Industrial Technology in 2026: Where Manufacturers Should Invest and What Can Go Wrong

Manufacturers are investing in automation, robotics, connected equipment, and artificial intelligence to improve productivity, reduce costs, and strengthen operational resilience.

But buying the technology doesn’t guarantee the result.

A manufacturer might successfully test predictive maintenance on one production line, only to discover that expanding the solution requires new integrations, additional security controls, workforce training, and agreement across departments.

The pilot works. The business case looks promising. Yet the expected improvement never reaches the rest of the operation.

For manufacturing leaders, that’s the real challenge of industrial technology investment: turning promising capabilities into measurable improvements in throughput, cost, quality, and reliability.

Industrial technology includes the equipment, software, and systems used to produce, move, and maintain physical goods. In 2026, it increasingly connects factory automation with operational data, enterprise software, analytics, and AI.

The opportunity is significant. So are the risks of disconnected systems, unclear priorities, and investments that never scale.

This guide examines where manufacturers are investing, what can prevent those investments from delivering results, and how leaders can make better technology decisions.

Industrial technology refers to the equipment, software, systems, and applied technical practices used to manufacture products and manage industrial operations.

It includes traditional production equipment as well as connected technologies that help organizations monitor, automate, and improve manufacturing processes.

Common examples include:

• Industrial robotics and collaborative robots
• Programmable logic controllers (PLCs)
• Supervisory control and data acquisition (SCADA) systems
• Computer numerical control (CNC) equipment
• Industrial Internet of Things (IIoT) sensors
• Manufacturing execution systems (MES)
• Machine vision and automated quality inspection
• Additive manufacturing and 3D printing
• Digital twins and simulation software
• Predictive maintenance and AI-driven analytics

Industrial technology also refers to an applied academic field combining engineering, technology, and management skills for manufacturing and industrial environments.

For business leaders, however, the more important definition is operational.

Industrial technology is the combination of systems and capabilities that determines how efficiently, reliably, and competitively a manufacturer can operate.

The US Department of Energy’s industrial efficiency and decarbonization work reflects the importance of improving industrial processes, energy efficiency, and technology adoption.

The department also supports an Industrial Technology Validation program, which evaluates emerging technologies in operating industrial environments.

The distinction between testing a technology and proving its value in actual operations is important.

Industrial technology vs. industrial engineering

Industrial technology and industrial engineering are closely related, but they emphasize different aspects of manufacturing.

Industrial engineering focuses on designing, analyzing, and optimizing systems that involve people, equipment, materials, and processes.

Industrial technology focuses more directly on applying, operating, and improving the technologies those systems depend on.

In practice, the two work together.

Industrial engineering may identify a production bottleneck, while industrial technology provides the equipment, controls, and data capabilities needed to address it.

For manufacturers, neither discipline delivers its full value without alignment to operational goals.

Industrial technology works best when its components operate as a connected system rather than a collection of independent investments.

A useful way to understand the environment is through six interconnected layers.

Technology layer	Primary function	Examples
Physical equipment and automation	Performs production work	Robots, CNC machines, conveyors, collaborative robots
Operational technology (OT) and control systems	Controls industrial equipment and processes	PLCs, SCADA, distributed control systems
Sensing and connectivity	Collects information from operations	IIoT sensors, machine vision, edge gateways
Data and integration	Connects information across systems	MES, historians, ERP integration, cloud data platforms
Analytics and AI	Supports prediction, optimization, and decision-making	Predictive maintenance, quality analytics, production scheduling
Security and governance	Protects systems and establishes operational accountability	Network segmentation, access controls, monitoring, backups

These layers aren’t necessarily implemented in a fixed sequence.

But their dependencies matter.

An AI system can’t reliably predict equipment failures without appropriate operational data.

A production dashboard can’t provide accurate insight when information from different systems is inconsistent.

And connecting equipment to enterprise networks without appropriate security controls can introduce operational risk.

Before investing in another automation, analytics, or AI initiative, manufacturers should determine whether the systems, data, security, and operational ownership required to support it are already in place.

That assessment can prevent promising projects from becoming expensive, disconnected experiments.

Manufacturers are continuing to invest in technologies that improve efficiency, flexibility, and operational resilience.

Research from Rockwell Automation and Deloitte highlights growing attention to connected manufacturing, automation, analytics, and AI.

Rockwell Automation’s 2026 State of Smart Manufacturing report examines how manufacturers are approaching smart manufacturing adoption.

