AI Implementation Strategy for Agencies: 7 Best Practices from Experts

In just a few years since AI took off, it has evolved from being the shiny new toy in marketing to the backbone of how agencies operate.

In fact, according to Forrester’s 2024 report, 91% of U.S. advertising agencies are either using or exploring generative AI. 

But implementing AI successfully isn’t as simple as plugging in a tool and expecting magic. You need a clear AI implementation strategy to make sure it enhances their work rather than disrupt it.

To help, we spoke with agency leaders and AI experts to uncover practical tips and best practices for integrating AI into your agency’s workflow. Here’s what they had to say.

1. Assign a clear AI champion

One of the biggest challenges agencies have with AI adoption is lack of focus. Agencies typically fall into one of two traps (or both):

Teams don’t have the time (or don’t set aside the time) to try out AI tools or properly train them. A few employees try out tools here and there, but no proper implementation follows across the board.

“If we were to say, ‘everybody needs to implement AI’ without anyone really driving it, we’d get nowhere,” says Artūras Lazejevas, CTO at Whatagraph.

The best way to combat this is to appoint someone who is responsible for driving AI adoption across departments. 

In most organizations, this is either the CEO or CMO, according to this 2024 State of Marketing AI report by Salesloft and Marketing AI Institute. Surprisingly, CTO ranks 4th, just below “no one owns AI”. 

2. Identify areas where your team time is wasted

Once you have an AI champion, their first task should be pinpointing where teams are losing time on repetitive tasks. 

Artūras suggests a simple exercise:

Track team hours for a week—what’s eating up time? Identify repetitive, low-value tasks—are manual reports, content creation, or data entry taking up valuable hours? Find quick automation wins—for example, Whatagraph’s SEO team saved hours by automating alt-text generation using a low-code AI tool.

But while you’re at this, it’s also important to assess whether a specific task or a process is actually necessary and worth automating with AI.

“A bad 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 started flowing in, but the sales didn’t because the team had no good workflow to handle the load.”

Before automating anything, audit your processes. Ask yourself:

Is this process valuable and actually necessary? What inefficiencies can we eliminate? Is there a smarter way to achieve the same goal?

Once you’ve identified that a specific process is necessary and valuable, you can set out to automate it with AI. 

3. Start small and build Proof of Concept

AI feels huge and overwhelming for most agencies, but it doesn’t have to be. Instead of overhauling entire workflows, start with small, low-risk pilot projects.

“Companies get stuck because AI feels like an elephant and they’re afraid they won’t be able to tackle it,” says Artūras. “But you can adopt it step by step. Start with simple tasks, for example using Perplexity AI for research instead of Google.”

His advice is to focus on:

Quick wins: Identify small, repetitive tasks that AI can automate. Fast iterations: Build and test simple solutions without overcomplicating them. Scalability: Once a concept works, scale it quickly to maximize its impact.

This is also how Justin Belmont, Founder and CEO at Prose and former Editor-in-Chief at Google implemented AI at his agency.

He tells us, “We started 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, but it doesn’t need to be a massive, resource-heavy initiative. 

“Training doesn’t need to be perfect; it just needs to happen,” 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 quick team session, recording it for future use.

“The prep work only took me four hours. I didn’t design a whole training program—I just went ahead and did it,” he shares. 

To train your team effectively:

Keep it simple: Focus on small, specific skills your team needs to adopt AI effectively. Start with quick wins: Choose a tool or process that provides immediate value, like improving prompt writing or automating repetitive tasks. Iterate as you go: Training doesn’t need to be perfect from the start. Run a quick session, gather feedback, and refine as needed.

5. Get your data ready

AI is only as good as the data it’s working with. If your data is messy, incomplete, or scattered across multiple platforms, even the most advanced AI tools won’t be able to deliver accurate insights.

“Data readiness and cleanliness are really important,” says Artūras. “If you consolidate cross-channel data and ask AI to generate summaries, your mind will be blown by how well AI can detect everything.”

Here’s how 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 ensure consistency. Test your outputs: Run small AI tasks, like generating a report summary, to see how well the data works in practice.

For instance, with a marketing intelligence platform like Whatagraph, you can integrate data from 55+ marketing channels and ask AI to give you instant insights, such as:

Which ad campaigns brought in the highest conversions? How much money did we spend on X campaign last quarter? Which blog pages converted 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 can be a sensitive topic to bring up with your team. With so much hype around AI in the past year, it’s natural for employees to feel anxious about being replaced.

The best way to tackle this is to:

Clearly communicate that AI is not a replacement, but an enhancement Encourage team members to boldly experiment with AI

“A year ago, there was fear and trepidation,” Robin Emiliani, Founder and CEO at Catalyst Marketing recalls. “But now, we have a team of bold, fearless people who are constantly experimenting.”

She compares AI adoption to past industry shifts: “The people who embraced social media when it exploded, or marketing automation when it first came out, were the ones who ended up ahead. AI is the same. 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 always hacking, testing, and trying new things. I believe that’s the mindset that wins.”

Agencies can create a safe AI testing environment by:

Encouraging teams to try AI tools on low-risk tasks Showcasing AI success stories internally Running team workshops to help employees explore AI tools without fear of failure

7. Be transparent with clients

And finally, if you’re planning to use AI in any way at your agency for client work, you need to let your clients know. 

AI’s role in marketing is still a sensitive topic. Some clients love it, while others are wary. And if you’re not transparent, you risk losing their trust, and their business.

“Many agencies do not 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 can 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, about a quarter say, ‘You cannot use AI with us.’ Others say, ‘You can use AI, but only these specific tools.’”

Her advice is to clarify AI policies upfront. Ask questions like these in your onboarding document for a new client:

Do you allow AI in our workflow? If yes, which tools are approved? Are there security restrictions we need to follow?

Then, go through this on an onboarding call to make sure both you and your client are aligned on AI. 

Robin advises, “Really get a clear 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 stay, and agencies that implement it strategically will gain a competitive advantage. But success depends more than just AI tools—it’s about having the right processes, culture, and leadership in place. 

To recap, here are the best practices from agency leaders and AI experts to implement AI effectively:

✅ Assign an AI champion to drive adoption

✅ Identify time-wasting tasks and processes worth automating

✅ Start small, build proof of concept, and scale gradually

✅ Train your team, but don’t overthink it

✅ Use a marketing intelligence platform like Whatagraph to make 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.