How to use AI to generate ideas for ad campaigns before the brief

AI can speed up the search for campaign ideas even before the brief is written, but only when it works on sensible data. In practice, this is not about automatically inventing one big idea, but about quickly generating many hypotheses to assess. That changes how the team works, because instead of starting from a blank page, it starts with an organised set of variants. The best results come from treating AI as a tool for divergent thinking, rather than as a replacement for the strategist. The key, then, is what you feed it and according to which criteria you later assess the output.

The role of AI in the process of creating advertising campaigns

AI in this process acts as an engine for quickly generating and organising many campaign variants. In a few minutes, it can develop several creative directions, set out different promises, headlines and message versions. This makes sense before the brief, because it lets you see the scale of options before the team chooses one direction. Instead of guessing, you compare alternatives straight away.

The most important thing is that AI does not make strategic decisions on behalf of a human. It does not independently know the margin, the priority of the objective or the brand’s history if you do not give it these. That is why it works best as a working layer between gathering data and assessing ideas. In practice, it saves time at the first stage and increases the number of sensible hypotheses for further selection.

AI is particularly useful where you need divergent thinking rather than one correct answer. It can show different ways of framing the same problem for different audiences or motivations. However, if the input is generic, the output will usually be generic too.

Analysing the business context and its impact on AI strategy

Business context affects AI strategy because it determines which ideas are realistic, profitable and aligned with the campaign objective. That context includes the product, USP, margin, objectives, budget, constraints and the history of previous activities. Without this data, AI generates aesthetic but often interchangeable proposals. With it, it starts working within specific boundaries and priorities.

Before the first prompt, it is worth gathering the operational minimum:

• The product and USP, because AI needs to know what really differentiates the offer.
• The campaign objective, because a message for sales looks different from one for leads.
• Margin and budget, because they affect the profitability of the proposed directions.
• Legal and brand constraints, because not every attractive message can be used.
• Campaign history, because there is no point in repeating overused motifs.

These data change the result more than teams usually assume. If the objective is quick sales, AI should look for simple, clear arguments and low entry friction. If lead quality matters, it is better to emphasise fit, credibility and the offer terms. When a brand has strict constraints, AI needs to be told them explicitly, otherwise it will suggest things that are eye-catching but useless.

The more accurate the business context, the less generic and the more testable the ideas AI will generate. This is especially important before the brief, because that is when it is easy to fall for seemingly interesting ideas with no connection to the offer. Good input does not limit AI’s creativity; it directs it where it can deliver real value.

Using Voice of Customer data for segmentation and JTBD

Voice of Customer data makes it possible to build segments based on real customer needs and the jobs they need to get done. As a result, AI does not create campaigns for an abstract group, but for people with a specific problem, motivation and objection to buying. That translates into sharper communication angles already at the idea-search stage. Instead of one general message, you get several sensible directions for different buying situations.

The most useful sources are SEO and SEM queries, People Also Ask questions, CRM data, analytics, social listening, reviews, and transcripts of conversations with support and sales. In these materials you can see how customers describe their problem, what they are afraid of and what they compare the offer with. That is more important than the internal brand language, because advertising has to sound like an answer to real intent. If a customer is looking for something fast, cheap or safe, that emphasis should feed the next stage of work with AI.

In practice, pre-brief segmentation should be based on JTBD, that is, on what the customer wants to achieve. One segment may be looking to save time, another to reduce risk, and a third for convenience or ease of implementation. For each of them, it is worth adding separate purchase barriers, stage in the journey and typical questions. Do not segment solely by demographics if they do not explain the purchase decision.

A good input material for AI is a short set of insights, not a raw dump of data. It is enough to list the most common questions, objections, comparisons, customer words and recurring motifs from conversations. Such input lets AI generate hypotheses grounded in the audience’s language, rather than in generic slogans. This is exactly where the quality advantage over a random brainstorm begins.

Prompt engineering as the key to effective hypothesis generation

Prompt engineering decides whether AI will generate useful campaign hypotheses or just a set of general ideas. The model itself does not know what your objective is, what the constraints are or what you will consider a good direction. The prompt therefore has to act as a working brief before the brief. The more precisely you set the task, the less time you will waste rejecting weak results.

