According to the KPMG Global Tech Report 2026: Industrial Manufacturing, industrial manufacturing is entering a decisive phase of technological transformation. What began as isolated digital pilots is rapidly evolving into enterprise-wide platforms powered by artificial intelligence, advanced analytics, and next-generation infrastructure.
KPMG’s report highlights how manufacturers are moving from technology ambition to execution. The report examines how organizations are scaling AI, strengthening data foundations, modernizing operations, and managing rising cyber and geopolitical risks.
While these initiatives may appear separate, they share one common requirement:
Accurate engineering and operational data flowing seamlessly across the enterprise.
For manufacturers, Engineering-to-ERP Integration is emerging as one of the most important foundational investments supporting AI, smart manufacturing, digital twins, advanced analytics, and future digital transformation initiatives.
By connecting engineering systems directly with ERP and operational platforms, manufacturers can establish the digital thread necessary to unlock greater value from next-generation technologies.
Source: KPMG Global Tech Report 2026: Industrial Manufacturing.
Top Metrics Snapshot #
KPMG’s report highlights several important technology trends shaping the future of industrial manufacturing.
| KPMG Technology Insight | Manufacturing Implication | Engineering-to-ERP Integration Opportunity |
|---|---|---|
| 49% of industrial manufacturing executives report active AI use cases already delivering business value. | AI is moving beyond experimentation into measurable execution. | Manufacturers need accurate product, BOM, revision, and operational data to support AI use cases. |
| 68% expect to be deploying AI at scale within the next 12 months. | AI readiness is becoming a near-term manufacturing priority. | Connected engineering and ERP data helps create the foundation needed to scale AI initiatives. |
| 80% say technology frequently improves the value generated from investments. | Technology investments must be tied to measurable business outcomes. | Engineering-to-ERP Integration can improve productivity, reduce manual work, and increase data accuracy. |
| 83% believe they are building strong AI data foundations, while 76% still cite unreliable data as a top AI risk. | Manufacturers recognize the importance of data but still face significant data quality challenges. | Automated integration helps reduce disconnected, duplicated, and manually maintained information. |
| 48% plan significant increases in cybersecurity investment. | Connected factories and technology environments require stronger controls. | Structured system integration can help reduce spreadsheet-based processes and uncontrolled data movement. |
| 89% agree that managing AI agents will become a critical workplace skill within five years. | Workforce readiness is becoming central to technology success. | Automation can reduce repetitive administrative work and help teams focus on higher-value activities. |
Source: KPMG Global Tech Report 2026: Industrial Manufacturing.
The New Manufacturing Reality: AI Is Only as Good as the Data Behind It #
Manufacturing organizations have moved beyond asking whether they should invest in artificial intelligence.
Today, the more important question is:
How can manufacturers ensure AI initiatives generate real business value?
KPMG’s report highlights growing investment in technologies such as artificial intelligence, predictive analytics, digital twins, edge computing, cloud platforms, intelligent automation, and connected operational environments.
These technologies promise significant benefits, including:
- Improved productivity
- Better decision-making
- Enhanced product quality
- Reduced operational risk
- Increased business agility
- Stronger resilience
However, many manufacturers face a major obstacle before these technologies can deliver their full value.
Their data resides in disconnected systems.
Engineering teams often work inside:
- CAD systems
- Product Data Management systems
- Product Lifecycle Management platforms
- Engineering change management tools
Operations and manufacturing teams rely on:
- ERP systems
- Manufacturing applications
- Supply chain systems
- Nesting software
- Service platforms
When information is fragmented across these environments, manufacturers struggle to create the trusted data foundation required for AI, advanced analytics, digital twins, and smart manufacturing initiatives.
What KPMG’s Findings Mean for Manufacturers #
One of the strongest themes throughout the KPMG report is the importance of connected, trusted, and governed data.
AI success is not solely about adopting new technology. It is about ensuring reliable information is available across the organization.
Manufacturers pursuing AI and digital transformation initiatives must address several foundational challenges.
Establish Trusted Data Foundations #
Manufacturers need consistent, accurate information that can be shared throughout engineering, manufacturing, procurement, sales, service, and operational functions.
