Buying an AI tool is easy. Working out whether it improved your marketing takes a little more effort. A platform might produce a blog post in seconds, but that means very little if an editor spends the next hour removing generic claims. The same way an image generator can deliver dozens of options without producing anything that fits the campaign.

The useful question is what happens to the work once the tool is involved. Does research become more focused? Are briefs stronger? Do fewer hours disappear into copying information between platforms?

Here are four functions where AI tools deserve serious consideration, with our pick for each and a clear view of where their usefulness stops.

1. Content and research: get to a stronger brief faster

Our pick: Claude

A general-purpose assistant earns its subscription when it helps a team work through information and develop ideas. Treating it as a machine for filling the content calendar sets a much lower bar.

Claude’s Research feature can run multiple searches, explore different angles, and return findings with citations. That makes it worth considering for content groundwork: exploring a subject, finding sources and identifying questions that need further investigation.

For example, ask it to investigate the objections buyers raise about a service, then separate documented concerns from assumptions. A writer can use that to build a more useful brief before speaking to a subject matter expert.

There is still an editorial job to do. Open the sources, check that they support the claims and challenge conclusions that sound suspiciously neat. A citation gives you somewhere to check; it doesn’t remove the need to check.

It earns its place if: Research and briefing take less time while giving writers better material. Count the checking and editing time before calling it a saving.

2. SEO and GEO: find gaps without writing to a score

Our pick: Surfer

Surfer’s Content Editor analyses top-ranking pages and guides relevant terms and article structure, alongside optimisation scores. For a content team, that offers a practical way to review whether a draft covers the subject thoroughly enough.

This is important for generative engine optimisation (GEO), as well. Surfer’s AI Tracker monitors brand mentions and cited domains across a set of prompts, helping teams examine their visibility in AI answers. Use that information to choose what to investigate and improve. If competitors repeatedly appear for a relevant question, review the sources and the answers those sources provide.

Neither a content score nor an AI mention guarantees enquiries. Keep tracking search performance, relevant AI citations and what visitors do after they arrive.

It earns its place if: It helps you prioritise worthwhile updates and produce more useful pages.

3. Visuals: explore ideas before committing production time

Our pick: Adobe Firefly

For a team already designing in Photoshop, Firefly is a sensible place to start. Photoshop’s Generate Image feature supports Adobe Firefly models and lets designers create image variations directly on the canvas from text prompts.

The strongest starting use is concept development. Before spending time producing a finished campaign asset, a designer can explore possible settings, compositions or visual directions and discuss them with the team. Comparing a few visual approaches gives everyone something specific to respond to.

The final image still needs to fit the brand and communicate the right message. Product details, visual consistency and the relationship between image and copy all need attention. 

It earns its place if: The team reaches an approved creative direction faster and produces assets it can actually use.

4. Workflow automation: remove the work between the work

Our pick: Make

Some of the most useful applications of AI sit in the unglamorous gaps between marketing tasks. Make connects applications through workflows and supports AI agents for tasks such as preparing content drafts, researching leads and assembling reports. That makes it worth considering when information repeatedly needs to move between the tools a team already uses.

A sensible first project could be a content handover. Design a workflow that takes an approved brief, creates the production task, attaches the relevant information and alerts the editor. Add an AI step if there is something useful to interpret or summarise.

Moving a file or sending a notification usually needs straightforward automation. Introducing AI makes sense when the task involves unstructured information, such as turning a long set of notes into a draft summary.

Start with a process someone can explain clearly. Decide what triggers it, what a successful result looks like and who deals with failures. Keep approval before customer-facing publication while you establish how reliably it works.

It earns its place if: It reduces manual handovers without creating an equally demanding job monitoring and repairing the workflow.

Make every subscription justify itself

A marketing team can get useful work from a small toolkit. Start with the task that repeatedly slows people down, then test a tool against that task. Before the trial, agree what you will measure:

• Time: Include briefing, checking, revisions and maintenance.
• Quality: Judge the finished work against your existing standards.
• Use: Check whether the team adopts it beyond the initial experiment.
• Value: Identify what the saved time or improved work lets you achieve.

Keep the tools that pass. Cancel the ones that produce impressive demonstrations but make little difference to delivery. The point of a marketing toolkit is to help a team do better work. Each subscription should have a clear answer to how.

Want AI to make a practical difference to your marketing?

At Axonn, we offer content, creative and SEO and GEO expertise to help brands reach their audiences. If your team needs help deciding where AI fits into that work, get in touch to discuss how we can improve your marketing efforts.