AI continues to see rapid growth in the digital marketing and design world, as an expanding range of companies harness machine learning to streamline and enhance their user experiences. 68% of CMOs report that their companies are currently using AI technology or are planning for business in the AI era — and they appear to be on the right track, with experts predicting that AI will enable 38% profit gains by 2035.

Despite this trend, however, many business leaders remain hesitant to step into the machine learning space. Common misconceptions— grounded in everything from business pain points to pop culture — are keeping too many companies on the sidelines as their competitors run with the opportunities AI presents.

Companies need to be ready to thrive in an AI-powered marketplace, and that starts with shaking off the assumptions that may be holding them back. Here are 3 common myths that are blocking companies from achieving their AI potential — and the realities that set them up for success.

Myth #1: You don’t have the time or resources to leverage AI effectively.

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Comparing themselves to the handful of tech giants who are best-known in the AI space, many companies see AI as a prohibitively costly and difficult undertaking. Where in their tight timelines, limited budgets and already overburdened teams, they wonder, would there be room to plan, implement and maintain an AI strategy?

Today’s intensely competitive and fast-moving marketplace isn’t an environment where a new initiative can be taken on lightly — especially if it could require extensive in-house development or even building a new team. It’s understandable that some risk-averse executives still eye AI with caution… especially if they’ve been burned in the past by trendy technologies that failed to deliver results.

Reality: You already have most of the pieces in place.

The closer we look at the realities of today’s AI landscape, the more we see that many companies are selling themselves short. Don’t have a chatbot or voice feature in the works? That doesn’t mean you’re not already leveraging AI — or just a couple steps away from it.

“When they think of AI, most people think of Siri and Alexa — but it’s already so much more varied and widespread,” explains Drew Harrison, who leads a team of UX Researchers and Designers for Microsoft’s Cognitive Services. “Bots and other AI tools are being used in ways that most of us don’t notice, and that has already become second nature.”

Think about the automation tools that streamline your daily processes, such as searching, categorizing, generating leads and scheduling appointments — features you may not currently be thinking of as AI. There’s a good chance that time-saving algorithms are already interwoven into your business; if that’s the case, your AI framework is up, running, and ready to build on. And even if your company isn’t yet using machine learning, the technology is well within reach.

The main reason? Today, getting started with AI doesn’t typically mean creating your own tools from scratch… or enlisting AI specialists to use them. Few outside of the field are aware of how many ready-made machine learning algorithms are already on the market — such as the 30 APIs in Microsoft’s Azure suite alone, which range from data clustering to sentiment analysis. Drew explains that today’s AI users “range from beginners to expert developers, and they’re using these APIs in a number of ways.”

The next step: Explore the tool kit that’s already at your fingertips.

Audit any machine learning tools you’re currently using in your research, sales, marketing, customer service or internal business processes. What’s saving you and your users time, and what’s not? What AI components could be improved by incorporating a different or additional algorithm? Where else could automation support your business goals?

Whatever stage you’re in, start by exploring the many existing tools you can utilize without investing in complex development or costly expertise. Especially if you’re struggling to get key stakeholders on board, keep in mind that, if done right, your AI strategy will generate long-term time and cost savings that far outweigh the initial legwork.

Myth #2: Your customers are skeptical about using AI.


There’s a sense in the industry that consumers are uncomfortable with companies’ increasing use of AI. 60% of CMOs believe that AI “is not yet ready for acceptance by the general population” — and considering the proportion of AI headlines that tend toward the anxious or even apocalyptic, that number isn’t shocking.

Any new technology brings with it practical and even ethical questions, and AI has presented some particularly complicated ones. Concerns surrounding privacy, security, job automation, or even a hostile takeover by “killer robots” (thanks, Elon Musk) are enough to make some companies balk at the idea of going deeper into AI. If you’re simply trying to save your users a few clicks, why invest in features they may not want — and that could even pull you into controversial territory?

Reality: Consumers aren’t just open to AI — they’re excited about it.

