Most digital marketing agencies are already using AI somewhere, even when nobody has formally called it an AI strategy. 

Your copywriters might use it to organise research, your account managers may use it to summarise meetings, your SEO team could be testing it for content analysis, while somebody in operations has quietly built an automation that saves them three hours every Friday. Meanwhile, leadership is looking at all of this activity and wondering whether the agency is genuinely moving forward or simply accumulating more subscriptions.

This is where an AI strategy for marketing agencies becomes useful because it gives all of those individual experiments a common direction. The goal is to work out where AI can make the agency easier to operate, where it can improve the quality or speed of delivery, what risks need to be managed, and which opportunities are worth investing time and money into. 

Recent enterprise guidance reflects a similar shift, with AI adoption increasingly being approached as a business transformation exercise involving goals, workflows, skills, governance and implementation plans rather than a technology purchase in isolation.

For an agency, though, that does not need to become a six-month consulting exercise involving a 96-page strategy document. It can start with a much more practical question: Where are we trying to get to, and which parts of the way we currently work are making that harder than it needs to be?

Using AI everywhere is not the same as having an AI strategy

There is a big difference between having a team that uses AI and having an agency that knows what it wants AI to achieve. One is activity, while the other is direction.

An agency might have ChatGPT, Gemini, Copilot, Claude, meeting transcription software, automated reporting tools and several AI features built into its existing marketing platforms, yet still have no shared understanding of which tools should be used, how they should be used or what business outcomes they are supposed to improve.

This often creates duplication. Two departments might pay for tools that solve almost identical problems, staff create their own processes without knowing what colleagues have already built, and potentially useful AI experiments remain personal productivity hacks because nobody has turned them into an agency-wide workflow.

A strategy starts by connecting AI use to agency priorities. Perhaps your agency wants to improve profit margins without compromising quality, increase the number of accounts each strategist can comfortably manage, shorten onboarding time, improve reporting, reduce production bottlenecks or create space for senior people to spend more time on strategy. Those objectives give AI somewhere to point.

Start with the agency problem

Imagine an agency owner says, “We want to automate our reporting.” That sounds like an AI project, but it is not quite a problem yet. Why does reporting need to change?

Perhaps account managers spend the last three working days of every month preparing reports, which means client communication slows down. Maybe specialists spend hours pulling information from different platforms, or perhaps reports are delivered on time, but clients struggle to understand what the numbers actually mean. Each of those problems requires a different response.

The first may need better data collection and workflow automation, the second could require integrations between reporting systems, while the third may benefit from AI-assisted interpretation that turns performance data into clearer explanations and suggested discussion points. 

The same thinking applies across the agency. 

• “We want AI content production” becomes: Our strategists spend too much time creating first drafts of briefs. 
• “We need an AI sales tool” becomes: Good leads are going cold because follow-up is inconsistent. 
• “We want an AI agent” becomes: Our account managers are constantly searching through project management systems, email threads and meeting transcripts before client calls.

Once the problem is specific, you can decide whether AI is actually the right solution. That part gets skipped surprisingly easily, particularly when new AI capabilities arrive so quickly. A demonstration can make a workflow look impressive, but an impressive workflow that solves a minor problem may still be a poor investment. 

Start with friction, cost, capacity or quality, then work backwards towards the technology.

Work out where AI can genuinely move the needle

Once you have identified the problems, the next step is prioritisation. You are unlikely to transform every department at once, and trying to do so can quickly turn your AI strategy into a collection of half-finished projects. A better approach is to compare opportunities according to business value and implementation effort. 

Consider a content agency producing hundreds of articles each month. A workflow that saves three minutes on every article could create meaningful capacity at that scale, although an automation that saves three minutes once a month clearly would not deserve the same attention. You also need to look beyond time saved.

An AI initiative may be valuable because it reduces errors, creates a more consistent client experience, shortens turnaround times, helps junior staff perform better, allows senior staff to focus on higher-value work or makes knowledge easier to access across the agency.

For example, improving the way a strategist prepares for every client meeting may have far more value than creating an impressive internal chatbot that people only occasionally use. You might therefore compare potential projects against questions such as:

• How often the task occurs
• How many people perform it
• How much time it currently takes
• How costly mistakes are 
• Whether the change would improve something the client actually notices

An effective AI strategy focuses your effort where those answers become meaningful.

 

 

Map the workflows before you start changing them

This is probably the least glamorous part of AI strategy, but it is often where the best opportunities become obvious. Take one agency process and map what genuinely happens from beginning to end. Client onboarding is a good example.

A new client signs a proposal, their information gets transferred into a project management system, someone sends an onboarding questionnaire, access details are requested, a kickoff meeting gets booked, the strategist reads through the proposal and sales notes, the meeting happens, somebody writes up actions, tasks are distributed and the first round of research begins. Once you see the whole workflow, you can ask better questions:

• Why is the same client information being typed into three systems? 
• Could the signed proposal automatically create the correct project template? 
• Can meeting notes be structured into agreed actions and assigned for human approval?
• Could information collected during sales and onboarding be turned into an initial client profile that the strategist reviews before kickoff?

AI becomes far more useful when it sits inside a process rather than appearing as an extra step employees need to remember. This is increasingly where AI development is heading. Google, for example, describes agentic systems as moving beyond individual chatbot interactions towards systems that can perform multi-step work across workflows, while current enterprise platforms are placing greater emphasis on orchestration, integrations, security and governance around those agents. 

An agency does not necessarily need sophisticated agents to benefit from that thinking. The principle is what matters: 

1. Look at the whole process 
2. Identify where information and decisions move
3. Decide where AI can remove unnecessary work

That is how an agency integrates AI into its existing business processes without forcing employees to completely change the way they operate overnight.