Reported findings include:

• 90% of surveyed manufacturers consider digital transformation essential to competitiveness.
• 59% report actively using smart manufacturing technology in operations.
• 18% report remaining in the pilot stage.
• AI-supported operations are expected to expand significantly by 2030.

Deloitte’s 2026 Manufacturing Industry Outlook also highlights investment in automation hardware, data analytics, sensors, and cloud platforms.

The broader business environment adds urgency.

Manufacturers face changing trade conditions, labor constraints, cost pressures, and increasing expectations for production flexibility.

Technology can help address those challenges.

But the value depends on how well investments support the manufacturer’s operating model.

The question isn’t what other manufacturers are buying

Industry trends can help leaders understand where technology is developing.

They shouldn’t determine investment priorities on their own.

A manufacturer struggling with unplanned downtime may benefit more from better maintenance data than from advanced robotics.

A company facing high scrap rates may find greater value in quality analytics or machine vision.

Another organization may need to improve ERP and production-system integration before additional automation makes financial sense.

The right investment depends on the business constraint.

Technology priorities should follow operational priorities - not the other way around.

Industrial robotics remains an important investment area for US manufacturers.

According to reporting based on International Federation of Robotics data, US industrial robot installations increased in 2025.

The US also maintains one of the world’s largest installed bases of industrial robots, although adoption patterns vary significantly by industry.

Automotive manufacturing continues to represent a substantial portion of robot installations.

International comparisons often focus on the pace of robotics adoption in China and other major manufacturing economies.

Those comparisons provide useful context.

But for an individual manufacturer, the more important question is whether a robot will improve the economics of a specific operation.

How should manufacturers evaluate robotics investments?

Before approving a robotics project, leaders should understand:

1. The operational problem: Is the investment addressing a production bottleneck, quality issue, safety concern, or labor constraint?
2. Expected business improvement: Will it increase throughput, reduce scrap, improve consistency, or lower operating costs?
3. Integration requirements: Can the equipment work with existing production systems and workflows?
4. Maintenance and support: Does the organization have the skills and resources to keep it operating?
5. Total cost: What will implementation, programming, maintenance, downtime, and training cost?
6. Scalability: Can the solution be replicated across additional lines or facilities?

A robot that increases production capacity but creates new maintenance problems may not deliver the expected return.

Likewise, automation that accelerates one production step without addressing downstream constraints may simply move the bottleneck.

The objective isn’t to automate the greatest number of tasks.

It’s to improve the performance of the manufacturing operation as a whole.

Artificial intelligence is becoming a more prominent part of industrial technology.

Manufacturers are exploring AI for predictive maintenance, quality inspection, production scheduling, supply chain planning, and operational analytics.

Deloitte’s manufacturing outlook also highlights emerging applications involving agentic AI and physical AI.

What is agentic AI in manufacturing?

Agentic AI refers to AI-enabled software capable of pursuing defined goals and taking actions within established permissions and constraints.

In manufacturing, potential applications include:

• Identifying supply chain disruptions and recommending alternatives
• Monitoring equipment data and initiating maintenance workflows
• Supporting production scheduling decisions
• Coordinating information across connected business systems

These applications require appropriate oversight, especially when automated decisions can affect production, inventory, safety, or customer commitments.

What is physical AI?

Physical AI refers to AI capabilities used in systems that interact with the physical environment.

Examples include robots that adapt to changing conditions, autonomous material-handling equipment, and advanced machine vision systems.

These technologies may eventually expand the range of tasks that industrial automation can perform.

But the business case still depends on reliability, operating costs, safety, and integration.

Why AI pilots fail to scale in manufacturing

A successful AI demonstration doesn’t automatically translate into a successful production implementation.

Common barriers include:

• Incomplete or inconsistent equipment data
• Disconnected production and enterprise systems
• Unclear ownership between IT and operations
• Insufficient workforce preparation
• Weak integration with existing workflows
• Uncertain implementation and operating costs
• Missing performance measures

Gartner’s 2025 forecast on agentic AI projects warned that more than 40% could be canceled by the end of 2027, citing factors including escalating costs, unclear business value, and inadequate risk controls.

That forecast spans industries and should not be interpreted as a manufacturing-specific failure rate.

Still, the underlying concerns are relevant.

The right AI strategy starts with a defined operational problem, reliable data, clear accountability, and a measurable business outcome.

