Generative AI shouldn’t reduce your startup’s marketing headcount

I spend most of my days talking to and working with chief marketing officers of startups and young companies, and several report rising tension in the C-suite over the potential of generative AI.

Many CEOs think marketing headcount can be cut as automated tools allow leaner teams to accomplish more. Remarkably, these top-job leaders are contemplating even broader cuts, with a recent survey indicating that 49% of CEOs believe most or all of their own jobs should be automated or replaced by AI.

But the majority of CMOs I work with see it differently: 81% of the marketers Norwest surveyed in April 2023 indicated they have no plans to reduce team size due to gen AI, with 22% of those saying they plan to add headcount based on the belief that gen AI will make marketing teams more productive.

Gen AI is not a quick fix, at least not yet. It’s too early to know what works, what doesn’t, and how gen AI applications will evolve. None of us can be sure what the potential impact will be on key issues such as employee morale and retention, copyright, bias amplification, and data privacy.

My advice to CEOs: Before making any staffing decisions, partner with your CMO to conduct focused and measurable trials of generative AI tools. Then apply the lessons learned to shape both your org design and marketing strategies.

Here are four concrete steps to optimize your investment in gen AI and drive positive business outcomes.

1. Ask your CMO to provide periodic briefings on generative AI use cases

The starting point for any discussion is an assessment of how gen AI is already being used by the marketing team. You might be surprised how widely it has been adopted: content production, corporate and product messaging, org design, image generation, presentation slides, meeting summaries, and more. Over time, you can identify trends and productivity gains to determine which tasks the marketing team should retain and which can be allocated to gen AI to free up staff for more strategic work.

Before making any staffing decisions, partner with your CMO to conduct focused and measurable trials of generative AI tools.

Execution of this task can be as simple as creating a spreadsheet. I’ve developed a system for tracking gen AI use cases at our portfolio companies that marketing leaders contribute to as a shared “database.”  It gathers date-marked inputs on the tools being used, best and worst use cases, query hacks, and other considerations. I use it to share lessons learned among colleagues and CMOs at portfolio companies. And it can easily be used within a single organization.

My recommendation is to establish a monthly or quarterly schedule for updates. The technology is evolving too quickly for a one-and-done conversation. The CMO’s reports should answer questions such as:

  • What has been the impact of gen AI on productivity?
  • What use cases are most productive (which you should standardize)? Which are least productive (which you should abandon)?
  • What key learnings will inform decisions on future adoption of gen AI tools?
  • What query hacks can all users benefit from?
  • What new opportunities do you foresee from continued experimentation?
  • What data has been collected outside the organization to get a broad view (e.g., from the CMO’s peers and networks)?

You also are going to want similar reports from team leads of other functions affected by gen AI, including finance, people management, and legal.

2. Request a plan for future use of gen AI in marketing

While it’s difficult to forecast how technology and tools will evolve over the coming months and years, it’s important to have a vision of how gen AI will be used in your marketing operations. Have your CMO and team paint a picture of how the tailwinds generated by AI today will free up marketers’ time in the future.

Building off this vision, the team should produce a plan that:

  • Establishes priorities among current and potential use cases.
  • Identifies and prioritizes which best-in-class tools should be utilized.
  • Forecasts opportunities for greater efficiency and output.
  • Sets goals for savings and process improvements, including metrics for tracking progress.
  • Defines a development plan to enhance team members’ skills and to redeploy time from tasks that gen AI can take over.
  • Applies lessons learned to formulating policies for use of gen AI.

3. Commission a company-wide policy for use of gen AI

Tools provide business value only when used properly. And with gen AI apps still relatively new, it’s even more important to establish guidelines for their use to both educate employees and avoid unintended negative consequences.

My conversations with CMOs about gen AI show high levels of concern about accuracy, quality control, data privacy, security, and legal and copyright issues. Despite these concerns, only a handful of companies I’ve talked with have a policy in place to mitigate risk. Most have not even started the process of developing policies and guidelines.

Without clear policies, individuals may apply AI tools in ways that could compromise sensitive corporate or client data without realizing it. The average corporate employee has limited knowledge of how large language models are trained and could easily share sensitive corporate or client data when querying gen AI tools like ChatGPT — a challenge similar to concerns about “shadow IT” in the early days of SaaS and open source software.

In addition to developing a policy, you can mitigate the risk of unintended data exposure by investing in an enterprise-grade gen AI solution. Standardizing on a service such as a bespoke large language model from Cohere or the data encryption and security certifications in ChatGPT for Enterprise will protect your data and send a clear signal to staff about the importance of data integrity.

4. Evaluate gen AI spending in the next budgeting cycle

Gen AI tools are currently affordable enough to be absorbed in the budget for marketing tools, which typically is about 10% of total program spend. The most popular tools (which CMOs tell us are ChatGPT, Jasper, and Bard) are in the range of $30 to $50 per person per month. Many tools are new to market, and their relatively low entry costs allow for plenty of experimentation and learning to gauge their impact.

You can expect costs to increase as gen AI companies solidify their products. By that time, you will likely have a short list of standardized tools for your organization.

Hiring plans for 2024 also must factor in the projected use of gen AI. What jobs can be redefined or enhanced? What opportunities exist for team members to learn new skills and pursue new growth paths? How should job descriptions for new positions be revised?

Think of AI as a new member of the marketing team: What roles will it, and won’t it, take on?

Key takeaways for gen AI adoption

  • We’re just at the beginning of the gen AI revolution, so be cognizant of drawing conclusions prematurely.
  • The choice is not between gen AI and humans; it’s about integrating gen AI into the marketing function to identify opportunities for productivity gains and innovation.
  • Start small, iterate, and learn from the process.
  • Closely follow developments and trends in gen AI and how gen AI is being applied.
  • Develop policies and guidelines to ensure data security.

Embrace gen AI in your organization, because it can relieve your marketing staff of tedious, low-value tasks and free them to perform higher-value work requiring business judgment, institutional knowledge, and visionary thinking. That, in turn, will allow you to redeploy your best performers on new strategic initiatives.

Marketing is already benefiting from gen AI in creativity, efficiency, and effectiveness, but expectations that it will replace many or all marketing headcount are so far unfounded. Instead, CEOs and CMOs should partner to iteratively expand their use of gen AI to drive greater business value from their marketing organization.