Improve Data Accessibility #
Critical product information should be available to the teams and systems that need it without requiring manual intervention, duplicate data entry, or spreadsheet-based workarounds.
Support Scalable Digital Transformation #
Technology deployments become significantly more effective when they are built on connected enterprise data rather than siloed departmental systems.
Deliver Measurable Business Outcomes #
Manufacturing technology investments must translate into operational improvements, productivity gains, improved visibility, and business value.
Each of these objectives aligns closely with the role Engineering-to-ERP Integration plays within manufacturing organizations.
Engineering-to-ERP Integration: The Digital Foundation for Manufacturing AI #
Engineering-to-ERP Integration connects engineering systems directly with ERP and operational platforms, creating an automated flow of product information across the business.
Information typically synchronized includes:
- Items
- Item masters
- Bills of Material
- Product revisions
- Engineering change information
- Documents and drawings
- Manufacturing routings
- Product lifecycle information
Instead of maintaining duplicate records across multiple systems, manufacturers create a synchronized digital thread connecting engineering with operations.
This improves:
- Data quality
- Process automation
- Product traceability
- Operational visibility
- Organizational alignment
- AI readiness
Engineering-to-ERP Integration helps manufacturers establish the connected information environment that next-generation technologies require.
Data Quality Is Emerging as a Competitive Advantage #
KPMG identifies data quality and data foundations as important factors in advanced technology success. The report also highlights a meaningful gap between confidence and capability, with many organizations believing they are building strong AI data foundations while also identifying unreliable data as a top AI risk.
For manufacturers, data quality challenges often begin when each department maintains its own version of product information.
Common examples include:
- Engineering managing one version of the bill of material
- Manufacturing working from another version
- Procurement maintaining separate supplier and component records
- Service teams relying on disconnected product history
- Operations teams manually validating information before production
This creates inefficiencies and limits confidence in business decisions.
Engineering-to-ERP Integration helps establish:
A Single Source of Truth #
Engineering and operational teams work from synchronized product data.
Greater BOM Accuracy #
Manufacturing receives accurate engineering information directly from source systems.
Improved Revision Control #
Product changes can be tracked and communicated more effectively across departments.
Better Enterprise Visibility #
Decision-makers gain confidence in the information used for planning, purchasing, manufacturing, costing, and service.
As AI becomes increasingly dependent on data quality, manufacturers that improve engineering and ERP connectivity may gain a significant competitive advantage.
From Data Silos to Digital Twins #
KPMG’s report highlights how manufacturers are building resilient data environments to support AI, digital twins, edge computing, and connected operations.
Digital twins depend on accurate representations of products, equipment, processes, and operations.
When engineering and ERP systems maintain separate versions of information, creating reliable digital models becomes significantly more difficult.
Engineering-to-ERP Integration helps create the connected environment required for:
- Digital twins
- Smart manufacturing
- AI-driven analytics
- Predictive maintenance
- Real-time operational visibility
- Continuous improvement initiatives
The more connected an organization’s product and operational data becomes, the greater the potential value of these technologies.
Workforce Readiness in the Age of AI #
KPMG also emphasizes workforce readiness as a critical factor in technology success. As AI becomes more embedded in manufacturing operations, employees will need to work effectively alongside emerging technologies and new operating models.
Yet many engineering and operations teams remain burdened by repetitive administrative activities such as:
- Re-entering BOM data
- Updating ERP records manually
- Maintaining spreadsheets
- Creating duplicate product information
- Coordinating revisions across departments
- Manually transferring data between engineering and operations
These activities consume valuable time while offering limited strategic value.
Engineering-to-ERP Integration automates many of these repetitive tasks, enabling teams to focus on:
- Product innovation
- Manufacturing optimization
- Engineering excellence
- Customer service
- Continuous improvement
- AI-enabled decision-making
This creates an environment where employees can spend more time solving business problems and less time managing disconnected data.
Cybersecurity, Governance, and Controlled Information Flow #
As manufacturers become increasingly connected, cybersecurity and governance remain critical concerns.
KPMG highlights cybersecurity as a board-level priority, with many executives planning significant increases in cybersecurity investment. Connected factories, AI adoption, and geopolitical instability increase the importance of secure and resilient technology environments.