Manufacturers should also evaluate whether AI is necessary at all.

In some cases, conventional automation, better integration, or improved process management may deliver the desired result with less complexity.

Industrial technology investments depend on people who can implement, operate, maintain, and improve the systems.

For many manufacturers, workforce capability is a significant constraint.

Research from Deloitte and the Manufacturing Institute estimates that US manufacturing could need as many as 3.8 million workers between 2024 and 2033.

The research also estimates that approximately 1.9 million positions could go unfilled if workforce and skills gaps aren’t addressed.

Those figures are projections, not guaranteed outcomes.

But they illustrate why workforce planning needs to be part of technology investment decisions.

As manufacturing systems become more connected, employees increasingly need skills involving data, automation, software, and systems integration.

Deloitte’s research also identified growing demand for simulation and simulation software capabilities.

What skills do manufacturers need?

Technology-enabled manufacturing may require combinations of:

• Automation and control-system knowledge
• Industrial networking and cybersecurity
• Data interpretation and analytics
• Robotics programming and maintenance
• Systems integration
• Process improvement
• Operational troubleshooting

Federal employment projections also show differences between industrial engineering occupations.

Occupation	Median annual pay, May 2024	Projected employment growth, 2024–2034
Industrial engineers	$101,140	11%
Industrial engineering technologists and technicians	$64,790	2%

These occupations perform different functions, so their growth rates shouldn’t be treated as a direct measure of all industrial technology skill requirements.

For manufacturing leaders, the practical issue is broader.

Buying technology without developing the capabilities to operate it can create new dependencies and reduce the expected return.

Upskill, hire, or bring in specialized expertise?

The right approach depends on the work.

Existing employees may be well positioned to develop skills in data interpretation, equipment monitoring, and automated workflows.

Specialized engineers or contractors may be appropriate for defined integration and implementation projects.

Fractional or external technology leadership may help when the organization needs stronger direction across multiple initiatives.

The important decision is identifying which capabilities must remain inside the business and which can be supported externally.

Workforce readiness should be part of the investment case - not something addressed after implementation.

Manufacturers increasingly connect operational technology (OT) with information technology (IT).

That connectivity enables valuable capabilities.

Production data can inform business decisions. Maintenance teams can monitor equipment remotely. Enterprise systems can provide better visibility into inventory, scheduling, and output.

But connectivity also creates dependencies that need to be managed.

A cybersecurity incident affecting enterprise systems can disrupt manufacturing operations even when attackers don’t directly compromise industrial control equipment.

For a manufacturer, that can mean lost production, missed customer commitments, recovery expenses, and operational disruption.

A manufacturing cybersecurity incident isn’t just an IT problem. It can become a business continuity problem.

What is IT/OT convergence?

IT/OT convergence is the integration of enterprise information systems with the technologies that monitor and control industrial operations.

IT typically supports business applications, communications, data management, and enterprise infrastructure.

OT supports physical processes, industrial equipment, and production control.

Connecting these environments can improve operational visibility and decision-making.

It also requires coordination between teams that may have different priorities, technical requirements, and risk tolerances.

What does the latest industrial cybersecurity research show?

The Dragos 2026 OT/ICS Cybersecurity Report, covering 2025 activity, describes ransomware and other threats affecting industrial organizations.

The report identifies manufacturing as a major target and highlights the importance of visibility, detection, and response capabilities.

The submitted research also references a 42-day average ransomware dwell time in OT environments and a substantially shorter response period among organizations with more complete OT visibility.

Those comparisons should be checked against the original report’s definitions and methodology before publication.

The broader lesson is clear.

Manufacturers need to understand which systems are connected, what those systems depend on, and how a disruption could affect production.

How should manufacturers manage IT/OT cybersecurity risk?

A practical approach includes:

1. Establish visibility. Maintain an inventory of connected industrial systems, devices, and important dependencies.
2. Assign shared accountability. Clarify responsibilities across IT, OT, operations, and security leadership.
3. Segment networks appropriately. Limit unnecessary connections between enterprise and industrial environments.
4. Control access. Review vendor connections, privileged accounts, remote access, and authentication.
5. Prepare for disruption. Establish and test recovery procedures that reflect production requirements.
6. Include security early. Evaluate cybersecurity requirements during project planning rather than after deployment.

Security controls should reflect the operating requirements of industrial environments.

Manufacturing systems often have availability and safety considerations that differ from conventional enterprise IT.

