Human-Machine Collaboration

Case studies

Real-world examples of how human-machine collaboration is evolving across manufacturing and supply chains.

Three themes in focus

Explore case studies developed with leading organizations that highlight how companies and ecosystems are redesigning work, building industrial skills and strengthening workforce coordination across public and private sector. 

Workflow redesign



Discover how organizations redesign work so that technology deployment and workforce development evolve together. 

Skilling and training strategies


Learn more about how organizations develop, reskill and redeploy their workforce as intelligent systems are introduced on the shop floor.

Ecosystem-level workforce coordination


Find out how governments, educational institutions and industry work together beyond the enterprise to build workforce capability at regional or national scale. 

Context: Ohio is one of the nation’s manufacturing leaders, employing more than 680,000 workers and generating nearly one fifth of the state’s gross domestic product. In recent years, Ohio has also experienced an unprecedented surge in domestic manufacturing investment across sectors such as semiconductors, automotive, aerospace and defense, energy and bioscience. These investments are projected to drive more than 35,000 additional manufacturing job openings by 2037, with the majority concentrated in technician and operator roles.These conditions created a clear business problem, a gap between talent demand and supply. The Ohio Manufacturing Talent Partnership was formed to address this challenge by uniting industry, state agencies, education providers, workforce organizations and economic development partners around a shared, scalable workforce strategy. Partnership Approach and Model Design: The Ohio Manufacturing Talent Partnership was established as the implementation vision for Ohio’s Manufacturing Workforce Blueprint. Rather than creating new systems from scratch, the partnership was intentionally designed to align and accelerate existing assets and initiatives across the state. At its core, the partnership is industry-led, statewide in scope, with regional execution. The Ohio Manufacturers’ Association serves as the primary convener, ensuring that manufacturers drive priorities and define workforce needs. Seven regional hubs, each led by an OMA-endorsed Industry Sector Partnership, coordinate local implementation and bring together manufacturers, community colleges, career centers, workforce development boards, community-based organizations and economic development partners. Statewide strategic alignment with JobsOhio, Ohio’s economic corporation and its regional network partners, drives additional expertise, market insights and industry connections. Education partners convened by Ohio TechNet bring the best of Ohio’s manufacturing and engineering education to the table, driving industry-curriculum alignment across the state. State partners like the Ohio Department of Job and Family Services, the Ohio Department of Education and Workforce and the Ohio Department of Higher Education bring system coordination and interconnection around industry and workers’ needs. The partnership is guided by five strategic pillars drawn from the Blueprint:Connecting workforce systems and priorities to improve coordination across agencies, regions and programmes.Growing career awareness and interest in manufacturing through aligned messaging and statewide campaigns.Broadening the manufacturing talent pool by expanding access for untapped populations and reducing barriers to entry and completion.Aligning and scaling education and training by standardizing competencies, increasing capacity and improving credit transfer.Expanding innovative earn and learn models that provide paid, work-based pathways into manufacturing careers. Several foundational assets support this approach. The Ohio Manufacturing Competency Model provides a shared, open-source definition of the skills required for high demand operator and technician roles. Ohio’s long-standing Industry Sector Partnership network enables sustained employer engagement at the regional level. Programmes and partners such as WorkAdvance, TechCred, the Ohio Association of Community Colleges and Apprentice Ohio, provide funding, implementation support and training infrastructure that can be aligned rather than duplicated. Results and Early Outcomes: Since its formation and, as of April 1 2026, the Ohio Manufacturing Talent Partnership has demonstrated tangible progress toward statewide workforce alignment. More than 70 organizations across industry, workforce, education, economic development and state government are actively engaged. Training programs supported or advised through partnership-aligned efforts have served more than 1,575 participants. Over $16 million in federal workforce funding and more than $8 million in state training investments have been secured and directed toward manufacturing-aligned initiatives. Beyond quantitative outcomes, the partnership has produced structural results that position Ohio for long-term success. The Manufacturing Workforce Activation Team and statewide working groups provide ongoing governance and coordination. Regional hubs now operate with common frameworks, shared language, and clearer roles. Educators and employers increasingly align curricula, equipment investments and credentials to the