Decide what stays human

An AI strategy also needs boundaries. There will be tasks your agency technically could automate that you decide to keep human because judgement, accountability, context or empathy matter more than speed. 

Client communication is an obvious example. AI might prepare an account manager for a difficult conversation by summarising recent results, identifying unresolved issues and finding relevant information from previous meetings, although you may decide that responses to an unhappy client always need to be written or approved by a person. 

The same principle could apply to strategic recommendations, final creative direction, hiring decisions, employee performance conversations, campaign sign-off and advice that could materially affect a client’s business. You can build different levels of AI involvement depending on the risk.

Some processes may be fully automated because the task is predictable and low risk, while others may use AI to produce a recommendation that requires human review. In another workflow, AI may simply organise information so that an employee can make the decision faster. The agency’s job is to decide this deliberately rather than allowing everyone to create their own boundaries.

That becomes even more important as AI systems gain the ability to complete longer sequences of work and interact with connected tools. Current guidance around AI governance increasingly emphasises visibility into what AI systems are doing, which information they can access and where human oversight remains necessary.

Which AI systems are we using?

What client information goes into them? Who has access? Which outputs require review? Who is responsible when the system gets something wrong? When those answers are clear, the team can use AI with much more confidence. Give your team rules, not just AI logins!

Buying an AI tool and emailing everyone the login details is not an adoption strategy. People will use it differently; some will use it badly, and others will quietly ignore it because they are unsure what they are allowed to do.

Your AI strategy therefore needs to include practical rules. Those rules should cover which platforms are approved, what information can be uploaded, how client confidentiality should be handled, when an output needs to be checked, who owns shared prompts or workflows and how staff report problems. They should also explain how the agency expects people to use AI in specific roles.

A content writer may have an agreed process for research, outlining and quality checking, while an account manager might have a different workflow for meeting preparation and call summaries. An SEO strategist may use AI for clustering, analysis and initial research, although they remain responsible for the recommendations that go to the client.

Training becomes much more useful when it is tied to these actual workflows. A two-hour session on “how to prompt ChatGPT” may create enthusiasm, but a session showing account managers exactly how the agency’s approved AI workflow can reduce the preparation required for monthly meetings is much easier to carry into Monday morning. This is another important part of how AI consultants help agencies scale because adoption depends on people being able to use the technology consistently, safely and confidently. A consultant can help design the workflow, but long-term value appears when the process becomes part of the team’s normal way of working.

Make AI part of how the agency operates

The strongest AI strategies eventually become less visible. Nobody says, “I am doing AI now” every time they open an automated workflow, just as your finance team does not announce that they are using cloud computing whenever they open their accounting software. The technology becomes part of the process. 

That might mean your onboarding system automatically organises information for strategists, your reporting workflow prepares an initial analysis for account managers to review, your content process brings together approved client context before a brief is created, or your internal knowledge system helps employees find answers without asking the same senior team member for the fifteenth time.

This is also where agencies need ownership. Every meaningful AI workflow should have someone responsible for it because models change, platforms update and the way your agency works will continue to evolve. You therefore need a simple review process: 

• Is the workflow still producing useful outputs? 
• Are people using it? Have errors appeared? 
• Has the task itself changed? 
• Is there now a better way to solve the problem? 
• Has the AI saved enough time or improved delivery enough to justify the cost?

Measurement keeps AI strategy connected to reality.

The most useful metric may not always be dramatic either. If an automation saves each of six account managers 90 minutes a week, that capacity can add up very quickly over a year. If an AI-supported briefing process reduces the number of revisions required on every piece of content, your gain may show up in margin and turnaround time rather than a headline-grabbing percentage. 

This is where our broader view on agency growth becomes particularly relevant: The agencies that create the most value will have an advantage over those that simply produce the most work. 

AI gives agencies an opportunity to create that value with less operational drag, but only when the technology is tied to how the business actually functions. A good AI strategy should make growth easier, not more complicated. There is a point where an AI strategy can become so ambitious that the agency starts serving the technology rather than the technology serving the agency.

You have 14 platforms, three experimental agents, eight internal AI meetings and a Notion page containing 62 prompts, yet your account managers still spend Thursday afternoons manually preparing reports. That is usually a sign to simplify.

A useful AI strategy for marketing agencies should make growth easier by increasing capacity, improving consistency and helping people spend more of their time on work that requires experience, judgement and relationships. For one agency, that may mean three carefully designed automations and a clear internal AI policy. For another, it could involve custom agents, connected data sources and AI workflows operating across several departments. The sophistication of the technology matters far less than the business value it creates. This is why there is no single AI strategy that every agency should copy.

Your service mix, team structure, client base, margins, existing technology and growth plans all shape where AI can create the most value. A ten-person SEO agency and a 70-person full-service agency may use many of the same models, but the way AI should fit into their operations could look completely different.

Start with the agency you actually have, understand the problems and choose the opportunities that matter. Map the workflows and give your team a clear way to work. Measure what changes, then expand from there.

AI is developing quickly, and there will always be another platform, agent or capability worth investigating. A strong strategy gives your agency a way to evaluate those developments without rebuilding the business every time something new launches. That, ultimately, is what a good AI strategy gives you: A framework for deciding where AI belongs, where it does not, and how it can help your agency build capacity and grow without making the operation more complicated along the way.

Ready to build a practical AI strategy for your agency?

Globital can help you develop an AI strategy for marketing agencies that starts with the way your business already works and identifies where AI could improve efficiency, client delivery and capacity. From workflow mapping and opportunity prioritisation to governance, implementation and team adoption, we can help you turn AI experimentation into a practical plan for growth.

Talk to Globital about building an AI strategy around the agency you want to become.