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To help companies remain competitive amidst changing markets, the Solutions Review editors have outlined an example AI readiness assessment framework for manufacturing companies to use as they work toward AI adoption.

Manufacturing companies are facing unprecedented pressure to implement AI systems. The industry has always been a poster child for advanced technologies—look at how industrial automation revolutionized the sector in the 1950s—but the rise of AI seems poised to eclipse it entirely. However, implementing AI is easier said than done, especially since manufacturers must maintain operational excellence, regulatory compliance, and competitive positioning during the implementation.

Most AI readiness assessments fail manufacturing organizations because they apply generic frameworks designed for service industries or technology companies. Manufacturing requires specialized evaluation criteria that account for physical production constraints, supply chain complexity, safety requirements, global concerns, and the unique interplay between human expertise and automated systems.

That’s what this framework aims to provide, as it outlines a comprehensive assessment methodology specifically calibrated for manufacturing environments. This will help teams address the distinctive challenges of integrating AI into production systems, quality control, predictive maintenance, and operational optimization.

Core Assessment Dimensions

Production System Integration Readiness

Manufacturing AI implementations must interface with existing production control systems, often requiring real-time decision-making capabilities that service-sector AI applications rarely encounter. The assessment begins with evaluating the compatibility of the Manufacturing Execution System (MES), the potential for Supervisory Control and Data Acquisition (SCADA) integration, and the readiness of the Enterprise Resource Planning (ERP) system for AI-driven insights.

Critical evaluation points include data flow architecture between production systems, latency requirements for real-time AI decision-making, and the ability to implement AI recommendations without disrupting established production sequences. Manufacturing systems typically operate on deterministic schedules with tight tolerances, making AI integration significantly more complex than administrative or customer service applications.

A well-designed framework will examine whether current system architectures can support bi-directional data exchange, real-time inference capabilities, and fail-safe mechanisms that prevent AI-driven decisions from compromising production safety or quality standards. This includes assessing the robustness of existing communication protocols, data validation procedures, and exception handling capabilities within the manufacturing technology stack.

Operational Technology Security Posture

Manufacturing environments present unique cybersecurity challenges that differ substantially from traditional IT security frameworks. Operational Technology (OT) networks often contain legacy systems with limited security features, creating vulnerability vectors that AI implementations can inadvertently exploit or expand. One area companies should assess is network segmentation between IT and OT environments, which covers air-gapped system protection and the security implications of introducing AI-enabled data collection and processing capabilities. Manufacturing AI systems frequently require access to production data, sensor readings, and control system parameters representing critical intellectual property and operational vulnerabilities.

Particular attention focuses on evaluating Industrial Internet of Things (IIoT) device security, edge computing security protocols, and the potential for AI systems to create new attack vectors through expanded data access requirements. The framework assesses whether organizations can implement AI solutions while maintaining compliance with industrial security standards and protecting against cyber and physical threats.

Data Quality and Availability Assessment

Manufacturing generates vast data sets, but much of this information exists in formats, systems, and contexts that resist straightforward AI implementation. Before implementing AI, a manufacturer’s readiness assessment should evaluate data completeness, accuracy, temporal consistency, and semantic coherence across production systems.

Manufacturing data presents unique challenges, as it encompasses factors like sensor drift, calibration inconsistencies, maintenance-related data gaps, and the complex relationships between process parameters and quality outcomes. Unlike transactional business data, manufacturing information often requires domain-specific preprocessing, cleaning, and contextualization before AI algorithms can extract meaningful insights.

Assessments should examine data governance practices, standardization procedures, and the availability of historical data sufficient for model training and validation. Manufacturing AI applications typically require extensive historical datasets that capture seasonal variations, equipment aging effects, and process optimization cycles that may span months or years.

Human Capital and Change Management Readiness

Manufacturing organizations often maintain deeply embedded expertise in production processes, quality control, and equipment operation that AI systems cannot easily replace. The framework evaluates how effectively organizations can integrate AI capabilities with existing human expertise rather than attempting to replace skilled operators and technicians wholesale. Assessment criteria for this area include:

• The willingness of production staff to work alongside AI systems.
• The availability of personnel capable of maintaining and troubleshooting AI implementations.
• The organizational capacity to retrain workers for AI-augmented roles.

Manufacturing environments typically require specialized knowledge that combines theoretical understanding with practical experience gained through years of hands-on operation, and the human capital assessment must reflect that reality. This can take the form of examining whether organizations can develop hybrid human-AI workflows that leverage the strengths of both artificial and human intelligence, particularly in areas requiring judgment, creativity, and adaptation to unexpected circumstances that frequently arise in manufacturing environments.

Technical Infrastructure Evaluation

Edge Computing and Real-Time Processing Capabilities

Manufacturing AI applications frequently require real-time or near-real-time processing capabilities that not every solution can reliably provide. Network latency, connectivity interruptions, and bandwidth limitations can severely compromise AI system effectiveness in production environments, requiring manufacturers to evaluate their existing edge computing infrastructure, local processing capabilities, and the ability to implement AI inference at the point of data generation. Another point to consider is that manufacturing environments often require AI systems that can operate independently of external connectivity while maintaining synchronization with broader organizational systems.

Critical evaluation points include local storage capacity for AI models and training data, processing power sufficient for real-time inference, and redundancy mechanisms that ensure continued operation during network or system failures. The framework assesses whether organizations can implement distributed AI architectures that balance local responsiveness with centralized coordination and control.

Sensor Integration and Data Collection Infrastructure

Manufacturing AI systems depend on comprehensive, accurate, and timely data collection from production processes. As such, assessments must assess existing sensor networks, data acquisition systems, and the potential for expanding data collection capabilities to support AI implementations. This can be complicated by environments that contain a mixture of legacy sensors, modern digital instruments, and hybrid systems that require different data collection approaches, all common in manufacturing.