competency model, accelerating talent readiness. Design Principles for Replication: The Ohio Manufacturing Talent Partnership offers several design principles that translate to other states and regions seeking to build durable public private workforce partnerships:First, industry leadership is essential. Employers must define skill needs and remain continuously engaged, not consulted episodically. Second, leverage existing infrastructure before creating new programmes. Ohio built scale by aligning institutions, partnerships, and funding streams that already existed. Third, balance statewide strategy with regional execution. Local intermediaries are critical for trust, customization and sustained participation. Fourth, adopt a shared competency framework and talent demand definition to anchor alignment across education, workforce and industry. Finally, invest in governance and activation capacity. A partnership requires dedicated structures to manage coordination, measure progress and sustain momentum over time.Together, these principles demonstrate how the Ohio Manufacturing Talent Partnership functions not as a one-time initiative, but as a replicable, industry led public private model for solving workforce challenges at scale.
Ohio Manufacturing Talent Partnership: A Replicable Model for Public Private Workforce Collaboration
Context: Foxconn Industrial Internet is deploying humanoid robots and AI agents in production workflows as part of a broader human-machine collaboration strategy. Implementation is rooted in frontline technical expertise, with operator teams identifying tasks that robots and agents can perform, running those tasks across production cycles and defining the handoffs required to keep throughput stable.Approach: Foxconn structures every workflow around two explicit paths. The standard path covers repeatable cases suited to augmentation and automation. The exception path routes edge cases to human operators, then returns work to the standard path once resolved. When a robot or agent signals failure or uncertainty, an operator completes the task, recalibrates the technology, or reroutes the work so the line does not stall. Teleoperation, delivered through devices such as the Apple Vision Pro, enables workers to teach and correct robots efficiently.Foxconn uses the number of robots one operator can supervise at current reliability levels as a workforce planning metric, connecting technology investment directly to labour productivity. The organization has identified two foundational skill categories for workers operating in this model: prompt engineering and data feedback. Data feedback is the ability to identify the edge cases and unusual failure modes that actually improve model performance over time. Operators have shifted from executing work toward supervising machines, intervening on exceptions and contributing to performance improvement through training and calibration. Supervisors manage safety boundaries, confirm technological and human readiness at the start of each shift, reduce the frequency of recurring exception types and improve the quality of handoffs between the standard and exception paths.Results: Each operator supervises three humanoid robots, each completing assigned operations at an 80% success rate, with operators intervening through teleoperation for the remaining 20%. One person managing three robots at that reliability produces about two and a half times the output they would produce working alone, an output leverage ratio of approximately 1:2.5. Separately, Foxconn's AI agents, which handle planning, scheduling and decision-support tasks, operate autonomously 90% of the time, with human takeover required for the remaining 10%. The current learning curve for a robot to adapt to a new operational task is approximately two weeks; Foxconn’s target is to reduce that learning to three days. Approximately 150 operators have transitioned into robot trainer and supervisor roles as of April 2026. 
Foxconn: Robots-per-Operator as a Workforce Planning Discipline
OCP Group, a global leader in phosphate-based plant nutrition solutions, is undertaking one of the most ambitious industrial transformations in Africa. While digitalization and automation are central to this journey, the Group quickly learned that technology alone does not deliver sustainable change. What determines success is people – their readiness, ownership and ability to shape the transformation from within. As one OCP talent leader put it: “When you involve people in the project, they have ownership. When you have ownership, you are 90% sure you will succeed.” This insight led OCP to reimagine its operating model around human-machine collaboration – weaving automation, capability building and organizational learning into a single, unified strategy. OCP Group’s approach rests on three pillars:1. Cross-functional ownershipEach automation project is led not by a central digital team, but by a multidisciplinary group that includes control room operators, maintenance technicians, process engineers, production engineers and, for the first time, data scientists working directly with field teams from the outset. A pilot project focused on automating a phosphoric acid production line demonstrated the power of this model. It succeeded where previous top-down initiatives had failed and quickly generated interest from other teams eager to adopt the same approach.2. Reskilling designed with