, and even Tier 4 suppliers and partners. This mapping should not be a static exercise. Instead, executives should rate each supplier based on its criticality to business continuity, connectivity to internal systems and data, and adherence to safety, quality, and regulatory compliance standards.

“Data organization and clear data governance are foundational for modernizing third-party risk management processes and achieving real-time supply chain visibility.”

While this task may seem daunting, advanced technologies enable organizations to map and monitor their supplier ecosystem with unprecedented depth, speed, and precision, including understanding downstream supplier dependencies and their impact on operations. Even manufacturers lacking the capacity to build this assessment and monitoring system internally can leverage external advisors to implement technology solutions to manage supply chains and inventory.

Once the ecosystem is mapped, next steps include the following:

Segmenting and prioritizing suppliers and partners: Segmenting suppliers by risk profile and exposure should consider financial implications, regulatory oversight, and operational dependencies. Developing a comprehensive risk profile for each supplier can help manufacturers identify performance issues, quality concerns, and capacity constraints before they cause major disruptions.
Updating sales and operations planning (S&OP) processes: Embedding supplier risk profiles into the organization’s S&OP processes reinforces risk awareness as a core element of strategic decision-making. Manufacturers can also integrate this information with broader enterprise risk management efforts for a more holistic approach to mitigating potential disruption.
Implementing continuous monitoring and governance: Manufacturers need to assess suppliers beyond the onboarding phase. Risk monitoring must continue throughout the entire lifecycle of the supplier relationship, from initial due diligence to eventual offboarding or vendor retirement. Proactive risk identification and mitigation can strengthen existing supplier relationships and show commitment to long-term partnerships.

For effective third-party risk management processes, manufacturers need clear cross-functional ownership structures and defined escalation protocols for addressing issues. IT, compliance, procurement, and operations must collaborate to ensure that risk data is integrated, accurate, and actionable.

How Can Data Enable Real-Time Visibility?

Data organization and clear data governance are foundational for modernizing third-party risk management processes and achieving real-time supply chain visibility. Advanced technologies can help harness data to its fullest potential. Dashboards, Internet of Things-enabled devices, sensors, and connected equipment all feed critical data into centralized platforms that provide actionable insights across the organization. Advanced analytics and machine learning tools can sift through vast quantities of supplier data to flag anomalies, forecast potential disruptions, and recommend proactive mitigation strategies.

Data-driven insights are increasingly becoming essential for companies across all industries. According to a 2025 RSM US LLP report on supply chain issues, most of the 309 executives surveyed said their organizations “already have systems in place to harness data throughout their supply chains.”

On a scale of 1 to 5, respondents reported a surprisingly high level of digital maturity in their supply chains:

0% rated their digital maturity at Level 1 (data is gathered ad hoc and manually)
3% rated their digital maturity at Level 2 (data is available but inconsistently entered and maintained)
28% rated their digital maturity at Level 3 (the company has a big data solution and gathers data from critical inputs)
47% rated their digital maturity at Level 4 (data is gathered from every function and automatically analyzed by BI or another data stack)
21% rated their digital maturity at Level 5 (enterprise data is unified to a single source of truth)

To remain competitive, manufacturers need to prioritize data architecture and data governance practices. Architectural infrastructure investments, such as cloud-based platforms and integrated data lakes, enable seamless data sharing and real-time monitoring across functions. High-quality, reliable data is the foundation for these efforts; without it, even the most sophisticated technology solutions will fail.

Manufacturers should prioritize data quality initiatives, using advanced technologies to identify and resolve discrepancies and ensure that risk signals are based on accurate, current information. This approach strengthens third-party risk management and enhances overall operational agility.

“Reassessing third-party risk management processes and frameworks begins with a holistic understanding of the company’s supply network.”

Looking Ahead

For manufacturing C-suite executives, reassessing third-party risk management is a strategic imperative for building resilient, transparent, and responsive supply chains in an era of constant change. By embracing a technology-enabled approach anchored in real-time data, continuous monitoring, and cross-functional governance, manufacturers can mitigate risks, identify new opportunities, and deliver greater value to customers, partners, and other stakeholders. M

About the authors:

Katie Landy is a Principal at RSM US LLP.

Jake Winquist is a Principal at RSM US LLP.

Manufacturers can follow strategic steps to build resilience across IT and OT environments while advancing their business objectives.

TAKEAWAYS:
● Manufacturers must engineer resilience through secure-by-design, standards-based architectures that future-proof operations against connectivity failures.
● Edge AI ensures operational continuity by eliminating cloud dependency and preserving real-time decision support during disruptions.
● Lasting cyber resilience requires commitment from IT and OT teams to collaborate and maximize risk reduction.

In today’s rapidly evolving business landscape, manufacturers must accelerate digital adoption to maintain a competitive edge. Embracing advanced technologies drives measurable value by minimizing waste, maximizing uptime, and reducing labor costs. This is achieved through the continuous flow of data across the manufacturing stack, accelerating performance, and unlocking new operational efficiencies. However, increased connectivity between systems introduces increased—and often insufficiently quantified—cybersecurity risk. While manufacturers have always managed threats such as physical access control, and even natural disasters, accelerated digital adoption now exposes operations to more frequent and sophisticated cyber threats than ever before. As these risks intensify each year, the imperative for manufacturers remains urgent and clear: innovate and grow, or lose ground to the competition.

