AI deployments have an extremely high failure rate. Jeff Wood from CanvassAI explains how industrials can make 2022 the year of AI success.

Industrial organizations are looking to redefine their complex processes with greater resiliency, flexibility and agility to better respond to evolving market conditions. Artificial Intelligence (AI) is emerging as a key enabler of the digital transformation of industrials to help them leverage the shifting market conditions. It is easy to see why AI makes for a compelling business case. AI-powered automation not only promises to help them enhance productivity and operational efficiency but also accelerate their journey to net-zero targets. 

While there is no doubt that AI is the future, there is something else you should know before you take the plunge. AI deployments have an extremely high failure rate. So much so that a staggering 80% of AI projects fail! With such high odds against you, what steps can you take to make 2022 the year of AI success?

Addressing the following three challenges can lay the foundations for successful AI implementation but can help industrials achieve real business value from AI as quickly as possible.

1. Select the right use case

Ensuring that your AI use case aligns with your business objectives and data availability is critical for successful AI deployment. However, it is also crucial to demonstrate the viability and get buy-in from all stakeholders. The million-dollar question here is: how do you select the right use case?

You need to select a use case that demonstrates a clear value or achieves a key benefit. A good example would be one that substantially cuts down operational expenditure or increases productivity. Next, you need to see if the use case already has data that you can work with. AI thrives on data, and needs it to identify trends, correlate behaviour, and ultimately deliver predictions that will help you to control processes and manage your assets. Therefore, picking a use case where you are already collecting the relevant data is a must.

Additionally, check if your use case has a measurable Return On Investment (ROI). While your project may be successful on a small scale, the value may come to a standstill if you cannot lead an enterprise-wide deployment. Hence, select a use case where you can measure RoI and demonstrate value to upper management in a language that appeals to them. Taking this point further, you need a use case that is easily scalable to other processes. This way, the framework and learnings of the project can be applied to other functions to fast-track your AI deployment.

2. Extract deeper insights from your data

Typically, industrials collect a massive amount of data but struggle to derive actionable insights from them. This can be addressed by leveraging digital tools that have been designed for your specific industry.  This is particularly true for the industrial sector. Generic AI tools, i.e. one size fits all enterprise sectors such as financial, retail etc, are geared to address general issues and have not been built to take into consideration the nature of problems to be tackled in an industrial operation. Consequently, this also means that the gains from AI deployment using generic AI tools will be minimal. Quite simply, industrial companies need data analytical tools and AI platforms that can handle the volume, variety, velocity and veracity of industrial data and the dynamic environments that manufacturers operate in.

You need to lean on the right digital tools that can give you critical insights into your business needs and offer solutions to improve plant operations, enhance quality, reduce waste, optimize energy consumption, decrease asset failure, accelerate net-zero ambitions, and more. 

3. Accelerate the time to impact 

Many businesses are apprehensive of AI because of the perception that it takes a long time before they start benefiting from it. Typically, industrials work with data scientists and external consultants to make sense of data. However, since they are not domain experts, this approach ends up complicating and delaying the deployment. Using a digital tool that empowers your workforce is critical to accelerating the time to impact. Instead of empowering the data science specialists, put the power of AI in the hands of your industrial workforce – your engineers.

You may argue that your engineers are not trained in AI and hence may struggle with it. However, there are several AI solutions available today that do not need data science or coding expertise. They are designed to make AI accessible to industrial engineers so that they can define and develop real-world use cases, quickly extract value from data, and drive impact across their operations. After all, who better to lead your AI initiative than the engineers who understand your business inside and out!

Industrials need to accelerate their digital transformation to stay relevant and gain a competitive edge. Addressing these three key challenges can help you leverage AI to drive a greater level of profitability and sustainability, while empowering your workforce.  

Jeff Wood Canvass Ai, Industry Today
Jeff Wood

About the author
Jeff Wood is the Director of Product Management at Canvass AI. In this role, Jeff is responsible for leading the product management team in defining our product vision and roadmap and building market leading product capabilities. Prior to Canvass AI, Jeff held a variety of enterprise software product management roles. Jeff has an Engineering Physics degree from Queen’s University and an MBA from the Ivey Business School at Western University.

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