Marketing teams are under growing pressure to use AI. Tools are evolving quickly, use cases are multiplying, and the expectation to experiment is increasing. The challenge is to bring AI into daily work without causing more disruption, uncertainty, or disconnected tools. That makes AI adoption a leadership and workflow challenge, not just a technology rollout.
The goal should not be AI for AI’s sake. It should be better outcomes with less friction. For marketing leaders, that means helping teams understand where AI can make work clearer, faster, or more focused — and where human judgment must remain central.
A practical approach starts with three moves: begin where work already happens, refine AI output to protect brand voice, and build confidence through small experiments.
Start Where Work Already Happens
AI adoption works best when it is built into the way teams already work. If teams must leave their normal workflow to find value, AI can feel like another task on top of the day job. If it helps inside the tools and systems teams already use, adoption feels more like an upgrade than a disruption. That means using embedded AI features within project management, collaboration, content operations, CRM, or creative platforms can help teams work faster and with less friction. For example, an AI-enabled project management workflow might help create a first-pass estimate, flag a potential budget issue, or turn meeting notes into action items without asking the team to leave the system they already use.
The important question is not “Where can we add AI?” It is “Where is the work already creating friction?” Adding AI as the 11th step in a 10-step process will not create operational harmony. It may simply create another process to manage. But using AI to reduce manual effort, surface patterns, or accelerate repetitive tasks can create capacity for higher-value thinking. Even deciding where and how to use AI is valuable because it helps teams examine workflows more critically: What is repetitive? What requires judgment? What can be accelerated? What must remain human-led?
Refine AI Output to Protect Brand Voice
AI can help teams get to a starting point faster. It can draft, summarize, organize, compare, and generate options. But AI output should not be treated as the final answer. The last thing marketers want is to dilute their brand’s message and authenticity with standard issue AI-generated text. Keeping brand integrity and voice intact requires human expertise and empathy. As a result, marketers have become more important, not less. It’s essential for marketers to evaluate whether AI output is accurate, relevant, differentiated, and appropriate for the audience. To avoid aesthetic drift, they need to refine tone, challenge assumptions, verify claims, and protect the nuance that makes a brand recognizable.
To improve AI output, feed it stronger inputs. Instead of vague prompts, teams should use approved source material, messaging frameworks, brand guidelines, audience insights, and examples of what “on brand” and “off brand” look like. A creative team might use AI to generate a rough wireframe, mood board, or first set of message options, then rely on human review to refine the idea for brand voice, audience fit, accuracy, and compliance. Leaders can also encourage teams to ask AI for options rather than finished work. A first draft, outline, concept, or set of variations gives the team something to react to, improve, and shape. The value of AI is not that it replaces judgment. It gives teams something to apply judgment to sooner and creates a clearer starting point.
Build Confidence Through Small Experiments
AI anxiety often comes from uncertainty. What is allowed? What is safe? What if the output is wrong? What does this mean for my role? Leaders can help by making experimentation practical, visible, and low risk. The best starting points are simple, internal, and tied to a real pain point. A useful first experiment might be summarizing a meeting, drafting a brief, creating variations from approved messaging, organizing research themes, or identifying gaps in a document before moving into anything customer-facing. These experiments make the payoff easier to see and help teams feel the value of AI in real work.Confidence grows when experimentation is paired with feedback. Rather than measuring success only by time saved, feedback loops that capture what worked or what required human judgement, over time compound into better AI practice and increased trust for the team.
Small wins matter because they make AI feel useful, not theoretical. Teams are more likely to engage when they experience what AI just did for them. The message should not be, “Use AI because everyone else is.” It should be, “Let’s find where AI can give time back, reduce friction, and help us focus on the work that matters most.” That is the clearest payoff for teams.
Make AI Practical, Safe, and Connected to Outcomes
AI will continue to become part of the tools marketers already use. The differentiator will not be access alone. It will be how thoughtfully teams apply it. Marketing leaders can help by creating the conditions for practical, safe, outcome-focused experimentation: start where work already happens, keep humans in the refinement loop, and build confidence through use cases that create visible value. That is what turns adoption into better work.
The goal is not to make teams use AI more. It is to help them use AI where it improves the work, protects the brand, and creates capacity for better thinking. That is the payoff.
Kate Roberts, Director, Strategic Accounts & Managed Services at Cella, has over 30 years of creative operations, marketing and leadership experience. She previously ran an in-house agency for a global Fortune 500 financial services firm where she guided the international expansion of her team, across the U.S., Canada, Mexico, Poland, India and Malaysia. Kate takes a client-centric approach in her responsibilities and values diverse insights, open discussion, and collaboration – and believes leaders need to foster an environment for success.
