Generative AI Training for Marketing Teams: SEO, Ads, and Landing Page Optimization

Keyword design and clarifying search intent

The first step in using AI for SEO is keyword design and clarifying search intent.

When writing an article for the keyword “marketing generative AI,” for instance, rather than having AI write the body straight off, you first break down the reader’s concerns.

• Who is searching
• What business problems they face
• Whether they are in the information-gathering stage or the adoption-consideration stage
• Whether they want to compare options or learn a concrete procedure
• What related keywords are likely to come up

Having AI help with this clarification makes it easier to align the premises for article production.

That said, the search intent AI produces is a hypothesis. It needs to be checked against actual search results, Search Console, ad data and the voice of the sales floor.

In my experience, whether you can hold this sense of “treating it as a hypothesis” makes a great deal of difference to the quality of AI use. Treat AI’s output not as the right answer but as a starting point for discussion. That stance matters especially in a marketing team.

Clarifying personas and messaging angles

In marketing, whom you are addressing and what you are conveying both matter.

Generative AI helps with first-cut personas and with broadening the range of messaging angles.

Even for the same “generative-AI training,” the concerns of an executive, an IT lead and a marketing or sales lead differ. Executives weigh return on investment, company-wide transformation and the impact on competitiveness. IT leads weigh security, governance, system integration and operational management. Marketing and sales leads weigh lead generation, conversion-to-deal rates, content production and the improvement of sales materials.

By using AI, you can sort out these reader-specific concerns and produce several messaging angles as options.

What I often do in the field is have AI produce several personas, then sit down with sales and customer-success colleagues to check “how close is this to a real customer.” AI is good at producing hypotheses, but it is the people on the ground who know the customer’s temperature. Combining the two moves you from a theoretical persona to one you can actually use in a campaign.

Copywriting and first drafts

The area where content-production AI is most readily used is copywriting and first drafts.

Article titles, headings, lead paragraphs, ad copy, landing-page hero sections, CTA wording — marketing teams have no shortage of moments where value must be conveyed in a few words.

With generative AI, you can produce several expression options from a single pitch in short order.

For ad copy, you can vary the output along lines such as the following.

• Problem-led messaging
• Outcome-led messaging
• Reassurance-led messaging
• Messaging for those comparing options
• Messaging for the adoption lead
• Messaging for senior management

For landing-page improvement, you can compare hero-section headings, sub-copy, CTAs and the presentation of case studies as several options.

What matters is not asking AI to “give me the single best option” but having it produce several and letting people judge. Think of AI as widening the choices and people as evaluating them, and it slots neatly into practice.

I often describe this step as “work that cuts down the time spent frozen in front of a blank page.” What people ought to agonise over is not writing the first sentence from nothing. It is choosing, from a set of options, the expression that fits the company’s strategy and the customer’s context.

Fact-checking and brand guidelines

What must always go into the training when using generative AI is fact-checking and brand guidelines.

AI is good at producing natural-sounding text, but it cannot handle every figure, quotation, proper noun, regulation or competitor detail accurately.

In SEO articles, ad copy and landing pages especially, information bearing on the reader’s trust is involved.

• Performance figures
• Customer case studies
• Number of adopting companies
• Survey data
• Competitor comparisons
• Pricing information
• Laws and regulatory frameworks
• Industry trends

Leaving these entirely to AI risks putting incorrect information out into the world.

So the training instils the premise that AI-produced text is never adopted as-is. Go back to the source for figures, confirm primary sources for quotations, fix expressions that don’t fit the brand, avoid over-assertion. Holding such review points as a shared team checklist is important.

When using an environment such as Kanata, where prompts and training data can be shared within the team, it becomes easier to work while referring to brand guidelines, past articles, product materials and sales decks. That said, selecting any tool presupposes checking your own security requirements, access management and fit with existing work.

Clarifying search intent from keywords

In the first exercise, you clarify search intent based on the target keyword.

For the keyword “SEO AI,” for example, you put questions like these to the AI.

• Who searches with this keyword
• What the searcher wants to know
• What the overt and latent problems are
• Which services or approaches they are weighing up
• Which questions the article must answer without fail

This exercise makes the premises of article production clear.

What matters here is not using AI’s answer as-is but checking it among team members. By discussing “does this search intent genuinely exist,” “does it match the concerns we hear in actual deals,” and “does it overlap with existing articles,” AI’s output turns into information usable in practice.

Producing an article outline

Next, you use AI to produce a heading structure.

Here you do more than line up H2s and H3s — you also sort out the role of each heading.

