The conversation about AI tools for marketing tends to get noisy fast — everyone has a favourite stack to recommend, and most recommendations are based on trial runs of a few weeks. At Choco Media, we have been running AI tooling in production for clients across content creation, SEO, and paid media since early 2024. What follows is an honest account of what made the cut, what didn’t, and why the gap between “impressive demo” and “reliable production tool” is wider than most people admit.
This post is written for marketing teams and agency operators who are past the hype stage and want to know what actually holds up when you’re pushing real work through it every week. We are not affiliated with any of these tools, and we pay for all of them. Where a tool failed us, we will say so plainly.
We will cover the full stack: content generation, image and creative production, SEO and research, automation and workflow, and the tools we tested and removed. By the end you will have a working picture of how a small AI-first marketing agency in 2026 is actually wired together — and where the human hand still has to stay on the wheel.
Why the “best AI tool” question is the wrong question
When a client or fellow agency owner asks “what’s the best AI tool for marketing?” we usually pause. The question sounds specific but it’s actually very broad. The answer depends entirely on what work you are trying to do, how much editorial control you need, and how much you trust your team to catch AI errors before they reach a client or go live.
The honest framing is this: AI tools reduce the cost of a first draft. That’s the core value proposition. They do not reduce the cost of a good final output if you skip review. In client work we have found that the teams who save the most time are the ones who treat AI output as raw material — fast, cheap raw material — not as finished work. The teams who lose time are the ones who trusted the first draft and had to fix it in public.
- Define the job first: are you producing volume (short-form content at scale) or quality (one flagship piece per month)?
- Match the tool to the constraint: speed-constrained work needs fast generation; quality-constrained work needs strong editing interfaces.
- Budget for review time: every AI-assisted workflow still has an editing step. Factor it into your estimates.
With that framing established, here is what we actually use.
Content generation: what survived the first year
Claude (Anthropic)
Claude is our primary writing tool for long-form content — blog posts, service page copy, email sequences, and client-facing reports. It handles nuance better than most alternatives we have tested, and it is considerably less likely to produce confident-sounding inaccuracies on topics it is uncertain about. The instruction-following is reliable enough that we can give it a detailed brief with voice guidelines and expect consistent output across a team.
Where Claude is weaker: it does not have native image generation, and its web browsing is limited compared to research-first alternatives. We do not use it for anything requiring real-time data without first feeding it fresh source material.
ChatGPT (GPT-4o)
We keep ChatGPT in the stack primarily for its versatility across formats and its strength in structured data tasks — turning a spreadsheet export into an analysis, building a comparison table from a prompt, drafting a structured brief from a rough voice note. The browsing capability is genuinely useful for quick competitor research when we do not want to open ten tabs.
One thing we noticed early: GPT-4o has a tendency toward a certain kind of confident corporate tone that has to be actively edited out. If your brand voice is direct and a little dry, you will spend time correcting for optimism and enthusiasm the model seems to default to.
What we dropped: Jasper
We used Jasper for about four months in early 2024. The template library is extensive, and the team onboarding is smooth. But the output quality plateaued in a way that became a problem at the volume we were running. We found ourselves editing Jasper outputs more heavily than Claude or GPT outputs, which erased the time savings. At the price point Jasper commands, the value-to-effort ratio did not hold up. We cancelled in Q2 2024 and have not missed it.
“The right question is not which tool writes the best first draft. It is which tool produces a first draft that requires the least intervention to become something you would actually publish.”
AI tools for SEO and content research
Perplexity
Perplexity has become one of the more useful research tools in our stack — not for writing, but for sourced synthesis. When we are pulling together background on a topic before writing a post, Perplexity gives us a quick oriented view with citations we can verify. It has meaningfully reduced the time we spend on initial desk research.
We are also watching it as an answer engine from an SEO and generative engine optimization perspective. In 2026, appearing in Perplexity answers is a distribution channel in its own right, and the structural content signals that drive Perplexity citations (clear claims, cited sources, FAQ-style answers) overlap significantly with what we optimize for in traditional SEO. Our SEO service now explicitly includes GEO elements for this reason.
Ahrefs and Semrush
Neither of these is an AI tool in the strict sense, but both have added AI-assisted features we use regularly. Ahrefs’ AI-generated content gap analysis has saved several hours per client per month. Semrush’s writing assistant is useful for checking keyword density in drafts without breaking flow.
We use Ahrefs as the primary source for keyword research and backlink auditing. Semrush sits alongside it for its competitive intelligence features, which we find marginally better for agency-to-agency comparison work.
Creative production: images, video, and ad creative
Midjourney
Midjourney is the tool we use most for marketing imagery — specifically for editorial illustrations, conceptual hero images, and campaign visual concepts that do not require photorealistic accuracy. The output quality is high enough for social content and blog imagery without post-processing, which matters at scale.
Where we use it carefully: anything involving people. Generating realistic faces of people for marketing contexts creates brand and legal risk we are not willing to carry. We use Midjourney for environments, objects, abstract concepts, and illustrated styles — not for synthetic people presented as real.
Adobe Firefly
Firefly is our second image tool, primarily because it is commercially safe by design — trained on licensed content, which matters when we are producing work for clients who need clean IP. For social post graphics, background removal, and quick content-aware fill work, it integrates well into existing Adobe workflows our clients already use.
The creative ceiling is lower than Midjourney. For high-concept visual work, Midjourney wins. For safe, brand-consistent production work at scale, Firefly is more predictable.
What we dropped: DALL-E via the API
We ran DALL-E (via the OpenAI API) as our image generation layer for about three months. The prompt adherence is good, but the visual style has a flatness that we found hard to work around for marketing contexts where the image needs to hold attention. We kept it for icon and simple diagram generation briefly, then removed it entirely when Firefly covered that use case at comparable speed and better consistency.
Paid media and ad creative tools
AI has entered the paid media workflow in two distinct places: creative generation and campaign management. We are cautious about the second category and more embracing of the first.
Motion (creative analytics)
Motion is not an AI generation tool — it is an AI-assisted creative analytics platform that tells you which ad creatives are performing and why. For the accounts we manage, it has become a near-mandatory layer. The time saved in manually pulling performance data by creative variant, and the speed with which we can identify hooks or formats that are winning, has been significant.
If you run paid social at any volume, and you are not using some form of creative analytics tooling, you are flying partly blind. Motion is the one we have stuck with longest. It connects directly to our paid media work and informs the creative briefs we send into production.
Meta Advantage+ and Google Performance Max
Both platforms now use AI-driven campaign management by default. Our relationship with these features is complicated. Advantage+ campaigns can perform very well for accounts with strong creative libraries and enough conversion data for the algorithm to learn from. For accounts under roughly €3,000/month spend without conversion history, they tend to underperform manual campaigns in our experience.
- We use Advantage+ for accounts with 50+ conversions per month and strong creative assets.
- We use manual campaign structures for newer accounts or those with limited data.
- Performance Max we treat as a supplementary channel rather than a primary one in most cases.
Automation and workflow tools
Make (formerly Integromat)
Make is the backbone of most of the automation we have built for ourselves and for clients. It is not an AI tool in the traditional sense, but AI steps (Claude API calls, image generation, content classification) slot into Make scenarios cleanly. The combination of Make + Claude API is how we handle things like automated first-draft generation from a content brief, or routing inbound lead data to a qualified summary before a human reviews it.
If you are evaluating automation platforms and have not looked at Make, it is worth an hour. Zapier is more widely known but Make’s scenario depth and pricing at volume are better for the kind of complex multi-step workflows marketing automation requires.
Notion AI
We already use Notion heavily as our content operations layer — as we covered in our post about building a content engine in Notion. The AI features within Notion have become genuinely useful for internal summaries, meeting note processing, and first-pass brief generation within existing documents. It is not a replacement for Claude for careful long-form writing, but for internal operational writing it earns its place.
The tools we evaluated and did not adopt
Not everything we tested earned a place in the stack. A few categories where we looked and stepped back:
- AI video generation (Sora, Runway, Pika): We tested these for social video production. The output is impressive in isolation but requires significant prompt iteration and still produces artifacts that matter in professional contexts. We have not found a workflow where they produce client-ready output fast enough to justify the iteration cost. We expect this to change within 12 months.
- AI voice cloning for ads: The legal and consent framework around voice cloning for commercial use is still not settled in the EU. We are not adopting it until it is.
- Autonomous social posting agents: Several tools promise to run your social accounts end-to-end with AI. We tried two. The brand voice drift within a few weeks of autonomous operation was significant enough that we would not recommend this for any client with a brand they care about. Human approval before publish is a line we hold.
How to build your own stack without over-investing
If you are starting from scratch or auditing what you have, the order we would suggest:
- Pick one writing model and learn it deeply before adding a second. The productivity gain comes from knowing your tool’s strengths and failure modes, not from having more tools.
- Add research tooling early — Perplexity or equivalent. It saves proportionally more time than any other addition.
- Add creative production tooling when you have a clear use case, not speculatively.
- Add automation after you have stable, repeatable workflows to automate. Automating broken processes makes broken processes faster — not better.
The honest number: running our current full stack costs us between €600 and €900 per month in tool subscriptions. For an agency billing at professional rates, that is not a significant overhead. For a solo operator or a brand handling this in-house, the cost can be reduced substantially by picking two or three tools from the list above and being patient about expanding.
What we keep human, no matter what
For all the tools we use, there are categories of work we do not delegate to AI, and this is not likely to change soon. Strategy decisions — which channels to prioritise, which creative direction to back, how to position a client — remain human. Client relationships remain human. Final editorial review before anything goes live remains human.
AI tools are good at generation and synthesis. They are not good at judgement under uncertainty, and marketing is largely about judgement under uncertainty. We think this is the productive frame: AI handles the repeatable, generatable work; humans handle the consequential decisions.
If you would like to talk through how AI tooling might fit into your specific marketing operation, we are happy to have that conversation. We do a short discovery call before any engagement — no pitch, just a clear look at where the opportunities actually are.
