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Busting the Myths about Artificial Intelligence

Jane Zavalishina
By Jane Zavalishina CEO, Yandex Data Factory

Manufacturers face a difficult task juggling the current “innovation agenda.” Today, the Industrial Internet of Things (IIoT), robotic automation and artificial intelligence (AI) are all poised to be the next big thing. But those on front line of manufacturing are cautious to embrace innovation—and rightly so. Too often expectations are unfulfilled, capital investments are made in vain, and experimentation doesn’t translate positively into profits.

Instead, many enterprises take a wait-and-see approach. They wait for leading companies, with bigger budgets, to figure out how to make these new technologies viable, in the process educating the rest of the market. But AI is different. Industrial AI is focused on using data from equipment and sensors to make intelligent predictions and automate operational decision-making. Manufacturers cannot afford to wait around to implement industrial AI—the rewards are far too great. Despite the myths about it, Industrial AI is a rare case of affordable innovation without inherent flaws. Let’s go through the myths one by one.

Myth #1: AI is Expensive

While all innovations have the potential to improve manufacturing, they often require large investments. But AI can achieve tangible results without significant investment. The secret lies in knowing how to apply it and taking advantage of the R&D efforts already made by internet-based companies. Indeed, algorithms used by Amazon and Netflix can now be transferred to offline shop-floor implementations. For manufacturers, the heavy lifting–developing and testing the core technology–has already been accomplished and paid for.

However, manufacturers should understand where on the shop floor AI will be best applied. Do not be misled by the futuristic idea of “connected factories.” AI can come in a much less extravagant, very practical format: optimizing existing processes with existing data. Given manufacturing’s traditional processes—established workflows, 24/7 operations, and long equipment lifecycles—AI has plenty to work with.

This will soon be the AI we know. Invisibly integrated, it will improve areas such as raw material spending, energy efficiency, and throughput with more precise decision-making at every step. What’s more, no capital expense or new hardware will be required.

Myth #2: AI Only Delivers Real Results in the Long-Term

Upfront cost isn’t the only fear manufacturers have when investing in innovation. Concern about the time required for a return on investment (ROI) can also overshadow technological ambitions. In manufacturing, deployment of innovative technology can take years, with ROI sometimes measured in decades. Other priorities intervene and managers may become less incentivized when the end results are not guaranteed.

The situation is different with industrial AI. Building AI-based models takes months, not years. Testing to measure the results of AI on continuous processes requires only days or weeks. Once the model is applied, it immediately generates value by producing results that guide further strategic changes.

Myth #3: AI Disrupts Existing Processes

People are naturally apprehensive about change, especially when it involves altering a process that already works. One change often leads to another, and, as experienced managers know, even when technology works the integration and adoption process can be challenging. However, when AI is used to optimize processes, none of this applies.

Where AI is used for optimization, there is no need to revamp the production line or train staff to use new process controls. Nor are complex IT integration projects—often the cause of grievances among CIOs and end users—necessary. Instead, the same business processes are carried out by the same means, but in a way that is far more efficient. For example, AI can suggest the best modes of equipment operation or the exact amount of raw materials required, all in the same interface your operators already use. The only thing affected by AI is the manufacturer’s bottom line.

AI has long been on the manufacturing radar. But today, with both sufficient computational power and critical data available, AI can be effectively pursued. There are few reasons to delay an AI project; the technology is already here and fears about innovation do not apply. In the case of AI, there really is no time like the present.

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