The resulting reality for manufacturers is a conundrum: ambitions to scale and improve operations can appear at odds with security goals to safeguard assets and data and to maintain control. However, manufacturers can balance these objectives by adopting a secure-by-design, standards-based architecture that delivers resilience against cyber and operational disruptions while allowing for seamless connectivity. Because there is no one universal architecture, manufacturing leaders must assess the unique risks connectivity poses to their operations and ensure targeted redundancy planning in the event of disruption. Ultimately, the critical question every manufacturing executive must answer is: can your operations continue when systems go down?

This article outlines the first steps leaders should take to build resilience across IT and OT environments while advancing their business objectives.

First: Business Continuity Planning for Connected Operations

Determining the specifications for the right secure-by-design architecture for your connected operation begins with a business impact analysis (BIA). A BIA analyzes operational functions and the potential effects of a disruption without interrupting or modifying any systems. Mapping assets, systems, and processes into a plant hierarchy helps leaders understand which disruptions would have the greatest operational impact. A separate cybersecurity risk assessment then evaluates threats, vulnerabilities, exploitability, and existing controls for the most critical functions and zones.

During a BIA, leaders must continually ask which parts of the operation are production-critical; that is, which areas must be made resilient to connectivity failure through segmentation and/or redundancy? This analysis provides a clear picture of each asset’s criticality, how failures propagate, and whether existing controls already mitigate the risk. When performed correctly, a BIA will establish the foundation for an architecture tailored to your operations, helping your organization avoid settling for a one-size-fits-all solution that demands costly process changes to function.

Then: Explore Edge AI Architecture for True Resilience

Organizations use BIAs to identify business-critical systems that must remain resilient during disruptions. This drives interest in edge-based intelligence, which, when explicitly designed and governed, can sustain monitoring, control, and safe shutdown functions even when cloud connectivity or other dependent systems are unavailable. By enabling AI at the edge, organizations can shield the “brain” of their operations from disconnection and protect against downtime. This resilience stems from the ability to run AI on-premises. While most AI tools today—especially generative AI—operate in the cloud and can be powerful solutions for tasks like reporting, they are not resilient enough for production-critical tasks because they depend on stable connectivity, external vendors, and infrastructure. Consequently, a failed cloud connection severs the intelligence governing operations.

Unlike larger, general cloud-based AI models, tailored small language models (SLMs) can run directly on industrial PCs, edge gateways, and controllers. This ensures that key decision-support capabilities remain available even without cloud access. Furthermore, SLMs can be tuned more precisely than larger, more general models, allowing them to specialize in specific tasks and provide more efficient real-time troubleshooting and decision support.

“A separate cybersecurity risk assessment then evaluates threats, vulnerabilities, exploitability, and existing controls for the most critical functions and zones.”

Consider this hypothetical scenario: a manufacturing operation, responsible for a significant share of global supply, experiences a connection outage due to an outdated architecture. Because of the nature of its production, any lapse in connectivity, operator visibility, control, or remote support triggers the dumping of all product for safety reasons—an extremely costly and environmentally impactful emergency measure. However, if edge-based intelligence were enabled, it could continue to guide operators by interpreting alarms, troubleshooting issues, and explaining failure modes and safe shutdown or recovery procedures, potentially averting unnecessary product loss.

The challenge with AI lies in selecting the right platform from the many available. Because most AI vendors are cloud-first and lack a deep understanding of industrial edge environments, carefully selecting a partner with operating experience at the edge is the best way to achieve a solution that supports the specific requirements of your operation.

Don’t Forget: Build a Culture of Cyber Resilience

For new approaches to succeed, they must be adopted and sustained by your employees and organizational culture. Leaders can guide their organization’s culture to evolve alongside new technologies by fostering a security-first mindset, treating security as a continuous practice rather than a one-time milestone. Teams should also shift away from a traditional ROI mindset—where investments are justified by direct financial return—and instead prioritize risk, making year-over-year investments to reduce the likelihood and impact of disruptions.

Much of this cultural shift centers on IT and OT teams, which traditionally have had divergent priorities: confidentiality and integrity (CIA) versus safety and availability (SAIC), respectively. True resilience lies at the intersection of IT and OT domains; resilience can only be achieved when these teams collaborate on architecture decisions, risk assessments, incident response planning, and governance. This achievement is a shared responsibility.

Step by Step: What Leaders Can Do Now

Figure 1: Steps Leaders Can Take Toward Resilience

Building resilience is not a one-time initiative but an ongoing commitment (Figure 1). It requires a secure-by-design architecture, edge-ready intelligence, and a culture that treats cybersecurity as foundational to operational excellence. As connectivity deepens and threats evolve, leaders who invest in continuity planning, adopt technologies purpose‑built for the industrial edge, and unite IT and OT teams around shared responsibility will position their organizations for success. Resilience is no longer achievable through a default defense posture. Manufacturers can only confidently operate, grow sustainably, and field emerging threats in an increasingly connected world through bold innovation and strategic thinking. M

About the authors:

Chris Hamilton is a Digital Consulting Sr. Manager & Cybersecurity Platform Lead at Rockwell Automation Digital.

Will Rosengarten is the Data Strategy & Architecture Lead at Rockwell Automation Digital.

Learn more about MLC membership.

