Generative AI (GenAI) isn’t the shiny novelty in our martech stacks anymore. It’s taken its place among the many useful tools effective marketing calls for, and creatives and marketers alike use it every day to crank out content, enhance visuals and draft campaigns. Yet most of that energy still goes toward speed and one-off experimentation, not real strategic transformation.
That’s a missed opportunity to turn GenAI into a true competitive advantage.
GenAI can power strategy, hyperpersonalized customer experiences, real-time content creation and data-driven creative execution at scale, driving business outcomes. But to get there, marketers need to move beyond experimentation and operationalize GenAI across the marketing function.
Here’s how smart marketers are making that leap.
Step 1: Know What You’re Solving For
Before picking a tool or setting up guardrails, define your use cases. That means understanding the specific job you’re trying to do and whether GenAI or some other AI/non-AI tool (or a combination) is the perfect fit.
Start by asking: Are we solving for insight or creation?
- If you’re trying to predict behavior (e.g., customer churn), forecast outcomes (e.g., campaign performance) or recommend next steps, predictive AI can perform those tasks. It works on structured data like purchase history or engagement metrics and helps answer the question: What should we do next?
- If you’re trying to generate content, like a message, product description or image, that’s where GenAI excels. It works with natural language inputs (like prompts or context) and produces creative assets like copy, visuals and video.
Smart marketing combines the two
For example, let predictive AI identify the right customer segment, and let GenAI tailor the message. If predictive AI predicts a customer is likely to buy running shoes, GenAI can draft a personalized email with relevant messaging and product descriptions.
Define the outcome. Then align the AI to the job.
Step 2: Focus on High-Impact, Low-Friction Use Cases
You don’t need to boil the ocean to start seeing ROI that can be generated using GenAI. Instead, identify two or three priority use cases where GenAI can deliver immediate value with minimal risk or complexity.
Here are some good starter use cases:
- Personalizing product descriptions at scale
- Drafting subject line or CTA variations for A/B testing
- Generating campaign copy
Avoid starting with high-stakes, high-risk tasks like legal sensitive brand messaging. GenAI isn’t ready to fly solo in those spaces and neither are most governance frameworks.
Step 3: Choose the Right Tool and the Right Input Strategy
GenAI success depends as much on what you feed the model as on the model itself. Most tools are trained on public data, which works well for general content but breaks down when brand specificity, accuracy or regulatory compliance come into play. That’s why you need to match input strategy to content risk.
Here’s a helpful framework to think about tools and data strategy.
Tool Type |
Great For |
Watch Out For |
What It Uses |
---|---|---|---|
Off-the-shelf models (e.g., ChatGPT, Gemini) |
Ideation, rapid drafts, generic content |
High hallucination risk, limited brand fidelity |
Public internet data only |
Retrieval-augmented generation (RAG) (e.g., Jasper AI’s Custom Apps) |
Injecting brand, product, or regulatory context |
Still needs monitoring, but lower risk |
External documents, FAQs, content management system (CMS) inputs |
Custom-trained models |
High-volume, high-risk content (e.g., financial services, pharma) |
Expensive, slower to implement |
Proprietary, structured/unstructured, internal-only |
Also, make compliance part of the system. As you scale GenAI, build in the right legal checks, data privacy safeguards and transparency standards from the start. Even the best tools create risk without solid review and governance.
Step 4: Define the Human Layer
GenAI doesn’t have to be customer-facing to drive value. But when it is, the cost of getting it wrong can be far higher than the productivity gains of automating it. You need to define how humans interact with AI across different risk tiers.
Common Human/AI Workflows
Workflow |
Use When |
Example |
AI to Human to Customer |
Quality and brand nuance matter (ads, emails, campaign copy) |
GenAI drafts. Human edits. Final copy goes live. |
AI to Customer (with clear rules on what it can/can’t say) |
Speed is critical and risk is low (live chat, reviews, FAQs) |
GenAI powers customer service chatbot with tight constraints. |
Human to AI to Human |
Supporting internal teams (briefs, summaries, ideation) |
Marketer prompts GenAI to summarize campaign performance, then uses insights to guide creative direction. |
Step 5: Build the Operating System, Not Just the Stack
Most marketers are focused on selecting tools. But the real differentiator is the operating model that governs how those tools are used.
- Who owns prompt libraries and AI playbooks?
- How do we track content versioning and legal compliance?
- What’s the review and revision flow?
- How do we govern usage across teams and vendors?
- Are we training people for new roles?
- How do we measure value beyond speed (brand consistency, message accuracy, business impact)?
Future-Proof Marketing Teams Invest In:
- Governance: Clear AI policies, review standards and approval workflows
- Skill development: Cross-training, mentorship and AI fluency at every level
- Roles evolution: New hybrid roles that blend strategy, creativity and machine fluency
Tools Don’t Differentiate; People Do
Generative AI is a multiplier. But what it multiplies depends on what you put into it, i.e., the quality of your data, your people, your planning, your processes and your judgment.
What the 2025 Cella Intelligence Report says about AI and creative teams applies equally to marketing: The teams that balance AI-powered efficiency with human ingenuity will define the next era of marketing.
Ready to move beyond experimentation?
CTA: Register now for Illuminate 2025: The Efficiency Effect to take the conversation on AI further with creative, marketing and digital leaders. Don’t miss out — save your seat at the event shaping how we use AI to work smarter, create better and move faster!