Many companies are exploring and integrating AI into their marketing strategies and across business functions. While every organization has its own characteristics, the main goal of integration is to boost efficiency, gather valuable insights and enhance marketing outcomes with less effort.
This article, the second in a series, outlines the essential steps and factors to consider when selecting the right AI solutions for your marketing ecosystem. Building on the previous article that explored setting the right goals for AI integration, this piece emphasizes the importance of choosing AI tools that are tailored to your specific marketing environment and can help achieve your desired outcomes.
Choosing the right AI platforms
First, make sure you choose the right tool(s) for the job. While a handful of platforms are getting a lot of attention in AI, thousands of others can provide writing assistance, content and image generation, predictive analytics and much more.
Based on your goals, make a short list of some of the biggest challenges that AI can help your team solve. Here are a few ideas:
- Spellcheck and fix writing for errors more easily.
- Turn meeting minutes into project briefs and action items for marketing campaigns.
- Generate campaign concept ideas more quickly.
- Quickly analyze datasets using natural language prompts.
- Create first drafts of marketing content, such as blogs, emails and brochure copy.
- Turn a single image into multiple versions for multiple channels, all using different size requirements.
- Generate personalized product images.
- Anticipate customer churn using predictive analytics.
- Route customers to different automated journeys depending on their. engagement and behavior.
Based on the examples above, you can use several types of AI tools, depending on the challenges you choose to focus on. For instance, a tool like Grammarly might be great to adopt as a common spellcheck and writing tool, but another tool will need to be used to analyze datasets. Still, another would be needed to understand customer churn and route those customers to the right automated journey.
Once you’ve identified and prioritized the marketing areas where AI can help the most, it’s time to examine platforms closely. Consider the following criteria:
- Scalability, or how well it can support your organization beyond an initial proof of concept or test.
- Integration with your existing martech stack, so that you have minimal efficiency losses from switching between applications.
- User-friendliness, or how well your current team and new team members who will join over time will be able to utilize it within their day-to-day work.
- Privacy and security and how secure your company’s data is when feeding it to the tool’s machine learning models and other AI processors.
- Total cost of ownership, or how much cost savings or revenue generation occurs from using the product vs. the cost of implementation and maintenance over time.
Existing products may also not fit the use case, security or other marketing or technical requirements. Thus, choosing between building a custom solution or buying off-the-shelf can be complex and unique to each company.
Custom-built platforms can offer tailored features and tighter control over data privacy. In contrast, pre-packaged solutions might offer faster time-to-value and easier implementation due to existing API connectors. Yet, proprietary systems could lead to longer-term costs and reduced influence over product development.
Dig deeper: How to decide which generative AI tools fit your organization
Getting the data (lake)house in order
“Garbage in, garbage out” is crucial when implementing AI in a business setting. An intelligent system is useless if it doesn’t have quality data to learn from and adapt to. Therefore, for AI to work effectively, it needs data and the state of your data storage platforms, like data warehouses or lakes, significantly influences the outcomes.
It’s also essential to ensure that the necessary data is easily accessible and shareable across systems while safeguarding consumer privacy and sensitive company information that should remain confidential. This is why generic AI solutions may not be as suitable for businesses as those tailored for enterprises or custom applications developed with these factors in mind. Given the growing regulatory focus on data handling practices, prioritizing practices and business security considerations in data strategies is crucial.
Dig deeper: How to make sure your data is AI-ready
Understanding and mapping process changes
Integrating AI into your MOps workflows requires a clear understanding of current processes to identify where changes can make the biggest impact. A good starting point is to map the current state against the desired future state involving AI.
With this start, you can then find areas for improvement in the form of:
- Bottlenecks that some increased automation could mitigate.
- Areas where there are not enough resources to do the desired work.
- Instances where generative AI tools could do more work with fewer resources.
- Opportunities to automate reporting and analysis tasks that help marketing teams get better recommendations.
Guide teams through the transition using an interactive process where tests and proofs of concept are used and learned from and where feedback is gathered from everyone involved. Taking an approach like this ensures you don’t make large, untested changes that may not achieve success and minimizes resistance to change by the people you need to support the long-term adoption of these changes.
Incorporating AI into marketing activities goes beyond choosing the technology; it involves preparing your company’s data infrastructure, grasping the process changes and readying your team for the transition. This approach can revolutionize your MOps with AI, resulting in enhanced ROI and customer interactions.
The upcoming segment of this series will explore how to combine these components to implement AI in marketing strategies and projects.
Fuel for your marketing strategy.
Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here.
