You Can’t Just Use AI – You Must Lead With AI

The following is adapted from Real World AI.

AI will soon be a necessary part of doing business. Every company will have an AI strategy in the same way that they have a social media strategy, branding strategy, talent strategy, etc.

So if you want to stand out from the competition and gain an edge, you can’t just use AI; you must lead with AI.

What does leading with AI look like?

To lead with AI, you can’t simply set up a small innovation team to solve point problems. You must embed AI throughout the organization, such that every team is taking advantage of the technology and incorporating it into their operations.

The specifics will vary from company to company, but there are some general strategies that will help you begin leading with AI.

Be Persistent

Leading with AI doesn’t just happen in the blink of an eye. It’s going to take time and hard work, and it’s critical you stay persistent.

Amazon, a leader in machine learning technology applications, didn’t start out using advanced AI techniques on day one. Just like everyone else, they had to go on a journey filled with discovery, successes—and the occasional course correction. For example, in 2017, Amazon launched a TV ad that accidentally triggered Alexa devices in its customers’ homes to purchase an expensive dollhouse. Not ideal, and not the best advertisement for the convenience of their machine learning devices.

They stuck with progress, though, and learned from their mistakes. They were persistent, even though they started out behind the pack. Their focus on incremental progress meant that it only took five short years for Alexa to beat out Siri and Google Assistant in the smart home speaker market. Alexa may have had a slow start, but now she’s a very strong player in the market.

Persistence and determination are the ingredients that build leaders in AI—not perfection from the start.

Train Your Leaders to Look for AI Solutions

Leading with AI

Before a business can lead with AI, it has to identify problems AI can solve. You can’t accomplish this by building an AI tiger team and turning them loose to hunt through each department for inefficiencies. That AI team wouldn’t have the necessary context, and all its time would be spent coming up to speed on the operations of the various departments.

Each division or department is familiar with its own operations and has the context to identify the most important problems. What they might lack is an understanding of AI and the types of problems it can solve. Giving them that training is the first step.

Organizations that do this successfully start with the belief that every department can use AI to solve some of its problems and work toward enabling their leaders to identify and solve those use cases. For example, a CFO, who is presumably good at what they do, probably doesn’t have any experience in AI—their expertise lies in finance. That CFO will need training to develop an AI-aware mindset, a capability of identifying the places within the finance department where AI can solve problems.

Obviously, your leaders don’t need to understand everything about AI; they just need to know enough to be able to identify when AI is a good solution.

Create Cross-Functional Teams

When you think of AI, your mind might jump to data scientists, but there’s a lot at play in an AI model. It’s a marriage of technology and business, which means you need cross-functional teams to develop the best solutions. Because of this, the organization as a whole must become highly effective at multidisciplinary communication and collaboration.

At its simplest, this might involve rolling out something like Slack to improve communication across departments. Some companies might adopt Agile processes and workflows to encourage collaborative planning of requirements, or they might institute regular all-hands meetings to sync up business priorities all at once and provide transparency.

The details will be different for every organization, but every company will have a need for departments to collaborate more than before to identify common problems, prepare and share data, and develop related models. In some cases, this may be best served by restructuring the organization and reporting lines; in others, a department that expects an extensive adoption of AI—marketing, for example—may need to establish its own data science team.

This degree of change may seem overwhelming. It’s important, however, for organizations to avoid the short-term solution of creating an AI team that’s shared across the company, or that team will end up being the primary bottleneck blocking AI adoption.

Budget and Allocate Resources

Nothing happens in a company without a budget and allocated resources. Finding the budget to implement AI solutions—to buy off-the-shelf products, hire people with the needed skills, spend time and resources annotating data—is critical, but challenging, especially because a good portion of the investment in AI has to be made company-wide up front.

Leading with AI can take reallocation of significant money and people, both of which are, in most cases, already budgeted out to normal operations. Deciding how much to reallocate and from where will impact the entire organization.

Cutting costs is always unpopular, but you must look at it as an investment. Take, for example, a company eager to invest in AI that runs a call center to capture support calls, return requests, and complaints. There’s no magical pool of money sitting around to invest in AI to improve this call center.

Instead, the company will have to take money out of the yearly budget to spend on the AI initiative and just accept that the time to handle a call will go up for a period because fewer agents are deployed to receive those calls. The promise, of course, is that the investment in AI will lead to a chatbot that can divert 15 percent of those incoming calls, requiring fewer agents to handle calls and improving the call-handling time overall.

Clearly articulating the long-term gains of AI will make it easier to secure buy-in across the company, so that the needed resources will be allocated.

Leading With AI

Leading with AI is hard. It will take time and massive investment. It’s never easy to switch up the operation of huge chunks of a business, reorganize reporting structures, and refocus priorities; it will almost certainly require killing some sacred cows.

Amazon, for example, spent years and a lot of investment to make the transition to leading with AI. Now, on the other side, they’ve maintained their status as a leader in their industry, with the infrastructure and culture that enables them to implement new AI solutions throughout their businesses as opportunities and problems arise.

With persistence, AI-aware leaders, cross-functional teams, and the right budget and resources, you, too, can begin leading with AI, ensuring a future competitive advantage.

For more advice on leading with AI, you can find Real World AI on Amazon.

Alyssa Rochwerger is a customer-driven product leader dedicated to building products that solve hard problems for real people. She delights in bringing products to market that make a positive impact for customers. Her experience in scaling products from concept to large-scale ROI has been proven at both startups and large enterprises alike. She has held numerous product leadership roles for machine learning organizations. She served as VP of product for Figure Eight (acquired by Appen), VP of AI and data at Appen, and director of product at IBM Watson. She recently left the space to pursue her dream of using technology to improve healthcare. Currently, she serves as director of product at Blue Shield of California, where she is happily surrounded by lots of data, many hard problems, and nothing but opportunities to make a positive impact. She is thrilled to pursue the mission of providing access to high-quality, affordable healthcare that is worthy of our families and friends. Alyssa was born and raised in San Francisco, California, and holds a BA in American studies from Trinity College. When she is not geeking out on data and technology, she can be found hiking, cooking, and dining at “off the beaten path” restaurants with her family.

Wilson Pang joined Appen in November 2018 as CTO and is responsible for the company’s products and technology. Wilson has over nineteen years’ experience in software engineering and data science. Prior to joining Appen, Wilson was chief data officer of Ctrip in China, the second-largest online travel agency company in the world, where he led data engineers, analysts, data product managers, and scientists to improve user experience and increase operational efficiency that grew the business. Before that, he was senior director of engineering at eBay in California and provided leadership in various domains, including data service and solutions, search science, marketing technology, and billing systems. He worked as an architect at IBM prior to eBay, building technology solutions for various clients. Wilson obtained his master’s and bachelor’s degrees in electrical engineering from Zhejiang University in China.

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