CRM/Marketing Automation consultant leading the practice for EigenX, specializing in manufacturing, higher education and financial services.
With the emergence of ChatGPT and generative AI technologies, there is a lot of buzz around AI in every aspect of the marketing world, from tools to processes and use cases. As a marketing leader, before you decide on the use of a tool, it’s important to consider some questions: Is my marketing operations ready for AI? What tools should be purchased and implemented? What kind of resources and skills are needed to incorporate this into our operations?
This article will focus on three simple ways you can measure your marketing team readiness before you decide on a tool and use case.
1. Do we have an experimentation and feedback loop process in our organization?
With AB testing being part of most marketing operations like campaigns, ads and content, marketing operations are a great department to leverage AI. As you explore the usage of testing in different areas in your department, the key criteria for the successful implementation of AI is to collect feedback on experimentation and make decisions on experimentation.
Here are the questions you should ask to verify your marketing operation readiness.
1. Do we have a process to collect feedback on experimentation, and is the feedback captured digitally?
2. Do we have a decision-making process, and are we empowering team members in making decisions?
3. Do we have a framework for experimentation, and do we have a way to capture variables, factors, etc.?
This is a great opportunity for marketers to create a foundational process for defining experimentation, inputs and outputs and have ways to leverage this for AI. As a marketing leader, before you start on generative AI, it is important to ask your existing marketing teams these questions and identify a baseline process to define experimentation, decision factors and criteria.
Generative AI is a long game subject to experimentation, failure and feedback, so defining experimentation or leveraging an existing process is important before launching a generative AI project.
2. Do we have the right data and quality for implementing a generative AI project?
For most marketing departments, marketing data resides in multiple systems from CRM, marketing automation, data warehouses, online prospecting tools and others. The ideal state would be to have connected data where there is a single view of your prospect and customer across connected systems. But this could lead to a huge data project with IT and will lead to huge delays in leveraging generative AI.
Before getting overwhelmed by the right data, here are some questions you can ask that will help you get a headstart on your AI project.
1. What are two to three use cases you can use for your AI project? For example, leveraging different subject lines for your email campaigns, different content based on a persona for your blog, emails, and social posts and leveraging content for your podcasts with generative AI are some use cases to start with.
2. Do you have three to five years of historical data for your use cases for the AI tool to generate meaningful content?
3. Does the IT team have a data strategy to measure data quality on the datasets and future projects on integrating the data that would help in decision making?
By partnering with IT organizations and asking them these questions, marketing departments can effectively measure data readiness, which is critical for your next generative AI project.
3. Do we have the right team with the skills and mindset to work on the next AI project?
Once you solve the data and process problem, it is time to focus on the right people to engage in the AI project. This would be a cross-functional team with a combination of subject matter experts (SMEs), technologists, early adopter mindset, customer experience teams and experienced people who would help in driving the success of the AI project.
Before jumping on the flashy AI tool, here are some questions which you can ask internally to measure people’s readiness for your AI project.
1. Do we have people who have done one or two experimentation projects, and are they willing to share their failures and successes equally? AI is a mindset-first, people-second process, followed by a tool that is going to help make it successful.
2. Do we have team members in customer service who know the persona of customers, marketing teams who know more about the prospects, IT teams who know the data and sources, customer experience teams who know the journeys for prospects and customers and SMEs on pocket areas like ERP, finance, contracts and more?
If you are struggling with who would comprise the ideal team for the project, begin with a group of folks with representation in sales, service, marketing, finance, IT and customer experience.
3. Do we have seasoned project managers who can execute the project dealing with external and internal team members? This is very critical for the success of the project. Instead of just choosing a random marketing team member, it is important to choose an experienced marketing team member who has worked with different teams to head the project. Engage an external project manager who can fill the gaps with your internal teams to drive success.
In Conclusion
To summarize, marketing teams are primed for AI deployment and implementation. Ensure you have the right experimentation process, at least two to three use cases to experiment with and the right team to start your marketing AI project.
Finally, communicate with the team that AI is not taking their jobs but helping them. Reassure mistakes are OK through an experimentation culture and incentivize SMEs to give feedback to help implement your use cases.
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