5 CMO Pain Points around AI and How to Overcome Them

Share

Summary

What are CMOs saying about AI? Here are the top 5 pain points discussed in a CMO Coffee Talk session. CMOs get real and discuss their concerns about AI and what they really think about it. In this post you'll take away ideas on how to overcome each pain point.

By Brenna Lofquist, Client Services Operations Manager at Heinz Marketing

It’s no secret AI has been a hot topic for awhile now. It has been around for quite some time but within the past year or so it’s really exploded. Now a days you can’t go on LinkedIn without seeing a handful of posts about the topic.

AI is something most companies are still navigating and will be for a long time. So what are CMOs saying about it? What are they concerned about when it comes to incorporating AI into their organizations?

Well, I thought what could be better than pulling insights right from the source. Back in March, our CMO Coffee Talk session was about how CMOs really think and feel about AI.

A little back story first. Matt Heinz started CMO Coffee Talk as a Slack community of B2B CMOs and Heads of Marketing. The community is now over 3,000 and every Friday morning they hop on Zoom to discuss a variety of topics. It’s never recorded so participants can share candidly with each other. It can get emotional, and it often gets very, very real.

After each session, Matt takes to LinkedIn and posts some of the comments from the conversation – a little sneak peek into the discussion. I took the comments, pasted them into ChatGPT and asked it to pull out the top 5 pain points expressed by the CMOs. And now here we go!

Fueling Growth Through Change Guide

Top 5 Pain Points

Overwhelmed and Information Overload

“There is SO much info out there on AI for marketing, it’s hard to know what’s good vs bad, what’s right for your needs, what’s fluff, etc.”

“I’m overwhelmed by every email being about a webinar/training/blog about AI and that THAT one is the best intro…”

This is definitely true for most people. There is an infinite abundance of information out there and it very easily gets overwhelming, to the point where you might just give up, at least in that moment.

Here are a few ideas on how to overcome the information overload:

  1. Curated learning: Take the time to identify a list of reliable sources of AI information your organization could reference. Limiting sources can help filter out the noise and ensure everyone is operating off the same information.
  2. Dedicated time blocks: Carve out specific time each week (or whatever cadence works best) for learning about AI. Focus on catching up with the latest developments or reading something from your curated list of resources.
  3. AI Specialists or Consultants: Hire or consult with AI specialists who can provide guidance tailored to your company’s needs. (Check out Nicole Leffer)
  4. Internal Knowledge Sharing: Establish an internal knowledge-sharing platform or location where team member can share key insights and useful resources. You could even create a Slack channel (or similar) for quick tidbits.

Fear of Change and Uncertainty

“Can we talk about that fear of change? The degree of fear people seem to have these days (across companies) within Marketing seems elevated… or is that just me???”

“The AI revolution is giving everyone FOMO and nobody wants to be last to the table. It’s actual madness and likely a huge distraction to what we need to achieve.”

“Change fatigue is a real factor – think about what we’ve all been through over the last 4 ys.”

Change is always hard, especially over the past few years when companies have had to adapt due to COVID. Then you add AI into the mix and there’s even more change happening. Change fatigue is real and you must be really careful about how you implement and communicate changes with your organization.

On the flip side companies, more so employees, are feeling uncertain about the impact of their role with the transition to AI. It’s hard to know what will happen in the future as AI becomes more ingrained in a company’s day-to-day processes.

Here are a few ideas on how to overcome the fear of change and uncertainty:

  1. Change Management Programs: Implement structured change management programs that include gathering feedback, training, clear communication, and support for employees to adapt to AI integration.
  2. Small-Scale Pilots or Tests: Start with small-scale AI projects so you can experiment and work out the kinks before rolling out to the broader group. This way you demonstrate the value without overwhelming the team. Success with pilots can build confidence and reduce fear.
  3. Open Discussions: Foster a culture of open discussion where fears and uncertainties about AI can be expressed and addressed. This could be through regular meetings or anonymous feedback, even a combination of both. Feedback and communication are key to change.
  4. Success Stories and Case Studies: Share success stories and case studies of AI implementation from similar companies to illustrate tangible benefits and reduce anxiety.

