This blog is based on Frost & Sullivan’s recent analysis, Growth Opportunities in Emerging Industrial AI Ecosystem, Global, 2025–2029, authored by growth expert Sebastián Trolli from the Industrial Practice Area
Executive Summary:
Industrial automation software is becoming harder to evaluate as automation, data, cloud, AI, and services vendors converge around similar language. Architecture ownership, plant-level proof, governance, and repeat deployment economics now provide clearer measures of vendor value. The New Industrial Tech Stack makes these differences more visible, while Industrial AI, contextual data, systems of action, and software-defined automation are increasing the importance of controlled execution and scalable deployment across plants and sites.
Key takeaways:
- How vendor roles differ across the New Industrial Tech Stack despite similar market language
- What separates plant-level technical proof from enterprise-ready deployment
- Where data context, workflow ownership, and governed action create lasting commercial value
- Why the second deployment provides a clearer test of scalability than the first
What Is Industrial Automation Software?
Industrial automation software connects plant assets, operational data, engineering tools, and production workflows. It includes manufacturing execution systems (MES), industrial data platforms, quality and asset applications, digital twins, engineering software, and software-defined control environments. A digital twin in manufacturing connects design, simulation, commissioning, and operating data to support testing, engineering, and lifecycle decisions.
Its role now extends beyond monitoring and deterministic control into contextual data, intelligent workflows, and governed execution. This wider scope allows software to coordinate information and actions across machines, lines, and sites while supporting engineering reuse, lifecycle management, and selective modernization.
Why Is Industrial Automation Software Becoming Harder to Evaluate?
At ProveIt! Conference 2026, four vendors from different market segments described their offerings using nearly identical terms: unified data, context, AI, agents, autonomy, and openness. Yet each product served a different purpose across industrial data, workflows, and execution.
Which Industrial Software Vendors Can Deliver Scalable Value?
Compare architecture ownership, deployment proof, and vendor dependencies to identify scalable growth opportunities.
The same category confusion emerged in nearly 100 discussions with vendors and manufacturers at ProveIt! Conference and Hannover Messe. Automation vendors, industrial software providers, cloud companies, AI firms, and system integrators are expanding into adjacent technology segments faster than traditional market categories can distinguish them. Industrial artificial intelligence (AI) adds to this complexity because its applications span multiple layers of the technology stack.
Four considerations are gaining importance in industrial technology evaluation:
- Architecture fit: Understanding which layer a product controls and how it connects with existing systems.
- Ownership and dependencies: Identifying where data and context reside, which partners are involved, and what responsibilities remain with the customer.
- Deployment evidence: Assessing integration effort, implementation time, costs, and performance under realistic operating conditions.
- Enterprise scalability: Establishing whether plant-level applications can meet wider support, governance, and deployment requirements.
For manufacturers, these distinctions provide greater clarity on vendor capabilities, implementation costs, and long-term dependencies. Specialist providers also gain access to larger manufacturing automation programs through defined roles in data context, workflow execution, engineering software, or governance.
Two Market Tests Are Redefining Vendor Credibility
ProveIt! Conference and Hannover Messe examined different aspects of industrial technology performance. ProveIt! exposed the practical demands of deploying applications in plant conditions, while Hannover Messe highlighted the architecture, partnerships, and support capabilities behind enterprise-scale solutions.
Two Market Tests, Different Measures of Vendor Credibility
| Evaluation Area | ProveIt! Conference | Hannover Messe |
| Primary focus | Application performance under realistic plant conditions | Enterprise architecture and scalability |
| What it reveals | Data preparation, integration effort, workflow assumptions, time, and cost | Partner reach, governance, infrastructure, security, and support capabilities |
| Commercial relevance | Establishes whether an application can work at plant level | Establishes whether the vendor can support and scale the solution across the enterprise |
Operational Evidence: At ProveIt!, the biopharma virtual factory brought together paper records, isolated information, single-use equipment, and manual decisions. These conditions exposed data preparation and integration challenges that conventional product demonstrations could overlook.
