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5 Common Fears About AI for Marketing – And How to Address Them

This is the second post in our “AI for CX & Marketing” series – check out the first post here.

If you’re trying to integrate AI into your marketing technology stack, you may encounter some internal resistance. This could be from your colleagues as you try to convince them to get on board, or even from yourself as you question whether AI is a truly smart investment.

It’s no surprise. Straddling the line between science fiction and high-tech reality, AI comes with a lot of unknowns. To help you address these concerns, we’ve compiled a list of 5 common fears about using AI in marketing, along with practical solutions to tackle them.

Fear 1. “It will make my role (or team) redundant.”

Why they’re worried: Automation, especially powered by AI, can certainly pose a risk to jobs and even entire industries. As AI gets smarter at completing human tasks, many of your colleagues may wonder if algorithms could replace their roles too.

The reality: With marketing teams and budgets frequently under-resourced, many teams end up bogged down in repetitive tactical work, leaving little time for strategic thinking. AI has the potential to free up this time by automating these tasks. Thanks to its knack for data analysis and pattern recognition, AI can also deliver far deeper and more valuable insights. This allows you to make smarter, more strategic decisions with greater agility, something all marketing teams could benefit from.

How to address the concern: Start by looking at areas that cause both customers and your team pain. These are the use cases to target first. Look for ways that AI can make these tasks less of a headache for your team. If you can show how it will take the load off or make things more efficient, it will be easier to get buy-in.

Fear 2. “We won’t know why it does what it does.”

Why they’re worried: There’s a reason the EU included “transparency” in its recent set of AI ethics guidelines. AI isn’t infallible; it learns from the data it’s given. But this data might be inaccurate, biased, or simply lack the context for accurate decision-making. It’s crucial to understand how a decision has been made so you can fix any issues if necessary. Additionally, there’s business value in understanding an algorithm’s decisions. Without understanding why a customer is predicted to purchase a particular product, it’s hard to gain actionable insights for your broader marketing strategy.

The reality: Understanding why an algorithm makes the decisions and predictions it does can be challenging, as thousands of data points may have been processed and analyzed to reach a conclusion. Fortunately, AI vendors are starting to realize the need for “explainable” algorithms. This doesn’t mean diving into every piece of data, but platforms should offer some indication of why a decision or prediction was made.

How to address the concern: Look for tools or platforms with plenty of visibility and control built in. These should give you deeper insight beyond surface-level predictions and allow you to approve or tweak actions before going ahead.

Fear 3. “AI is just another fad – it will burn out soon.”

Why they’re worried: Marketers have been known to chase the latest and most exciting tech, often before its usefulness is truly validated. If AI is to be worth the investment, it needs to create long-term value.

The reality: While it’s fair to ask whether AI is just the next marketing buzzword, it’s also clear that it’s much more than hype. AI is a decades-old discipline with significant advancements that have already impacted our lives. Machine learning (a subcategory of AI) drives predictive personalization, from Google search suggestions to your Netflix recommendations. AI-powered assistants like Alexa and Siri are gradually finding their way into more homes and pockets. McKinsey predicts that AI applied to marketing and sales functions could create up to $2.6 trillion in value across industries.

How to address the concern: Show that AI has staying power and creates value for businesses like yours. Research current applications in your sector and potential use cases that might benefit your team. Look for tools geared towards solving specific business problems to easily measure their value. Some vendors may offer custom ROI predictions.

Fear 4. “It will make our marketing seem less human.”

Why they’re worried: When we talk about CX these days, it’s primarily about “human” things — emotion, connection, and personalization. So, bringing in artificial intelligence and machine learning can seem counterintuitive.

When we talk about machines being involved in the customer experience, many people think of automated phone systems that send callers in frustrating circles.

The reality: Counterintuitive as it may seem, AI is actually essential to achieving true one-to-one personalization.

After all, it’s unlikely that humans are manually creating personalized experiences for your customers right now — that would take a lot of team members (or a really small customer base).

So, whatever level of personalization you’re offering already relies on software to some extent — probably rule- or segment-based marketing automation.

AI simply takes this existing software to the next level.

By analyzing millions of customer records and data points, an algorithm can understand each customer’s needs and predict the next “best action” for every individual. That means experiences and interactions can be more personalized than ever.

How to address the concern: Ensuring that AI-powered customer interactions still feel “human” — that is, not too cold or impersonal — is mostly down to the quality of the tools you choose.

Look for software that gives you plenty of insight, integrates with all your main data sources, and offers customization at set up, plus ongoing flexibility.

Fear 5. “It will be too expensive or complicated to implement.”

Why they’re worried: AI has traditionally been a very technical discipline. To make it a reality, most companies have built solutions in-house by employing teams of data scientists — and that means a big budget is a must.

The reality: More recently, “productized” solutions have reduced the technical requirement and made AI more readily available. Rather than having to build and train algorithms in-house, companies can roll out pre-built solutions that are then customized to their needs.

It’s easier than doing it yourself, but it can still be time-consuming, requiring input and approval from multiple stakeholders.

There are also self-serve options gradually coming to market, which significantly reduce the costs and internal development needed. These tend to be focused around a specific use case — but often that’s a good thing when you’re getting started.

How to address the concern: Do plenty of research beforehand into potential solutions. It’s not just cost — find out how easy it will be to implement, the integrations that are available, and how much support you’ll need from IT.

And think long-term — once you’ve rolled out the initial project, how much control will you have? Will you need additional support (and budget) to make changes, or can you manage things yourself?

SaaS platforms are good for this as they tend to be hands-on — just make sure you get something user-friendly.

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