Manufacturers need:
- Controlled access to information
- Reliable data governance
- Consistent business processes
- Secure integration between systems
- Reduced reliance on uncontrolled spreadsheets and manual data handling
Engineering-to-ERP Integration can help reduce the risks associated with disconnected tools, duplicate databases, and manual data transfer processes by creating structured and controlled information flows throughout the organization.
The QBuild Perspective #
The KPMG Global Tech Report 2026 delivers a clear message for manufacturers:
Technology investments are increasingly focused on AI, automation, digital twins, analytics, and measurable business outcomes. However, success depends on having trusted, connected data available across the enterprise.
Whether manufacturers are pursuing:
- Artificial Intelligence
- Smart Manufacturing
- Digital Twins
- Advanced Analytics
- Operational Excellence
- Supply Chain Optimization
- Product Lifecycle Transformation
the foundation remains the same:
Connected engineering and operational information.
Engineering-to-ERP Integration creates the digital thread that connects CAD, PDM, PLM, engineering change management, manufacturing operations, ERP systems, nesting software, and downstream business processes into a unified information environment.
For manufacturers looking to maximize the value of AI and digital transformation investments, Engineering-to-ERP Integration may be one of the most important foundational capabilities available today.
Related Engineering-to-ERP Integration Opportunities #
Organizations building trusted data foundations often evaluate multiple integration opportunities across the enterprise.
CADLink #
CADLink connects CAD systems with ERP to automate BOMs, items, revisions, and engineering data synchronization.
PLMSync #
PLMSync connects PDM and PLM systems with ERP to create a unified product lifecycle data environment.
ECx Manager #
ECx Manager supports engineering change management by helping manufacturers manage engineering change requests, engineering change orders, engineering change notices, and related workflows.
NestLink #
NestLink connects ERP systems and nesting software so job, material, work order, and production-related data can move automatically between systems in the background.
Together, these integration opportunities help manufacturers establish the connected data foundation required to support AI, automation, digital twins, and future digital transformation initiatives.
Frequently Asked Questions #
What is Engineering-to-ERP Integration? #
Engineering-to-ERP Integration connects engineering systems such as CAD, PDM, and PLM with ERP systems so product data, BOMs, revisions, documents, and engineering changes can flow automatically across the organization.
Why is Engineering-to-ERP Integration important for manufacturing AI? #
Manufacturing AI depends on accurate and connected data. Engineering-to-ERP Integration helps manufacturers improve product data quality, synchronize engineering and ERP information, and create a stronger foundation for AI initiatives.
How does Engineering-to-ERP Integration support digital twins? #
Digital twins require accurate product, process, and operational information. Engineering-to-ERP Integration helps connect engineering and ERP data so manufacturers can create more reliable digital representations of products and operations.
How does Engineering-to-ERP Integration improve data quality? #
Engineering-to-ERP Integration reduces duplicate data entry, spreadsheet-based processes, and disconnected records by synchronizing product information directly between engineering and operational systems.
How does Engineering-to-ERP Integration help workforce productivity? #
Engineering-to-ERP Integration automates repetitive administrative work such as BOM re-entry, item creation, revision updates, and manual data transfer, allowing engineering and operations teams to focus on higher-value activities.
How does Engineering-to-ERP Integration support cybersecurity and governance? #
Engineering-to-ERP Integration can help manufacturers create more structured and controlled information flows between systems, reducing reliance on uncontrolled spreadsheets, duplicate databases, and manual data handling.
Conclusion #
KPMG’s Global Tech Report 2026 highlights a manufacturing sector moving rapidly toward AI, advanced analytics, digital twins, edge computing, cybersecurity investment, and workforce transformation.
However, the success of these initiatives depends on a common foundation: connected, accurate, and trusted data.
Engineering-to-ERP Integration gives manufacturers a practical way to build that foundation by connecting product design, engineering change management, manufacturing operations, nesting software, and ERP systems into a unified digital thread.
For manufacturers preparing for an AI-enabled future, Engineering-to-ERP Integration is no longer simply an efficiency improvement. It is becoming a strategic capability that supports data quality, technology scalability, operational resilience, and long-term competitive advantage.