Commercial Implication: Plant-level performance establishes application viability, while enterprise validation reveals the support, governance, and implementation commitments involved in wider deployment. Both forms of evidence provide a clearer basis for assessing vendor credibility, deployment costs, and scalability.
📖Explore how digital thread ecosystems improve data continuity across connected manufacturing?
The New Industrial Tech Stack Is Making Vendor Differences Visible
ISA-95 and the Purdue Model remain relevant for organizing industrial operations and network segmentation. However, vendor evaluation now extends across industrial data, semantic context, digital twins, AI models, workflows, applications, governance, and service dependencies.
The New Industrial Tech Stack maps nine layers, from Physical and Control through Application and Execution. Governance, Validation, and Security, along with Ecosystem and Services, span the architecture. The framework makes it easier to distinguish between vendors that control data foundations, semantic models, operational workflows, or execution environments.
Three Implications for Vendor Positioning
- Hard-to-replace control points: Ownership of industrial data, semantic context, workflows, and validated execution influences application strategies, switching costs, and customer flexibility.
- Installed-base advantage: Automation vendors can extend established control relationships into engineering software, lifecycle services, and operational applications while maintaining reliability and support.
- Reusable service models: Standardized data contracts, governance methods, validation frameworks, and deployment patterns allow system integrators and technology services firms to reduce custom work across sites.
Commercial Implication: No vendor has equal depth across all nine layers. Partnerships connect complementary capabilities, while clearly defined ownership and dependencies give buyers greater flexibility. For technology providers, a distinct architecture position creates access to larger automation programs without requiring control of the entire stack.
Where do vendors hold control across the industrial technology stack, and how do their dependencies affect long-term value?
Three Industrial AI Proof Points Strengthening the Commercial Case
Industrial AI is delivering measurable improvements in selected engineering and plant workflows. Early 2026 examples demonstrate the value of clearly defined tasks, established operating limits, and human oversight. These results reflect individual applications rather than industry-wide productivity gains.
Evidence of Operational Value
- Engineering deployment 2–5x faster
An engineering agent generated verified controller code, built operator screens, and ran diagnostics using plain-language instructions, enabling faster deployment than manual methods.
2. Up to 50% less configuration and documentation time
A control-engineering copilot reduced time spent on configuration and documentation, shortening work previously measured in weeks to hours.
3. Batch scanning reduced from 2 minutes to 20 seconds
A handheld reader captured an entire batch from a single image, demonstrating a measurable improvement in a traceability-focused workflow.
Commercial Significance
These workflow-level results provide measurable evidence for Industrial AI investment. Wider deployment brings additional requirements around data reliability, decision limits, validation, cybersecurity, and rollback. Vendors that can meet these requirements gain a stronger basis for extending proven applications into additional workflows.
Top Three Growth Positions Across Industrial Automation Software
Three Growth Positions offer vendors opportunities to reduce implementation costs, expand customer relationships, and generate recurring revenue.
- Contextual Industrial Data and Semantic Infrastructure
Industrial DataOps, semantic models, and data contracts create reusable context for AI, digital twins, and workflows. Cognite illustrates industrial data contextualization, while HighByte focuses on data modeling and orchestration.
Business Implication: Lower integration costs strengthen market access for data-platform vendors, semantic specialists, and system integrators while preserving customer control over data.
- Governed Agentic Workflows and Systems of Action
Engineering, maintenance, and quality workflows offer defined applications for agentic software. Anthropic provides the Model Context Protocol for connecting AI systems with data and tools, while Siemens applies Industrial AI to automation code generation through its Industrial Copilot.
Business Implication: Proven workflows create entry points for agentic software providers, with recurring opportunities across validation, cybersecurity, governance, and support.
- Software-defined Automation and Operational Technology Lifecycle Management
Version control, simulation, virtual commissioning, and rollback extend automation value beyond initial deployment. Schneider Electric and Copia Automation illustrate developments in software-defined control and engineering lifecycle management.