and for workersTraining is not developed in isolation. For each automation project, OCP Group convenes business leaders, technical experts and senior operators to define the new competencies required and translate them into targeted learning modules.For example, as control room operators transitioned from manual supervision to automated line management, their role evolved toward more advanced monitoring, analysis and optimization tasks. Building on their strong operational expertise, they further strengthened their understanding of process chemistry to better interpret system behaviours, diagnose anomalies and enhance performance, while progressively developing capabilities in using data and AI-enabled tools to support deeper analysis, anticipate complex deviations and improve decision-making.3. Communities of practice to sustain learningTo ensure continuous capability building, OCP Group has created communities of practice in automation, metrology, instrumentation and process engineering. These networks enable decentralized problem-solving, peer-to-peer learning and the rapid diffusion of best practices across sites.Innovative learning tools, including virtual reality for safety-critical training and simulation platforms for complex operational scenarios, reinforce this ecosystem.Results: Higher Engagement, Stronger Performance and a Scalable ModelThe Group is now scaling this participatory model across its major industrial programs. Staff involved in automation design report higher engagement, a clearer sense of how their roles are evolving and greater confidence in the transition.The Group’s internal framing is explicit: time unlocked through automation is reinvested in higher-value activities such as continuous improvement and process optimization. This reframes automation not as a threat but as a career accelerator.Operational results are already visible. On one automated acid production line, OCP Group recorded:7% reduction in raw water consumption;17% reduction in sea water consumption;0.5‑point increase in yield; andGreater process stability and product quality.These gains support the Group’s broader ambition to build a decarbonized, circular industrial model powered by innovation, talent and responsible stewardship of natural resources.Looking Ahead: Scaling Human-Machine Collaboration Across OCP GroupAs part of its Future of Work agenda, OCP Group aims to free up 20% of employees’ time to contribute to cross-functional, high-impact projects. Automation is a key enabler - but only when paired with the right human capabilities, organizational culture and learning systems.By embedding human-machine collaboration at the heart of its transformation, the Group is demonstrating how an African industrial champion can combine technological excellence, people-centered change and sustainable growth – turning its mission, Bringing Phosphorus to Life, into action.
OCP Group's Participatory Model for Automation
Context: New England's industrial base is concentrated in defense supply chains, aerospace and precision manufacturing, sectors experiencing rapid technological change and talent challenges; the supply of graduates from technical programs is increasingly insufficient and the skillsets of incumbent workers are lagging industry requirements. New England is characterized by smaller, specialized manufacturers with limited capacity to design or fund workforce programs. The region needed a coordinating mechanism that could connect government, education, and industry into a functioning system.   Approach: The Center for Advanced Manufacturing (CAM), a division of the Massachusetts Technology Collaborative (MassTech), focuses on fostering a connected manufacturing ecosystem and supporting the state’s industrial base. Through the Massachusetts Manufacturing Accelerate Program (MMAP), CAM issues capital equipment grants to small- and mid-sized manufacturers, conditioning that funding on engagement with technical assistance providers who establish a business case, assess workforce implications and identify training before acquisition. This addresses the common failure mode in which technology is purchased but not fully deployed. Partner nonprofits such as the Berkshire Innovation Center (BIC) and the Massachusetts Manufacturing Extension Partnership (MassMEP) provide expert guidance, providing companies with an outside perspective on how to modernize their operations and set their workforce up for success. Through the Massachusetts Manufacturing Innovation Initiative (M2I2), CAM also invests in collaborations between academic research institutions and startups, providing funding for projects that move products towards commercialization and equipment that community college and university students can be trained on. Finally, curriculum alignment is anchored to validated industry frameworks; CAM works with Manufacturing USA institutes and manufacturers statewide, to identify core competency standards that have become a shared reference point for colleges building new programs and updating existing programs.  