In just a number of years since AI took off, it has evolved from being the shiny recent toy in marketing to the backbone of how agencies operate.
In fact, based on Forrester’s 2024 report, 91% of U.S. promoting agencies are either using or exploring generative AI.
But implementing AI successfully isn’t so simple as plugging in a tool and expecting magic. You need a transparent AI implementation technique to be sure it enhances their work fairly than disrupt it.
To help, we spoke with agency leaders and AI experts to uncover practical suggestions and best practices for integrating AI into your agency’s workflow. Here’s what they’d to say.
1. Assign a transparent AI champion
One of the most important challenges agencies have with AI adoption is lack of focus. Agencies typically fall into one in all two traps (or each):
- Teams don’t have the time (or don’t put aside the time) to check out AI tools or properly train them.
- A number of employees check out tools here and there, but no proper implementation follows across the board.
“If we were to say, ‘everybody must implement AI’ without anyone really driving it, we’d get nowhere,” says Artūras Lazejevas, CTO at Whatagraph.
The best method to combat that is to appoint someone who’s responsible for driving AI adoption across departments.
In most organizations, that is either the CEO or CMO, based on this 2024 State of Marketing AI report by Salesloft and Marketing AI Institute. (*7*), CTO ranks 4th, just under “nobody owns AI”.
2. Identify areas where your team time is wasted
Once you have got an AI champion, their first task ought to be pinpointing where teams are losing time on repetitive tasks.
Artūras suggests an easy exercise:
- Track team hours for per week—what’s eating up time?
- Identify repetitive, low-value tasks—are manual reports, content creation, or data entry taking on worthwhile hours?
- Find quick automation wins—for example, Whatagraph’s website positioning team saved hours by automating alt-text generation using a low-code AI tool.
But whilst you’re at this, it’s also necessary to evaluate whether a selected task or a process is definitely needed and price automating with AI.
“A foul process, when automated, just becomes a faster bad process,” says Peter Lewis, Founder & CEO at Strategic Pete, a marketing consultancy agency.
“I’ve seen agencies jump into AI for lead generation with no good system in place to follow up on the leads. The leads began flowing in, however the sales didn’t since the team had no good workflow to handle the load.”
Before automating anything, audit your processes. Ask yourself:
- Is this process worthwhile and really needed?
- What inefficiencies can we eliminate?
- Is there a better method to achieve the identical goal?
Once you’ve identified that a selected process is needed and worthwhile, you’ll be able to got down to automate it with AI.
3. Start small and construct Proof of Concept
AI feels huge and overwhelming for most agencies, however it doesn’t must be. Instead of overhauling entire workflows, start with small, low-risk pilot projects.
“Companies get stuck because AI seems like an elephant they usually’re afraid they won’t have the opportunity to tackle it,” says Artūras. “But you’ll be able to adopt it step-by-step. Start with easy tasks, for example using Perplexity AI for research as a substitute of Google.”
His advice is to give attention to:
- Quick wins: Identify small, repetitive tasks that AI can automate.
- Fast iterations: Build and test easy solutions without overcomplicating them.
- Scalability: Once an idea works, scale it quickly to maximise its impact.
This can be how Justin Belmont, Founder and CEO at Prose and former Editor-in-Chief at Google implemented AI at his agency.
He tells us, “We began small—testing tools on internal projects and seeing what stuck. Once we nailed down the workflow, we scaled it up for client work. It wasn’t a big-bang launch; it was a series of little wins.”
4. Train your team, but don’t overthink it
Another key area of successful AI implementation strategy is training, however it doesn’t should be a large, resource-heavy initiative.
“Training doesn’t should be perfect; it just must occur,” says Artūras.
For example, when he wanted non-engineers to learn prompt writing, Artūras quickly found Anthropic’s slides online, trained himself on them, then held a fast team session, recording it for future use.
“The prep work only took me 4 hours. I didn’t design an entire training program—I just went ahead and did it,” he shares.
To train your team effectively:
- Keep it easy: Focus on small, specific skills your team must adopt AI effectively.
- Start with quick wins: Choose a tool or process that gives immediate value, like improving prompt writing or automating repetitive tasks.
- Iterate as you go: Training doesn’t should be perfect from the beginning. Run a fast session, gather feedback, and refine as needed.
