I grew up in the Star Wars era. I remember sitting rapt in the theater, watching Luke Skywalker and Han Solo battle the dark forces in one intergalactic battle after another. So it’s not lost on me when I read an article like this one in Popular Mechanics detailing how Artificial Intelligence (AI) is putting us closer to a Star Wars world every day. 

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We've found that companies that scale AI successfully actually spend less than their counterparts. But they're more careful and thoughtful in their approach to AI. 

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Beyond the clouds, AI is helping citizens prepare for the COVID-19 crisis. Not to mention recognizing and interpreting human emotions.

But to reap the full value of AI, for business and for society, we need to scale it and accelerate adoption. And we need to “scale smart.”  

Many AI initiatives stop after the proof of concept, even as executives recognize that scaling AI is a major priority. Taking something from a pilot program to enterprise-wide use is a challenge which we have embraced ourselves at Accenture, as well as with our clients. I’m here to bust a few myths around that process, in the hopes you can learn from those who have gone before you. 

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Myth #1: Doing it right means doing it fast.

Effectively scaling AI typically doesn't happen in a matter of hours, days, weeks, or even months. Successfully scaling AI means doing it purposefully...and that can take years. We conducted a global study—surveying 1,500 executives across 16 industries—and found that companies that have had the most success scaling their AI efforts are 65% more likely than their less successful peers to set timelines of one to two years. 

Myth #2: Big spenders win.

We've found that companies that scale AI successfully actually spend less than their counterparts. They're more careful and thoughtful in their approach to AI. They rely on clearly defined strategies and operating models to make enterprise-wide AI a reality. But they don’t spend more. 

Scaling AI effectively costs less than you might think because the technology itself is already being delivered on a massive scale today by major technology providers. For example, chatbots are fairly inexpensive to implement yet can result in major ROI. 

We recently helped two businesses, both mobile network companies, introduce virtual assistant chatbots into their customer service operations. Those bots now handle millions of customer calls each year, saving our clients tens of millions of dollars in call-center costs. 

Myth #3: More data is better.

Companies often get off track when they focus on data issues before determining whether they have the right business processes in place. When they nail the business processes first, it becomes easier to approach data issues strategically, identifying and structuring the information necessary for AI initiatives. 

We recently helped a tech client do just that with their sales operations. The client wanted to automate commission payments to sales executives. Before working with them on a solution, we first examined how they tracked their sales data, how they targeted clients, and other business practices. We found inefficiencies and broken processes. With our guidance, the client fixed and streamlined those processes before we looked at data. 

Only after that did we evaluate the data the client truly needed. And we determined that they already possessed the information necessary for finetuning their commission process. Armed with this data, we devised a solution that ultimately saved them hundreds of millions of dollars while also leading to improved sales. 

Myth #4: IT departments own AI operating models. 

Too often, we meet with company leaders who believe an AI initiative should be launched by a chief technology officer or a chief information officer. But the companies that scale AI successfully have CEOs who actively champion the technology themselves, focus on a specific business outcome, and make clear that AI is a priority for the entire organization. 

What's more, successful AI efforts are often undertaken by an interdisciplinary team. They involve not just engineers and IT professionals, but also data scientists and business experts to ensure that the technical architecture of the initiative meets the company's needs. As AI is adopted, members of just about every business function, from sales to supply chain, are included to help the implementation roll out across the organization. 

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92%

of strategic AI scalers use multi-disciplinary teams to do so.

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Myth #5: Big companies = Big AI pain.

When scaling AI, giant entities are no less successful than small, agile companies. When we analyzed organizations by size, we found no significant relationship between scaling success rates or return on investment. 

Why? Because large companies have grown to their current size for a reason. They know how to scale, and they're good at it. They apply those capabilities to their AI initiatives. And like any other company that journeys beyond proof of concept, they're using the proven strategies to do it. When it comes to AI, they've separated fact from fiction, yielding very real results.

 

Greg Douglass​

Senior Managing Director – Technology Strategy & Advisory​

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