anufacturers face a diffi cult task juggling the current “innovation agenda.” Today, the Industrial Internet of Things (IIoT), robotic automation and artifi cial intel-

ligence (AI) are all poised to be the next big thing. But those on front line of manufacturing are cautious to embrace innova- tion—and rightly so. Too often expectations are unfulfi lled, capital investments are made in vain, and experimentation doesn’t translate positively into profi ts.

Instead, many enterprises take a wait-and-see approach. They wait for leading companies, with bigger budgets, to fi gure out how to make these new technologies viable, in the process educating the rest of the market. But AI is differ- ent. 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 abou t it, Industrial AI is a rare case of affordable innovation without inherent fl aws. Let’s go through the myths one by one.

Myth #1: AI is Expensive

While all innovations have the potential to improve manufac- turing, they often require large investments. But AI can achieve tangible results without signifi cant 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 Netfl ix can now be transferred to offl ine shop-fl oor 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 fl oor AI will be best applied. Do not be misled by the futur- istic 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 workfl ows, 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 ef- fi ciency, and throughput with more precise decision-making at

96 | August 2017 Busting the Myths about Artifi cial Intelligence

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 technol- ogy can take years, with ROI sometimes measured in decades. Other priorities intervene and managers may become less incen- tivized 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 adop- tion 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 effi cient. For ex- ample, 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 suffi cient computational power and critical data avail- able, 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.

Jane Zavalishina CEO

Yandex Data Factory

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