To that end, a manufacturing-centric AI readiness framework examines whether current sensor infrastructure can provide the data quality, quantity, and temporal resolution required for successful AI model training and operation. Particular attention focuses on evaluating sensor calibration procedures, data validation mechanisms, and the ability to integrate data from disparate sources into coherent datasets suitable for AI processing.

Model Deployment and Maintenance Infrastructure

Manufacturing AI systems requires specialized deployment and maintenance procedures that account for production schedule constraints, safety requirements, and the need for continuous operation. Assessments evaluate the organizational capacity to implement, monitor, and update AI models within manufacturing environments. Critical evaluation points include:

• The ability to test AI models in production environments without disrupting operations
• Procedures for validating model performance against manufacturing outcomes.
• Mechanisms for updating models based on changing production conditions.

Manufacturing environments often require AI systems that can adapt to equipment modifications, process changes, and evolving quality requirements, so organizations should identify whether they can implement the model versioning, rollback capabilities, and performance monitoring systems that ensure AI implementations continue to deliver value over time.

Domain-Specific Readiness Factors

Quality Control and Statistical Process Control Integration

Manufacturing quality control systems often rely on Statistical Process Control (SPC) methodologies refined over decades of industrial practice. AI implementations must integrate with existing quality frameworks while enhancing rather than replacing proven statistical methods. Additionally, manufacturing quality control typically requires explainable AI approaches that can provide a clear rationale for quality decisions and recommendations.

Readiness assessments will explore how effectively an organization can combine AI-driven quality predictions with traditional SPC approaches, maintaining compliance with industry quality standards while leveraging AI capabilities for early defect detection and process optimization. Evaluation points include:

• The ability to maintain traceability between AI-driven quality decisions and final product outcomes.
• Integration with existing quality management systems.
• Compliance with regulatory requirements that may mandate specific quality control procedures.

Frameworks must assess whether organizations can implement AI quality control systems that enhance rather than compromise existing quality assurance processes.

Predictive Maintenance and Asset Management Readiness

Manufacturing equipment maintenance represents one of the most promising applications for AI technology, but successful implementation requires a sophisticated understanding of equipment behavior, failure modes, and maintenance practices. The assessment evaluates organizational readiness to implement AI-driven predictive maintenance while maintaining existing preventive maintenance programs.

Frameworks will examine whether organizations can collect and analyze the diverse data types required for effective predictive maintenance, including vibration analysis, thermal imaging, oil analysis, and operational parameter monitoring. Manufacturing predictive maintenance AI systems must account for the complex relationships between equipment condition, production demands, and maintenance scheduling constraints. This entails evaluating the availability of historical maintenance data, the ability to correlate equipment condition with production quality outcomes, and the organizational capacity to act on AI-generated maintenance recommendations.

Supply Chain and Inventory Optimization Readiness

Manufacturing supply chain management involves complex interdependencies between material availability, production scheduling, quality requirements, and customer demand patterns. AI implementations must account for these relationships while providing actionable insights for inventory optimization and supply chain coordination. This includes evaluating the availability of supply chain data, the ability to integrate AI recommendations with existing planning systems, and the organizational capacity to implement dynamic inventory management based on AI-driven demand forecasting.

Evaluation points include the ability to model supply chain disruptions, integrate AI recommendations with production planning systems, and maintain appropriate inventory levels while minimizing carrying costs. The framework assesses whether organizations can implement AI-driven supply chain optimization that enhances rather than disrupts existing supplier relationships and procurement processes.

Implementation Readiness Assessment

Pilot Project Selection and Validation Framework

Manufacturing AI implementations benefit from carefully selected pilot projects that demonstrate value while minimizing risk to production operations. For this phase of the assessment framework, manufacturers will explore their capacity to identify, implement, and validate AI pilot projects within manufacturing environments. Effective pilot project selection requires balancing potential AI benefits against implementation complexity, data availability, and the ability to measure success using manufacturing-specific metrics.

Industry-specific AI pilots typically require more extensive validation procedures than applications in other industries due to the potential consequences of incorrect predictions or recommendations. As such, the most relevant points to assess include the ability to isolate pilot project variables, measure AI impact on manufacturing outcomes, and scale successful pilot implementations across broader production systems.

Regulatory Compliance and Industry Standards Alignment

Manufacturing industries often operate under strict regulatory requirements that govern product quality, safety procedures, and environmental compliance. Like the World Economic Forum explains, “the convergence of ethical principles with AI deployment becomes imperative to ensure responsible innovation and sustainable progress.” This is especially crucial for manufacturers, who must maintain compliance with existing regulations while potentially enabling enhanced compliance monitoring and reporting capabilities.

Assessments should evaluate how AI implementations interact with these regulatory requirements, covering audit trails for AI-driven decisions and the organizational capacity to demonstrate compliance with industry standards. Specific points to examine include the ability to maintain regulatory compliance during AI adoption, the potential for AI systems to enhance compliance monitoring, and the organizational capacity to adapt AI implementations to evolving regulatory requirements.

Vendor Selection and Partnership Evaluation

Manufacturing AI implementations often require specialized vendors with an extensive understanding of manufacturing processes, equipment, and operational constraints. The assessment evaluates organizational capacity to select and manage AI vendor relationships within manufacturing contexts. For example, an effective vendor selection requires balancing AI technical capabilities with manufacturing domain expertise, implementation experience, and long-term support capabilities.

Assessments should examine whether organizations can identify vendors capable of delivering AI solutions that integrate effectively with existing manufacturing systems and processes. For example, priorities in readiness assessments include vendor experience with manufacturing AI implementations, the ability to provide ongoing support and maintenance, and the alignment between vendor capabilities and organizational AI objectives.