Sthitie Bom

VP Global Manufacturing IT
Seagate

https://www.seagate.com/

https://www.linkedin.com/in/sthitie/

Tim Brown

VP, Information Technology
RYAM

https://ryam.com/

https://www.linkedin.com/in/timothy-brown-ba894013/

Jeff Elkin

SVP Information Technology
Marvin

https://www.marvin.com/

https://www.linkedin.com/in/jeff-elkin-16382b4/

Ed Maier

President and CEO
G.W. Lisk

https://www.gwlisk.com/

https://www.linkedin.com/in/edward-maier-b2b3051a/

Kerry McQuone

CMO
GrayMatter Systems

https://graymattersystems.com/

https://www.linkedin.com/in/kerry-mcquone-995a406a/

Danny Seigle

VP, Partnerships and Alliances
MaintainX

https://www.getmaintainx.com/

https://www.linkedin.com/in/dannyseigle/

Lance Whitacre

Senior Vice President, Manufacturing and Logistics
Andersen Windows & Doors

https://www.andersenwindows.com/

https://www.linkedin.com/in/lance-whitacre/

Alex White

Vice President, Strategic Engagements
Infor

https://www.infor.com/

http://linkedin.com/in/alexanderleewhite/

Manufacturers push deeper into execution as smart factory strategies mature, AI advances and digital transformation gains as a competitive advantage.

KEY TAKEAWAYS:
● Smart factories have entered the execution era with manufacturers now wrestling with how to scale digital transformation efforts
● AI is maturing beyond experiment into value-driven, smart factory deployments
● Digital transformation is shifting from table stakes to a game-changing advantage

The Manufacturing Leadership Council’s 2026 Smart Factories and Digital Production Survey shows an industry that has moved beyond experimentation and into a more disciplined phase of execution, integration and operation. Manufacturers are increasingly committed to digital transformation and are now figuring out how to scale it.

While economic optimism and investment intent remain strong, the tone of this year’s survey reflects a more mature mindset. Expectations remain high for AI, automation and end-to-end digitization, but respondents also demonstrate a clearer understanding of the legacy, data and organizational challenges that accompany scale. Compared to 2025’s resurgence of momentum and 2024’s momentary hesitation, 2026 signals a new phase: steady progress, pragmatic confidence and a sharper focus on execution.

SECTION 1: Economic Outlook and Investment Trends

Manufacturers enter 2026 with cautious optimism about the broader economy. A strong majority (67%) expect moderate growth, while only 10% anticipate a significant downturn (Chart 1). These numbers remain similar to the 2025 survey results where 69% expected moderate growth while 8% expected an economic slowdown.

Meanwhile, nearly half of the respondents (48%) say U.S. tariffs are having a moderate or significant impact on their smart factory implementation (Chart 2). While those sentiments are worth watching going forward, the tariff negatives are not enough to undermine Manufacturing 4.0 digital investments. More than 90% of respondents say they expect to maintain or increase smart factory and production technology investments in 2026, with a sizable share planning increases rather than flat spending (Chart 3). The data suggests that digital investment has become embedded in long-term operating plans rather than driven by short-term economic sentiment.

1. Strong majority expect moderate economic growth ahead

Q: What is your company’s outlook for the economy in 2026? (Select one)

2. Nearly half experiencing negative impact from U.S. tariffs

Q: What impact are U.S. tariffs having on your company’s smart factory implementation? (Select one)

3. More than 90% plan to maintain or increase smart factory investments

Q: How do you expect your company’s outlook for the economy to influence M4.0 smart factory and production technology investments for 2026? (Select one)

SECTION 2: Smart Factory Maturity and Adoption

Smart factory maturity is a moving target. When new transformative technologies, like generative AI in 2022 and 2023, find their way into factories, it is natural for manufacturers to reassess how mature they truly are. This is one possible cause for the dip in maturity that our 2024 survey unveiled. As manufacturers have gotten more comfortable with GenAI and other emerging technologies, they’ve been able to find their footing and move forward on the maturity scale, and our survey saw a strong rebound in 2025 that remains consistent in 2026.

The vast majority still place themselves at a mid-level maturity (72% in 2026 vs. 75% in 2025), but our survey shows that those placing themselves firmly at the middle level, or a 5, has hit a high-water mark. This year, more than one-third of respondents say they are at a 5, compared to 22%, 11% and 16%, in 2025, 2024 and 2023, respectively. Growth at the highest maturity levels (8-10) remains incremental—rising from 9% in 2025 to 10% in 2026—reinforcing that advanced smart factories are still the exception rather than the rule. (Chart 4)

This pattern is echoed in how manufacturers describe their digital activities. Fewer than 10% of respondents report being in the learning stage. Meanwhile, a nearly equal number report that their company is scaling at either a single factory or rolling out initiatives across multiple sites (49%) compared to those in the learning or experimental stages (51%). (Chart 5)

Looking ahead, confidence in future progress remains strong. While only a minority (28%) consider their factories very or somewhat smart today, a substantial majority (88%) expect to reach those levels by 2028. (Chart 6)

The data suggests that manufacturers believe the foundation is now in place, and they are heading toward the destination. Diving into the digital adoption of specific functions bears this out. It is clear that digital efforts are still a work in progress. Only product design, production/assembly and R&D rise above 15% of respondents reporting that these functions are at an advanced level for their company. The good news is that more than 40% of respondents say they are at least at an intermediate level for every single function we asked about in our survey. (Chart 7)

4. Smart factory maturity continues to advance

Q: How would you assess the maturity level of your smart factory journey? (Scale of 1–10, with 10 being the highest level of digital maturity)

5. Those scaling nearly equal to those in the experimentation and learning stages

Q: Which activity best describes the primary stage of your company’s M4.0 digital efforts today? (Select one)

6. Manufacturers expect smarter factories ahead

Q: How “smart” do you consider your factory and plant operations to be today, and what do you anticipate they will be by 2028?