The objective isn’t to eliminate connectivity.

It’s to support useful connectivity without creating unmanaged operational risk.

With so many technologies competing for attention, manufacturers need a consistent way to decide what deserves funding.

The starting point should be the business outcome.

Is the organization trying to reduce downtime, improve throughput, increase quality, lower cost per unit, strengthen resilience, or support growth?

Once that outcome is clear, leaders can evaluate the technology decisions required to achieve it.

A practical framework is to determine what to fund, fix, scale, stop, or monitor.

Decision	When it makes sense	Example
Fund	A defined operational problem has a credible business case and clear ownership	Automating a repetitive process with measurable labor or quality benefits
Fix	A valuable initiative is blocked by underlying system, data, or capability gaps	Improving MES and ERP integration before expanding analytics
Scale	A pilot has demonstrated repeatable results and the organization can support broader deployment	Extending successful predictive maintenance across additional production lines
Stop	The expected value no longer justifies the cost, risk, or complexity	Discontinuing a pilot that cannot meet production requirements
Monitor	A technology is promising but not yet justified by current needs	Tracking emerging physical AI capabilities without committing to premature investment

This approach helps leaders distinguish between technologies that are interesting and investments that are justified.

Five questions to ask before approving an investment

1. What business problem are we solving?

Define the operational constraint and its impact on cost, growth, quality, or risk.

2. What needs to be in place for the technology to work?

Identify dependencies involving data, systems, workforce capabilities, vendors, and security.

3. Who owns the business outcome?

Assign responsibility for operational performance, not just technical implementation.

4. What will it cost to operate at scale?

Include implementation, maintenance, training, support, integration, and security.

5. How will we know whether the investment succeeded?

Establish baseline measures and expected improvements before the project begins.

These questions help prevent disconnected projects and create a stronger foundation for leadership decisions.

Manufacturers don’t need to adopt every emerging technology.

They need to identify where technology can improve performance and make sure the organization is prepared to realize that value.

Six actions deserve attention.

1. Start with the most consequential operational constraints

Identify where downtime, quality problems, labor shortages, or inefficient processes are creating meaningful costs.

Prioritize the problems that matter most to business performance.

2. Assess data and integration readiness

Determine whether existing equipment, applications, and data systems can support the improvements you’re considering.

Address critical gaps before expanding investments.

3. Plan for scale before launching pilots

Define success measures, ownership, implementation requirements, and rollout criteria early.

A pilot should help answer whether an initiative is worth scaling - not simply demonstrate that the technology works.

4. Align IT, OT, and operations leadership

Establish shared priorities and clear decision-making responsibilities.

Connected manufacturing initiatives require coordination across technical and operational teams.

5. Invest in workforce readiness

Identify the skills required to operate and sustain new systems.

Determine which capabilities can be developed internally and where outside expertise may be appropriate.

6. Measure business performance

Evaluate investments using measures that reflect manufacturing outcomes.

Depending on the initiative, those may include:

• Equipment uptime
• Overall equipment effectiveness (OEE)
• Throughput
• Scrap and rework
• Cost per unit
• Quality performance
• Production safety
• Recovery time following disruptions

Not every metric will apply to every project.

Choose the measures that connect directly to the original business problem.

The strongest industrial technology strategy isn’t the one with the most automation. It’s the one that delivers measurable operational improvement without unnecessary complexity.

Manufacturers don’t need more disconnected technology investments.

They need greater confidence that the systems they’re funding will improve operational performance.

STG Consulting is a business technology consulting firm that helps leaders see how technology affects growth, cost, and risk.

Using the STG Strategic Technology Framework®, STG helps organizations evaluate their current technology environment, identify gaps between business priorities and execution, and establish a practical roadmap for improvement.

For manufacturers, that may mean clarifying which automation initiatives deserve investment, identifying data and integration barriers, strengthening accountability across IT and operations, or determining which pilots are ready to scale.

STG helps leaders create the clarity and alignment needed to make informed decisions and move forward with confidence.

The objective isn’t to introduce technology for its own sake.

It’s to help leadership understand what to fund, what to fix, and where technology can deliver meaningful business value.

Is your industrial technology investment delivering the results you expected?

Before committing to another automation, analytics, or AI initiative, understand where your technology is helping - and where disconnected systems, unclear priorities, or execution gaps may be limiting results.

Take the Business Technology Assessment.