Business Implication: Release management, validation, and multi-site support create recurring revenue opportunities for automation vendors, engineering software providers, and integrators.
🔍 How are hybrid intelligence and software-defined automation influencing industrial performance?
Frost & Sullivan Perspective
Frost & Sullivan believes the next phase of industrial automation software growth will depend on vendors establishing distinct, defensible positions across the technology stack. Providers that combine specialized capabilities with clear ownership, dependable partnerships, and lifecycle support will be better positioned to expand within industrial accounts. Differentiation will rest on the value a vendor controls and its ability to sustain that position as applications and deployments expand.
Which growth positions are creating opportunities for vendor differentiation and recurring revenue?
Download the Whitepaper to explore all five growth Positions across the New Industrial Tech Stack.
Future Outlook: The Second Deployment Will Define Scalable Industrial Software Growth
An initial project may succeed through custom engineering, site-specific integration, and additional vendor support. The second deployment reveals whether established context, interfaces, validation, and governance can reduce that effort across subsequent sites or workflows.
Clear responsibilities among vendors, implementation partners, and customers also affect repeatability. Lower engineering and support requirements can improve deployment economics and recurring revenue potential, while persistent customization limits the commercial value of expansion.
How effectively is your industrial technology strategy converting initial deployment success into repeatable commercial value?
Connect with our growth experts at [email protected] to explore vendor positioning, partnership opportunities, and scalable industrial software growth.
Ready to Lead the Transformation?
- Schedule a Growth Strategy Dialog: Align your growth roadmap with Frost & Sullivan’s Visionary Growth Pipeline™ Dialog.
- Engage with Growth Experts: Co-design AI-enabled, data-driven operating models that scale industry-specific and commercial impact.
- Showcase Your Transformational Leadership: Position your organization as a transformation leader through Frost & Sullivan’s Transformational Growth Leadership platform.
- Join the Growth Council: Collaborate with industry leaders shaping the future of your ecosystem.
- Explore Best Practices Recognition: Be recognized for excellence in growth strategy, execution, and customer impact.
- Benchmark Your Industry Positioning on the Frost Radar™: Benchmark your growth performance and innovation strength against industry competitors.
- Tell Your Story: Accelerate awareness, engagement, and revenue growth through integrated brand and demand generation strategies.
Connect with our growth experts at [email protected].
FAQs
1. What is industrial automation software and what does it do?
Industrial automation software connects machines, control systems, plant data, engineering tools, and production workflows. It supports monitoring, analysis, workflow coordination, governed execution, and lifecycle management across machines, lines, and sites.
2. How is Industrial AI used in manufacturing?
Industrial AI supports engineering assistance, diagnostics, maintenance planning, quality assessment, operator guidance, and process optimization. Its value depends on reliable plant context, defined decision boundaries, and auditable workflows.
3. What is a manufacturing execution system, and how does it support governed execution?
A manufacturing execution system (MES) coordinates production information, work instructions, quality records, and operational workflows. Its role can extend toward governed execution when connected systems support defined recommendations or actions with visible approvals and accountability.
4. What is software-defined automation, and why does it matter?
Software-defined automation separates automation applications from fixed hardware lifecycles. Version control, simulation, virtual commissioning, rollback, and modular deployment support selective modernization without requiring the wholesale replacement of dependable control assets.
5. How does a digital twin in manufacturing create value?
A digital twin in manufacturing connects design, simulation, commissioning, and live operating data. This improves virtual testing, engineering speed, change management, and deployment consistency before changes reach production.
6. What is driving manufacturing automation growth?
Manufacturing automation growth is being driven by productivity pressure, quality and traceability requirements, rising software value, and demand for more adaptable production. Contextual data, Industrial AI, and governed workflows are expanding the value available above traditional control systems.
7. What are the growth opportunities offered by industrial automation software?
Industrial automation software offers growth opportunities across contextual data, governed agentic workflows, software-defined automation, AI-enabled engineering, and operational technology trust infrastructure. This blog examines three positions, while the whitepaper explores all five.