Maturity Assessment and Progression Framework

Foundational Readiness Level

Organizations at the foundational level possess basic data collection capabilities, established quality control procedures, and general awareness of AI potential within manufacturing contexts. Manufacturers require significant infrastructure development and capability building before implementing production AI systems. Enterprises should focus on improving data quality and availability, establishing basic analytics capabilities, and developing an organizational understanding of AI applications within manufacturing environments.

Progression paths emphasize building technical infrastructure and human capabilities that support future AI implementations. Additionally, manufacturing organizations at this level may benefit from focusing on Industry 4.0 initiatives that create the data infrastructure necessary for eventual AI implementation, rather than attempting direct AI deployment.

Intermediate Readiness Level

Intermediate organizations possess established data collection and analysis capabilities, more experience with automation technologies, and some pilot AI implementations within manufacturing contexts. These organizations typically require focused development in specific AI application areas and integration capabilities. These organizations should focus on expanding successful pilot implementations, developing specialized AI capabilities for manufacturing applications, and building organizational expertise in AI system management and maintenance. The progression path for these organizations emphasizes scaling existing capabilities and developing specialized manufacturing AI expertise.

Advanced Readiness Level

Advanced organizations possess comprehensive AI capabilities, established manufacturing AI implementations, and organizational cultures capable of integrating AI insights into production decision-making. Because of these capabilities, advanced-level organizations typically focus on optimizing existing AI systems and exploring advanced AI applications.

At this stage, manufacturers should focus on developing cutting-edge AI capabilities, exploring emerging AI technologies, and sharing expertise with industry partners and suppliers. The progression path emphasizes innovation, optimization, and leadership in manufacturing AI implementation.

Conclusion

Manufacturing AI readiness requires specialized assessment approaches that account for the unique challenges and opportunities within production environments. This framework provides a comprehensive methodology for evaluating organizational readiness while recognizing the distinctive requirements of manufacturing AI projects.

Successful manufacturing AI initiatives require careful integration with existing production systems, comprehensive data quality management, and organizational cultures that effectively combine human expertise with AI capabilities, which a properly configured AI readiness assessment framework can provide. When manufacturers carefully assess their readiness and develop targeted improvement strategies, they will be better positioned to realize the substantial benefits that AI can provide within manufacturing contexts.

Want more insights like this? Register for Insight Jam, Solutions Review’s enterprise tech community, which enables human conversation on AI. You can gain access for free here!

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Alexandra Conza, Senior Strategic Content Marketing Manager at Smartcat, explains why AI translation tools are essential to manufacturing growth and efficiency. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

The manufacturing sector is inherently global. From sourcing raw materials to distributing finished goods and training diverse workforces, manufacturers operate across borders, languages, and cultures. Navigating this complex landscape requires seamless, accurate, and rapid communication, which has traditionally been a challenge marked by delays and high costs.

However, AI translation platforms fundamentally change the game, enabling manufacturers to expand more effectively, optimize operations, reduce expenditure, and empower their global teams. Leading manufacturing companies are leveraging AI-powered translation and localization to achieve measurable outcomes and gain a strategic advantage.

Accelerating Global Expansion and Marketing Reach

Entering and succeeding in new international markets hinges on effective communication. This means localizing everything from product strategy, content, and technical marketing collateral to vehicle launch materials. Translation and localization AI platforms dramatically speed up this process, delivering high-quality multilingual content for automotive manufacturers to internal training documents, videos, and audio files for multinational teams.

The impact on speed is significant. On average, automotive manufacturers achieved a 50 percent faster turnaround time with Smartcat, and one major global car manufacturer reduced its average translation project times from over two months to just two to three weeks. This accelerated pace allows for rapid global deployment of localized marketing campaigns and product information, fueling timely and impactful marketing and sales efforts.

Streamlining Operations and Improving Workflows

Operational efficiency is paramount in manufacturing, and traditional translation methods often involve fragmented processes and excessive manual work. AI translation and localization platforms can integrate with enterprise tools, eliminating repetitive tasks and reducing the time spent on tasks. This enables organizations like a global optical instrument manufacturer to complete more than 50 percent more projects simultaneously. This automation also mitigates the risk of errors, leading to up to a 70 percent workload reduction on editing and review.

For example, a leading optical instruments manufacturer saves an estimated two hours per global content project with AI translation and localization, reducing the time to produce one e-learning course in over ten languages from thirteen to eleven hours. Automated project management solutions, sometimes called “Lights Out” management, can handle routine operations like task assignments and payment management, yielding substantial time savings. These automated project management features are monitored 24/7 with complete transparency.

Furthermore, AI translation and localization platforms centralize each client’s unique linguistic assets like translation memories and glossaries, ensuring consistency and quality across projects and teams. Collaborative workflows allow for seamless teamwork with real-time collaboration and task assignment. This collaborative environment has helped companies eliminate the chance of ‘doing the work twice’ that occurred with offline processes. Average project durations have decreased by up to two weeks, with some manufacturers reporting a remarkable 400 percent improvement in turnaround time, from ten days to two to three.

Delivering Substantial Cost Reductions

Cost savings are a major driver for adopting translation and localization AI, with average cost savings typically falling between 50 percent and 70 percent compared to traditional translation and localization methods. For example, one leading medical technology company could translate twice as much with the same budget as previous language service providers.

These savings are achieved through various mechanisms, including leveraging AI translation that continuously learns and applies insights from existing linguistic data. This means that AI-enhanced translations can deliver cost savings, greater speed, and improved accuracy.

Enhancing Employee Training and Communication

In manufacturing, a well-trained global workforce is essential for safety, efficiency, and product understanding. Localizing technical documents, user manuals, and industry-specific e-learning is critical. AI translation and localization platforms make this process faster, more cost-effective, and scalable.