7. Advanced digital adoption remains a work in progress across functions

Q: At what stage of digital adoption are the following functions in your company? (Rate as early, intermediate, or advanced)

SECTION 3: Digitization and Automation Growth

Digitization across factory operations is currently mixed. When considering production, maintenance, quality, planning scheduling and support functions, an equal number of respondents report partial end-to-end digitization or that end-to-end digitization is in the planning stage (45% each). At the extreme ends of the spectrum, 5% say they are extensively digitized or have no plans to digitize. But there are reasons for optimism, with 98% saying that their factory operations will be extensively or partially digitized end-to-end by 2028. (Chart 8)

Production and assembly processes remain the focal point of digital efforts today. While extensive digitization is still limited to 11% today, expectations for the future are strikingly high, with most respondents anticipating at least partial digitization by 2028. (Chart 9)

Integration beyond the factory, however, continues to lag. Digital integration with suppliers and customers shows improvement but remains well behind internal digitization efforts. This gap highlights both the complexity of ecosystem integration and a major opportunity for future value creation. (Chart 10)

8. End-to-end factory digitization expected to accelerate

Q: Thinking about your overall factory operations (production, maintenance, quality, planning, scheduling, support functions, etc.), to what extent are they fully digitized end-to-end today, and to what extent do you anticipate they will be by 2028? (Select one for today and one for 2028)?

9. Assembly process digitization on the horizon

Q: Focusing specifically on your core production and assembly processes (activities taking place on the manufacturing line), to what extent are these processes digitized today, and to what extent do you anticipate they will be by 2028? (Select one for today and one for 2028)

10. Customer and supplier digital integration lags behind other functions

Q: To what extent are your production functions digitally integrated with customers and suppliers today, and what do you anticipate they will be by 2028? (Select one for today and one for 2028)

SECTION 4: Future Factory Models and AI-Driven Operations

Manufacturers continue to envision a hybrid future for their factory networks. Most expect a mix of large-scale facilities and smaller, more specialized sites, balancing efficiency with flexibility. Compared to 2025, the biggest shifts occurred around the size of the future factory footprint. In 2025, 40% expected their future factory model would be a large factory or network of large factories, compared to just 28% in 2026. Meanwhile, the expectation to have a small factory or network of small factories grew from 10% in 2025 to 19% in 2026. (Chart 11)

Confidence in AI-driven operations has remained steady. In 2026, 79% of respondents tell us they fully or partially agree that factories will evolve into self-managing, self-learning facilities. This aligns closely with last year’s survey where 80% fully or partially agreed. In both years, it’s important to note that those who partially agree far outweigh those who fully agree. This outlook has increased significantly since 2024, reflecting a more informed outlook that is, perhaps, shaped by leaders’ early experiences with AI technologies. (Chart 12)

11. Hybrid factory model remains dominant future strategy

Q: As you think about your factory footprint in the future, what is the expected future state of your factory model? (Select one)

12. Most partially or fully agree that tomorrow’s factories will be self-managing/learning

Q: Thinking about the impact of technologies such as AI and machine learning, to what extent would you agree or disagree with the following statement: “Tomorrow’s factory will evolve to be a self-managing and self-learning facility.” (Select one)

SECTION 5: Technology Adoption and Priorities

To that end, technology adoption in production operations continues to expand. Cybersecurity, manufacturing execution systems (MES), cloud computing, product lifecycle management software (PLM) and robotic process automation are the highest technologies deployed at scale today. These technologies are increasingly viewed as foundational rather than optional. Looking ahead to 2028, smart planning and scheduling tools, adaptive process control, digital twins, and digital thread are poised to make the largest leap forward. (Chart 13)

AI adoption is also advancing, with at least two-thirds of manufacturers reporting active deployment of traditional AI tools like vision systems (76%) and machine learning (66%). Across the board, every AI solution we asked about is expected to grow in its usage by 2028. Edge AI, causal AI and physical AI are expected to see the largest increases in use in that timeframe, while machine learning is expected to just surpass vision systems to become the most deployed AI solution. (Chart 14)

This measured pace is reflected in perceptions of AI’s impact. A growing percentage of respondents describe AI’s future impact on production as very significant (39%), continuing the upward trend from 2025 (34%) and 2024 (10%). (Chart 15)

Together, these findings suggest AI has moved beyond hype, entering a phase of value-driven deployment.

13. Multiple production operation technologies now surpass 50% scaling mark

Q: Where does your company stand in regard to the following technologies in its production operations? (Select one answer per technology)

14. Future growth ahead for every type of AI solution

Q: What types of AI solutions are you using today? (Select all that apply)

15. AI expected to have significant impact on production operations

Q: Looking ahead over the next few years, how significant an impact will AI have on your production operations? (Select one)

SECTION 6: Challenges and Benefits of Digital Transformation

As digital transformation matures, so do the challenges manufacturers face. Legacy equipment remains the most frequently cited obstacle, though this concern has dropped slightly from 2025’s survey. Meanwhile, there has been a sharp increase in those identifying data interoperability as a roadblock to their smart factory strategy. In 2025, only 22% of respondents identified data issues as a primary roadblock, but that has increased to 37% in 2026. Only cybersecurity had a larger increase. In a sign of progress, five roadblocks fell by at least seven percentage points from 2025 to 2026: lack of leadership buy-in, organizational structure or culture that resists change, lack of a roadmap, difficulty moving from pilot to scale, and lack of skilled employees. (Chart 16)

