What is keeping manufacturing operations from leveraging all of their rich data to optimize and use AI effectively? Often, it’s the lack of a structured, coherent, well-managed way to ensure everyone can leverage all the right data at the right time. This Industrial Intelligence Platform Buyer’s Guide discusses what to consider and the many facets of such a system to support operational excellence.
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Table of Contents
- Manufacturing and Service Need Intelligence
- Business Urgency
- Industrial Intelligence Platform
- Industrial Intelligence Platform Options
- Evaluating Industrial Intelligence Platforms
- Functionality
- Technology & Security
- AI Capabilities
- Provider and Service
- Enterprise Considerations
- Industrial Intelligence Platform Benefits
- Recommendations for Full Value
- Acknowledgments
Industrial Intelligence Platform
Trusted Insights
Companies need trusted insights that are grounded, contextual, explainable, governed and bounded. Reliability and safety depend on it.
Starting from an Industrial Platform
Do-it-yourself (DIY) can be dangerous, with the need to create guardrails and consider all constraints from scratch. Industrial companies should not need their IT group to become software architects or developers in industrial realms. We define an Industrial Intelligence Platform in the sidebar on this page. The Industrial Intelligence Platform should inherently provide a foundation and many building blocks:
- IIoT device monitoring and management
- Integration with a wide array of data sources
- Industrial data structuring and management, such as normalization and contextualization
- Data analytics and AI
- Low-code development with starting points to confidently create tailored applications with built-in security.
In Operation
Costs and revenue rely heavily on frontline teams in manufacturing and service making good decisions every minute of every shift. Manufacturers have been building toward industrial intelligence for some time, but many have recently found that dashboards and traditional analytics are no longer keeping them competitive. These are often generic, miss the priorities of an operation, are too slow for the task (though better than static reports), or are unable to answer current questions.
AI Style Intelligence
With these rich capabilities, an Industrial Intelligence Platform may ease AI startup and facilitate scaling beyond pilots to full production in multiple use cases. Every manufacturer has specific challenges for their data and AI to solve, so it’s best to find a platform that helps operations experts create the code needed to support manufacturing and service processes. For example, purpose-built AI assistants and agent services can deliver faster time to value and a trusted starting point.
Evaluating Industrial Intelligence Platforms
Considerations in Buying
We have identified six major categories of issues crucial to making a sound investment decision for an Industrial Intelligence Platform. Beyond the software’s functionality and technology as part of the infrastructure, the provider also matters. Choosing the best-fit platform can determine time to value, the agility of intelligence to match operations, long-term costs, scalability, and operational adoption.
Functionality
Capabilities for ingesting, harmonizing, managing, contextualizing, analyzing, and creating applications with a wide range of industrial data are fundamental. Many functions are needed to leverage diverse, fast-moving, and granular industrial data at the performance levels needed to support manufacturing, maintenance, or service operations.
Technology
The platform must be open to connect with diverse data sources, yet be fully protected by cybersecurity measures. Industrial settings also require high performance to ensure timely alerts and action while operations are in progress. Industrial Intelligence Platforms must also be able to orchestrate not only data streams, workflows, and UI mashups, but also AI agents.
Industrial Infrastructure
This platform needs to blend into the data infrastructure over the long term. This includes many hosting options, a robust edge, and intelligence for every employee as needed. Ideally, it streamlines licensing by including AI in the platform.
AI Capabilities
An Industrial Intelligence Platform must appropriately incorporate and manage AI. Per the definition, it is added into the analysis capabilities to improve the ability to convert data into actionable insights and intelligence. Agentic AI assistants for specific domains, such as quality or service, can improve the value of software developed.
Provider & Service
The company providing the Industrial Intelligence Platform is ideally ready to support your company in many ways over the long haul. They need to have deep experience in industrial settings and, ideally, have customers with use cases similar to those your company has in manufacturing and service operations. An ecosystem of partners can also be useful.
Enterprise Considerations
Industrial Intelligence Platforms come in many forms, and not all are ready to scale and deliver the reliable performance larger companies need to standardize across their manufacturing and/or service operations. Scalability, reuse, and proven applicability in all of the areas your company wants to orchestrate now and in the future are crucial.
Recommendations for Full Value
- Multi-Function: Seek a platform that offers AI, IIoT, a development platform for agility, pre-made applications, templates, and ways to reuse all code effectively.
- Both-And: Often, we see companies make trade-offs between options (e.g., agility to develop vs. speed to start, up-front investment vs. long-term benefits, fast time to value vs. tailored to specific needs). Ask the question: Are these really tradeoffs, or can one vendor help me get it all?
- Balance of Expertise: Build your team’s skills, but only as you must. Industrial software development includes: security, infrastructure, connectivity, data modeling, DevOps, and more. Ideally, you get support from the platform provider and its partners, and best practices are built into the platform and its training.
- Openness: Avoid vendor lock-in with standards-based connectivity for OT, IoT, and enterprise data sources and MCP for AI.
- Secure: Assess many aspects of cybersecurity, as industrial companies are increasingly targeted. Find a vendor that regularly undergoes third-party security audits and has deep knowledge and commitments around the security of all the areas the platform will touch.
- Partner: Be sure the platform and company can support you, no matter where your enterprise and each location stand in terms of digital maturity.
- Check References: Be sure to evaluate the performance and talk to customers with a similar scale and/or type of operation to your company’s.
- Broad Buying Team: Pull together a buying team that is not only cross-functional but also cross-divisional, multi-level (individual contributors, managers, and executives), and spanning many sites and regions.
- Scorecard: Measure the success of the Industrial Intelligence Platform in terms of time to value from idea, speed of development and continuous improvement projects, team adoption, and operational impact over time.
- Derisk AI: Be the exception and use a purpose-built Industrial Intelligence Platform to ensure data quality, harmonization, and context are in place. Then use those coherent, trustworthy datasets to create custom applications and insights that reliably deliver business value.
- Long-Term TCO: Total cost of ownership (TCO) for an Industrial Intelligence Platform rests on many factors. These include the use of tokens on a hyperscaler’s platform, governance of both AI and all developed applications, built-in industrial knowledge, and change management needed for application developers and operations users to adopt.
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