Marketers have never been able to produce more content, more quickly. The tools are here, and they’re only getting more powerful and accessible. But now that everyone has AI, access alone isn’t an advantage.
The real challenge in today’s environment is differentiation rather than production. When every marketer in the industry can generate a Medicare enrollment email or a market commentary in seconds, the bar for what makes content effective and worth reading has never been higher.
Jason Mack, Vice President of Applied AI, Enterprise Data and Analytics at AmeriLife, leads the company’s artificial intelligence strategy and data science initiatives across its Health and Wealth businesses. His focus is turning AI from a buzzword into practical tools that make marketing smarter, faster, and more effective without sacrificing the intentional strategy that separates great marketing from noise.
“The sea of sameness is already here,” Jason said. “If everyone’s using the same tool, everyone’s just taking ChatGPT and putting in simple prompts like ‘write me a Medicare enrollment email,’ the tone, the structure is all going to converge on kind of the same thing.”
The inputs are the key, not the outputs
When every marketer has access to the same platforms, like Copilot, Claude, ChatGPT, and Writer, the tools stop being the difference maker. What separates strong marketing from mediocre marketing comes down to what you feed into them and the strategy behind it.
Proprietary data, such as enrollment trends, producer feedback, real call center transcripts, or client objection patterns, is what makes AI output genuinely useful in our crowded industry. Two marketers using the same tool will get very different results depending on the context and prompting they provide.
“If one marketer just says, ‘Write me a market update,’ they’re going to get a pretty mediocre result that looks like the inbox that everyone’s getting,” Jason said. “But the other that pastes in their firm’s house view, the top three client concerns from that week, and their firm’s compliance guardrails will see a night-and-day difference in what’s going to come back. So, strategy still needs to live between the marketer’s ears.”
Brand voice doesn’t survive by accident
One of the most consistent risks Jason sees in organizations adopting AI is the erosion of brand voice, not because the technology is incapable of sounding human, but because most organizations haven’t defined their voice in the first place.
Many companies have brand guidance covering things like colors, logos, and fonts. However, very few have documented their brand voice in a way that an AI model (or even a new employee) could reliably replicate.
“AI is incredibly good at the zero to 80%,” Jason said. “That first draft, getting the structure right, the research synthesis. It’s getting from 80% up to 100%, which is where the emotional nuance, cultural sensitivity, the ‘does it sound like us’ gut check comes in, and that’s where our human marketers earn their keep.”
Jason recommends every marketing team build what he calls an AI voice brief: a short document with five or six examples that show what the brand sounds like and what it doesn’t. That document becomes part of the context fed into every AI-assisted interaction, from emails to campaign strategies to social content.
The marketers who win won’t be the ones publishing whatever the first prompt produces. They’ll be the ones using AI to generate ten options and relying on their own strategy and vision to pick the right one.
Compliance as a design requirement
In regulated industries like insurance and financial services, compliance isn’t just a final checkpoint. Compliant practices should be woven into every step of the content creation process. When AI joins that workflow, the rules don’t change, but the risks of getting it wrong multiply.
CMS requires that Medicare marketing materials be filed and approved before being used by marketers or producers. FINRA and SEC regulations strictly govern what financial professionals can communicate to clients. That scrutiny has real-world consequences, and AI’s tendency to produce content that sounds authoritative even when it’s slightly wrong can create new compliance exposure.
“AI will confidently produce content that sounds perfect, but it could contain subtle errors,” Jason said. “Maybe it’s a slightly wrong plan benefit, or an outdated performance figure, or a missing compliance disclaimer. That’s the kind of error in a FINRA-regulated communication that could create a real regulatory issue.”
Three steps marketers should take right now
For organizations still deciding whether to invest in AI for marketing, Jason is direct: the cost of waiting isn’t just missed efficiency but missed learning as well.
“Every month you delay, someone else is building that muscle memory that’s going to separate the marketing leaders from the followers in the space,” Jason said.
That doesn’t mean overhauling the entire marketing operation. Instead, Jason’s advice is practical: start small, pick one workflow (email campaigns, social content, long-form repurposing, etc.) and learn what works before scaling.
The second principle is equally important. “Your knowledge of your audience, your understanding of what moves a 67-year-old woman in Tampa to pick up the phone, your feel for what that pre-retiree in Chicago needs to hear about their 401k- that expertise is what turns AI output into marketing that works,” Jason said.
The third principle is governance. Build a simple framework now, including what tools are approved, what the review and approval process looks like, and document it. Regulation is coming, and organizations with a thoughtful, responsible approach already in place will be better positioned to scale when it does.
Ready for what comes next?
The current wave of AI adoption with prompting tools, generating drafts, and repurposing content is only the beginning. Jason sees the next meaningful shift coming in the form of agentic AI workflows.
Where today’s AI tools operate on a call-and-response basis, agentic AI will manage entire content pipelines autonomously, including researching, drafting, routing for compliance review, optimizing based on campaign performance, and recommending what to create next.
“Marketing teams will manage these AI agents the way they manage junior team members today,” Jason said. “We’ll be able to get to that hyper-personalization at an individual level.”
The industry’s direction is clear. AI will continue to become more capable, more embedded in marketing workflows, and more critical to how organizations reach and serve their audiences. The organizations that start building their AI marketing strategy, their governance, and their team’s proficiency now will be the ones best positioned to stand out from the flood of content creation.
“AI is a powerful tool, but it’s still just a tool,” Jason said. “The strategy behind it is what is going to separate the leaders from the followers.”
Frequently asked questions
How does proprietary data improve an AI marketing strategy?
The quality of AI output is directly tied to the quality of the context fed into it. A marketer who prompts an AI tool with nothing but a topic will get a generic result. A marketer who includes their organization’s house view, recent client concerns, and compliance guardrails will get something meaningfully different. Proprietary data, audience insight, and institutional knowledge are the inputs that provide a competitive edge with AI.
Where does human judgment remain essential in an AI marketing strategy?
AI excels at producing first drafts, synthesizing research, and getting content to a workable starting point quickly. But the final stretch of the content creation process, where emotional nuance, cultural sensitivity, and brand voice come into play, still requires human judgment. An AI marketing strategy that skips that final human review risks publishing content that is technically complete but misses the mark on tone, relatability, or brand consistency.
How should regulated industries handle compliance within an AI marketing strategy?
In insurance and financial services marketing, compliance requirements don’t disappear just because AI is involved in the content creation process. CMS requires that Medicare marketing materials be filed and approved before use, and FINRA and SEC regulations strictly govern what financial professionals can communicate to clients. AI’s tendency to produce content that sounds authoritative even when details are slightly off creates real exposure. Building compliance review into every stage of the AI marketing workflow is not optional in regulated industries.
What is the first step for organizations building an AI marketing strategy from scratch?
Jason’s practical advice for organizations at the beginning of their AI marketing journey is to start small and build from one focused workflow rather than attempting to overhaul all marketing operations at once. Picking a single use case, such as email campaigns, social content, or long-form repurposing, and learning what works before scaling creates the muscle memory that separates marketing leaders from followers over time.
