AI is a big gift for marketeers, brands and agencies alike - from personalising customer experiences, optimising ad campaigns, to shortening client and category knowledge build during a pitch process. However, despite these benefits - many organisations struggle to adopt AI in a way that is effective and genuinely beneficial. Having undertaken a number of fractional CMO roles over the past couple of years has shone a light on the issues marketing teams have when exploring AI solutions.
- Hype, misinformation and misleading AI solutions. It feels like we’re in classic ‘peak of inflated expectations’ territory. I’ve seen first hand some of the misleading claims - either from an external partner or internal teams - how AI tools are a panacea to a digital transformation project - which requires a massive amount of data, advanced infrastructure and heavy financial investment. In reality, solutions which are expensive and often unnecessary - and difficult to gain traction
- Tool proliferation and tech overwhelm. The array of tools available can be overwhelming - a quick search highlights how thousands of tools being added to product hunt daily - and it’s reported that marketing teams utilise up to 10 AI enabled tools. Ultimately this will lead to underutilised tools, wasted resources and hefty subscriptions that often don’t deliver on their promises. The lack of a structured approach to choosing AI tools means that teams end up with overlapping or redundant capabilities, complicating workflows rather than simplifying them
- Uncertainty around implementation. Many marketing teams lack AI expertise, creating a sense of uncertainty around where to start, how to implement and how to measure the impact. This uncertainty leads to either stagnation, or hasty decisions driven by short term goals that don’t align with the broader strategy
How we suggest approaching it:
- Step 1 - Start with the problem, not the tool. AI is a tool to solve problems; it’s not a solution in itself - or as the often misquoted Teddy Levitt put it “people want a ¼ inch hole, not a ¼ inch drill”. For instance, are you looking to improve customer segmentation, optimise ad spend, or streamline your content creation? Clearly defining the problem allows you to explore AI solutions with focus and precision, ensuring that any investment aligns with your actual needs
- Step 2 - Once you have clarity on the problem, focus on finding a tool that aligns with your specific requirements. Building a knowledge bank that categorises tools by their functions, strengths, and limitations can be helpful. This resource will serve as a go-to for your team, saving time and reducing the confusion of sifting through endless options. The DMI have a great resource to help you build from. However, you need to build other factors into your decision making criteria - a simple framework which looks at scalability, ease of integration and a basic cost/benefit analysis (based on your specific marketing problem) e.g to increase lead conversion - assess operational costs, conversion rate, average order value, customer satisfaction, and marketing efficiency
- Step 3 - Finally, Start small by piloting AI in a limited area of your marketing processes. Implement, measure, and iterate based on performance data to understand the actual impact before expanding further. Incremental adoption also means integrating AI into your workflows gradually, allowing your team to get comfortable with the tools and processes
Remember, AI should serve your strategy—not drive it. With the right mindset and approach, AI can be a game-changer for your team.