AI translation platforms empower manufacturing companies to significantly enhance global employee training and communication. Organizations are leveraging these tools to train thousands of employees globally, creating comprehensive multilingual learning paths supporting new equipment operation, critical safety protocols, or widespread strategy adoption.

This technology facilitates a rapid expansion of linguistic reach, with some companies easily increasing their target languages and supporting projects across dozens of countries. This also enables the consistent creation of a high volume of localized content, with some organizations producing an average of 18 online learning courses per language every quarter. For a global tire manufacturer, the speed of localizing training videos dramatically improved: video translation projects, including dubbing, can be completed in as little as one hour per file, while initial subtitling translation is reduced from days to just 15 minutes with AI.

Driving Precision and Quality Assurance

In a sector where precision, compliance, and safety are paramount, translation quality is non-negotiable. AI alone isn’t enough; combining AI and human expertise is key. An AI that provides high-quality, brand-consistent translations must be able to learn from its expert human reviewers to continuously improve its accuracy and adapt to specific brand terminology or technical terms.

Human proofreaders at a leading global water technology manufacturer reported accuracy rates well above 90 percent for complex languages like Chinese, Korean, and Japanese when using Smartcat’s AI. Reviewers at other organizations noted a significantly lower number of corrections than traditional agencies, accelerating the editing process and improving efficiency.

The Future is AI-Human Collaboration

The strategic adoption of AI translation platforms enables many manufacturing companies to achieve significant advancements in efficiency, cost reduction, and global reach. These comprehensive solutions empower organizations to accelerate AI adoption across business units and achieve targeted global results, and are fast becoming fundamental platforms in the modern manufacturing landscape.

Looking ahead, integrating human expertise with adaptive AI systems will continue redefining global communication. This collaborative approach is a force multiplier, holding enormous potential for future worldwide communication and growth, particularly for highly regulated sectors like manufacturing, where linguistic barriers historically limited expansion.

For modern manufacturing, AI translation is a strategic necessity for scaling efficiently, reducing costs, and empowering teams worldwide. When AI and human experts work together, manufacturers gain the accuracy, reliability, and domain-specific nuance required to innovate and compete in an increasingly interconnected world.

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Travis Rothstein, the senior manager of Acquisition Sales and Integrations at Advantive, explains how integrated platforms can bring unified B2B commerce to fast-moving consumer goods (FMCG) distributors. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Unified B2B commerce has hit a roadblock in the fast-moving consumer goods (FMCG) sector. Despite significant technological advancements, many FMCG distributors struggle to integrate the customer-facing and back-office systems that power their operations. Without system interoperability, organizations face a disconnect that leads to siloed data, inconsistent customer experiences, and limited flexibility in responding to shifting market demands.

Companies are well aware of these challenges and the need to facilitate easy data exchanges between all stakeholders to achieve unified commerce. In their active efforts to solve the issues, 80 percent of B2B organizations are pursuing ways to connect their eCommerce platforms with modern enterprise resource planning (ERP) systems. When successfully integrated, those systems can streamline operations so that FMCG businesses can scale, adapt, and deliver exceptional customer value.

The Value of Data in B2B E-Commerce Cannot be Overstated

Data is the backbone of every B2B e-commerce system, and FMCG businesses handle large amounts of it, including customer orders, supply chain details, product catalogs, sales analytics, and financial transactions. Without centralizing all that data to enable standardization and accessibility, organizations cannot achieve operational efficiency.

Centralizing company data within unified systems improves accuracy and consistency and offers a deeper understanding of business processes. Quality data allows businesses to identify potential errors early before they lead to significant problems. FMCG distributors that standardize key data points across sales, inventory, and finance can gain crucial operational visibility and a clear picture of both daily functions and long-term growth opportunities.

For example, if a distributor receives a surge of similar datasets from multiple sources and they all vary in format, each set might contain identical or overlapping bits of information. However, the slightly different formats and files can create differentiation issues, requiring companies to allocate more resources to manage datasets that are essential to their business. When formats are standardized across the board, distributors can more easily ingest, distinguish, and transact on large-scale data.

This strategy saves both time and money. Research by McKinsey & Company found that organizations that embraced the approach lowered the total cost of data ownership by as much as 30 percent.

Benefits of B2B E-Commerce Platforms Include Real-Time Insights and Better Accessibility

Modern B2B e-commerce platforms are built to meet FMCG distributors’ diverse needs when it comes to data management. These systems address the unique challenges faced by various stakeholders, including sales representatives, retail buyers, and customer service teams, while supporting companies’ efforts to grow and succeed in high-pressure markets.

One of the most significant advantages e-commerce platforms offer is the ability to bridge the gap between front-end customer interactions and back-end operational systems. Innovative solutions that consolidate omnichannel sales into a single platform provide a streamlined approach to keep companies organized and competitive. Businesses can more easily ensure a consistent buyer experience across online and offline channels thanks to the real-time insights they get on buying trends, promotions, and sell-through performance. In addition, simplified sales management lets sales representatives secure deals on the go with mobile CRM access.

B2B e-commerce platforms can also integrate with on-premise and cloud-based ERP systems, providing instant access to vital data regardless of team members’ locations. With flexible features like online product availability and offline route accounting, field sales representatives with mobile devices have direct insight into inventory and customer order history. Plus, stakeholders can make better data-driven decisions since inventory sync helps businesses avoid overselling or understocking, which are common and costly pitfalls in distribution. At $1.77 trillion, the cost of inventory distortion is a growing global issue. Between 2022 and 2023, out-of-stocks increased 17.7 percent in North America alone.

