Most agencies give you one of two lines on AI. Either they're not using it – which is rarely the whole story. Or they're using it for everything – which is definitely a problem.

Here's the honest version: AI sits inside our workflow – research, brainstorming, and sometimes first-pass drafting that we then rebuild. What you receive at the end of it is content that's been strategically thought through, rewritten, and checked by us. We don't supply wholly AI-generated content, and we never would – because that's where generic AI slop comes from.

The short version: we don't use AI to replace thinking, we use it to sharpen it.

Where AI earns its place in our process

BCG's 2026 AI at Work survey found that marketing professionals who use AI regularly are the most likely of any function to save a full working day a week (60% of respondents report saving 8 hours). The question isn't whether AI belongs in your content process in 2026, it's how to make sure that time is going somewhere worthwhile.

Research and competitor analysis

When we're building a content strategy or launching a thought leadership campaign, one of the first things we do is understand the audience and competitors. AI has a useful role here.

For example, we often use deep research tools to map the competitor content landscape, providing a snapshot of what messages are being used, where the trends are, and – crucially – where the gaps are. This gives provides valuable input, efficiently, in identifying where your campaign has opportunities to stand out.

We also use AI as part of persona research: the questions your target audience is actually asking, the language they use when they're not in a marketing meeting, and the long-tail search and LLM terms that reveal what they're genuinely worried about. That insight goes into how we shape the content.

Tone of voice refinement and compliance

Once we've defined the right voice for a client's content, AI tools help us test and tighten it efficiently. If a line is close but not quite there – a fraction too formal, or not sharp enough for the audience – AI can suggest alternatives fast.

It's also useful when multiple stakeholders are contributing to content across a campaign. AI can help keep the voice consistent against a clear set of guidelines, which saves a lot of editorial rework at the end.

For example, recently, we built out tone of voice and messaging guidelines for a tech client, which included key words and phrases that must be avoided in marketing to support GEO and correct market misconceptions about the product. We (and the internal content writers) can now use AI to conduct final checks to ensure content is compliant before publication.

Summarising and synthesis

When we're conducting customer interviews as part of a content strategy process, AI helps us extract patterns – recurring language, common pain points, the specific phrases that capture how a problem feels from the inside.

We're not outsourcing the analysis, we're accelerating the extraction so more time goes into the thinking about what the insight means and how to use it.

When AI gets creative

This is the part that surprises people – and gets our creative juices flowing

For example, for a cloud technology roundtable, we used generative AI to help come up with cocktail names built around cloud puns to serve at the bar after the event. It sounds like a small detail, but it's the kind of touch that makes an event memorable and reinforces the brand’s personality.

We obviously needed to do extensive QA on the suggestions, as many didn’t make logical sense and would stand out as meaningless to a CTO. But having the creative sparring partner helps open new ideas, get our synapses connecting in new ways, and get to a great result faster.

What AI can't replace

AI generates output based on patterns in what it has already seen. That makes it genuinely useful for research and synthesis. It also means, by definition, that it can’t give you a new perspective.

What AI output tends to lack – and what experienced B2B copywriters supply – is the ability to distinguish what will resonate from what merely sounds reasonable. There's a difference between copy that reads well on the surface and copy that's cuts through with your audience, their drivers, and what will actually persuade them.

We know what makes B2B content work. Not because we've been trained on internet articles, because we've spent years in the room with CMOs, Managing Directors, Heads of AI, Plant Managers, Operations Directors, architects, and specifiers – asking the questions that produce the insight and then translating that into messaging that lands.

That means when we use AI output as an input, we can separate the insight worth keeping from the anodyne, generic observations that would lose a reader by paragraph two. The final copy is always written and edited by a person, grounded in a deep understanding of your sector and what your audience actually cares about.

That's a long way from AI writing a whitepaper. It's using a tool to do something specific and fast, in service of an idea a person had. The judgement – that this is the right approach for this client and this audience – was ours. That's true across every piece of content.

What this means for your content process

The right question isn't whether AI has a role in B2B content: it does. The question is whether it's being used as a thinking tool or as a shortcut to bypass thinking. The same BCG research reinforces why: in their survey of close to 12,000 employees across more than a dozen global markets, they found that people who receive clear strategic direction on how to use AI outperform those who simply have greater access to AI tools. Therefore, what determines whether AI produces anything useful is the quality of the thinking that goes into it.

