How to Get Started with Practical AI

In 2025, it feels like everyone - investors, clients, the media - is asking the same question: What’s your AI strategy? For agencies, the pressure is real. But rushing to spin up AI pilots without a practical plan often leads to a graveyard of half-finished experiments, unused tools and few measurable results.

The key isn’t just doing AI, it’s doing it well, in ways that solve real business problems, integrate into your team’s daily work and deliver results. That starts with focusing on Practical AI: the kind that enhances workflows, not distracts from them.

Practical AI starts with strategy

Once your agency has decided it’s time to seriously explore AI, and not just dabble, it’s crucial to approach it with structure and intent. A desire to “do AI” isn’t enough. Without the right foundations, even the best tech won’t stick.

Here are the core pillars to set your AI journey up for success:

Align AI projects to business outcomes

In marketing, AI shouldn’t be a science experiment, it should be a lever for better client results, greater efficiency or higher margins. Ask yourself:

• Will this reduce the manual load on our team (e.g., admin tasks, reporting, revisions)?

• Does it support a service offering clients already value?

• Can this project help us increase campaign velocity?

Start by targeting pain points your team already complains about - repetitive, time-consuming work like drafting social copy, QA'ing ad variations or juggling post-campaign reporting. Solve those problems first and the ROI becomes self-evident.

Set a realistic, dedicated AI budget

No budget, no traction. If your team, spending time here and there, has to “make do” or rely solely on free tools, you’re unlikely to build momentum.

The first step is to ensure you are committing internal resources to spend time on the AI project. Followed by allocating a modest but focused AI budget. This ensures your team has access to:

• Premium AI tools and plugins

• Custom workflow automations

• External guidance when needed

Think of this not as a tech investment, but a productivity multiplier that helps your agency do more with less.

Access the right expertise

If you don’t have AI engineers or automation specialists in-house, that’s okay, most agencies don’t. But that also means external guidance is essential.

An experienced partner can help:

• Identify and prioritize the AI use cases which are highest value are worth pursuing (and avoid the bad ones that waste effort with little ROI)

• Design agents and agentic workflows (e.g. AI systems that support business processes with a level of autonomy)

• Prepare and ingest your business data to power AI agents and models

• Securely integrate AI agents with business applications like Slack, HubSpot, Google Workspace, or Figma

Focus on team adoption, not just tools

The biggest barrier to AI impact? Lack of adoption.

Your team needs to see how AI helps them, not fear it. That means:

• Choosing problems they care about solving

• Avoiding tool overload - start small and go deep, not wide

• Offering practical training, not just one-off demos

• Highlighting wins and giving recognition to early adopters

A powerful approach here is using AI to solve repetitive tasks people dislike that frees up time for your team to focus on higher value client work.

Integrate AI into your current stack

Don’t expect your team to log in to a dozen new applications. The best practical AI lives inside the tools you already use.

• Add specialized agents to Slack or Teams, relevant to the channel e.g., for the marketing department or a specific project

• Design agents to be triggered by events from existing applications e.g. a new lead in the CRM and delivery value across the process - such as researching the prospect, drafting an email and preparing a presentation

• Build agents that push reminders, generate reports or auto-classify leads inside existing workflows

This approach removes friction and makes adoption feel effortless.

Get leadership involved - actively

AI can’t be something that “the ops team is testing.” It needs visible support from agency leadership. That means:

• Actively using the tools yourself

• Talking about AI wins (and learnings) openly

• Budgeting time and space for experimentation

• Setting realistic expectations and celebrating progress

Also: be transparent about concerns your team may have, especially around job security. Framing AI as an enabler of higher-value work is key to building trust and excitement.

The bottom line: start small, start smart

Practical AI isn’t about overhauling your agency in a quarter. It’s about solving real problems in smart, repeatable ways. With the right strategy, tools, and team engagement, you can go from AI-curious to AI-capable faster than you think.

Whether it’s using agents to streamline post-campaign workflows or to accelerate content creation, the opportunity isn’t in the far-off future - it’s here now.

At Decoder, we specialize in helping agencies take the guesswork out of AI adoption. We guide teams from first experiments to full-scale, agentic workflows that drive efficiency, value and impact.