Industrial AI

Five Tips from AI Experts on how to implement a successful AI Strategy

Published on:
March 22, 2022

Artificial intelligence and machine learning are no longer concepts of science fiction. Businesses across the globe are increasingly looking for ways to adopt AI to maximise ROI and drive greater operational efficiencies. According to a forecast by Gartner, Inc., revenue from artificial intelligence (AI) software worldwide is forecast to total $62.5 billion in 2022, an increase of 21.3% from 2021. A massive spike in digital innovation spurred by Covid-19, has fuelled the adoption of AI, with many organisations ramping up their data-driven initiatives and AI strategy to drive faster results. When deployed correctly, AI can radically enhance operational efficiency, improve quality, reduce downtime and lower carbon emissions, enabling industrials to generate greater profits.  

While AI holds vast promises for industrials - success will depend on how we are able to harness its full potential to support overall business strategy. This can be an overwhelming process. Shortage of AI talent, technology infrastructure, legacy equipment & processes and quality of data collected are some of the challenges hindering the successful deployment of AI.  

“Enabling operational agility with a digitally connected workforce provides a competitive edge that is now more critical than ever”, says Matthew Littlefield, Principal Analyst at LNS Research. So, what does this mean for industrials as we move into an era fuelled by innovation? How can industrials harness the true power of data and fast-track their time to AI impact? How can companies create an AI-enabled enterprise? To answer these questions and more, Canvass brings you the 'Expert's Guide to Getting Started with Industrial AI' – a blueprint to help industrials start their AI journey on the right foot. It lists out three crucial steps in ensuring AI success: 

Getting your data ready

As AI is data-dependent, the first step of any AI journey is to assess data readiness. You can do this by analyzing data, learning from it, and culling out actionable insights. The data needs to be relevant, multifaceted and must include large quantities of historical as well as real-time information. The quality and depth of the data will determine the level of AI integration you can achieve. Simply put – the data you collect needs to be relevant for a particular use case or for a problem you’re trying to solve. While large volumes are crucial to the AI algorithms, the data should be representative of the process or asset for you to gain meaningful and actionable insights. “AI can only work with what you feed them, and again it’s technology; it’s not magic. So, having an excellent data set that fits the purpose, the problem you’re trying to solve is essential,” says Lindsay Dempsey, Senior Lead Optimization Engineer, Enmax Energy.

Identifying the right use case

Identifying the right use case sets the ball rolling for a successful AI rollout. It helps you demonstrate viability, obtain buy-in, and create favourable conditions for scaling. A successful use case is one that helps you demonstrate clear value, has sufficient and relevant data to work with, augments your existing process or asset, demonstrates tangible results, and is scalable to other processes. According to Gartner, when AI is applied to the right set of problems and business issues, it can demonstrate great value and by addressing those problems CIOs can inject confidence in senior management and secure funding for future initiatives.  

However, AI may not be suitable for everything and works best for processes and assets that are repeatable and have historical data, where models can be developed and validated. For example, AI can be used to predict failures in a process that keeps happening repeatedly; but would unlikely be able to predict surge in sales of a product that occurred at the beginning of a pandemic due to unavailability of historical data. Popular industrial AI use cases include optimizing assets and processes to improve product yield, reducing water and energy consumption to enhance sustainability, enabling predictive maintenance to cut unplanned downtime, optimizing energy to cut rising costs and more.

Addressing the people factor

Employees are the backbone of any organization. Innovation cannot succeed unless the entire workforce – right from operations floor to mid-level to top management is ready to embrace the change. Having the right technology infrastructure is not enough, you need the entire workforce to back your AI strategy in order to build an intelligent, agile and innovative workplace. Failure to do this has been a key contributor to why 85% of AI projects have failed, according to Gartner.  

“The most important thing is that you need to put people at the center of your AI-powered transformation. In the end, it is the people that will make decisions based on that data and take action. Data without insights is irrelevant, and insights without somebody acting on them are useless, which is why we believe people are at the center of driving transformations with AI at scale, as you create a high-performance culture and become a data-driven company” shares Sachin Lulla, Global Digital Strategy and Transformation Leader, Ernst and Young.

For AI initiatives to be successful, organisations need to put long-term effort in reskilling and enhancing the learning curve to drive impactful results and maximise ROI.  

Selecting an AI platform built for industrial environments

AI platforms come in different shapes and sizes that may not be suitable to the industrial environment or speak directly to the targeted users. Industrial companies need the depth of industrial-specific expertise that can manage the volume, variety and velocity of industrial data, and the variability that is typical in industrial environments. What’s more, in addition to data scientists, AI platforms need to be custom-built for SMEs who are integral to the digital transformation process but may lack coding expertise. Canvass AI provides a no-code platform that is built to support industrial operational environments and empower industrial operators. By choosing Canvass, industrial engineers can extract value from their data in days not months – without requiring coding expertise. The solution comes with pre-built AI templates for industrial solutions and cloud-based on-demand infrastructure that responds instantaneously to scaling needs.  

These tips and more insights from industry experts and companies, who have successfully implemented AI and are leveraging it successfully to gain a competitive edge in the crowded marketplace, are included in the ebook 'Expert's Guide to Getting Started with Industrial AI' eBook. Download the eBook here.