ERP and CRM Integration Supports the Seamless Exchange of Data 

ERP and CRM integration are non-negotiable to truly enable unified B2B commerce. Both systems are central to managing sales, inventory, customer interactions, and financial data. When integrated with eCommerce platforms, they create a cohesive network that enhances visibility across all areas of business. Most B2B e-commerce solutions now include built-in ERP and CRM integrations, acting as the connective tissue that ensures automatic, real-time updates across all systems and defines the flow of data on products, customers, receivables, and more.

Configurability is a key factor in making these integrations effective. With configurable plugins, companies can tailor scheduled synchronizations to automate certain processes that don’t require frequent changes. Businesses value the flexibility to align integration with their unique workflows and operational priorities.

Gains in speed and accuracy are equally important. The automated flow of information between systems means companies can eliminate manual data entry and reduce the risk of human error. Sales transactions, for example, are instantly recorded in ERP systems, helping inventory, finance, and customer service teams stay on the same page and respond quickly to changing demands. Improved operational agility also enhances timely order fulfillment and communication, leading to higher customer satisfaction.

No longer a future goal, unified B2B commerce has become a present-day differentiator for FMCG businesses aiming to stay ahead. When ERP, CRM, and e-commerce systems work together, companies get the agility and insight to deliver consistent value and customer satisfaction, even as consumer demands evolve.

Adaptability is everything in fast-paced industries, and organizations that invest in seamless, integrated systems are most favorably positioned for success. With measurable gains like increased revenue and stronger customer loyalty to be made, the case for unified commerce is undeniable. Moving beyond disconnected tools and legacy systems in favor of integration will be the difference between merely keeping up and leading the way forward.

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Lavi Sharma from Genpact outlines the value that a three-tiered, AI-first approach to finance in manufacturing can provide. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Thanks to cutting-edge advanced technologies, including agentic AI, the manufacturing landscape is changing fast. From autonomous warehouses that run like clockwork to touchless production plants and intelligent AI agents empowering defect detection systems, AI isn’t just improving processes; it’s accelerating time to value, scaling operations, and setting the bar high for companies to redefine customer experience.

As manufacturers embrace the principles of Industry 4.0 and the Internet of Things (IoT), a new revolution is quietly gaining momentum—Industry 5.0. This emerging vision goes beyond automation, advocating a harmonious collaboration between humans and advanced technologies to create a sustainable, resilient business model. These innovations are setting new standards in precision, scalability, and efficiency and reshaping the future of manufacturing as we know it.

In my conversations with clients, I often discover that even though CFOs play a key role in shaping business strategy, financial planning, and accounting operations, the manufacturing industry hasn’t prioritized finance and accounting (F&A) transformation. Why? Most manufacturing companies have grown by acquisition, inheriting diverse technology landscapes that often use fragmented legacy systems that require significant manual efforts.

The solution? Shift from a lean manufacturing mindset to an AI-first approach.

An AI-first approach requires a systematic ecosystem shift, not a set of incremental changes. Just as the invention of electricity completely changed society, industries, and lives, so will AI-first transform how we operate.

So, how can you reimagine every end-to-end process within manufacturers’ F&A functions? A three-tiered adoption approach is necessary—empowering the workforce, augmenting operations, and using AI to power finance solutions.

1) Build an AI-empowered finance workforce

At a granular level, AI can act as the fuel that supercharges finance teams to gain insights through knowledge management systems and be an intelligent assistant to accountants. For example, AI has the potential to enable transaction matching for voluminous inventory reconciliation with greater speed and precision than humans.

You can use gen AI-powered virtual assistants to provide next best action recommendations to team members, enabling synchronized actions across finance, supply chain, and sales teams—all in real-time. It’s like having a subject matter expert available 24/7 to help resolve contextual queries, improve data consistency, and quality.

2) Enable AI-augmented finance operations

Focusing on driving last-mile automation offers organizations better insights, improves visibility, and increases processing speed. Agentic action-enabled tools that run on AI models can perform tasks independently, take actions, and solve complex problems, thereby enabling autonomous processing.

For example, when leveraged within an accounts payable (AP) finance function, you can use agentic AI to analyze the sentiment and tone of a vendor’s emails and chat conversations. It can help extract data from enterprise resource planning and AP platforms, map it to the vendor’s query, and draft an appropriate response.

Generating proactive email responses with adjustable levels of courtesy can minimize delays, resolve conflicts, and close pending payments. This way, finance professionals can avoid frequent credit hold situations and reduce turnaround time.

3) Design AI-powered finance solutions

Specialized finance AI solutions are game changers. Whether they use AI-driven journal entry automation, transaction matching, anomaly detection, or third-party risk management solutions, the technology can streamline F&A operations.

I’ve seen how well customized AI solutions work in this area. Some of the world’s largest companies have invested in developing finance data fabrics to generate real-time and on-demand predictive insights that support decision-making with recommended next-best actions—all through a single source of truth.

Armed with a suite of analytics solutions to streamline auditing and reporting processes and improve user experiences with personalized insights to drive actions, users report a reduction in manual efforts by more than 50 percent and up to 40 percent improvement in their insight-to-action journey.

Build a robust finance organization that runs on AI

Implementing AI requires a clear strategy, organizational flexibility, a data-driven environment, and team collaboration.

Reality check: your AI models are as good as your data. One way to get your AI models to churn out near-accurate results is by integrating data intelligence platforms. These solve many challenges by breaking the barriers of siloed data across supply chain, financial planning, tax, and treasury, and providing one central platform with necessary data management and governance.

This way, you can empower teams to quickly understand and reconcile trends, patterns, and behaviors and trust AI-driven insights to make confident decisions.

As front-end functions in manufacturing have embraced AI, it’s time for accounting and finance to follow a similar path. As AI becomes more sophisticated, it’s poised to transform the financial world and the role of finance teams to make way for autonomous and predictive operations.