• Headings that make the reader’s problem clear
• Headings that lay out the overall picture of the solution
• Headings that explain concrete steps
• Headings that show pitfalls and examples of failure
• Headings that lead to the next action

In SEO articles, quality is largely decided at the outline stage. Using AI makes it easier to check for gaps and to produce heading options from a different angle.

After having AI produce an outline, I always revisit it through the lens of “will this structure put the reader’s worries to rest.” What matters is not only headings aimed at the search engine but whether the flow makes sense as the reader works through it.

Sorting out the information before producing a first draft

Before having AI write the body, sorting out the information also matters.

You might, for instance, pull together the following beforehand.

• Intended reader
• The reader’s problem
• The aim of the article
• The relationship to your own service
• Keywords to use
• Expressions to avoid
• Internal materials to draw on
• Passages requiring fact verification

Instructing AI without this preparation tends to yield text heavy on generalities.

As far as I see in the field, most cases of dissatisfaction with AI’s accuracy stem less from AI itself than from a shortage of the background information being handed to it. Good output requires good input.

Reviewing on the premise of fact-checking

In SEO articles, a pre-publication review is indispensable.

Against AI-produced text, you check on the following points.

• Is there a source for the figures
• Are competitor comparisons inaccurate
• Is there any expression that misleads the reader
• Has it tipped into over-assertion
• Does it fit your brand tone
• Are keywords crammed in unnaturally

Using AI makes first drafts quicker, but you cannot hand the responsibility for publication over to AI. In training, it is important to confirm this dividing line again and again.

Producing several messaging angles

Before writing ad copy, you first sort out the messaging angles.

When pitching generative-AI training, for instance, angles such as the following come to mind.

• Operational efficiency
• Talent development
• Reducing key-person dependency
• Department-by-department use
• Security response
• Post-training embedding support
• Faster content production

By having AI produce several messaging angles, you can design ad copy not as mere rewording but as distinct hypotheses.

Here I recommend instructing it not to “produce ten similar expressions” but to “produce messaging aimed at different buying psychologies.” The words that land differ between someone wanting reassurance, someone in a hurry for results, and someone struggling to explain things internally.

Producing ad copy by persona

Even for the same service, the words that land differ by reader.

For executives, words such as company-wide adoption, return on investment and competitiveness can be effective. For IT leads, security, access management, data handling and governance matter. For marketing and sales leads, lead generation, sales materials, ad improvement and content production tend to be the concerns.

In training, you run an exercise producing ad copy by persona from the same product.

After having AI produce ad copy, people evaluate it on the following points.

• Is it clear whom the ad copy is for
• Is the problem concrete
• Has it become an exaggeration
• Is the CTA natural
• Does it match the landing-page content

Including this evaluation lets you use AI not as a “tool for producing ad copy” but as a “partner for multiplying hypotheses.”

Producing A/B-test ideas

In ad improvement, designing A/B tests also matters. An A/B test is a method of comparing several patterns — with part of the ad copy or landing page changed — to verify which is more likely to lead to results.

With generative AI, you can sort out test ideas like the following in short order.

• Verify a difference in headings
• Compare problem-led and outcome-led messaging
• Compare CTA wording
• Compare messaging by persona
• Compare messaging with figures against messaging without
• Compare reassurance-led copy against forward-looking benefit-led messaging

That said, A/B tests aren’t something to multiply on a whim. You need to decide what you want to verify, which metric you will watch, and over what period you will judge.

When producing A/B-test ideas, I always make a point of writing down “what is this test for learning.” In ad improvement, gaining a learning you can use in the next move matters more than merely seeing who wins.

Improving the hero section

In a landing page’s hero section, you need to convey briefly whom the service is for, which problem it solves and why it is worth looking at now. The hero section is the area visible on screen the moment the page opens.

Using AI, you can produce several improvement options based on the existing hero section.

You might, for instance, vary the output along these lines.

• An option pressing hard on the problem
• An option presenting the ideal picture after adoption
• An option making the target audience explicit
• An option bringing out concrete work scenes
• An option that puts in figures or timeframes
• An option conveying reassurance and a support structure

That said, the copy AI produces isn’t necessarily usable as-is. With a landing page, you need to check consistency with the actual service content, the customer’s consideration stage and the ad copy.

Reconsidering the CTA

In landing-page improvement, the CTA matters too. CTA stands for “call to action” — the element prompting the reader towards a desired action such as requesting materials, enquiring or signing up.

“Contact us” alone can set the reader’s psychological hurdle high. Using generative AI lets you sort out CTA options suited to the reader’s stage.

There are, for instance, options like these.

• Download the materials
• Discuss the training content
• See examples of use by department
• Check pricing and plans
• Hear first what adoption would look like
• Discuss a training theme that suits your company

In training, you consider not only the CTA wording but also what information to place before the CTA. A CTA isn’t decided by the button’s words alone; it is decided by the context leading up to it.