At the same time, perceptions of benefit continue to strengthen. Operational efficiency remains the top expected outcome, with greater speed and flexibility and cost reduction close behind. These results reinforce the growing belief that digital transformation is about more than technological advancement—it delivers measurable business value. (Chart 17)

Importantly, while the largest group of manufacturers continue to believe that digital transformation is table stakes to stay in the game, 43% now view digital transformation as a competitive advantage—the highest percentage in the past three years. Because different companies progress on their digital transformation journey at different paces and with different success rates, those who are more successful are seeing a competitive advantage arise. While table stakes still dominate, the momentum is unmistakable. (Chart 18)

16. Legacy equipment, data and workforce are top roadblocks

Q: What do you feel are your company’s primary roadblocks to implementing your smart factory strategy? (Select top three)

Note: horizontal and vertical integration difficulties were not included on 2025 or 2024 surveys

17. Better operational efficiency remains top benefit of smart factories

Q: What are the most important benefits and opportunities your company hopes to realize from embracing a smart factory strategy? (Select top three)

18. Digital transformation seen as table stakes by nearly half

Q: Do you believe that digital transformation of your company’s manufacturing operations will create a unique competitive advantage for your company or is it merely table stakes to remain in the game?

SECTION 7: The Strategic Value of Digital Transformation

Manufacturers’ views on the strategic importance of digital transformation remain overwhelmingly positive. Perhaps that is why we have seen an increase in those reporting better integration of their smart factory strategy with their company’s overall business strategy. In 2026, 58% say they have passed the midway point (6 or higher) on their integration journey. That is up 11 percentage points from 2025 and a full 30 percentage points from 2024. (Chart 19)

As digital transformation becomes more aligned with overall business strategy, it is not surprising that a clear majority believe digital transformation represents a fundamental shift in the manufacturing industry. Consistent with sentiment seen in 2025 and well above the dip recorded in 2024, 61% of respondents tell us that digital transformation is a game changer, indicating a truly new era for manufacturers. (Chart 20)

19. Smart factory strategy shows stronger alignment with business goals

Q: To what extent has your smart factory strategy been integrated with the company’s overall business strategy? (Scale of 1–10, where 10 is fully integrated)

20. Optimism around digital transformation impact remains steady

Q: Ultimately, how significant an impact will digital transformation have on the manufacturing industry?

The Bottom Line

What has changed is tone. The 2026 survey reflects less exuberance and more resolve. Digital transformation is shifting from proving its value to delivering on it. As manufacturers move deeper into execution, the 2026 Smart Factories and Digital Production Survey confirms that Manufacturing 4.0 has entered perhaps its most consequential phase yet. The leaders of the next decade will be defined by who executes best on the new technologies available to manufacturers. M

About the author:

Jeff Puma is content director at the NAM’s Manufacturing Leadership Council.

David R. Brousell - Future of Manufacturing Project 2025

For tomorrow’s manufacturing leaders, technological expertise, agility to thrive in flatter organizations, and the ability to orchestrate across functions won’t be optional—they’ll be the baseline.

Good morning, everyone, and welcome to the fifth annual Future of Manufacturing Project event.

In the months preceding the launch of the Project in December of 2021, MLC undertook an in-depth research project to examine the economic, demographic, and technological trends that would shape manufacturing by 2030.

What resulted was a 52-page white paper entitled “The Next Phase of Digital Evolution” that laid out a set of decision points for the manufacturing industry in the years ahead. Whether it was the strategic use of data, the challenge of defining the human-machine relationship in the age of AI, the prospect of autonomous operations, or the emergence of digitally-driven business ecosystems, one imperative stood out across all trends – the importance of enlightened leadership.

Since its formation in 2005, MLC has believed that enlightened leadership is the key to successful digital transformation. MLC’s definition of enlightened leadership has three main components:

The ability of manufacturing leaders to understand the potential of advanced technologies to reshape how things are made, how people work, and how operations can be made more efficient, productive, and faster.
The ability of manufacturing leaders to manage and orchestrate change in flatter, more collaborative and cross functional organizational structures characterized by data-driven decision-making processes and information-empowered workers.
The ability of leaders to adopt and practice a digital first mindset that emphasizes continuous organizational learning, agile response to change, the ability to form and manage multi-constituent networks, and a heightened willingness to take risks.

Of course, all of these digitally inspired skills, competencies, and behaviors need to be layered on a set of basic business competencies that are eternal. Every manufacturing leader needs to understand the business they are in, what their customers want, how to motivate people, and how to make a profit. They need stamina and passion, and even a sense of humor, particularly in times of disruption. They need to inspire, create a culture of trust and fairness, and always wonder what’s next.

All of these basic things are true and will remain so.

***

Our task today is to try to understand how digital leadership might change in the years ahead. I pose it this way because this is not a question. It is a certainty. Our jobs are about to become more complicated and challenging, but also more exciting.

To understand what digital leadership will need to look like in the years ahead, we need to take a page from that MLC white paper I mentioned and think anew about the larger trends shaping our world.

Contrary to what some would wish for, the world is getting more interdependent, not less.

In an essay last month entitled “Welcome to Our New Era. What Do We Call It?” New York Times columnist Tom Friedman said that old binary left-right concepts and systems have given way to multiple, interconnected ones.

We had thought of the world as a set of binaries – East/West, Communist/Capitalist, North/South, Aligned/Non-Aligned. Communities had been defined by a single ethnicity or faith and are now what he calls “polyglot, polychromatic, and polyreligious.” National economies were based on bilateral trade of discrete goods between countries with clear borders and self-contained industries.