5. Get your data ready
AI is simply pretty much as good as the info it’s working with. If your data is messy, incomplete, or scattered across multiple platforms, even probably the most advanced AI tools won’t have the opportunity to deliver accurate insights.
“Data readiness and cleanliness are really necessary,” says Artūras. “If you consolidate cross-channel data and ask AI to generate summaries, your mind will likely be blown by how well AI can detect every thing.”
Here’s the way to ensure your data is AI-ready:
- Centralize your data: Use a platform or tool that consolidates data from multiple sources, like ad platforms, analytics tools, and CRMs.
- Clean your data: Remove duplicates, fix errors, and standardize formats to make sure consistency.
- Test your outputs: Run small AI tasks, like generating a report summary, to see how well the info works in practice.
For instance, with a marketing intelligence platform like Whatagraph, you’ll be able to integrate data from 55+ marketing channels and ask AI to present you fast insights, corresponding to:
- Which ad campaigns brought in the best conversions?
- How much money did we spend on X campaign last quarter?
- Which blog pages converted probably the most?
This helps you understand marketing performance in as quickly as 3 seconds and optimize your campaigns for higher conversions and ROI.
6. Foster a culture of experimentation
AI is usually a sensitive topic to bring up together with your team. With a lot hype around AI previously 12 months, it’s natural for employees to feel anxious about being replaced.
The best method to tackle that is to:
- Clearly communicate that AI is just not a substitute, but an enhancement
- Encourage team members to boldly experiment with AI
“A 12 months ago, there was fear and trepidation,” Robin Emiliani, Founder and CEO at Catalyst Marketing recalls. “But now, now we have a team of daring, fearless people who find themselves continually experimenting.”
She compares AI adoption to past industry shifts: “The individuals who embraced social media when it exploded, or marketing automation when it first got here out, were those who ended up ahead. AI is identical. This is the time to double down.”
Instead of hesitating, agencies should test, iterate, and explore. Robin puts it bluntly: “We’re a growth marketing company—we’re all the time hacking, testing, and trying recent things. I imagine that’s the mindset that wins.”
Agencies can create a secure AI testing environment by:
- Encouraging teams to try AI tools on low-risk tasks
- Showcasing AI success stories internally
- Running team workshops to assist employees explore AI tools without fear of failure
7. Be transparent with clients
And finally, in case you’re planning to make use of AI in any way at your agency for client work, it’s essential let your clients know.
AI’s role in marketing continues to be a sensitive topic. Some clients find it irresistible, while others are wary. And in case you’re not transparent, you risk losing their trust, and their business.
“Many agencies don’t tell, advertise, or are transparent about their use of AI in client-facing work,” says Ryan Anderson, President at Markiserv, a creative design agency. “We’ve had clients switch to us because their previous agencies used AI without disclosing it.”
Lack of transparency may cause serious trust and compliance issues. Some brands have strict policies against AI-generated content, while others only allow specific tools.
Robin Emiliani, CEO at Catalyst Marketing agency, shares how her agency navigates this:
“Out of our 25-30 clients, a couple of quarter say, ‘You cannot use AI with us.’ Others say, ‘You can use AI, but only these specific tools.’”
Her advice is to make clear AI policies upfront. Ask questions like these in your onboarding document for a brand new client:
- Do you permit AI in our workflow?
- If yes, which tools are approved?
- Are there security restrictions we’d like to follow?
Then, undergo this on an onboarding call to be sure each you and your client are aligned on AI.
Robin advises, “Really get a transparent understanding from the clients, what you’re allowed to do and what you’re not allowed to do before going all in.”
The Bottom Line
AI is here to remain, and agencies that implement it strategically will gain a competitive advantage. But success depends greater than just AI tools—it’s about having the fitting processes, culture, and leadership in place.
To recap, listed below are the perfect practices from agency leaders and AI experts to implement AI effectively:
✅ Assign an AI champion to drive adoption
✅ Identify time-wasting tasks and processes value automating
✅ Start small, construct proof of concept, and scale regularly
✅ Train your team, but don’t overthink it
✅ Use a marketing intelligence platform like Whatagraph to be sure your data is AI-ready
✅ Foster a culture of experimentation so teams embrace AI, not fear it
✅ Be transparent with clients about AI usage
Want more practical strategies and AI tool recommendations from agency leaders? Download our AI Playbook for Agencies in 2025.
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