Serai Schueller, content strategist at Widen, delves into why marketers are behind when it comes to AI adoption, and how they can experiment with it to overcome tech-phobia
Artificial intelligence (AI) is a popular topic these days. From Tesla’s self-driving car to fearmonger headlines about personal data loss, it seems to find its way into everyday conversation. Deemed by analysts, vendors, and reporters as the next big thing, it certainly is hard to ignore.
However, for marketers, is AI just a buzzword to latch onto or is it something that will actually take hold? Are marketers simply giving this popular topic lip service or are they putting it into action?
Our latest Widen Connectivity research shows that the verdict is still out. Marketers acknowledge that AI can do big things, but when it comes to implementing it in their work, they are slow to adopt.
Marketers lag in AI adoption despite priorities
For our annual Connectivity Report, we conducted 32 phone interviews and 506 online surveys with marketing professionals. All 32 phone interview participants agree that AI will have a big impact on the creative and marketing industry in the next three years, with the most massive applications coming down the pike in the next five to 10 years. However, 86% of respondents told us that they’re not using AI in their work.
These results are particularly surprising, considering that AI is a key way marketers can achieve personalisation in their efforts. And, with respondents indicating that “personalising the customer experience” is the number one marketing, technology, or business trend they are focused on, you’d think AI would play a more active role. After all, isn’t there a tremendous opportunity for AI-powered personalisation? Whether it’s automated data collection, AI-fueled chatbots, or intelligent content curation for email marketing, AI has a lot to offer on the personalisation front.
Furthermore, AI is alive and well outside of the office. Gallup research finds that almost 85% of consumers already use AI in their personal lives. Consumers are rapidly adopting devices, programs, and services with AI capabilities, such as self-learning thermostats, navigation apps, or streaming subscriptions. Yet, these same people aren’t necessarily applying AI to their business lives. Where’s the disconnect?
Why marketers resist AI at work
AI is still in the early stages. And, while some marketers are undoubtedly eager to find applications for it in their work, they may be holding off until the technology can meet their exact needs. For others, there’s more to it. They may have deeper-rooted hesitations related to one of these three reasons:
1. Lack of understanding and how to apply it
When we asked respondents what AI means, over 50% said that it either reminded them of futuristic movies and robots or they didn’t know. And, while 93% of respondents feel that personalisation at scale is attainable, a whopping 58% are unsure of how to achieve it (be that through AI or other). But, it’s not just marketers. We find that most people are confused by AI and how to translate it to their business needs. So, doesn’t that make sense? If people don’t have a firm grasp on what AI means — or more importantly what it means for them — why would they go through the trouble of changing the way they do things?
2. Unproven means greater risk
One reason consumers are quicker than professionals to adopt AI is that the risk is lower. If someone subscribes to Netflix and is disappointed with the recommendation results, so what? They cancel. Give the navigation app Waze a try, and it fails? No big deal, pull up a map.
However, the consequences of trial and error for business professionals aren’t as easy to absorb. In many cases, implementing AI is an expensive investment, requiring upfront costs and time. Businesses must not only put in the hours to train their teams to use it, but they must also advocate for its adoption. And, since it’s relatively unproven, there’s a lot of reputation equity at stake for individuals that push for a technology that could fall short.
It’s a vicious circle. If marketers aren’t implementing AI, they aren’t testing it. If they aren’t testing it, they don’t know that it works. And, if they don’t feel confident in AI and its likelihood of success, they’ll probably forgo it. So, therein lies the problem — round and round we go!
3. Fear
Fueled by the media, many professionals are apprehensive to invite AI into their existing business ecosystems. Many worry that AI will automate their role, leaving them without a job. Others are concerned that AI will expose their errors or contradict the way they’ve always done things, making them look bad. Or, even more detrimental, some professionals don’t want to give up control out of fear that something catastrophic, like a data breach or loss of revenue, will happen with AI in the driver’s seat.
While a certain level of hesitation is expected, marketers that adopt AI in their professional lives have the advantage. Sure, AI will likely automate some jobs. However, it will also open up others. If organisations can use AI to automate manual, repetitive tasks, they can free up time for more strategic, business-driving roles. And, at the end of the day, AI and humans should coexist. AI can do some pretty powerful stuff, but unlike humans, it doesn’t have skills like emotional intelligence or strategic creativity. It can’t make a client laugh, negotiate a deal, build team morale, or find creative solutions to age-old problems. There’s really room for both.
How marketers can move cautiously forward with AI
Marketers are right to stop and think before moving forward with AI. But rather than coming to a complete standstill, they should find ways to experiment with it. There are many approaches organisations can take, but here are a few recommendations:
1. Challenge vendors
Educate yourself, but also push your vendors to explain their AI capabilities in terms that make sense. Chances are that if marketers are confused by AI, many vendors selling AI technologies are, too. Be fearless in your line of questioning. Ask questions to understand how their tools are intelligent, what their AI roadmap looks like, and even how their AI tech collects and stores data.
2. Find test cases
Access the needs of your business by looking at your responsibilities and those of your team. What tasks could you automate? What capabilities do you wish you had? What gaps exist within the technologies you use? From here, determine how AI can help you address your greatest business needs. Starting small, find ways to prove out the success of AI before making large financial or time commitments. Talking to your vendors can help. Many tech providers are starting to integrate AI capabilities into their tools. Check out your options. Chances are, there are plenty of opportunities to run small pilots before making a long-term commitment.
3. Have a change-management strategy
Organisations preparing for technology-driven change will likely run into resistance along the way. Whether it’s out of fear or a fixed mindset, not everyone wants to trade out the processes, tools, or ways of thinking they’ve always relied on. To gain corporate-wide adoption of the changes that technology can bring, begin to lay the groundwork early. Have a strategic vision, goal, and roadmap in place before rolling out any changes, no matter how small. And on top of providing the tools and training to support change, make sure to consistently highlight the benefits and opportunities that the technology will bring.
Being an early adopter is thrilling. But, to marketers’ defence, there is still a lot to learn about AI, so a little caution isn’t always a bad thing. But at some point, hesitation becomes paralysis so marketers must strike a balance.
While AI is still in the early phases of development and people are still figuring out how to harness its power, there is something there. Marketers must find efficient ways to scale their efforts and feed the customer-experience engine. So, while it’s good to stop and question AI, don’t stop for too long or you’ll end up chasing the leaders.
