Why AI Marketing Fails and How to Fix It

I get asked frequently about why an investment in their time, energy and commitment to AI Marketing is failing or has failed. While still bullish on AI marketing, I’m always a proponent of marketing teams who should take a measured approach to AI in any industry.

Recently, we worked directly with several dozen mid-large size firms and recently surveyed 162 companies with $5M or more decision at stake on their post AI marketing efforts.

Here were some of the most common challenges:

1. Lost Customer Centricity

Sixty-three percent (63%) shared that they felt they lost sight of the customer in applying AI technology to marketing, sales, and customer support data. Outsourcing entire AI projects led to a near complete focus on data and analytics and a near loss of empathy and customer experience journey expectations.

Lesson: Partial AI efforts can be outsourced, but a brand must continue to own in the process. You can either seek firms who have deep experience in your industry/target market or consider planning for a more integrated collaborative approach with your support firm. Data alone tells only a partial picture. Without intuition and experience, your brand will fail to shed the light on the critical context. 

2. Wrong Bus, Direction, and Rules of Engagement

Seven-two percent (72%) felt they there were gaps in skills, roles, and policies. As brand re-architect their go-to-market efforts leveraging AI and data, many of these leaders expected a learn as you go approach. The quandary for these executives is between pilots that show little real results or greater investment and time to execute greater scoped strategies.

Lesson: People still are at the heart of AI efforts.

First, no matter the size in scope, buy-in at C-level leadership is required for any customer experience related AI efforts. Directly or indirectly, real results can only come from real engagement from the data or orchestration. Not just an aspiration, this executive support should coincide with a clearly defined vision with goals and objectives.

Next, defining roles and responsibilities ahead of time are critical. There are many different facets of AI enabled marketing beyond data science. Legal, customer success, partnerships, and operations are just a few non-traditional ones named throughout the survey. Defining roles aids in focusing deepening educational efforts.

Finally, with a collective diverse team, outline rules and policies of using AI.  The scope AI enabled marketing policies range from technical, social, regulatory, and ethical.

3. Garbage In, Garbage Out

Forty-four percent (44%) of respondents cited data and analytics challenges. Nearly every article out there speaks to the need for better data and use of analytics for when leveraging AI. It does seem surprising that many initiatives still suffer on this front. But why? 

Lesson: What I had uncovered was a failure to align the AI project with C-Level goals.  Most of the data and analytics KPIs were too broad or poorly outlined. For instance, the KPIs were focused on activity, but not engagement or returns. While not all metrics lead directly to top-line revenue, you should find the metrics that tie to CXO level shareholder value, brand value, careers elevation, greater margins, influence, and/or retention goals. Also, consider incorporating operational and customer data across the organization to enhance the visibility of effect of marketing AI efforts.

4. Two Buzz Words Don’t Make a Right

A surprising eight-one percent (81%) felt they attempted too much with AI on their first attempt. Martech is both a noisy and more importantly constantly innovating industry. It becomes easy to be drawn into FOMA (fear of missing out) conversations on the latest buzzwords and acronyms. For one, I love true innovation in tactics and technology. I encourage ‘play’ where you deliver on the t, however, a leap in innovation still comes with incremental steps.

Lesson: CX, CDP, ABM, MAP, Content Marketing, Adtech, Blockchain, etc… All great initiatives. Applying AI in marketing should be the next step after developing the experience in an area. This 81% of respondents attempted to combine a new area and apply AI initiatives in conjunction. Remember, AI (or any tech) only reflects the people, process and data behind it. Either apply AI to marketing areas where you are seasoned or develop experience a new area before an application of AI.

5. Got a budget for AI? Double It

Sixty-five percent (65%) felt they didn’t end up having enough budget or account for the cost. Not that it’s expensive, but too many unknown factors lead to mistakes or unforeseen issues during education, implementation or deployment.

Lesson: AI, like Chess or Go, today’s level of AI in marketing is easy to learn and know conceptually how to apply them in a vacuum. However, the application of AI within the complexities of an organization alongside a dynamic marketplace is another order of magnitude. Review the budget by cross-referencing some of the lessons here along with clear value objectives for AI orchestration, insights or discovery.

AI marketing impact has little to no value when siloed. It’s impact and return can be magnified when aligned with other parts of the organization. Take the extra time to set the long-term vision, clear responsibilities and appropriate budget to set up for success.

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