Pressure from Leadership

“I’d be curious to know how many of us are getting pressure from our CEOs or others on the leadership team when it comes to AI. – as an Insight-backed company, there’s some pressure there at the board level (for me).”

“We have our annual board meet next week and everyone is asked to incorporate AI in every slide that they present (like how they leverage AI in their work).”

Depending on the company, you might be getting pressure from leadership or your board to implement and adopt more AI. It’s inevitable but how do you handle that?

Here are a few ideas to deal with pressure from leadership:

  1. Align Expectations: Open conversations with leadership will help to align on realistic expectations for AI integration and its timeline. Clarify what can be achieved in the short term versus the long term.
  2. Showcase Early Wins: Demonstrate quick wins or early successes of AI initiatives to build trust and gain buy-in from leadership. Outcomes from tests or pilots would be a great example of this.
  3. Develop a Roadmap: Create a clear AI implementation roadmap that outlines stages, expected outcomes, and resource requirements. Presenting and aligning on the roadmap with leadership will help manage their expectations.
  4. Regular Updates: Provide regular updates to leadership on AI progress, including challenges and adjustments to the plan. Transparency will help manage pressure and build understanding.

Balancing Strategic and Tactical Work

“100% and you get dinged for not being more strategic when the resources you have require you to get in the weeds.”

“Change management is more of my job these days than strategic planning/experimentation.”

It’s easy to get into the weeds when it comes to AI. To be able to use it, or even instruct your teams how to use it, you have to understand it. And no one is exempt from this. However, it can be difficult to find the balance between strategic and tactical work.

Here are a few ideas on how to find the balance:

  1. Delegation and Team Structure: Delegate tactical work to specialized team members or hire additional staff if possible, allowing CMOs to focus more on strategic planning.
  2. Strategic Prioritization: Use prioritization frameworks to categorize tasks and focus on those that align with strategic goals. Ensure tactical tasks support strategic initiatives.
  3. AI Tools for Efficiency: Leverage AI tools to automate repetitive tactical tasks, freeing up time for strategic thinking and planning.
  4. Time Management Techniques: Implement time management techniques such as time blocking or a regular meeting, to ensure dedicated time for strategic activities.

Integration and Practical Application

“GenAI is not something a CMO can ignore, but that doesn’t mean we need to be an expert. Like any other transformational technology, we need to understand it well enough to know when to use it (and not).”

“AI doesn’t fix what’s broken. It amplifies what’s there. So we don’t want it to amplify what’s broken.”

At this point you might have all the other things figured out or at least you’re working through it. Next comes the actual application of AI. It’s easy to think you can throw AI at a problem and it will fix it but that’s not always the case. Don’t fall into the shiny object syndrome like most companies do where they often buy tech willy nilly, often adding to the problem.

Here are a few ideas to overcome integration and practical application:

  1. Practical Training: Offer practical training sessions focused on integrating AI tools into everyday workflows. This can include hands-on workshops and real-world scenarios. I would recommend documenting the workflow first so everyone is operating from the same process.
  2. Cross-Functional Teams: Form cross-functional teams that include AI experts and end-users to work together on integrating AI solutions. This ensures that practical needs are considered during implementation.
  3. Pilot Projects with Clear KPIs: Start with pilot projects that have clear key performance indicators (KPIs) to measure success. Use these projects to refine AI applications before broader rollouts.
  4. Feedback Loops: Establish feedback loops where users can report back on the performance of AI tools, enabling continuous improvement and adaptation to practical needs.

In summary

There is not a one-size-fits-all approach to adopting AI but hopefully these ideas will get you started. It will take time to figure out the needs of your organization and what approach works best. As I mentioned previously, feedback and communication are key! Listen to your teams and make sure they feel heard. Boosting confidence is also important. There’s a lot of anxiety around AI and you want to make sure your employees feel confident.

Interested in discussing AI more? Reach out for a free consultation with one of our experts. 

Image by Freepik