Talent Requirements for the AI Era in the Manufacturing Industry

Talent Requirements for the AI Era in the Manufacturing Industry

Several large players in the manufacturing industry have embraced AI and other digital technologies and have started realising significant outcomes for their businesses, resulting in many of them committing to an AI-first strategic approach. It is also the industry where we find AI getting embedded in and integrated with various other technologies. Hence, there is a rapid change in the manner in which business processes are getting transformed. As a result, as the manufacturing industry readies itself for the future waves of AI, its leaders have recognised the need to reskill their workforce continually and also identify the roles that are becoming redundant or getting redefined.

Let us examine the changes in the roles as a result of the transition in the manufacturing industry in Tables 6.1 and 6.2.

Table 6.1 Evolving Roles in the Manufacturing Industry (India & Asia-Pacific Context)

2D-only design engineers Digital twins, simulation-led engineering, generative design Generative design & simulation engineer •Uses AI and physics-based models to optimise components/products •Skills in computer-aided manufacturing + AI tools and digital twin modelling Prototype engineers (physical-first) Virtual prototyping & additive manufacturing Additive manufacturing & rapid prototyping specialist •Operates 3D-printing ecosystems, topology optimisation, materials science & short-run prototyping Production & shop floor operations Assembly line operators (manual repetitive tasks) Industrial robotics, cobots, automation Human–robot collaboration technician •Works with cobots, configures safe human–machine workflows •Requires robotics basics, safety & calibration Manual machine setters/operators Computer numerical control auto-calibration, remote monitoring Smart factory technician (IIoT-enabled) •Monitors machine health dashboards •Configures IIoT devices •Performs data-led interventions & root-cause checks Quality & inspection Manual quality control inspectors (visual/manual checks) Vision systems, non-destructive testing automation, predictive quality AI-powered quality analytics lead •Uses computer vision, statistical models & anomaly detection for real-time quality assurance •Statistical process control and data skills Paper-based quality documentation roles Digital quality management systems, blockchain traceability Digital quality & traceability analyst •Manages digital quality records, blockchain trails & compliance dashboards across suppliers and plants Maintenance & asset management Reactive maintenance technicians Predictive maintenance, digital twins, sensorisation Predictive maintenance & reliability engineer •Uses vibration/thermal data, machine learning models & comprehensive enterprise facility management platform to forecast failures •Requires condition-monitoring & analytics skills Separate mechanical/electrical maintenance roles Convergence of mechatronics & automation Mechatronics & automation engineer •Integrates mechanical, electrical, PLC, robotics & automation tech for end-to-end equipment reliability Supply chain & logistics Manual planners & schedulers (spreadsheet-led) Real-time supply chain visibility, AI planning Connected supply chain planner (control-tower model) •Uses digital control towers with scenario planning •Skills in sales and operations planning, network optimisation & AI planning tools Storekeepers & manual inventory tracking RFID, IoT, automated warehouses Smart warehouse & intralogistics coordinator •Operates autonomous mobile robots, automated storage systems & sensor-based inventory •Skills in warehouse management system, robotics & safety ESG, energy & sustainability Generic environmental, health & safety/corporate social responsibility officers Carbon mandates, renewable integration, circular production Decarbonisation & circular manufacturing lead •Designs Scope 1–3 reduction plans, circular material loops, green certifications & supplier ESG programmes Manual waste handling staff Zero-waste factories, resource recovery Industrial waste-to-value engineer •Converts process waste into energy or raw materials •Skills in industrial waste chemistry, ESG & partner ecosystems Workforce, safety & skill development Safety officers (compliance-only) Connected workers, wearables, AR/VR safety training Connected worker experience & safety technologist •Deploys wearables, AR/VR safety simulations & digital standard operating procedures •Skills in health, safety and environment + digital adoption Classroom-only trainers AR/VR, microlearning, skill passports Immersive learning & skills mobility designer •Builds VR/AR modules, competency-based credentials & internal talent mobility pathways IT/operational technology (OT), cyber & data Traditional IT support roles IT–OT convergence, cybersecurity for factories OT cybersecurity & industrial resilience architect •Secures Supervisory Control and Data Acquisition system (SCADA), PLC, IIoT networks •Incident response & cyber-physical security expertise Manual data entry/reporting roles Smart manufacturing execution system, AI copilots & automation Manufacturing data product analyst •Builds digital workflows for shop floor data, business intelligence dashboards & AI use cases •SQL, analytics & domain expertise Procurement & vendor management Manual vendor selection & audits Smart contracts, supplier risk analytics Supplier risk & digital procurement analyst •Uses AI for vendor scoring, ESG compliance & smart contracting •Strong analytics + negotiation knowledge After-sales, service & product life cycle Field service technicians (reactive) IoT-enabled remote service, servitisation Product service & remote diagnostics engineer •Uses IoT data for remote troubleshooting & predictive service •Skills in customer-centric service analytics Smart manufacturing & Industry 4.0 transformation Lean officers (process only, no tech) Industry 4.0, smart plant networks, AI-first operations Industry 4.0 transformation manager •Leads road map for automation, AI, IIoT, digital twin & workforce upskilling •Strong change management + tech fluency Innovation & new business models Traditional product managers (hardware-only mindset) ‘Product + Service + Data’ business models Product-as-a-service business architect •Designs subscription/usage-based models for industrial products, integrating service, data & digital ecosystems

