Bridging the reality gap for manufacturing technology

Joanne Goh
Practice Leader, Manufacturing Technology

The manufacturing reality gap is a chain of interconnected gaps across supply, people, and technology that prevents AI and automation investments from scaling. The supply gap reflects fragile, opaque supply chains that cannot provide the real-time visibility automation requires. The people gap reflects a shortage of skilled talent capable of operating and maintaining AI-driven systems at scale. The technology gap reflects legacy infrastructure and disconnected systems that cannot support the integration AI and automation demand. Global manufacturing is facing increasing uncertainty as these three gaps compound each other.

Supply is becoming less predictable. Geopolitical tensions, trade restrictions, localization policies, and shifting demand are reshaping global supply chains. The challenge is no longer about securing capacity, but about having the right products, technologies, and suppliers in the right geography.

People remain a critical constraint. Manufacturers are adopting automation partly to address labor shortages, yet they simultaneously face a shortage of workers with the skills to deploy, integrate, and maintain increasingly complex systems. At the same time, experienced workers are retiring, creating a risk of losing valuable institutional knowledge.

Technology is advancing faster than many factories can adopt and deploy it. AI, robotics, edge computing, and connected-factory technologies are moving rapidly from experimentation toward deployment, but legacy systems, fragmented data, and integration challenges continue to slow scale-up.

Why the gaps across supply, people, and technology compound each other

Understanding your reality gap

Matching the right technology and capacity to the right market at the right time

The core challenge in manufacturing technology is matching the right technology and capacity to the right market at the right time, amid rising geopolitical and operational complexity. Global manufacturing is going through uncertainty and volatility. Geopolitical tensions, trade restrictions, and supply-chain regionalization are forcing manufacturers to rethink where they source, produce, and invest. Tariffs, export controls, and localization policies are increasing the complexity and cost of operating across multiple markets.

At the same time, manufacturers face a widening technology and skills gap. Automation, AI, robotics, and connected-factory technologies are advancing rapidly, but many manufacturing companies struggle to integrate new technologies with legacy systems and existing processes. The shortage of skilled engineers and technicians further limits the ability to deploy and scale these solutions.

The supply-demand gap is also becoming more complex. Rapid growth in areas such as AI infrastructure, semiconductors, electrification, and advanced automation is creating localized capacity constraints, while excess capacity can persist in other products or regions. The challenge is increasingly about matching the right technology and capacity to the right market at the right time.

Meanwhile, manufacturers are under growing pressure to improve productivity, energy efficiency, resilience, and sustainability simultaneously. These competing priorities are increasing the need for better data, smarter automation, and more flexible operating models.

These challenges are interconnected: supply determines availability, technology determines deployability, and skills determine scalability. Closing the gap therefore requires more than investing in new technology. Manufacturers need greater supply-chain visibility, targeted technology adoption and workforce strategies that enable people to work effectively alongside automation and AI.

The path forward is likely to be pragmatic rather than transformational overnight: prioritize high-value use cases, leverage existing assets, build flexible and regional supply ecosystems, and invest in skills alongside technology.

Industry-specific challenges & solutions

Making AI and connected-factory technologies work with existing systems

Closing the technology gap

The technology gap in manufacturing is no longer about access to AI, robotics, or connected-factory technologies; it is about making these technologies work with existing machines, systems, processes, and data.

Manufacturers should focus first on low-hanging fruits, meaning applications where value can be demonstrated quickly, such as predictive maintenance, energy monitoring, machine vision, quality inspection, and production optimization. Edge computing can provide a practical bridge, connecting legacy equipment and processing data closer to the machine without requiring wholesale replacement of PLC (Programmable Logic Controller), SCADA (Supervisory Control and Data Acquisition), or control architectures.

Data preparation is where 80% of AI projects in manufacturing fail, while data quality, integration, and governance remain major scaling challenges.

Omdia recommends the 6S framework for moving manufacturing AI projects from experimentation to scale:

Specify the right problem to solve.
Success - establish measurable outcomes.
Start small with focused, high-value use cases.
Senior support - secure leadership backing.
Shared responsibility across IT, OT, and operational teams.
Staff - equip people with the skills to sustain the deployment.

This provides a practical framework for moving from experimentation to scale. It emphasizes defining the right problem, establishing measurable outcomes, starting with focused use cases, securing leadership support, and bringing IT, OT, and operational teams together.

Making the industrial workforce AI-capable, not just AI-staffed

The skills gap is becoming a critical constraint on technology adoption in the manufacturing world. Only around 10% of manufacturers are implementing AI at scale, according to Omdia estimates, highlighting the gap between experimentation and operational deployment. The challenge is not simply a shortage of AI specialists; manufacturers need people who understand how to apply AI within complex production environments.

Closing the skills gap means making the industrial workforce AI-capable, not simply hiring more AI talent. Manufacturers should therefore focus on augmenting existing expertise, combining domain knowledge with AI, automation, and data skills. Experienced engineers and technicians possess critical institutional knowledge that can be amplified through AI-assisted troubleshooting, programming, documentation, and decision support.

Leadership and organizational alignment are equally important. The ability to identify the right use cases, define success, and integrate AI into existing workflows requires cross-functional ownership, not a technology team working in isolation. Ultimately, the competitive advantage will come from combining human expertise, AI assistance, and cross-functional IT/OT skills to scale what works.

Closing the skills gap

Closing the manufacturing supply gap requires shifting from traditional supply-chain optimization toward supply-chain orchestration, which balances cost, resilience, capacity, and regional requirements.

Approach	Primary focus	Limitation in current environment
Supply-chain optimization	Cost and efficiency through single-source scale	Vulnerable to geopolitical disruption and localization policy shifts
Supply-chain orchestration	Balancing cost, resilience, capacity, and regional requirements	Requires greater visibility and more complex supplier management

Resilience does not mean localizing everything. It means knowing what to localize, where to diversify, and when to activate alternative capacity. Selective localization should focus on strategic components and technologies where geopolitical exposure, long lead times, or limited substitutes create the greatest risk. At the same time, multi-regional sourcing and qualified alternative suppliers can reduce dependency on individual countries and suppliers.

Visibility will become a critical competitive advantage. Manufacturers need to look beyond Tier 1 suppliers to understand sub-tier dependencies, capacity constraints, lead times, and geographic exposure. For technology vendors, understanding where manufacturing investment is moving will be increasingly important. Demand for automation, electrification, and digital infrastructure will follow new factories, reshoring, and localization initiatives. Ultimately, manufacturers need to build resilient local ecosystems while retaining diversified global supply.

Closing the supply gap

Shifting from supply-chain optimization to orchestration

Bridging the gap between ambition and execution

Conclusion

Manufacturing transformation is no longer constrained by technology alone. The real challenge is bridging the gaps between ambition and execution across supply, technology, and skills.

Geopolitical uncertainty and localization are reshaping supply chains, requiring manufacturers to balance global scale with regional resilience, while skills shortages and organizational readiness determine whether new technologies can actually be deployed and scaled.

Closing these gaps requires a pragmatic, connected approach: build resilient supply ecosystems, start with high-value technology applications, strengthen the data foundation, and equip people to work alongside AI and automation. The opportunity is not to transform everything at once, but to start small, prove value, and scale what works.

Joanne Goh
Practice Leader,
Manufacturing Technology

This research referenced is from: