AI really helps speed up processes and bring a project to a result faster. But with the advent of thousands of AI tools, it has become more difficult to choose the one that will really suit your team. In this article, we will analyze which solutions are more effective for small agencies and which ones are more effective for large teams. We will also show how the choice of a tool depends on the type of task: be it strategy, creativity, analytics, or automation.
Before choosing anything, it is important to remember: there is no universal tool. One AI helps with generating ideas, another finds behavioral patterns in the audience, and a third processes feedback and builds segments. Everything depends on the internal structure of the agency, specialization, nature of projects, and maturity of processes. The main thing is not how popular the tool is, but how well it solves a specific problem within your team.
A simple guideline is to ask yourself: what operation do you want to speed up, and what result do you expect? This could be a reduction in the time it takes to create advertising creatives. Or the automation of base segmentation. Or the generation of UTM tags taking into account the context. Implementation for the sake of “trying AI” does not produce results if there is no measurable benefit.
Therefore, it is important not just to choose one tool, but to test several at once. On the same tasks. With the participation of the same team. With clear metrics: how much time was spent, how many errors were saved, how involved the specialist remained, and what result each of the tools gave. This approach eliminates subjectivity and makes it possible to make a decision not under the influence of a trend, but based on benefits.
The main tasks performed by marketing agencies
Marketing agencies rarely specialize in one thing. Their work covers strategy, creativity, channels and analytics. Depending on the niche and structure of client requests, agencies usually solve problems in the following areas:
1. Strategy development
Agencies help to define the target audience, form a value proposition and select key communication channels. This includes competitor analysis, segmentation, creating customer portraits and building positioning logic.
2. Brand creation
Branding is not only a logo. Agencies develop the name, visual system, corporate identity, brand voice, as well as rules for using all these elements in different channels.
3. Preparation of websites and landing pages
From structure to texts. Agencies design pages, write content, select visuals, focus on loading speed and SEO optimization. The main goal is for the page to work for conversion and strengthen positioning.
4. Content production and campaign launch
Posts, banners, videos, email newsletters, presentations — agencies create a full set of content, adapting it to the platform format and the client's tasks. Includes copywriting, design, production and publication.
5. Paid advertising management
Working with advertising accounts of Google, Meta, LinkedIn, TikTok and other platforms: setting up campaigns, testing creatives, tracking ROAS, budget optimization and bid management.
6. SEO and organic growth
Agencies create a content promotion strategy, select keywords, conduct technical audits, set up internal optimization, work with links, and prepare SEO content.
7. Email marketing and funnel automation
Creating and launching onboarding, warm-up chains, retention, and re-engagement campaigns. Agencies deal not only with texts but also with segmentation, trigger logic, and performance analytics.
8. Analytics and reporting
Agencies collect and visualize data, generate reports for the client, build funnels, and conduct campaign and segment analyses. They can explain what worked, what didn’t, and why.
9. Project management and client communication
Calls, presentations, briefings, task coordination, feedback processing — everything that helps the client see the result and understand what they are paying for. Account managers build the process and are responsible for transparency.
These tasks rarely exist separately. In reality, they are intertwined. The more harmoniously they work, the higher the result. This is where AI tools provide the greatest advantage: they speed up decision-making, simplify routines, and help the team act faster.
Top AI tools for marketing agencies
In 2025, marketing agencies no longer ask themselves whether to use AI. They decide which tool will speed up the team and give the client the best result. Below is a selection of solutions that really solve the problems that agencies face every day. Without "magic buttons", only what works.
1. M1 Project (Elsa AI)
If you work with B2B, B2C clients of SaaS companies, then creating an ICP, audience segmentation and structuring positioning is a key stage. M1 Project allows agencies to quickly collect data on the target audience, create client profiles and turn them into understandable messaging. It speeds up research by 3-5 times and simplifies communication with the client at the start of the project. Tools such as ICP generator and Marketing strategy builder can save up to 90% of time at the audience research and positioning stages.
2. Jasper AI
Useful for agencies with a large volume of content tasks: landing pages, email chains, advertising creatives. Scripts are adapted to the brand voice, segment and goal. The main thing is to use it as an assistant, not a replacement for a copywriter. At the draft stage, it saves up to 50% of the team's time.
3. Surfer SEO
For agencies that offer SEO strategies and create articles. Surfer selects the optimal content structure for keywords, shows which topics are not sufficiently covered, helps maintain an SEO balance between density, length and relevance. Integrates perfectly with the content pipeline.
4. ChatGPT (with custom GPTs)
Scripts for a client's brief, draft generation, feedback analytics, explanation of complex terms, and tone of voice adaptation - through custom GPT modules, ChatGPT turns into a universal assistant for marketers. But it requires customization for agency processes.
5. Midjourney / DALL-E 3
Indispensable for quickly creating visual concepts, mockups, covers, banners, and moodboards. For agencies with a strong visual component, they allow you to speed up presentations and conceptual development.
6. Ocoya
A platform for creating and automatically publishing content on social networks with AI copywriting, templates, and analytics. Convenient for SMM agencies that manage several accounts and want to centralize the process.
7. Brand24 with AI signals
Monitoring mentions, analyzing tonality, and tracking reactions to PR activities. Especially useful if the agency is engaged not only in advertising, but also in reputation marketing.
Each of these tools solves a specific problem. But their strength is in their combination. Teams that know how to integrate AI into their flow not only save time, but also increase productivity without losing quality.