Today, our economy is “an interdependent web of knowledge, skills, technology, and talent”.

Take, for example, the chip in your smart phone. The essay points out that the chip was “imagined in California, designed using software from the US and Europe, manufactured in Taiwan using Dutch lithography machines and material science innovations from Japan and Silicon Valley, and delivered by a global logistics network”.

A similar wave of interconnectedness and interdependency is playing out in our companies as Manufacturing 4.0 matures.

The years we have spent trying to implement digital manufacturing, and the challenges that have arisen as a result, have shown us that a holistic, cross-functional approach to digital manufacturing that leads to full organizational integration is required to achieve true game-changing results.

As an article in the Harvard Business Review in October, by Herminia Ibara and Michael G. Jacobides, said “Failure to capture value from technology is typically about failing to align technology to the value proposition and missing the opportunity to leverage technology to change the organization.”

Organizational alignment, adapting processes to take advantage of what technology has to offer, and rethinking how people, teams and functional domains work are key orchestration challenges facing manufacturing leadership today and in the future. You can’t manage digital transformation effectively in a linear fashion.

According to former MLC Board member Pietro D’Arpa of P&G, there are three types of orchestrations manufacturing leaders need to master. They are:

Technical orchestration: bringing together IT, OT, data, and AI into a coherent and manageable whole;
Organizational orchestration: aligning roles, incentives, workflows, and governance with the way digital operations actually work;
Ecosystem orchestration: working more closely with suppliers, customers, institutions, and technology partners in a world that is becoming more interconnected.

***

If that isn’t enough, things are about to get more complicated as the age of AI unfolds.

As AI, which MLC calls a pervasive technology, is incorporated into the many systems used to run factories and plants, and as it increasingly augments human activities from complex data analysis to report writing, manufacturing leaders need to address serious and consequential questions surrounding the human-machine relationship.

To what extent should we allow intelligent machines to act? What types of decisions should they be empowered to make? Should they be trusted? Can they be co-equal partners with human beings? How shall we define the status of AI-powered intelligent machines in relation to human beings? And to what extent do we want to embrace autonomous manufacturing?

It is incumbent upon us as manufacturing leaders to define where the lines are of what is permissible and desirable and where the guard rails should be even as the AI technology matures and becomes more powerful and capable. This is a moving target, and it is accelerating.

Without trying to be hyperbolic, I think this is the existential challenge we face as manufacturing leaders in the years ahead. Just about all else pales in comparison.

***

David R. Brousell - Future of Manufacturing Project 2025

In researching the literature on digital leadership as I was preparing this speech, I was struck by how often the word “agility” appears in the many lists of recommendations on how to lead in the digital era. The literature abounds with titles such as “5 Digital Leadership Skills”, “8 Components for Being an Effective Digital Leader”, “5 Critical Skills Leaders Need in the Age of AI”, and the “5 Cs, 3 Ls and 4 Ps of Leadership.” Almost all of them mention agility as a key competency, usually married to the phrase “in times of disruption.”

Frankly, disruption has been a part of the human condition since we began to stand upright. What’s changed, and what I think underlies the emphasis on agility in times of disruption, is the almost instantaneous communication of change we have today.

So, when things speed up too much, leaders need to slow them down and allow time for careful consideration. A proper reaction, the solution to a problem, a change in strategy may not at first be obvious or clear. As Peter Drucker once famously said: “In times of disruption, it is not the disruption that matters. It is acting with yesterday’s logic.”

The point about agility leads me to some other thoughts about the requirements for digital leadership in the future. Here are a few recommendations:

Craft a Digital Vision – Establishing an idea of what your business could be in the digital age is critical to give the organization and the people in it a sense of destination, of where you are going – with the understanding that the direction and ultimate destination can and probably will change over time as technological developments occur. And support the vision with no more than three goals. Focus is key.
Keep Up with Technology – I know this is easy to say but hard to do. MLC research has shown for years that staying abreast of new technological developments is one of the biggest challenges operational executives say they face. But, going back to what I said about the three main components of enlightened leadership, understanding the potential of new technologies will help you avoid a “Kodak moment” of surprise and even disintermediation. How do you do this? Task a person or a team to function as a kind of lab or investigative unit. Put them off on the side and let them run.
Diversify the Bench – As you think about and plan your leadership team for the future, in conjunction with your HR people, think about hiring in an unconventional manner. Consider hiring people with degrees in philosophy, the arts, in literature. Think about brain power not in a linear or binary fashion, but in a network fashion. There is great strength in diversity and in our increasingly poly world we need all we can get. And always be recruiting for the best minds you can find whether you have a job opening at a particular moment in time or not.
Have the Courage of Your Convictions – Success with digital requires seemingly endless communications with multiple constituencies inside and outside the organization. The ability to communicate clearly, comprehensively, and persistently Is at a premium in our complex age. What becomes key is what Pietro D’Arpa calls Narrative Agility, the leader’s ability to frame and reframe the meaning of change as technology continues to reshape the business. Helping people understand why shifts are happening, what they imply, and how they fit into the organization’s evolving purpose is increasingly essential in a fast-moving environment.

And this needs to be done in a collaborative environment where a leader often must function more as a coach, often employing Socratic technique. But decisiveness and responsibility are also important. People need to know where the buck stops, as Harry Truman once said. And this is where courage comes in. Have the courage of your convictions.

