CMOs on AI Adoption: Overcoming AI Marketing Challenges

CMOs Reveal Why AI in Marketing isn't as Easy as It Sounds - Key Challenges in AI Implementation 

Key marketing functions driven by cutting-edge AI tools sound like the future—until you try to make it work in a real-world setting. Beneath the hype, real challenges can pose tough obstacles, even for the most thought-out plans. 

While surveying marketing executives and CMOs as a part of my doctoral thesis, I got a reality check on how some solid real-world challenges are thwarting their AI implementation plans. But, before I discuss the challenges, let's look at the key marketing verticals in which marketing executives plan to derive maximum benefit from AI tools in the next 12 months: 

The survey shows more than 1 in 3 (34.5%) executives anticipate Gen-AI-based content creation as a key marketing game changer. Other major areas offering high potential for fruitful AI implementation include recommendation engines (25.40%), telesales (24.50%), and customer interaction through Chatbots, as well as other AI tools (22.70%). Additionally, around 1 in 10 executives (9.10%) think lead generation and qualification can be one of the main beneficiaries of AI tools. 

AI Marketing - A Holy Grail or an Effective Sidekick?

Another interesting study finding is that 28% of respondents believe AI is a holy grail for marketing (potentially replacing human marketing agents in core functions). In comparison, 72% think it's an effective sidekick, i.e., it offers an effective complementary role to the human marketing agent. 

As the majority believes in AI's potential to empower human marketers and make them more effective (rather than making them redundant), the fear of large-scale job loss in marketing due to AI seems a little too far-fetched. 

While most marketing professionals are excited about exploring AI tools and how they can add value to their marketing functions, they face several critical challenges while transforming ideas and technologies into solid business solutions. 

Let's explore these challenges. 

Main Barriers to AI Adoption in Marketing 

Many of the AI implementation challenges marketers face are not unique to AI. We have observed many of these challenges during major technological upgrades, be they computerization or the interaction of Internet technologies. In that sense, my research findings align with McKinsey's global survey, which found that change management and strategy-related factors pose significant challenges to adaptation. 

Lack of Awareness

1 in every 2 (49%) marketing executives surveyed in my study revealed that a lack of awareness of the available tools is the primary barrier to AI adaptation. Given the rapid proliferation of AI tools following the buzzing entry of ChatGPT, it's overwhelming for marketers (many of whom are non-technical people) to evaluate and compare tools and select the right ones. This barrier can be eased when AI development stabilizes, and the key players are established.  

Data Privacy, Ethical and Legal Concerns

In December 2023, The New York Times sued OpenAI and its major investor, Microsoft, alleging copyright infringement for scraping its articles to train ChatGPT without permission. The lawsuit highlights concerns over using vast data in AI training without proper acknowledgment or compensation. In my study, 24% of respondents think data privacy and ethical and legal concerns are the primary barriers to AI implementation. 

AI models require vast amounts of data, raising copyright, plagiarism, and privacy issues. Businesses using off-the-shelf models or training their own face potential legal challenges, particularly with copyright and data privacy. For example, using sensitive customer data, such as patient information, to train AI models can breach privacy and confidentiality. Additionally, integrating third-party AI models with existing systems, like CRM platforms, can introduce data security risks like breaches.

Difficulty in Justifying Investment vs. ROI 

In a competitive business environment, strategic decisions cannot be taken in isolation without considering ROI. It's difficult to isolate productivity gain directly attributed to AI implementation. 26% of survey respondents in my study think the difficulty in determining ROI vis-a-vis AI investment is a major barrier to implementation.  

Employee Concerns Regarding Fear of Job Loss 

Fear of job loss is a significant concern among marketers when embracing AI. A recent Forbes Advisor survey revealed that nearly four out of five respondents (77%) are either 'somewhat concerned' or 'very concerned' about AI tools potentially taking over jobs within the next year. 

Source: Forbes Advisor

In contrast, only 10% are either 'somewhat unconcerned' or 'very unconcerned.' This anxiety over job displacement and career uncertainty fuels resistance to AI implementation. In my study, 24% of executives cited their employees' concerns regarding job loss as a primary barrier to implementing AI solutions. 

Technical Complexities and Implementation Challenges

Let's admit it: GenAI's underlying technologies are complex. It's quite challenging for an everyday marketer to make sense of these technologies and make an informed decision. Moreover, marketers often face technical challenges like data readiness and integrating AI tools with existing marketing systems. 

Quality data is the foundation of large AI models powering intelligent chatbots, creative content generators, and advanced image generators. To extract valuable insights from your organization's customer data, it's essential to have a robust system in place to collect, clean, organize, and make vast amounts of data accessible to AI tools. However, in many organizations, data is often siloed and fragmented, which hinders the efficient implementation of AI. 26% of respondents I surveyed cited technological complexities and implementation challenges that constitute critical implementation barriers concerning AI.  

Management Concerns in Committign Large Investment 

While off-the-shelf AI tools are cost-effective, they could be better suited for companies worried about data security, protection of prosperity data, and other ethical and legal concerns. In that case, one alternative is to develop and train in-house AI models, which can be costly. Additionally, organization-wide implementation of ready-made AI tools can raise a hefty bill. 25% of respondents think management's unwillingness to commit to large-scale investment, especially in an evolving technology like AI, is the primary implementation barrier. 

CMOs' Final Thoughts on AI Adoption in Marketing

While multiple barriers exist in AI marketing, CMOs emphasize that they can be overcome with a strategic approach to AI adoption. To successfully integrate AI into marketing strategies, organizations must adopt robust AI marketing practices that align with business goals and customer needs. This includes recruiting and nurturing AI talent, building the necessary infrastructure, fostering a culture of innovation and risk-taking, promoting transparent communication to ease apprehensions, gaining management support, and implementing governance models that ensure accountability in AI usage.

Moreover, since customers are key to driving growth, they should remain central to all AI design principles. By leveraging AI and other innovative technologies, organizations can deliver a seamless, multi-channel, multi-touchpoint experience that enhances customer satisfaction.

What barriers do you see in your organization or your client's organization when it comes to adopting AI for solving business challenges?