AI influence in product leadership: How teams must adapt

Key takeaways

• Solve problems, don’t just deploy tech. Successful AI influence in product starts with a clear business pain point, not a mandate to “use AI.”
• Human behavior is the biggest hurdle. The technology is ready, but shifting team habits and workflows requires intentional leadership.
• Trust is your currency. As AI agents handle more interactions, brand trust becomes fragile. You need guardrails to ensure consistency.
• Strategy remains human. While AI handles execution and analysis, strategic direction and “gut feel” are still distinctly human traits.

AI is disrupting business strategy and content creation, but navigating AI influence in product leadership requires a steady hand. On a recent episode of the Markup AI podcast, we sat down with Kayce Danna, our VP of Product. Kayce shared her journey from AI skeptic to power user, the lessons she’s learned building AI products, and why she believes the future of technology relies heavily on human trust.

Here are the highlights from our conversation.

From skeptic to daily user

Like many, Kayce didn’t immediately buy into the AI hype. “I admit to being shortsighted,” she says. “It felt like a toy.”

Her perspective changed not through a single flashy demo, but through practical utility. As a verbal processor working remotely, she found that Large Language Models (LLMs) could act as a sounding board, organizing her “stream of consciousness” thoughts better than she could alone.

Today, AI is embedded in her personal operating system. She uses it to generate step-by-step tech support guides for her parents, decipher complex real estate contracts, and even automate a personal concert calendar for her friends.

AI influence in product: The trap of “cool tech”

In the product world, it is easy to get wrapped up in innovation for innovation’s sake. Kayce shared a humbling experience where she built a prototype based on cutting-edge LLM prompts. The dev team loved it, but the customers were unimpressed.

“Customers don’t care what’s happening behind the scenes,” Kayce explains. “They want an outcome. They want a tool to solve a job.”

This reinforced a critical lesson for Markup AI: We must lead with the problem. Whether you are a developer or a marketer, if you focus on the technology rather than the pain point, you will miss the mark.

Overcoming the human hurdle

When asked about the biggest opportunities for businesses, Kayce points to automating repetitive tasks—like competitive research and data analysis. However, she notes that the primary limitation isn’t the technology; it’s human behavior.

“Getting people there is really challenging,” she says. “Not everyone is comfortable just rolling up their sleeves and trying something new.”

Successful adoption requires a mix of top-down leadership and bottom-up knowledge sharing. It’s not enough to buy Copilot licenses and walk away. Teams need frameworks, training, and permission to experiment without fear of failure.

Trust is the new currency

As content explodes and personalization scales, the room for error increases. If an AI agent generates off-brand content or hallucinates facts, the user doesn’t blame the AI—they blame the brand.

“You get very few chances to screw up before people walk,” Kayce warns.

This is where Content Guardian Agents℠ become essential. To scale confidently, you need a system that can automatically scan, score, and rewrite content to ensure it meets your compliance and brand standards. We treat these guardrails as enablers, not blockers. They allow you to move faster because you know your output is safe.

What AI can’t replicate

Despite her enthusiasm for automation, Kayce believes humans remain essential for the “last mile” of creation. In the near term, AI struggles with:

• Strategy: Determining what to build or say, rather than just how to build or say it.
• Intuition: The “gut feel” that drives differentiation.
• Context: AI is only as good as the data it accesses.

“If you ask an AI what to build, you’re going to get cookie-cutter answers,” she notes. The human element provides the strategic direction that separates a generic brand from a market leader.

Focus on the problem

Kayce’s final advice for businesses deploying AI is simple: Don’t start with the solution.

“Use more AI” is not a strategy. Instead, ask “What problems do we need to solve?” and “What tasks do we want to automate?” Once AI touches your workflow, ensure you have clear governance and ownership.

By focusing on the problem and integrating the right guardrails, you can empower your team to scale AI safely and effectively.

Frequently asked questions

How can I encourage my team to adopt AI tools? Adoption requires more than a mandate. Provide training, share success stories from within the team (bottom-up leadership), and ensure leadership models the behavior (top-down). Focus on how the tools solve specific pain points rather than just the novelty of the technology.

Why are guardrails important for AI-generated content? As you scale content production with AI, the risk of errors and brand inconsistencies increases. Guardrails, such as Content Guardian Agents, allow you to scan, score, and rewrite content automatically. This ensures every asset aligns with your compliance and brand voice before it reaches the customer.

Will AI replace the need for human strategy? Not in the near future. While AI excels at execution, data analysis, and pattern recognition, it struggles with strategic intuition and the “gut feel” required for true market differentiation. Humans are still required to set the direction and provide the necessary context.