Results: Community colleges across Massachusetts are incorporating AI, robotics, automation and model-based definition into curricula in response to employer demand signals. Apprenticeship programs historically underutilized in the region are expanding, with Massachusetts receiving dedicated funding to scale these pathways. Most significantly, pairing capital equipment grants with workforce development support has begun to shift the unit of intervention from choosing between technology investment and talent development to a system that incorporates talent upscaling and development to improve business strategic alignment. The scale of CAM’s investment portfolio reinforces these systemic gains. To date (April 2026),  Through M2I2, CAM has funded 100 projects, invested over $100 million in Massachusetts organizations and leveraged more than $370 million from other sources, supporting more than 100 new products, assisting over 400 entrepreneurs, training more than 1,000 students and workers and creating over 1,000 jobs. Through MMAP, CAM has made 128 awards totaling $22.3 million in capital equipment grants, attracted $55.7 million in committed matching funds and supported the creation of over 800 new advanced manufacturing jobs.
Massachusetts Center for Advanced Manufacturing: Building a Coordinated Workforce and Technology Ecosystem
Context: As manufacturers integrate robots, autonomous equipment and AI agents into production lines, the shape of the safety challenge is changing. Total recordable injuries are declining in advanced economies, but serious injuries and fatalities are not falling at the same rate and in some sectors are rising. Larger machinery, faster cycles and human-robot interaction zones mean that a single unsafe act, a bypassed light curtain, an entered restricted zone, an unsafe hand placement, can produce catastrophic outcomes that traditional safety architectures were not designed to prevent. Engineering controls such as light curtains, pressure mats and lockout-tagout remain necessary but cannot keep pace with environments where humans, autonomous systems and conventional machinery operate in close proximity. The gap requires a layer of real-time intelligence that can detect what engineering controls miss and a deployment model that earns the trust of frontline workers.Approach: AI computer vision, applied to existing CCTV infrastructure, provides continuous detection across more than fifty categories of unsafe acts and conditions: PPE non-compliance, restricted-area entries, ergonomic deviations and unsafe proximity to machinery and vehicles. Video is processed at the edge and personally identifiable information is stripped before any data enters the analytics layer, enabling anonymized outputs without sacrificing detection fidelity. When integrated with programmable logic controllers, the same capability can trigger sub-second emergency stops when safeguards are bypassed, collapsing the response time between detection and intervention, with automatic restart once the hazard clears.Aggregate signal, not individual events, is where the diagnostic value sits. Heat maps and temporal patterns surface which workflows, layouts and operating conditions generate unsafe acts repeatedly, shifting the unit of analysis from the worker to the system. When unsafe behaviour is observed, the relevant question is what conditions made that behaviour rational. In one documented case, repeated machine-bypass incidents traced back to equipment that could only be diagnosed while running, under a zero-downtime mandate. The process was structured to produce the unsafe act. That reframe, from behaviour to conditions, is what allows the data to drive operational redesign rather than individual correction.Trust is a precondition for this model to function. Anonymized reporting, with heat maps and aggregated counts placed in front of operators at every daily shift change and no worker images or identities surfaced, converts continuous observation from a surveillance mechanism into a shared diagnostic tool. Worker feedback loops directly into how detectors are configured, making the system's behaviour visibly responsive to frontline input. Across deployments, this combination produces an observable shift in how workers relate to the technology within four to six weeks of go-live. At one global flexible packaging manufacturer, plant leadership reported that operators came to describe the capability as "our additional eye that never sleeps" rather than an oversight mechanism, a shift that emerged within the first month of structured daily shift-change conversations.The capability is also reshaping adjacent roles. Two customer organizations have created EHS AI Specialist positions to manage the interface between continuous machine observation and human safety decision-making. Time previously spent on manual walkdowns is being redirected toward root-cause analysis, exception review and controls design, a reallocation of human attention toward higher-order judgement.Results: Across three independently documented deployments, continuous AI observation has produced measurable reductions in audited recordable injuries. At a North American manufacturer of engineered steel structures, Total Recordable Injury Rate in pilot departments at one of the company's largest facilities fell from 9.7 to 2.1 over three years, a 78% reduction and total recordable injuries across the site fell from 33 to 2. Improvement extended well beyond the original pilot departments, indicating a site-wide safety-culture lift rather than localized compliance. At a global flexible packaging manufacturer, a four-site pilot across three continents produced a 44% improvement in monthly Total Recordable Injuries, with monitored sites averaging a TRI of 1.56 versus 2.81 at comparable