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Brian Martensen, Product Manager at Plex, by Rockwell Automation, explains why quality control and management are essential to resilient manufacturing processes. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

As consumer expectations and demand continue to rise, achieving quality in product development isn’t just a goal for manufacturers to achieve—it’s the foundation of the business. Recalls and costly delays plague businesses that fail to go the distance and many high-profile moments in recent years have proven to be case studies on what not to do. The costs of not achieving quality go beyond delays.

When quality standards slip, the financial, reputational, and operational costs are enormous. On the other hand, high-quality standards drive customer satisfaction, protect brand reputation, and ensure long-term profitability. Yet achieving these standards isn’t simple, especially in an environment defined by supply chain complexities, evolving customer demands, and the rapid pace of digital transformation.

The challenge for manufacturers is clear: How do you embed quality into the DNA of your organization while navigating these complexities? The answer lies in the intersection of leadership and technology. Quality must start from the top of leadership, setting an example by leveraging solutions that empower teams, streamline processes, and transform quality management into a strategic advantage.

For the second year in a row, a recent survey of manufacturers found that “improved quality” was the top outcome manufacturers and leaders hope to achieve from existing smart manufacturing technology. This consistent priority signals a broader industry trend: quality must be at the core of the business strategy to drive operational success.

From Compliance to Culture: Technology’s Role in Quality

Traditionally, manufacturers have often viewed quality management through the lens of compliance to meet regulatory standards and avoid penalties. However, the modern manufacturer knows that true excellence requires going beyond compliance to build a culture of quality. This culture is not just about checking boxes. It’s about continuous improvement and a shared commitment to delivering exceptional products and services. Smart manufacturing technologies, like digital quality management systems (QMS), are key to enabling this shift. These tools provide real-time data, actionable insights, and the ability to address potential quality issues before they escalate. However, technology must be combined with leadership and strategic implementation to help establish an environment that prioritizes quality at all levels.

Quality isn’t just the responsibility of one team—it requires cross-functional collaboration. But it also must start from the top. Leaders play a key role in modeling this culture of quality by demonstrating the importance of excellence in every decision and action. They must also set measurable goals, foster open communication, and build trust. In addition to fostering a collaborative, quality-focused mindset among teams, leaders can also champion smart manufacturing solutions that ensure collaborative teams can achieve quality goals by tracking their progress toward these goals in real-time.

This technology can also break down organizational silos by centralizing data and fostering seamless communication. For example, a food manufacturer leveraging a QMS system can monitor production quality in real-time. Automating defect detection and leveraging predictive analytics helps the company reduce waste, minimize recalls, and maintain compliance with industry standards. This real-time visibility helps them improve their efficiency, lower their costs, and build trust with customers by consistently creating quality products.

Building a Quality-Driven Culture

Creating a culture of quality begins with leadership but is sustained through the strategic use of technology. Here are four key strategies manufacturers can adopt:

Define Clear Quality Objectives

Leadership must set measurable quality goals that align with broader organizational objectives. For example, instead of simply aiming to “reduce defects,” a manufacturer could set a specific target to reduce defect rates by 20 percent within a year.

Invest in Training and Empowerment

Technology is only as effective as the people using it. Regular training and access to digital tools aren’t just perks—they’re essential. Instruction and practice are key to ensuring employees are equipped with smart manufacturing tools and understand how their roles impact quality outcomes. Leaders should invest in intuitive platforms and provide employees with the resources they need to make data-driven decisions.

Foster Collaboration Across Departments

Effective quality management is not limited to quality control or production teams. A culture of quality thrives with cross-functional collaboration. Tools like collaborative ERP systems foster communication between departments, breaking down barriers and enabling a more holistic approach to quality management. For example, when production and procurement teams share real-time data on material quality, they can quickly address issues before they escalate.

Empower Continuous Improvement

Continuous improvement is at the heart of a thriving quality culture. Leaders must create systems that empower employees to share ideas, identify inefficiencies, and propose solutions—all with the support of real-time data and actionable insights. By leveraging modern IT tools to establish transparent feedback loops, organizations can proactively evaluate and refine processes while encouraging employee engagement.

The Future of Manufacturing: A Technology-Driven Culture of Quality

As manufacturing continues to evolve and grow in complexity, organizations that embrace technology to build a culture of quality will lead the industry. These companies will not only meet customer expectations but exceed them, setting new standards for excellence. The result is a resilient, future-ready organization prepared to thrive in a shifting industry. In the end, a culture of quality isn’t just about avoiding mistakes. It’s about delivering value, fostering trust, and achieving long-term success. Businesses that combine leadership with the right technology solutions can transform quality management into a strategic advantage.

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Steve Bassaw, a Product Manager at SYSPRO Americas, shares his expertise on the potential supply chain disruption that could arrive in 2025. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Just as global supply chains have finally recovered from the unprecedented disruption caused by the COVID-19 pandemic, new threats are emerging that could destabilize the delicate balance once again. Three major forces—potential trade wars, climate change, and geopolitical conflicts—are converging to create what experts warn could be a perfect storm for supply chain managers in 2025.

The most immediate concern stems from the possibility of increased tariffs. All tariffs will have a significant impact, but the ones that would have the greatest effect would be on imported goods from Canada and Mexico. These are America’s largest trading partners and North American supply chains have become deeply integrated since the US-Canada-Mexico Agreement (USCMA) eliminated most trade restrictions in 2020. Many products cross borders multiple times during manufacturing, compounding the tariff effect at each crossing. This could lead manufacturers to completely reassess their operations, potentially relocating facilities and finding new suppliers within their target markets.

Climate change presents another significant challenge. Recent events like Hurricane Helene have demonstrated how extreme weather can severely impact critical supply chain nodes. When the hurricane hit North Carolina, it temporarily shut down mining operations in Spruce Pine, disrupting the global supply of high-purity quartz essential for chip manufacturing.

