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AI tools for marketing, AI tools for marketers
AI marketing tools
AI marketing tools range from general assistants such as ChatGPT, Gemini and Microsoft Copilot to features built into the email, social, CRM and design software a team already pays for. This guide does not rank products; rankings date quickly and depend on your stack.
Instead it sets out the categories of AI tools for marketing, the questions to ask about data, brand control and measurement, and how to run a fair trial before rolling anything out to marketers.
Leave with a category map of your current and candidate tools, a checklist for data, brand and measurement, and a simple trial design.
OpenAI states that by default it does not use data from ChatGPT Business, Enterprise or the API for training its models. OpenAI: business data privacy, security and compliance ↗
Microsoft states that prompts, responses and Graph data are not used to train the foundation models. Microsoft Learn: Copilot data, privacy and security ↗
New to the topic? Read the background on artificial intelligence.
AI tools for marketing, AI tools for marketers
what categories of AI marketing tools are there?
Broadly: general assistants, AI features inside existing platforms, specialist content and creative tools, and analytics or automation tools.
General assistants handle drafting, research preparation and summarising across many tasks. They are flexible but know nothing about your brand unless you give it to them.
Embedded features sit inside software you already use, such as your email platform, CRM, design tool or office suite. They often have the advantage of working where your data already lives, under contracts you already have.
Specialist tools focus on one job, such as image generation, video editing, SEO briefs or ad variants. They can be strong at that job but add another vendor, another data flow and another login.
Analytics and automation tools summarise performance, suggest segments or trigger workflows. They need the most care, because they act on customer data and can change what customers receive.
General assistants for flexible drafting.
Embedded features where your data already lives.
Specialist tools only where a job justifies another vendor.
AI tools for marketing, AI tools for marketers
how should we check data handling?
Read the vendor's own data documentation for the plan you will actually buy, and map what customer or confidential data would enter the tool.
Data terms differ between consumer and business plans. OpenAI, for example, states on its business data page that by default it does not use data from ChatGPT Enterprise, Business or its API platform for training its models.
Microsoft states that Microsoft 365 Copilot prompts, responses and Graph data are not used to train foundation models, and that it only surfaces content each user already has permission to view, which makes your existing permissions hygiene part of the evaluation.
For any of the AI marketing tools on your list, ask where data is stored, how long it is retained, whether it is used for training, and which sub-processors are involved. Then compare that with what your team would actually paste in.
Where personal data is involved, the ICO's guidance on AI and data protection is the UK reference point for accountability and impact assessment.
AI tools for marketing, AI tools for marketers
how do we keep brand and claims under control?
Prefer tools that let you supply approved sources and voice guidance, and keep human approval in the publishing route.
A useful test of AI marketing tools is whether you can ground them in your own material: brand guidelines, product fact sheets, approved claims and examples of good copy.
Check whether outputs can be reviewed and edited before anything is published or sent. Tools that publish directly to channels need permission settings that match your approval process.
Remember that the ASA says the advertising codes apply in the same way whether or not AI is used. A tool cannot take on that responsibility for you.
If a tool generates images or video, record what inputs were used. The UK consultation on copyright and AI has closed with the outcome still pending, so ownership questions are not fully settled.
| Check | What to establish |
|---|---|
| 1 | Ground outputs in approved brand material. |
| 2 | Keep review before publishing. |
| 3 | Record inputs for generated images and video. |
AI tools for marketing, AI tools for marketers
how do we measure whether a tool is worth it?
Baseline the task first, then measure time, edit effort and quality on the same task with the tool.
Vendor claims about time saved are not evidence for your team. Pick one recurring task, such as a monthly newsletter or a set of ad variants, and record how long it takes today and who touches it.
Run the same task with the tool for a few cycles and record time, the number of edits needed before approval and any errors caught in review.
Quality matters as much as speed. A draft that saves an hour but needs heavy fact-checking may not be a saving at all.
This is a workflow baseline, not a promised saving. Our guide to AI use cases for business applies the same principle: measure your own process before and after.
AI tools for marketing, AI tools for marketers
should we buy new AI marketing tools or use what we have?
Start with the AI features in tools you already license, and add new vendors only where a clear gap remains.
Many teams already have AI features available in their office suite, CRM or email platform that nobody has been shown how to use well.
Using those first reduces new contracts and data flows, and lets the team build habits on familiar software before comparing alternatives.
Where a gap remains, such as specialist video or design work, a short structured trial against your baseline is better than a long procurement exercise driven by demos.
Whichever AI marketing tools you choose, the deciding factor is usually how well people use them, which is why we pair evaluation with AI marketing training for teams.
Audit the AI features you already pay for.
Add vendors only for clear gaps.
Trial against a baseline, not a demo.
AI tools for marketing, AI tools for marketers
how do we roll a tool out safely?
Agree approved tools, data rules and review steps in writing, then train the team on its own tasks.
A short written policy names the approved tools, what data may and may not go into them and who approves outputs. Our AI policy template gives a starting structure.
A risk framework helps larger teams think it through; the NIST AI Risk Management Framework is voluntary guidance covering design, use and evaluation of AI systems.
Training should use the team's real briefs and channels. For the wider picture of use cases and UK rules, see our guide to AI for marketing.
When you want a second opinion on a shortlist, contact us with the tasks you want to improve and the tools you already have.
Write down approved tools and data rules.
Use a recognised risk framework for larger rollouts.
Train on real tasks.
Make the decision concrete
What does the current task cost in time?
Choose a task connected to ai marketing tools and enter its current workload. This gives you a baseline to discuss before buying training or commissioning a build.
A baseline is useful even if you decide to keep the process manual. Measure the work that matters, then compare quality and review effort as well as time.
Assess the workflow before changing it, or plan business AI training if the gap is how people use existing tools.
Your current workload
Estimate how much time the existing task takes before discussing ai marketing tools.
16 hours per week
People × minutes × weekly tasks ÷ 60. A workload baseline from your inputs, not a prediction of AI time savings. Include review and support time when comparing a proposed workflow.
A useful AI investment leaves your team able to do the work and judge the result.
Continue with the right question
Where should you go next?
Use the overview for context, then follow the pages that match your delivery decision.
See our ai training service or read the ai for marketing overview.
FAQ
Questions, answered
Evidence and further reading
Which sources support the guidance?
Source links support the specific background described here. The practical checklists and illustrative examples are predeploy’s guidance.
OpenAI states that by default it does not use data from ChatGPT Business, Enterprise or the API for training its models. OpenAI: business data privacy, security and compliance ↗
Microsoft states that prompts, responses and Graph data are not used to train the foundation models. Microsoft Learn: Copilot data, privacy and security ↗
ICO guidance covers AI accountability, transparency and fairness; the guidance is currently under review. ICO: AI and data protection guidance ↗
The ASA (29 May 2025) says the CAP and BCAP Codes apply in the same way whether or not AI is used, there is no general UK legal requirement to disclose AI in ads, and disclosure generally cannot cure a misleading ad. ASA: Disclosure of AI in advertising ↗
Consultation ran 17 Dec 2024 to 25 Feb 2025 on AI training, transparency and computer-generated works; it is closed and the page says the government is analysing feedback. GOV.UK: Copyright and artificial intelligence consultation ↗
NIST describes its AI risk framework as voluntary guidance for design, use and evaluation. NIST: AI Risk Management Framework ↗
Background reading on the broader field of artificial intelligence. Wikipedia: artificial intelligence ↗
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AI for marketing
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