A number of recent studies challenge the myth that consumers are resistant to AI. In increasing numbers, people think of machine learning as a source of added convenience in their everyday lives… and as a whole, they want to see companies use more of it.

For instance, nearly 70% of consumers are open to AI assisting them with everyday tasks, especially if they “save time and cost.” When it comes to customer service, 40% of consumers say they don’t care whether it’s a human or an AI tool that’s assisting them — as long as they “get help quickly and easily.” For simple customer service tasks, that number jumps to 53%.

On a bigger-picture level, over 60% of respondents in a recent survey expressed the belief that AI will “make the world a better place,” using words like “optimistic” and “excited” to describe their feelings about the technology. Though the public isn’t about to uniformly embrace all forms of AI, it’s clear that consumer opinion is already much more positive than many in the industry believe it to be.

The next step: Understand your users’ concerns to create the types of experiences they want.

Public concerns surrounding AI technology aren’t going to disappear any time soon, and any company that wants to maintain its customer relationships and brand integrity need to be deeply sensitive to them. The key is using this knowledge as fuel for crafting a thoughtful, user-focused AI strategy… not as a reason to limit yourself to the status quo.

You know that great UX team you already have in place? They’ll be vital in making these experiences as usable, secure and inviting as possible for your customers… but more on that next.

Myth #3: Its robots or nothing.

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You’re not alone if the words “artificial intelligence” bring the Terminator or Westworld to mind. “Robots” was the most common word associated with AI in a recent study, with 22% of respondents mentioning it unprompted.

“I think a big part of the problem is that people often think about AI in a very science fiction sense,” explains UX Researcher Hannah Sherwood, who’s heading up the research side of Filter’s Microsoft Cognitive Services team. “They picture a machine that that can seamlessly interpret and respond to all kinds of visual and contextual information, just like we can.”

OK, we (mostly) know AI isn’t all about full-on robots. Still, the reality is that it’s the most complex, interactive, and arguably “human-like” developments that have long dominated mainstream discussion surrounding AI. A lack of exposure to other forms of machine learning has led to many to perceive AI as a single, all-or-nothing solution — one that’s reserved for the most intrepid fringes of the tech world.

Reality: Bots are just one branch of a wide (and growing) family tree.

AI isn’t the monolithic technology our culture often makes it out to be. In reality, it’s a family of distinct, highly specialized tools — linked by the central concept of “machine learning,” but extremely diverse in form, complexity and potential use cases. “I think the main takeaway,” Hannah says, “is that AI isn’t really one thing: it’s combinations of smaller parts that can be pieced together to solve a wide variety of problems.”

As companies learn more about how multifaceted AI really is, they’re discovering a breadth of new ways to incorporate it. Outside the limelight is a diverse and ever-expanding tool kit of lesser-known machine learning tools: subtle algorithms that work behind the scenes to automate precise moments in the user journey.

In many cases, teams are finding these features, or combinations of them, just as effective as their flashier, more interactive cousins when it comes to saving users time and effort… and depending on the task, often even more so.

The next step: Focus on UX to keep your AI strategy both grounded and inspired.

Many companies know from experience that the trendiest solution isn’t always the most useful, and that’s critical to keep in mind as you decide how to utilize AI. For your strategy to be successful, every component must target a real user need; no more, no less.

Keeping your AI strategy focused on “why” instead of “wow” comes down to a strong UX foundation. Leverage high-quality user research, testing and user-focused design throughout every phase of the process. Hannah explains:

“UX is helping AI technology concentrate on the core tasks at hand… [and] often, that means guiding companies away from what’s “cool” to what actually works best for their customers and their business goals.

When designing AI experiences, I think it’s incredibly important to take a step back and assess the reasoning behind our decisions. UX thinking can help us get to that big picture.”

The Takeaway

Challenging these longstanding assumptions about AI, and persuading your stakeholders to do the same, is no small task; but again, the work is well worth it. By making these three primary shifts in perspective, you’ll help clear the way for an AI strategy that’s both effective and affordable — and that solves real problems for both your customers and your business.

What other misconceptions do you think are holding companies back in the AI space? We’d love to hear your thoughts in the comments.