non-monitored sites. At a global industrial textile manufacturer, a rollout across 28 facilities delivered a 24% reduction in recordable injuries, more than 53,000 corrective actions completed in year one and 93% of workers reporting they feel safer at work.Pathway analytics have also surfaced operational inefficiencies invisible to conventional monitoring. At one discrete manufacturer, heat maps detected a sustained increase in late-shift walking distance between two workstations, with no incident, no near-miss and no complaint. Root-cause analysis traced it to a toolbox displaced during a prior layout change, forcing repeated unscheduled tool runs each shift. Standardizing placement across sites carrying the same pathway signature reclaimed thousands of labor hours annually. A separate manufacturer has extended the same capability to material flow and cycle-time analysis as a lean-operations tool, applying it to production efficiency independently of safety reporting.As automation density increases, the safety function shifts from auditing past failures to continuously engineering the conditions under which humans and machines share space. As of May 2026, this capability is active across 31 countries, 173 cities and 400+ facilities, processing more than 22 billion video frames per day. The monitored workforce exceeds 100,000, a conservative estimate reflecting headcount at actively deployed facilities, with the actual population meaningfully higher.
Intenseye Leading with Trust: AI-Powered Safety to Power Future Production Floors and Warehouses
Context: Western Digital (WD) builds the storage foundation that powers certainty in the AI‑driven data economy. WD’s commitment to scale, reliability and innovation shapes its internal operations, where the company manufactures high volumes of storage products each day across global sites. As WD began deploying AI agents across its manufacturing operations, the company recognized that workforce readiness was essential to realizing the technology's full potential. By investing early in targeted training programmes, WD empowered its engineers to confidently govern AI-enabled systems, accelerating adoption and amplifying impact across the organization. Approach: To ensure technology and workforce readiness advance together, WD combined two key elements: a dedicated organizational function to manage the transition and a focused pilot to develop and validate the approach before scaling.A Future of Work function, consisting of representatives from HR, IT and Operations, was established to connect AI-focused teams with operational teams. This group identifies how roles are evolving, defines new job streams and prepares engineers ahead of expanded AI capabilities and adoption. The Future of Work function oversees both building and deploying AI agents, ensuring they are designed effectively and operate safely over time. This effort began with a two-day workshop involving 25 cross-functional participants to align priorities and expectations. The programme is organized into six core workstreams: governance (data, AI agents and AI agent supervisors, including red-teaming and adversarial testing), AI agent creation, validation and testing, change management, skills mapping and training, and operational efficiency and productivity. Execution is coordinated across technology, operations and HR teams through weekly joint planning meetings, shared milestones (including minimum viable agent delivery, HR-led job and skills gap analysis, training curriculum and change initiatives) and regular reviews with executive sponsors. In parallel, a pilot programme focused on two manufacturing workflows to demonstrate proof of concept and value. Each workflow will be supported by an AI agent supervisor that empowers employees to take on higher‑value, more strategic responsibilities. WD is upskilling existing process engineers to supervise, train and set boundary conditions for AI agents within their domains — a model that leverages deep process expertise while enabling scalable AI governance. Engineers selected for these supervisor roles complete a competency-based certification covering AI and ML model outputs, data architecture and sources, model refinement, output auditing (including adversarial and unfairness testing and red teaming), stakeholder reporting and communications and incident documentation. As agents take on routine tasks, from defect detection to containment routing to disposition decisions, the engineer's role shifts from execution to oversight. Engineers can now focus on monitoring performance, optimizing the rules and boundaries the agents operate within and resolving complex or unexpected cases. Results: The combined approach of establishing a dedicated function and running a targeted pilot, successfully moved WD’s AI agent transformation to active execution within 3 months. As of May 2026, the two pilot workflows are on track to transition automated agent operations with a human AI agent supervisor role. With the new role structure, certification pathway and governance model are fully defined and operational. The Future of Work function is taking key learnings from the pilot programme into a scalable playbook for broader AI adoption and workflow transformation across the enterprise.
Governing AI Deployment through Workforce Readiness at Western Digital

Human-Machine Collaboration

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