Wildfires are increasingly affecting regions previously unaccustomed to them, such as northeastern North America, causing transportation disruptions and air quality issues that impact logistics operations. Rising temperatures are also shifting agricultural zones, with significant implications for food manufacturing supply chains.

Adding to these concerns is the volatile geopolitical landscape. Ongoing conflicts and tensions in multiple regions threaten to disrupt crucial shipping routes and access to raw materials. The potential realignment of longstanding international alliances could further complicate global trade relationships, requiring supply chain managers to develop new contingency plans and alternative routing strategies.

The complexity of managing these multiple threats simultaneously pushes supply chain operations beyond what can be effectively managed through traditional methods. As a result, artificial intelligence and advanced analytics are quickly transitioning from optional tools to essential capabilities for supply chain management. AI enables supply chain managers to handle multi-sourcing strategies effectively, run sophisticated what-if scenarios, and make accurate predictions about supplier performance. These capabilities will be crucial for maintaining operational resilience in an increasingly unpredictable environment.

Organizations that recognize and adapt to these challenges by investing in robust technology solutions and developing flexible supply chain strategies will likely gain significant competitive advantages. Those who don’t may struggle to maintain operations as disruptions become more frequent and severe.

The key to survival in this new environment will be building supply chains that quickly adapt to changing circumstances. This means leadership must:

• Develop multiple sourcing options for critical materials and components.
• Create flexible manufacturing and distribution networks that can quickly pivot when disruptions occur.
• Implement advanced technology solutions that can predict and respond to potential disruptions before they cause a significant impact.
• Maintain strong relationships with suppliers across different regions to ensure backup options are available.

As we move toward 2025, it’s clear that the supply chain landscape will become increasingly complex. Success will depend on organizations’ ability to anticipate and respond to disruptions quickly and effectively. Those who invest in the right technologies and strategies now will be best positioned to navigate the challenges ahead, while those who maintain a business-as-usual approach may find themselves at a significant disadvantage in this new era of constant supply chain disruption.

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Henry Ames, the General Manager of Logistics Orchestration at TraceLink, outlines six reasons companies should integrate trading partners across their end-to-end digital supply chain networks. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Supply chain disruptions are inevitable. In fact, according to BCI’s Supply Chain Resiliency Report, nearly 80 percent of organizations experienced supply chain disruptions in the past year, with most encountering between one and ten such events. The leading causes of these disruptions varied from third-party failures to cyber-attacks to adverse weather or natural disasters. Still, the consequences included loss of productivity (affecting almost 80 percent of organizations), service disruption (75.4 percent), and customer complaints (70.1 percent).

These disruptions–and the impact and risk to business–are driving factors for the digitalization and integration of trading partners across end-to-end (E2E) digital supply chain networks, often referred to as multi-enterprise networks and platforms. But what challenges do organizations face when trying to digitize their supply chain? And why should they integrate all trading partners across this type of network?

The Current State of Supply Chains and Their Challenges

Supply chains are more complex than ever, with suppliers, manufacturers, distributors, logistics providers, retailers, and more spread across the globe. As a result, it’s never been more important for partners to be seamlessly connected to drive interoperability, visibility, collaboration, agility, and compliance.

However, traditionally, organizations have relied on a combination of manual processes, legacy systems, and siloed data management for supply chain operations. As a result, organizations face inefficiencies, lack of visibility, limited agility, high risk of disruption, inconsistent data, compliance violations, and much more–which was on full display across the healthcare and pharma supply chain during the COVID-19 pandemic.

To address these challenges, companies embracing E2E digital supply chain networks will allow for seamless, cost-effective, and timely data exchange and partner collaboration. Using these networks–and the platforms that power them–breaks down silos and enables better orchestration of processes and decision-making in real-time. It also increases resilience by exposing vulnerabilities such as delays and inefficiencies, allowing supply chain partners to react quickly or anticipate changing market conditions and disruptions.

The Reasons to Integrate Partners on a Single End-to-End Digital Supply Chain Network

1) Increased outsourcing has led to massive complexity, resulting in a need for better supply chain visibility and collaboration.

Organizations are increasingly outsourcing manufacturing, logistics, and other functions, supporting a more dedicated focus on specialized core capabilities while also providing the opportunity to reduce costs. However, outsourcing these processes makes collaborating in real-time with acceptable levels of upstream and downstream visibility challenging.

As a result, companies are taking steps to reduce supply chain risk through supply chain digitalization, which includes joining an E2E digital supply chain network. IDC’s 2024 Worldwide Supply Chain Survey found that in the life sciences industry, 34 percent seek to improve supply chain visibility, 32 percent seek to improve supply chain agility, and 33 percent focus on end-to-end supply chain orchestration to improve visibility and reduce risk.

2) Electronic Data Interchange (EDI) and other Point-to-Point (P2P) connections are costly, difficult to manage, and lack scale.

Organizations want to integrate and collaborate with ALL trading partners in real-time to improve agility and mitigate disruptions. However, traditional methods, such as EDI and other P2P integrations, have been expensive and often don’t meet agility goals. As a result, most companies have only been able to integrate with a handful of their “top” partners, leaving a significant portion of an organization’s supply chain operating in a less efficient and highly manual process.

Conversely, E2E digital supply chain networks provide a fast and straightforward way to integrate with ALL suppliers and customers at a fraction of the cost of EDI or other point-to-point connections (and some let suppliers and customers use any transaction format or back-end systems and onboard additional partners at no cost).

3) These networks are proven to work, are readily available as a SaaS solution, and deliver business benefits today.