Building an AI-Powered Workflow
Choosing the right AI tools for marketing agencies in 2025 isn't just writing a tech stack—it's creating a workflow that compounds value at every step. Agencies will often fall into the trap of choosing tools in a vacuum, hoping one solution will gain a competitive edge. It won't. What works is a networked system where insights, content, and execution flow freely.
Start by mapping your high-friction areas. Is it audience discovery, campaign planning, or creative production? Once identified, integrate AI solutions that collaborate rather than compete. For example, the ICP Generator from M1-project.com defines hyper-specific audience clusters using behavioral and psychographic signals. Feed these segments into the Marketing Strategy Builder, and you’ve automated what used to take weeks—positioning, messaging pillars, and channel prioritization—into a responsive framework that updates in real time.
Then, scale velocity in content creation. Your research is for naught if delivery grinds to a halt. That's where the Social Media Content Generator delivers force: translating audience insights into deploy-ready assets synchronized to tone, format, and timing. Add that to AI-powered design software such as Midjourney or DALL·E for graphics, and your team moves from ideation obstacles to volume publishing without sacrificing quality.
Analytics don't have to be in a silo either. Best-of-breed agencies are incorporating AI-powered performance feedback loops into campaign orchestrations. Think of executing a Facebook ad campaign where creative iterations and bid tactics dynamically optimize based on predictive engagement models. According to McKinsey, agencies leveraging adaptive AI workflows lower CPA by 32% and boost ROAS by 27% within the first 90 days.
Below is a viable flow:
- Audience Intelligence: Develop micro-segmented profiles out of CRM and real-time market signals with ICP Generator.
- Strategy Automation: Bring such profiles into Marketing Strategy Builder for messaging blueprints and omnichannel mapping.
- Creative Acceleration: Trigger insights with Social Media Content Generator, complemented with AI-driven visuals for campaign-ready assets.
- Performance Feedback: Integrate analytics platforms to maximize creative and bidding logic in real-time.
Brian Balfour: "Growth isn't about hacks, it's about systems." AI marketing agency tools need to be that system—dependent, predictive, and constantly optimizing. When you leverage AI as an orchestrator, and not a set of apps, you don't just optimize work—you rewire the economics of your agency.
How to choose the right tool for your agency
Choosing an AI tool is not about choosing the “most powerful” solution. It’s about finding a match between the task, the team, and the workflow. What saves one team may hinder another. Therefore, the only working approach is to test consciously.
Start with a task map. Break down all the agency’s processes into segments: strategy, creative, advertising, analytics, reporting, customer support. For each of them, ask the question: where do we spend the most time? Where does overload occur? Where does quality most often fall? These are the points at which an AI tool can enhance the process, rather than replace a person.
Next comes the level of maturity of the team. If the agency has clear templates, action logic, and those responsible for the result, AI can be easily integrated. If everything is based on individual expertise, the tool will become a burden. It is important that the employee understands why and how to use the solution, and not just try to “play around”.
Now the selection criteria. Here are three that really work:
- Scalability: can the tool handle the growth in the number of clients, projects, or content?
- Integration: can AI be integrated into the current stack — Figma, Notion, Google Docs, CRM, Trello?
- Verifiable result: Can you measure time savings, quality improvement, and reduction in the number of edits?
The next step is testing not by demo videos or reviews, but by your tasks. Take a specific operation (writing an email, analyzing reviews, generating a landing page) and run it through several solutions. Measure the result. Let one person do it manually, the second with AI, and the third in a mixed mode. Only then will the real picture emerge.
And finally, don’t choose by the interface or trendy words. Choose by how understandable the tool is for the team, how much it reduces the effort, and how much its result does not require rework.
If AI is not just a "tool" in an agency, but part of the operational process, this gives a systemic advantage. Acceleration, flexibility, focus - without losses to chaos and blind experiments.
Integrating AI: Mindset and Training
Picking the right AI tool is half the battle. Successful implementation depends nearly as much on preparing your people to embrace new processes and rely on AI-facilitated assistance. Resistance to change or unfamiliarity has a way of overwhelming superior technology, turning promise into a source of frustration.
Start by getting the team aligned on why they're bringing in AI integration — not as some sort of magic bullet, but what it will do to augment expertise and free up time for work of higher value. Realistic expectations upfront prevent disappointment and make active engagement more likely.
Second, role-specific training investments to build trust. Show your specialists how AI tools automate routine tasks without substituting their judgment and creativity. Laboratory sessions, pilots on real projects, and open comment forums establish a culture of experimentation and continuous change.
Also, appoint internal champions who are familiar with the technology and can help others get past early stumbling blocks. They serve as a bridge between AI developers and end-users, allowing for better communication and handoff.
Finally, bake AI use into documented work flows and performance metrics. Where AI is included in standard operating procedures, not an add-on, teams learn faster and outcomes dramatically improve.
Finally, but not least, successful AI adoption takes more than a software choice—it takes leadership, training, and support to translate tools into actual productivity multipliers.
Conclusion
It is important to look at how a specific tool fits into the current system. What benefit does it provide? How does it scale? And how much does it make work easier, rather than adding a new layer of chaos?
The best way is not to believe in universal solutions, but to test within your team and on your tasks. And then AI tools in the agency will work not “somewhere nearby”, but where it really makes sense.
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