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When you’re a late adopter of new martech, it feels like being the last one picked for kickball. Okay, it doesn’t have quite the same emotional toll, but you’ll have to deal with the anxiety of the competition getting a leg up while you make a decision. Marketers are rushing to pick the most impactful AI-powered tools and nearly 90% report that their AI software budgets will increase in 2024. However, the latest wave of AI martech brings reason for pause, and marketers have some unique concerns about the new technology that can quickly become barriers to adoption.
The top 5 barriers to AI adoption cited in our recent State of AI in Digital Marketing Report are data security, lack of knowledge of AI solutions, lack of AI strategy, implementation cost, and unclear ROI. Let’s dig into each one and examine how you can overcome them.
Data security is the #1 barrier to marketing AI adoption, and for good reason. From customer data to proprietary product information, marketers handle a vast amount of sensitive data. With the new generation of generative AI, the concern that private data may be improperly secured, used to train publicly accessible AI models, or create security vulnerabilities is very real. The only solution to overcome this barrier is to do your due diligence.
First, it’s more important than ever that your infosec team is a part of your vendor selection process. Every vendor worth their salt will say their security is up to snuff, but your infosec experts are the ones who must validate their claims.
To ensure that your marketing data stays private, start with the basics. Check that your AI vendor employs robust encryption methods to safeguard your data both in transit and at rest. This is particularly important in industries that handle highly sensitive personally identifiable information (PII) like healthcare and financial services.
You should also ensure that financial information like credit card and Social Security numbers are automatically redacted in any AI tool that can transcribe or otherwise store this data. Compliance standards like the Payment Card Industry’s Data Security Standard (PCI DSS) require this.
Without proper encryption and redaction, your marketing insights, customer information, and even trade secrets could be vulnerable to unauthorized access. Check out this blog post to learn about the most important security and privacy questions you should ask when adopting new AI martech.
Nearly all of the marketers (93%) that we surveyed claim to have advanced knowledge of AI, yet a lack of knowledge of AI solutions was cited as the #2 barrier to AI adoption. This can be attributed to the sheer volume of new AI martech cropping up every day. Marketers may also feel other stakeholders in the decision-making process don’t have adequate AI knowledge to see the value in the new technology. Here’s how you can get past both of these barriers.
No matter how cool they are, you can’t pick up every piece of new AI martech. When deciding what AI you should bring on, first consider what problems you need to solve and if the AI can deliver clear value. Look for issues you can solve that can be tied to clear KPIs like conversion rates, acquisition costs, or revenue generation. Then look at the customer case studies for that solution. Are they delivering clear value or fluff?
Take advantage of free AI tools and AI that’s part of the technology you already use. A great example of this is Google Ads. We all use it, but not everyone uses the AI tools it offers. The most powerful and easy-to-implement solutions are Data-driven attribution (DDA) and Google Smart Bidding, as the two work hand-in-hand. Data-driven attribution uses machine learning to allocate credit to all the touchpoints in Google Ads based on their role in driving the conversion. This provides a clearer picture of what drives conversions than first-click or last-click attribution can.
You can then feed all of your conversion data from DDA to Google Ads Smart Bidding, which automatically adjusts your search ad bidding according to what is driving the most conversions.
Finally, there are conversational AI chatbots like ChatGPT and Google Bard that can save you time when creating content, ad copy, slide decks, and more. A word of warning: Don’t enter any proprietary information or private data in any public AI system. Your prompts are used to train the AI and your data could potentially be exposed to other users.
You can’t adopt any new technology without a clear strategy. Well, you can, but it’s a recipe for disaster! Having a strategy in place is particularly important when the technology is new or somewhat unproven. Here are the basic steps you can take to develop an AI strategy for marketing.
The first step you need to take is to determine what your pain points are and filter out the ones that AI can help solve. You don’t need to throw AI at processes that already work well. The most common use cases for AI in marketing are:
One of the biggest hurdles in developing a marketing AI strategy is that there are often many stakeholders involved. They may not even be in your department and can span sales, user experience, and more. When you’re evaluating solutions, be sure to involve everyone that it might impact. You also need to make sure that they know the value of the AI tools you’re considering—they may not be as well-versed as you so be prepared to do some education. Being a marketer, you should be pretty good at that!
As awesome as it would be to AI all the things, you can’t do it all at once. Start with some low-hanging fruit like using ChatGPT to write ad copy or using Google Smart Bidding and Performance Max to optimize your ad buys to drive more revenue. Once you can prove the results, then you can build on your success with more complex projects and platforms.
Sometimes the results of new AI martech software can be kind of squishy. It might save you time developing content or help you write better ad copy, but you may not be able to quantify it with hard data that shows ROI. When you’re first getting started, make sure that you establish your KPIs ahead of time and try to tie them directly into revenue or cost savings. This isn’t to say those squishy results aren’t worth pursuing, but maybe they’re not worth spending money and time on right now.
Your entire organization has to be on board with making AI a priority or you can’t make a case for the budget. These days, though, that shouldn’t be too hard to do. We found that nearly 90% of marketers said that they will increase their investment in AI next year and an equal number said they will have a budget dedicated to AI-powered martech in 2024.
Budgets are still tight these days and not everyone can get a dedicated budget to experiment with AI. To get around this, look for budget line items that already exist that you can add AI to. You can add AI features to your email marketing platform to help you craft better emails. You may be able to get personalization tools with the budget for your CMS. If you use call tracking to attribute calls to marketing, you can get more customer intent and conversion data by analyzing marketing-driven calls with AI.
You can also look to other stakeholders in your organization to contribute budget to new AI tools. If another division like sales, the contact center, or UX can benefit from the same tool, make sure to involve them in the decision-making process and see if there’s a way to split the cost.
If you can’t show that new AI martech is going to deliver value, it’s going to be a tough sell to get budget approval. Here are a few steps you can take to determine if the shiny new AI you’re looking at will deliver the goods and help you get the budget approval to adopt it.
There are barriers to AI adoption, but they are by no means insurmountable. Everyone wants to be an early AI adopter, but careful consideration of the barriers to adoption makes it more likely that you’ll also be an early success story.
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