05.17.2022 Executive Data Bytes: Ways To Overcome Industrial AI Roadblocks

Welcome to another edition of Executive Data Bytes! This week, we’re looking at Industrial AI roadblocks, and ways to overcome them.

Focus piece: “Three Ways To Overcome Industrial AI Roadblocks” 

Executive Summary

Industrial AI models depend on data quality and the ability to feed quality data into them. Data historians can help solve these problems by turning unstructured, unformatted data into secure and easier-to-manage data for the industrial AI model. This article by Forbes lays out the key steps needed for industrial AI to fully deliver its promised value.

Key Takeaways

• AI is becoming a disruptive force in the oil/gas and energy industries, and its role in the industrial sector will only continue to evolve in 2022 and beyond. The real issue is how to deploy industrial AI in a way that maximizes its value and ROI.
• A lack of data quality and management is stifling industrial AI's maximum value. Insecure data, large volumes of irrelevant data, and disparate locations and silos for storing data all clog up the ability to derive real value from data.
• The technology and team silos that stifle collaboration between team members are the main reason why industrial organizations have visibility into only two-thirds of their industrial data. This lack of collaboration hurts industrial AI because the insights returned by the AI model will also be incomplete.
• Lack of strategy around industrial AI deployments can result in productivity losses, greater downtime, cost inefficiencies and fortified data silos. The solution is clear: Plan your industrial AI deployments strategically
• Industrial AI can unlock game-changing benefits for the oil/gas and energy sectors, but major structural roadblocks must be overcome before industrial AI can be fully applied.

Focus piece: “Overcoming Real And Imagined Roadblocks To Automation”

Executive Summary

Many business leaders remain wary of automation technologies and are held back by outdated myths associated with them. This article from StrategicCFO360 outlines five ways to unlock the full potential of intelligent automation.

Key Takeaways

• The surge of technology investment in the past two years has transformed automation into an intelligent technology that enables innovative customer experiences and improved decision-making.
• Automation lets people focus on adding value with and for fellow humans, rather than routine tasks, and ultimately delivers better service for customers.
• Intelligent automation is an ongoing process that requires step-change improvements. To overcome obstacles, companies should have a clear plan for building an intelligent automation capability within the organization, and align the solutions with the overall business strategy.
• To make use of cutting-edge automation technologies, companies must simplify their tech environment, decouple data, infrastructure, and applications, and improve data management processes. This will allow them to leverage advanced AI and automation tools to create new forms of value.

Focus piece: “Common Roadblocks to Successful Enterprise AI Adoption (And How to Overcome Them)” 

Executive Summary

Businesses are investing in artificial intelligence (AI) to enhance their products, projects, and operations. However, AI comes with its own set of challenges and roadblocks in terms of technology, people, and organizational culture. Read about some of the top tips on how to get started in this article from eInfochips.

Key Takeaways

• The connection between adopting AI and gaining a competitive advantage is strong. However, the journey to deployment is often accompanied by a number of challenges.
• Many organizations are thinking about investing in emerging AI technologies, but they often fail because the whole organization does not share the same vision. This can lead to uneven adoption of technologies and conflict with an overall AI adoption strategy.
• 31% of organizations see the skills gap as one of their top three challenges in AI adoption.
• The lack of relevant and insightful data is the biggest roadblock in the journey of AI implementation. Companies need to collect the right kind of data, not just the most data.

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