5 Killer Bottlenecks to AI-driven IIoT Solution Success and How to Avoid Them

The promise of Industrial IoT (IIoT) and Artificial Intelligence (AI) to revolutionize industries is undeniable. However, Enterprise-Grade IIoT and AI implementations are complex. From the ‘Big Data’ infrastructure required, to the many subcomponents these systems seek. This article examines identifying and avoiding expensive ‘5 Key Bottlenecks’ that stifle innovation, inflate costs, and delay time-to-market for your next AI-driven IIoT Solution.

In a world where all IIoT & AI solutions were more or less the same, solving these bottlenecks would not be such a huge challenge, as off-the-shelf platforms would be available to simply configure and use. But this is not the case – and for good reason. The fact is that - as painful as it might be - customization and differentiation of IIoT & AI applications is at the core of the value they provide. These solutions all have a specific context related to the products they support, processes they enable, analytics they provide, and the end-user services they provide. But this results in a business conflict, because while customization and differentiation are essential, but it is also essential that IIoT & AI solution investments be efficient from a cost, risk, time to market, and cost to maintain perspective.  

So, this article progresses through these ‘5 Key Bottlenecks’ and then moves on to what we believe are the best ways to knock down these bottlenecks for far more efficient solution implementation and streamlined strategic product innovation.

5 Killer Bottlenecks to AI-driven IIoT Solution Success

• Conquering Device Chaos: The Onboarding Bottleneck:
  Why it's a bottleneck: The sheer variety of protocols, data formats, and semantic meanings across industrial devices makes integration complex and time-consuming. Handling this directly with coded solutions for each type of protocol, each data translation, and applying specialized context for each data stream is expensive, time-consuming, and slow.
  Why it's important to solve: Delays in onboarding translate to delayed ROI, contribute to excessive data silos, and increased application complexity. This all hinders future expansion and integration, as well as stalling near-term innovation.
• Streamlining Creation of Great Dashboards:
  Why it's a bottleneck: Contextualizing and processing complex IIoT data is the first step in creating meaningful dashboards. This requires interacting with system databases, creation of analytics and ‘derived data’, and even application of AI/ML models. Then there is the dashboard creation itself, which requires the creation of the visualization elements and organization of these into the dashboards themselves with into intuitive visualizations is crucial but often requires extensive coding and effort to create meaningful derived data.
  Why it's important to solve: Slow dashboard creation hinders user adoption, delays decision-making, misses optimization opportunities, and limits the solution's ability to evolve based on feedback. But still more important, where dashboard creation is expensive and time consuming, this stifles experimentation, iterations, and ultimately innovation, impacting the ultimate value of your solutions.

• Unlocking Maximum IIoT Value by Integrating with Enterprise Systems:
  Why it's a bottleneck: Connecting IIoT with ERP, MES, and CRM systems for a holistic view is complex due to disparate data structures and protocols. The bottleneck comes from the multiple APIs to connect to, the disparate formatting of the data from the different systems, and the ‘normalization’ needed to make this data appropriate for system level analytics.
  Why it's important to solve: Failing to integrate leads to data silos, limiting enterprise-wide intelligence, strategic decision-making, operational efficiency, and customer experiences, impacting revenue and profitability.
• Transforming Raw Data into Powerful Insights:
  Why it's a bottleneck: The volume and velocity of IIoT data require significant processing, leading to lengthy development cycles for extracting meaningful insights. The reason this is such a bottleneck is because the context of IIoT (or enterprise-systems) data is essential to its meaning. And, to provide valuable results, analytics of an asset across its different possible states is also essential. All of this involves careful coding and iterations to deliver the most value.
  Why it's important to solve: Long backlogs in this work slows down innovation, limits agility, and hinders the ability to respond to market changes or customer demands effectively.
• IIoT Solution Differentiation: Building Unique Business Context Value:
  Why it's a bottleneck: Implementing custom business logic and analytical models specific to an organization's needs often requires extensive coding, making it complex and difficult to adapt. And, in the end, this is where the most value for any IIoT or AI solution is derived – in the creation of differentiated solutions that drive strategic advantage for your business.
  Why it's important to solve: This hinders the creation of a competitive advantage by limiting solutions to generic functionalities and preventing the development of proprietary algorithms and tailored workflows.

How Flex Platform Solves These Critical Bottlenecks

The Flex Platform is specifically designed to dismantle these barriers, enabling practical customization and accelerating value creation:

• Solving the Onboarding Bottleneck: Flex Platform’s Onboarding & Ontology Services streamline data onboarding, normalization, and structuring of your IIoT data into a readily accessible data lake. This well-defined workflow handles diverse protocols and data types, the translation of this data into usable formats, and a uniform instantiation of this data into the system data lake - minimizing the need for custom coding and accelerating the time to access and utilize device data.
• Solving the Dashboard Creation Bottleneck: Flex provides a deep library of visualization ‘widgets’ and a drag-and-drop dashboard creator. Furthermore, Flex has multiple tools to speed the creation of analytics and custom business logic, making derived data immediately available to the dashboard services. This eliminates the need for extensive custom coding for both widgets and data preparation, enabling rapid dashboard creation and iteration.
• Solving the Enterprise System Integration Bottleneck: Flex is designed for seamless data flow across your enterprise with its Open APIs & Standard Protocols. It provides multiple connectors to commonly used sensor and edge devices as well as to ERP, MES, CRM, databases, and more, simplifying connectivity and data normalization. This creates a unified data context, breaking down silos and enabling enterprise-wide intelligence.
• Solving the Data Transformation and End-User Experience Bottleneck: Any data ingested into Flex using the Ontology Service is immediately available to the dashboard creation service – as well as to the analytics and custom business logic tools – and outputs from these analytics - the ‘derived data’ is also readily accessible to the platform components. Flex Platform’s data hander allows you to connect any asset data and build big-data transformations, rules, analytics, and custom dashboards.
• Solving the Business Context and Custom Logic Bottleneck: Flex provides a simple means to arrange application flows and allows for the easy integration of Logic & Analytics ‘Code Blocks’. When the included Rules Engine, Schedulers, Triggers, and Event Notifications aren't enough, users can add their own coded logic, algorithms, or AI/ML directly into the Flex processing engine. This empowers the creation of unique business logic and analytical context without extensive coding in the core platform.

Flex Platform: Architected for Practical Customization and Differentiation

IoT83's Flex Platform was built from the ground up to resolve the conflict between flexibility and cost & time-to-market. It achieves this by providing a comprehensive and yet modular 3-layer architecture:

• Flex Foundation: This foundational layer provides the core services for connectivity (open APIs, standard protocols), security, multi-tenancy, and a robust data lake. It ensures seamless data ingestion, normalization, and secure access, laying the groundwork for all subsequent application development. The value here is robustness, security, and seamless data management, providing a solid and reliable base that minimizes the need for custom infrastructure code.
• Flex Core Services: Building upon the Foundation, this layer offers pre-built, high-value services that address common IIoT and AI application needs. These include the Ontology Service for streamlined device onboarding and data contextualization, Dashboard Services for easy visualization and reporting, and connectivity tools for simplified enterprise system integration. The value proposition of this layer is accelerated development and reduced complexity, offering ready-to-use components for common tasks, significantly cutting down on the custom code required for these functionalities.
• Flex Catalyst: This is the application creation and orchestration engine. It empowers users to build custom business logic, analytics, and workflows with minimal coding through intuitive visual tools. It includes features like a rules engine, schedulers, triggers, event notifications, and the ability to seamlessly integrate custom code blocks (algorithms, AI/ML) for truly unique requirements. The primary value of the Catalyst layer is flexibility and rapid innovation, enabling the creation of highly differentiated applications tailored to specific business needs with significantly less custom code than traditional approaches.

The Strategic Advantage of Minimal Code

A core principle of the Flex Platform is the understanding that the number of new lines of code directly impacts the cost, schedule, and risk of any software project. Complex, enterprise-grade IIoT and AI solutions are no exception. Flex is designed to dramatically reduce the need for extensive custom coding through its layered architecture and pre-built services. By leveraging configuration over code and providing a rich set of functionalities out-of-the-box, Flex empowers users to build sophisticated applications with significantly less effort. This not only reduces development costs and accelerates time-to-market but also lowers the risk associated with complex custom code and simplifies ongoing maintenance.

Flex: Complementing and Expanding on Existing Investments  

The open nature of the Flex Platform allows it to seamlessly integrate with and enhance existing IIoT investments. Organizations can leverage their current infrastructure and connect it to Flex's powerful services. This complementary approach enables them to:

• Enhance Existing Systems: Add advanced functionalities like sophisticated analytics and visualization to their current systems without the need for a complete overhaul.
• Unlock Portfolio-Wide Connectivity Velocity: Achieve simplified and accelerated onboarding of diverse devices across their entire portfolio.
• Create a Normalized Data Lake: Achieve unified data insights across all connected assets, breaking down data silos and enabling faster innovation.
• Focus on Value Creation: Shift valuable resources from the complexities of basic connectivity and integration to creating strategic, value-added applications on top of a robust and enhanced data foundation.

This ability to complement and enrich prior investments allows organizations to move to new dimensions of strategic capability, achieve significant differentiation in the market, foster continuous innovation, and ultimately realize greater business value from their Industrial IoT and AI initiatives.

Conclusion: Enabling Practical Customization for Unprecedented IIoT Value

The bottlenecks inherent in traditional IIoT and AI development approaches hinder innovation and increase costs, directly impacting time-to-value, user adoption, enterprise-wide intelligence, speed of innovation, and competitive advantage. While the need for custom solutions for strategic differentiation is clear, building them with traditional methods can be impractical and expensive, often forcing a trade-off between flexibility and cost. The Flex Platform provides a revolutionary solution by directly addressing these bottlenecks through its 3-layer architecture, extensive pre-built services, and a core philosophy of minimizing custom coding. By eliminating these critical roadblocks and seamlessly complementing existing investments, Flex empowers organizations to build highly differentiated, custom IIoT and AI applications practically and profitably, unlocking unprecedented levels of strategic capability and business value.