According to IDC’s aforementioned survey, of those using an E2E digital supply chain network, 49 percent of respondents cited enhanced visibility into supply, 39 percent cited better supplier collaboration, 35 percent cited greater supply chain agility, and 31 percent cited improved regulatory compliance. The outcomes are broad, from reducing out-of-stock, optimizing inventory levels, and lowering operational costs to improving service levels and enabling faster product launches, supporting a new market entry, and creating new commerce channels.

4) CMOs, suppliers, and other partners will benefit greatly from being on one collective network.

E2E digital supply chain networks can ensure more precise production planning and better capacity utilization through real-time exchange of forecasts and POs. A supply chain network can also improve responsiveness to customer requests for PO changes, ensuring better on-time, in-full (OTIF) deliveries with higher operational efficiency. Furthermore, real-time information exchange can improve relationships with customers and suppliers (especially if there are no onboarding or integration costs). An additional meaningful benefit includes reducing IT costs related to maintaining outdated modes of information exchange. This ongoing benefit should not be overlooked.

5) From order-to-cash to OTIF, it improves important key performance indicators.

These networks allow organizations to digitalize all supply chain processes to improve KPIs such as revenue, costs, OTIF delivery performance, order cycle times, and much more. For example, contract manufacturers can collaborate on forecasts and purchase orders to improve OTIF. Distributors and wholesalers can digitalize order-to-cash to shorten cycle time. Direct suppliers can achieve more predictable procurement lead times to reduce safety stock inventory levels.

6) It allows for the next step in supply chain innovation – orchestration intelligence

By using advanced technologies, such as AI, machine learning, and real-time data analytics, organizations can automate workflows such as order processing, inventory management, and logistics planning, reducing manual intervention and improving accuracy. They can enhance real-time proactive decision-making and respond to disruptions quickly, such as rerouting shipments or adjusting product schedules. It improves end-to-end visibility so all stakeholders see the current order status, inventory levels, and potential bottlenecks. It can analyze historical and real-time data to predict potential (unforeseen) issues, such as demand fluctuations or supplier delays.

Supply chain disruptions significantly impact organizational cost, customer satisfaction, compliance, and more. The American Journal of Transportation noted in the first half of 2024 that these disruptions increased 30 percent over the first half of 2023, and most research groups predict a continued increase due to issues around labor, geopolitics, cyber-attacks, climate change, and more. Organizations need better solutions to help overcome these challenges and manage the risks and inefficiencies in today’s complex supply chains. End-to-end digital supply chain networks offer that lifeline and are essential to optimizing supply chain operations in today’s complex world.

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Josh Roth, the General Manager U.S. for Pipefy, explains why workflow automation for manufacturing doesn’t have to be “all or nothing.” This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

We all know people with an “all or nothing,” “now or never,” or “sink or swim” approach to life. While it may be impressive in a football game or the climax of a movie, the truth is an “all or nothing” mindset glorifies extremes with no middle ground. In business, implementing new technologies—even when transformative to your operation—can seem like an “all or nothing” proposition. The undertaking may appear overwhelming, but it doesn’t have to be if taken step by step. For manufacturing, workflow automation can immediately produce benefits, even based on automating small processes and building from there.

Overall, manufacturing businesses have done an excellent job modernizing factory floor operations. Unfortunately, beyond the floor, many front—and back-office workflows are still painfully slow, disjointed, and prone to countless errors. A company’s lack of workflow automation can negatively impact even the most impressive factory floor innovations.

First, let’s break down the problem many manufacturers face: their workflow process functions in some fashion—usually through a messy series of emails, spreadsheets, side discussions, and managers’ memories. Although imperfect, the operation is so entrenched that management fears breaking down the current system. But sometimes, you need to step away from the day-to-day operation and take an honest look at it.

The steps involved in a single manufacturing transaction may go from purchase requisition to manager approval, supplier outreach, budgeting, setting a delivery date, organizing production, ensuring quality control, and engineering. You can see how many vulnerable gaps exist if the workflow process is disjointed. A manager may forget to note a deadline, or an important email may be overlooked or sent to the wrong email. Many manufacturing businesses are sacrificing speed, efficiency, and improved quality control for the status quo, even with emerging technology solutions that can significantly transform operations.

To address this workflow disconnect between departments and functions, it is of the utmost importance to recognize that quality control is key. Little errors along the way can become big and disastrous in the end. Your business may have been lucky so far, but relying on old processes is riskier than biting the bullet and switching to automating workflow processes. Additionally, cost and time savings gained by automating workflows are huge bonuses to the operation and the bottom line.

For example, a major supplier in the energy segment recently adopted a workflow automation solution that reduced its turn-around time from 90 days to less than a week. They were able to scale faster while also significantly decreasing production errors. For this company, all areas—from the front office to the back office and the factory floor—were connected with workflow automation so that orders could move rapidly and accurately through measurement, purchase, and production.

The supplier was concerned with disrupting the business, so they started small by automating quality assurance first and then adding purchasing and maintenance requests. By automating quality assurance into a paperless process that used to be piles of paper, the manufacturing company can now easily trace the status and quality of all the parts it produces. By using a no-code AI-driven solution, the teams could quickly build workflows customized to their operation and/or tasks, and they could make changes easily as they gradually added more processes. By not relying solely on their IT to implement the solution, this approach was faster and easier to implement.

Many manufacturing companies find that the easiest tasks to automate first are the most repetitive, manual processes, such as purchasing and maintenance requests. After automating a few tasks, you can scale quickly and continue to chip away until the entire process is a fully integrated workflow. Truly, any manufacturer will find benefits to rethinking and closely assessing their workflow process so that they can achieve the highest levels of efficiency and product quality.

Don’t let “all or nothing” thinking prevent your manufacturing company from transforming to workflow automation. Take the first step, and you will soon be running full speed and never looking back.

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