If you're investing in AI tooling for your content – to maintain tone of voice across a distributed team, support content at scale, or surface audience insight more efficiently – the quality of the inputs matters as much as the LLM itself. Brief templates, tone of voice frameworks, strategic personas, effective prompts: these are what determine whether AI-assisted content cuts through or adds to the noise.

We work with clients to build that foundation. The content side of AI tooling tends to get less attention than the technology side, but it's often where the real difference is made.

If you're already using AI tools and want a practical benchmark for what good output actually looks like, our guide on how to check your AI-generated copy is a good place to start.

If you want to talk about your content approach – whether that's working together directly, or improving the results you're getting from AI tools you're already using – get in touch.

FAQs

Does Gunning Marketing use AI to write copy?

We don't supply wholly AI-generated content. AI features in our workflow – research, brainstorming, and sometimes first-pass drafting that we then rebuild – but what clients receive has been strategically thought through, rewritten, and checked by us. Exactly where AI sits in the process varies by project and client preference. Our generative AI policy sets out the detail.

How does AI help in B2B content strategy?

AI is useful for researching competitor messaging, identifying audience language and search terms, summarising interview data, and testing tone of voice consistency. In each case it accelerates a specific task. It doesn't replace the strategic judgement about which insights matter and how to apply them.

What's the difference between AI-generated and human-written B2B copy?

AI-generated copy tends to sound plausible but generic – it's built on patterns from existing content, which means it often reproduces the same ideas, phrases, and structures already in the market. Human-written copy, built on specific audience insight and domain expertise, is more likely to say something distinctive that actually resonates with the right reader.

Can AI tools help maintain brand voice across a large team?

Yes – but only if the voice is well defined first. AI can help enforce consistency against clear guidelines, but it can't develop those guidelines from scratch. The brief, tone of voice documentation, and audience understanding all need to exist before AI can reinforce them effectively, and the AI agent supporting the team must have the right instructions and knowledge base.

How do you use AI for audience research without losing the human angle?

We still do our own research – looking directly at competitor websites, analysing case studies and reviews, and conducting customer interviews. We use AI to surface patterns – the questions audiences are asking, the language they use, the themes that repeat across sources. The human work is combining the “manual” research with the AI-assisted research and interpreting what those patterns mean for a specific campaign and judging which insights are worth building content around. AI gives us a faster starting point; it doesn't give us the answer.

Video transcript

We don't use AI to replace thinking, we use it to sharpen it.

Market and audience research

That includes things like deep research, where we can use generative AI to do market research that looks at the competitor landscape, what competitor trends are out there, and what messaging competitors are using so we can identify different gaps and opportunities for your campaign content.

We also use it for persona-based research as a way to dig into target audiences, learn more about messages that will resonate, and learn more about different goals, pain points, and drivers that will really make the campaign and the content sing.

Refining tone and summarising insights

Tone of voice is another area where we use it a lot. Once we’ve defined the tone of voice for your content, we use it to do refinements and make suggestions that will make different bits of content align better with the tone of voice – make it punchier so it stands out in line with your goals.

We also use AI for simple things like summarising. So if we're doing customer interviews, we’ll use different generative AI tools to help us summarise and interrogate the different customer interviews to extract key messages.

Creative use case: cloud pun cocktails

We also use AI the way we use other writing tools like thesauruses and rhyming dictionaries to help with creative brainstorming around different phrases and words that will really make the content stand out.

For example, a really good use creative use case was an event for a cloud technology managed services provider, where they were doing a roundtable with key stakeholders. We used AI to help come up with cloud pun cocktail names that they could serve at the bar after the roundtable had taken place.

The value of effective human expertise

We stand out because we're not relying on generative AI to do the thinking for us. It's about taking our deep knowledge of the sectors in which we work and then being able to identify what in the AI output will really help make a campaign sing.

This is the same as you would with any other research or tool, whether that's customer interviews, market research, discussions with sales teams, and the like.

So it's always human-crafted final output informed by a deep understanding of what makes content and copywriting compelling and persuasive – and what messages are really going to stand out. Because you can tell when there's generic messaging that's gone into content, whether that's something that generative AI has fed you or whether that's something that a customer or a stakeholder interview has fed into it.

And part of the value that you get with working with domain and copy experts like Gunning Marketing is we have that insight and that nous to separate the wheat from the chaff – and really home in on those messages that will resonate.

We know what makes content great – what makes it persuasive – and therefore can separate the jargon and the anodyne AI output to make sure that the final result is compelling, going to achieve your goals, and going to resonate with your target audience.