Source: Curated with inputs from ChatGPT

Table 6.2 Understanding the New Roles in the Manufacturing Industry (India and Asia-Pacific, 2025–2030)

Net New Role Why This Role Is Emerging (Key Drivers) Manufacturing Segments Where This Appears Key Skills Needed

Digital twin systems architect Digital twins for plants, lines, machines & supply chains;

simulation-led decisions

Automotive, electronics, pharma, heavy engineering, FMCG

•Simulation tools •IIoT •Data modelling •Physics-based AI

AI-first production orchestration manager

AI-driven dynamic scheduling & production flow optimisation

Discrete & process manufacturing

•AI/machine learning for ops, manufacturing execution system + enterprise resource planning + IIoT integration, scenario planning

Robotics & cobotics fleet supervisor

Fleet of collaborative robots & autonomous systems on shop floor

Automotive, electronics, FMCG, food processing

•Robot ops, safety, programming, uptime & vendor management

Industrial metaverse experience designer

Virtual factories for training, design, maintenance & remote collaboration

High-tech, aerospace, automotive, tier-1 suppliers

•XR design •3D content •Human–machine interaction

Green hydrogen & clean energy systems lead

Transition to net-zero energy, hydrogen-based industrial fuels

Steel, cement, chemicals, heavy industries

•Hydrogen tech •Energy systems •Carbon accounting

Carbon & circular materials scientist

Low-carbon materials and circular product design

Packaging, textiles, automotive, electronics

•Material science •Life cycle assessment •Recycling tech •Circular design

Smart materials & nano-manufacturing engineer

Smart/self-healing materials, nano-precision manufacturing

Semiconductors, electronics, medical devices

•Nanotech •Cleanroom •Micro-fabrication

Industrial data marketplace product owner

Monetisation of machine & process data for ecosystem partners

Industrial original equipment manufacturers, tier-1 suppliers

•Data economics •API ecosystems •Platform strategy

Cyber-physical threat intelligence analyst

Security for converged OT+IT; attacks on PLCs/SCADA & IIoT

All asset-intensive sectors

•OT cybersecurity •Threat intel •Red teaming

Ethical AI & autonomous systems auditor

Governance of AI-led production, robotic decisions, safety & compliance

Smart factories & multinational plants

•AI governance •Safety standards •Compliance frameworks

Human-digital workplace experience designer

Designing worker-centric digital shop floor interactions

All modern factories, especially with ageing workforce

•UX for frontline tech •Change management •Ergonomics •Inclusion

Micro-factory & localised production network manager

Distributed, small-footprint, hyperlocal manufacturing networks

Apparel, food, electronics, 3D printing–enabled sectors

•Network design •Micro-plant ops •Community supply chains

Servitisation & outcome-based manufacturing lead

Shift to ‘product + service + performance guarantees’