A good prompt should include specific elements, because each of them affects the quality of the output:

• the role of AI, for example a D2C creative director or an e-commerce analyst,
• the task objective, for example generating hypotheses for sales or leads,
• the business context, that is the product, USP, budget, margin and constraints,
• input data from VoC, including questions, concerns, comparisons and customer vocabulary,
• the response format, so the output is immediately comparable,
• the assessment criteria by which the team is to select the variants.

The AI persona matters in practice, because it changes the perspective of generation. If you set the model as a creative strategist, you will usually get stronger hooks and promises. If you set it as an analyst, you will more often receive more disciplined variants based on data and segments. It is best to test both perspectives if you want to combine freshness with usefulness.

Equally important is the output format. Instead of asking for campaign ideas, it is better to request a set number of hypotheses broken down by segment, problem, hook, headline and rationale. That way it is easier to compare variants and quickly spot which ones are just the same idea reworded. The prompt that delivers the most quality is the one that limits freedom where relevance is needed.

At the end, add evaluation criteria, because without them AI produces volume, not value. You can tell it to reject messages that are too generic, inconsistent with the brand, or promising something the offer cannot deliver. It is also good practice to ask for risks and data gaps to be flagged for each hypothesis. This forces the model to be more disciplined and immediately makes further selection easier.

Defining an AI persona for better communication fit

An AI persona gives the model a concrete working perspective, so the communication better fits the objective and the segment. It is not a decorative prompt element, but a way of setting the model’s mindset. The same data set will produce different results when you ask for the analysis from a creative director, and different ones when an e-commerce analyst gets the role.

In practice, the persona is chosen for the task, not for your own preferences. If you want to draw out strong hooks and promises, use a role such as “creative director in a D2C agency”. If you care about structure, segments and data-based arguments, “data analyst specialising in e-commerce” will work better.

An AI persona does not replace the client persona or JTBD segmentation, but it helps translate them into communication more effectively. It is best to compare two or three persona variants on the same input and in the same response format. Avoid roles that are too broad or contradictory, because the model starts mixing style, strategy and evaluation.

Pitfalls and risks associated with using AI in campaigns

The biggest risks of working with AI in campaigns come from weak input, lack of verification and acting without a testing plan. The model can quickly generate seemingly sensible ideas that are not grounded in data. The larger the scale of generation, the faster the number of errors grows, and the harder they are to filter out later.

The first pitfall is an overly generic prompt. If you do not provide the objective, constraints, client language and campaign history, you will get messages that are interchangeable between brands. Such output may sound professional, but it does not give you an advantage when choosing a direction before the brief.

The second pitfall is hallucinations and a lack of fact-checking. AI may add product advantages that do not exist, or suggest promises that are risky legally, reputationally and from an advertising policy perspective. That is why every chosen idea must be checked against the offer, the brand and the realism of the message.

The third risk is ignoring the brand context and generating without a plan for further evaluation. If the team does not have selection criteria, AI produces volume instead of value. At this stage the aim is not the number of ideas, but a set of hypotheses that can be sensibly compared and filtered out.

Iteration and feedback loop in the campaign optimisation process

Iteration and the feedback loop mean that test results feed into the next round of hypothesis generation and improve subsequent prompts. As a result, AI does not work from scratch each time, but on increasingly better input material. In practice, after the first tests you already know which segment responds, which hook attracts attention and where the message loses consistency with the offer. This shortens the path to more sensible variants in subsequent rounds.

What matters most are not the results themselves, but the differences between the variants. If one message delivers a high CTR but a weak CVR, the problem often lies in the promise or in the mismatch with the landing page. If another variant generates fewer clicks but better lead quality, it may be responding to the right intent more effectively. Such observations should feed back both into the business context and into the structure of the prompt.

In practice, after each test series it is worth adding three things to the input: what worked, what did not work and why that might have happened. On that basis you update the segments, customer language, objection list and constraints for AI. Then you narrow down weak directions and develop those that have potential at an acceptable cost and with brand alignment. This loop turns AI from an idea generator into a tool for systematic campaign learning.