- Increase Your Stamina – Success, or victory, in the digital age may be less dependent on skill and talent than the wherewithal to see things through. The ability to persevere through wins and losses, good times and bad, often determines winners and losers. We know there is no finish line in the digital journey. Never give up.
- Mitigate Complexity – I’ve talked about this one before, but it bears repeating. We are building complex structures of IT and OT technologies in our companies, layer after layer of software and systems to run every aspect of operations and the business. Now, the AI wave is about to engulf us. Find ways to beat back complexity, to simplify wherever you can.
- Lastly, Maintain a Sense of Humor – This shouldn’t just be all work. Let’s have some fun with digital transformation. After all, we are in the process of creating a better future for manufacturing. Let’s celebrate and enjoy it! M

Photos by David Bohrer / National Assoc. of Manufacturers

A year of candid conversations with leaders on what it takes to lead in an era of digital transformation and rapid change

The pace of change in manufacturing leadership has never been faster, and 2025’s Executive Dialogue series captured that shift in real time. Across conversations with industry trailblazers, a clear theme emerged: the future of manufacturing will be defined not just by technology, but by the leaders who know how to harness it. From AI-driven operational excellence to workforce transformation and digital culture, this year’s Executive Dialogue interviews reveal how manufacturing leaders are navigating complexity with clarity, purpose and vision.

In this special best-of collection, hear how leaders are scaling Manufacturing 4.0 across global operations, building digitally fluent cultures, embracing new generations of talent, and preparing for the rise of agentic AI. Whether you’re accelerating your own digital roadmap or looking for breakthrough ideas to strengthen your organization, these interviews offer practical insights and a glimpse into the leadership mindset shaping manufacturing’s next era.

Dialogue: Finding the Human Edge in Manufacturing AI

Tim O’Neal, Dow, Inc. | February 2025

Dow’s Tim O’Neal discusses the intersection of talent, data and AI in manufacturing, sharing insights on how intentional leadership and digital innovation drive operational excellence. From predictive analytics to AI-powered decision-making, he explores the evolving role of technology in shaping the future of industrial performance.

Watch Now

Dialogue: Scaling Industry 4.0 at Eaton

Craig Sutton, Eaton Corporation | April 2025

How do you scale digital transformation across 200 global manufacturing plants? Craig Sutton, Vice President of Industry 4.0 at Eaton, shares how the company is driving Industry 4.0 adoption with a strategic focus on technology, culture and governance. In this conversation, he discusses the challenges of integrating digital tools across diverse operations, the importance of a strong tech stack, and why shifting mindsets is just as critical as deploying new solutions.

Watch Now

Digital Culture Begins with Executive Intent

John Dyck, CESMII | June 2025

CESMII CEO and MLC Board Member John Dyck shares why building a digital culture in manufacturing starts with executive intent. In this conversation with MLC’s Penelope Brown, Dyck explores the foundational role of data quality, the need for cultural transformation, and the importance of aligning IT and OT through strategic structure and incentives. He emphasizes that digital transformation requires more than tools—it demands leadership commitment, repeatable and trusted data, and a clear roadmap for change. Manufacturers, he says, must move beyond assumptions and build the organizational muscle to fully realize Industry 4.0’s value.

Watch Now

Dialogue: At Merck, It Takes a Digital Village

Besu Alemayehu, Merck & Co. | August 2025

Merck’s Besu Alemayehu, the Manufacturing Leadership Council’s 2025 Manufacturing Leader of the Year, says it starts with humility. “We think we know it all when we’ve been in the industry 30 years,” he says. “But this generation went to school with no textbooks. Their way of thinking about digital and technology and enablement is incredible.” Watch his full interview to hear how he’s helping shape a culture where fresh thinking and digital-native ideas can thrive.

Watch Now

Dialogue: Agentic AI Moves from Insights to Action

Prasoon Saxena, NTT DATA | October 2025

NTT DATA’s Prasoon Saxena sees agentic AI as the next leap in manufacturing—necessary to accelerate cycles, fill labor gaps, increase global competitiveness, and reshape how humans and AI work together. In this special Future of Manufacturing Project-focused Executive Dialogue, he shares why trust, governance and training are critical as manufacturers prepare for a future where humans and AI agents operate side by side.

Watch Now

About the author:

Jeff Puma is Content Director for the Manufacturing Leadership Council

From AI to digital resilience to autonomous operations, Rethink 2025 delivered the latest insights on Manufacturing 4.0.

Over the past two decades, Rethink: Accelerating Digital Transformation in Manufacturing has become a must-attend event for operations leaders. In 2025, attendees engaged in outstanding keynotes, insightful panel discussions, interactive Re(Think) Tanks, and more.

From a selection of outstanding sessions, here are a few standouts from last June.

Note: You will need to be logged into the MLC Member Portal to access these videos.

Keynote: Scaling Digital on Eaton’s Path for Growth

Craig Sutton, Vice President, Industry 4.0, Eaton Corporation

Scaling digital transformation across a global manufacturing enterprise requires more than just technology—it takes structure, strategy, and cultural change. Eaton is scaling its Manufacturing 4.0 journey from a single digital plant to a global network, using governance, process frameworks, and a tailored tech stack to drive real impact.

Watch Now

Case study: IPG’s Digital M4.0 Journey, from Big Data Analytics to AI/ML Implementation

Emmanuel Boullay, Senior Vice President, Operations, IPG

2024 Large Enterprise Manufacturer of the Year IPG shares its six-year transformative journey into Manufacturing 4.0. Taking a deliberate, step-by-step approach, IPG built its M4.0 foundation on plant-level employee engagement, a strong focus on quality, and measurable ROI. Their “clean data first” strategy enabled the development of diagnostic and prescriptive analytics, paving the way for advanced AI and machine learning models that deliver predictive insights and prescriptive capabilities.