Industrial original equipment manufacturers; machinery; heating, ventilation and air conditioning; elevators

•Contracting •Key performance indicators •Data-driven service-level agreements

Retrofit & brownfield digitalisation specialist

Need to digitise legacy plants instead of greenfield

Engineering, procurement & construction firms, MSME clusters

•OT/IIoT retrofitting •Vendor management •ROI modelling

AI-powered product teardown & re-design engineer

Circularity + cost pressure → redesign for reuse/refurbish

Electronics, automotive, consumer goods

•Value engineering •Teardown analysis •AI design tools

Quantum manufacturing R&D analyst (early-stage)

Future of quantum modelling for materials & optimisation

Advanced materials, pharma, high-tech

•Quantum basics •Simulation •Applied research

Sustainability-linked finance & compliance coordinator

Financing tied to ESG metrics & sustainable performance

Large manufacturers & green-fund recipients

•ESG reporting •Finance •Incentives & audit alignment

Industrial biomanufacturing process developer

Growth of bio-based manufacturing & synthetic biology

Textiles, packaging, chemical substitutes

•Bioprocess design •Fermentation •Scale-up

Workforce digital ethics & well-being officer

Automation, AI, augmentation → new well-being/ethics needs

Multi-factory enterprises

•Employee well-being •Tech-ethics •Policy •Inclusion

Industrial talent cloud & skills exchange manager

Talent sharing across factories, gig technicians & skill marketplaces

MSMEs, clusters, multi-plant enterprises

•Skill taxonomies •Gig workforce ops •Credentialing

Source: Curated with inputs from ChatGPT

Leadership Imperatives for the Manufacturing Industry in the AI Era

Leaders are aware that the changes in the way businesses would function and new jobs would be performed would impact the industry as a whole. As a result of changing business processes and the new roles being created, their workers have to be trained to embrace the change from being blue-collar workers to adapting to an environment where they would be guided by AI for diagnostics and maintenance on the shop floor. Thus, their focus would shift from supervising humans to enabling humans to accept cobots in the work environment and facilitating collaboration between humans and machines.

Disruptions caused by Covid-19 and the reality of wars in various parts of the world—Russia versus Ukraine, Israel versus Palestine and the turbulences in the Middle East—have been wake-up calls to the leaders of the manufacturing businesses to streamline their supply chains with better visibility across the value chain and robustness to avoid material shortages. This has resulted in the urgency of leaders to adopt AI-enabled predictive global supply chain systems that meet ethical and compliance standards.

Autonomous manufacturing lines are replacing manual or machine-supported assembly lines. Hence, it is critical for leaders to have a thorough understanding of cyber-physical systems. While decision-making could be centred on experiences, they would be driven by data and intelligence coming through multiple sources. Hence, an in-depth understanding of how to nurture the development of decision frameworks by integrating sensors, robotics, digital twins, safety, cybersecurity, diagnostics and predictive maintenance would be essential. All businesses would have access to a whole host of technology and AI tools. However, the decisions that leaders make about investments, their risk appetite on the future trajectories of how technology is likely to define their growth pathways and their choice of manufacturing technologies, products and locations, to name a few, would determine their current competitiveness and their survival in the future.

The questions of ethics, governance and data ownership are tough questions that do not have easy answers, and leaders of the manufacturing industry, like all other industry leaders, need to work in unison with hi-tech industry leaders, innovators and governments to find the pathways that would work for all. Table 6.3 provides a detailed description of leadership focus and priorities for the AI era.

Table 6.3 Leadership Imperatives for the Manufacturing Industry: Traditional vs AI Era

Source: Curated with inputs from ChatGPT

In this context, the life sciences industry is yet another industry that is grappling with a similar set of questions and challenges in the context of AI. Several functions such as design, production and supply chain have similar characteristics and performances, although there are other dimensions that are unique to the life sciences industry driven by its own ecosystem. It, therefore, felt prudent to examine some of the dimensions of this industry that would play dominant roles in defining the future of AI’s own trajectories while they are in the throes of immersion into AI.