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Operationalizing Machine Learning at Enterprise Scale LogoArtificial Intelligence (AI) and Machine Learning (ML) have become a top strategic priority for businesses across industries. According to a McKinsey Global Survey, approximately 30% of executives reported active pilot projects, while 71% were expecting a significant increase in AI investment. However, the survey found that progress remained slow, most companies didn’t have a clear strategy or infrastructure for sourcing data, and organizations were lacking the foundational building blocks to create value from AI at scale.

Challenges in Operationalizing AI

Deploying AI in industrial operations is difficult for a variety of reasons  – complex data management, challenging integration, enterprise security requirements, real-time analytics and capability to handle thousands of models in the production environment. However, a fundamental problem is finding skilled people to implement AI. To circumvent this issue, companies are relying on citizen data scientists – subject matter experts with domain expertise in operations – and providing them with advanced analytical tools. The biggest impediment to broader adoption by these citizen Data Scientists is poor usability, the ease of learning, effectiveness, and efficiency of AI tools. Also, AI does not garner trust easily and this problem is exacerbated for industrial operations.


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Achieving Impact with Predictive Operations

Software vendors have come up with point solutions for data discovery, data preparation, streaming analytics, visualization and model management. These solutions provide more options, but also make it more complex and confusing for technology buyers and end-users. Many data and analytics leaders are overwhelmed by the number of available solutions and struggle to understand the difference between them.

Digital Transformation leaders should look for collaborative AI systems – products that augment and leverage the SME’s domain knowledge. They need an automated ML system designed for the Operations team that is intuitive, easy to learn and use, and one that simplifies integration with existing infrastructure using standard APIs. This ML system should address multiple use cases, deliver results in months and provide a quick payback.

Machine Learning System with Prediction, Confidence and Explanation
Machine Learning System with Prediction, Confidence, and Explanation

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Here Are the Key Factors IT/OT Leaders Should Pay Attention to When Evaluating AI/ML Vendors

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Data Management

Practitioners need a scalable, robust, and high-performance environment for operational data management, review, learning, and analysis. There should be a central place to see patterns of interest over large amounts of data and native Data Visualization tools.

Automated Feature Learning

Manually creating features is difficult and time-consuming. Automated feature learning dramatically saves users time, identifies meaningful features and builds accurate predictive models.

Explainable AI

Explainable AI gives insight into model results and allows the user to understand how decisions were made. Explanation makes the Machine Learning process transparent, quantifies the contribution of each variable, and enables practitioners to accurately do root cause analysis.

ML at the Edge

Customers need flexible deployment options whether it is Cloud, On-Premises or the Edge depending on the application. For application scenarios that have poor connectivity, require real-time analytics with millisecond latencies and need to be resilient, the ability to deploy ML models on small-footprint devices at the edge, closer to the source of data, will be critical.

Operational Readiness

There are several other factors that should be taken into consideration when selecting an AI/ML vendor. How will the analytical product fit into the existing infrastructure? Can it be scaled for high dimensionality use cases? How secure is the product and does it meet IT policies? Will it be able to handle latency-sensitive applications? What approach does it have for air-gapped deployment? These are the critical areas that if overlooked can derail the project and hamper Digital Transformation progress.

A pragmatic approach to accelerate AI initiatives is to expand the population of citizen Data Scientists. Providing shop floor engineers with advanced analytical tools will empower them to drive data-driven operational improvements and build the foundation to create value from AI at scale. A collaborative ML system, purposely built for operations, with simple data management, automated feature learning and explanation is the panacea for most AI ailments.

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