Watch Now

Case study: Hershey’s Digital Factory – How Hershey is Driving Manufacturing Excellence

Will Bonifant, Vice President, Manufacturing and Engineering, The Hershey Company; Member, Board of Governors, Manufacturing Leadership Council
Christopher Gantz, Senior Manager, Digital Systems, The Hershey Company

In 2024, Hershey set out to make its conversion cost improvement target best in class. To get there, the company devised a holistic strategy to digitally upgrade all work, bringing automation and advanced analytics to its frontline teams and automating repetitive physical tasks for workers. This session focuses on how technology and workforce transformation are ushering in a new phase of manufacturing excellence.

Watch Now

Panel discussion: Bridging the Digital Divide – Accelerating Transformation for Small and Medium Manufacturers

Chris Droney, Chief Operations Officer, Milo’s Tea Company
Dan Dwight, President and CEO, Cooley Group; Chair, MLC Board of Governors; Executive Committee, NAM Board of Directors; Winner of the 2024 ML Awards Small/Medium Manufacturer of the Year; Winner of the 2024 Manufacturing Leader of the Year
Joe Pfaff, Vice President, Global Business Technology, Husco

As digital transformation reshapes the manufacturing landscape, small and medium manufacturers face unique challenges in keeping pace with larger enterprises and global competitors. However, with the right strategies, SMMs can harness emerging technologies to drive efficiency, agility, and growth. This panel brings together industry leaders to discuss practical approaches for accelerating digital transformation in SMMs.

Watch Now

Power Panel: Handicapping the Global Digital Transformation Race

Julie Fraser, Vice President of Research, Manufacturing and Operations, Tech-Clarity
Matthew Littlefield, Co-Founder, President and Research Lead, LNS Research
Bob Parker, Senior Vice President, Worldwide Research, IDC
Craig Resnick, Vice President, ARC Advisory Group
Facilitator: David Brousell, Founder, Vice President and Executive Director, Manufacturing Leadership Council

Where are U.S. manufacturers on the digital transformation journey in comparison to companies in Asia, Europe, and other parts of the world? Are U.S. manufacturers ahead or behind? This panel of distinguished manufacturing industry analysts assesses the digital competitive landscape and what U.S. companies can do to accelerate the digitalization and modernization of the U.S. industrial base.

Watch Now

About the author:

Penelope Brown is the Senior Content Director, Manufacturing Leadership Council

MLC’s 2025 research shows that operations leaders are resolved to continue scaling the digital maturity curve.

In 2025, the Manufacturing Leadership Council’s research efforts studied four areas that are formative to the current state of Manufacturing 4.0: Smart Factories and Digital Production; M4.0 Supply Chains, Artificial Intelligence, and Data Governance, Mastery, and Analytics.

In broad strokes, the surveys paint a picture of manufacturers moving up the digital maturity curve with strong expectations of continuing to advance. Operations leaders see the promise of digitalization, data and AI, but encounter roadblocks for access to capital investment, questions on the business case for technology deployment, and resistance from the workforce and sometimes the C-suite.

Here are the findings from MLC’s 2025 research surveys.

“M4.0 Appears Poised for a Significant Leap”, MLJ February 2025

MLC’s Smart Factories and Digital Production survey showed manufacturers are accelerating their digital transformation, with 60% calling it a defining shift and 75% rating their digital maturity as mid-level. Many companies expect to scale digitization rapidly by 2027—particularly in production, AI-driven operations and smart-factory integration. However, legacy equipment, lack of skilled workers and weak supplier/partner connectivity remain key hurdles.

Read Now

“Tariff Tumult Roils Supply Chains”, MLJ April 2025 – A For MLC’s M4.0 Supply Chains survey, manufacturers said that the Trump administration’s shifting tariff policies created significant supply chain disruption, with 43% experiencing negative impacts at the time of the survey and rising costs cited as the most damaging effect. Nearly half say it is still too early to fully assess the long-term impact, but uncertainty and business strain are widespread. In response, companies are accelerating the use of digital tools—especially supply chain analytics—to improve visibility and resilience in the face of ongoing trade turbulence.

Read Now

“Shaping the AI-Powered Factory of the Future”, May 2025 – The 2025 Future of Manufacturing Project survey found 68% of manufacturers believe AI will be foundational and most expecting increased investment and adoption over the next two years. While enthusiasm is high, AI use in operations is still limited, and many companies are only beginning to formalize governance and strategy. Significant barriers—including poor data quality, skill gaps, and legacy systems—remain, making steady, iterative progress the key to scaling AI successfully across the industry. Note that you must be logged into the MLC member portal or complete a contact form to access this survey.

MLC Members

MLC nonMembers

“Manufacturers See Data, Lack Strategy”, October 2025 – MLC’s Data Governance, Mastery, and Analytics survey found that manufacturers increasingly recognize data as a critical asset for digital transformation, driving measurable improvements in cost reduction, efficiency, quality, and decision-making. However, many companies still face significant obstacles, including disparate systems, legacy equipment, limited AI-ready data, and insufficient governance, with nearly half lacking a corporate-wide data strategy. Despite these challenges, firms that effectively harness and validate their data are seeing tangible business benefits and are laying the groundwork for more strategic, data-driven operations. M

read now

About the author:

Penelope Brown is the Senior Content Director, Manufacturing Leadership Council