When thinking about a CTA, I often check “what is the reader anxious about right now.” Is it the cost, the internal roll-out, the security? The CTA needs to be a natural next step in response to that anxiety.

Producing FAQ and reassurance content

On a landing page, content that puts the reader’s worries to rest also matters.

Using generative AI makes it easier to surface the likely questions.

For a generative-AI-training landing page, for instance, questions like these come to mind.

• Can beginners take part
• Can the content be varied by department
• Do you cover security caveats too
• Is there embedding support after the training
• Which job roles is it suited to
• Can we run exercises using our own business data
• How long is the training

Having AI produce FAQ options makes it easier to pick up the reader’s worries without gaps.

That said, the answers need to be checked with the relevant parties — sales, customer success, legal, IT and so on. Answers bearing on security or contracts in particular should not be decided by the marketing team alone.

Start with common tasks first

Trying to spread AI across all work from the outset throws the team into confusion.

It is best to begin with common tasks such as the following.

• Producing SEO article outlines
• Producing ad copy options
• Sorting out landing-page improvement ideas
• Drafting email copy
• Producing webinar announcement copy
• Producing white-paper outlines
• Producing first-cut sales-meeting materials

These are areas where the deliverable is clearly imagined and the effect of using AI is easy to feel.

When I run training, too, I pick “themes usable in tomorrow’s work” to begin with. Rather than a grand AI strategy, being usable for tomorrow’s ad copy, next week’s article outline and this month’s landing-page improvement is what tells on embedding in the field.

Don’t keep prompts in individual hands

As generative-AI use advances, each person ends up with “a good prompt only they use.”

In the short term this is handy, but seen across the whole team it leads to key-person dependency.

After the training, it is important to build a mechanism for sharing the prompts that worked well.

You might, for instance, manage prompts like these within the team.

• SEO article-outline prompt
• Ad-copy creation prompt
• Landing-page improvement prompt
• Persona-clarification prompt
• A/B-test idea prompt
• Fact-checking-point extraction prompt
• Brand-tone correction prompt

When using an environment such as Kanata, where prompts and training data can be managed on a per-project basis, it becomes easier to reuse frequently used instructions and reference materials within the team. This makes it easier to turn an individual’s ingenuity into a team asset.

Using brand guidelines as training data

When a marketing team uses generative AI, the connection to brand guidelines matters.

Even if AI can write natural text, without your company’s character it cannot be published as-is.

It is therefore effective to have materials such as the following in order.

• Brand guidelines
• Tone and manner
• A list of forbidden expressions
• Past ad copy
• Past landing pages
• Articles that performed well
• Sales materials
• Customer case studies
• Frequently asked questions

Working with AI while referring to these makes it easier to obtain output close to your company’s context rather than generalities.

That said, the information you feed in as training data needs to be managed in line with internal rules. Where you handle customer names, personal data, contract terms or undisclosed information, confirm the handling beforehand.

I also often hear, in the field of AI adoption, that “we can’t tell what we’re allowed to put in, so in the end we don’t use it.” That is precisely why it is important to confirm, within the training and with concrete examples, what information may be entered, what should be masked and what must never be entered.

Deciding the review structure

The more AI raises production speed, the more the review structure grows in importance.

Even if first drafts multiply, if few people can check them, the bottleneck simply shifts to the review stage.

It is therefore wise to decide roles such as the following at the training stage.

Dividing roles in this way makes it easier for AI use to take hold as a team operation rather than a personal task.

When I support a team, I check again and again that the review stage isn’t taken lightly. Even if AI makes production faster, quality won’t steady while review criteria remain vague. If anything, the more production volume grows, the higher the risk of checks being missed.

Don’t treat AI’s output as the right answer

AI can produce plausible-sounding text.

But being plausible and being correct are two different things.

In SEO articles, incorrect information risks drawing in search traffic. In advertising, exaggeration or inappropriate expression can lead to ad rejection or brand damage. On landing pages, expressions at odds with the facts can mislead customers.

AI’s output is, at most, a starting point. The final responsibility must rest with people.

In training, I often use the phrase “spar with AI” rather than “leave it to AI.” Sparring takes as its premise that you don’t adopt the answer that comes back as-is. You think, choose and correct for yourself. It is precisely that premise that makes AI safe and easy to use.

Don’t let it invent figures

In marketing, there are many occasions to use figures.

That said, you must never have AI invent figures with no basis.

Expressions such as “many adopting companies feel the results,” “major improvement in a short period,” or “CVR improved dramatically” should be avoided where there is no basis.

Where you use figures, you need to make the period, the subject, the conditions and the units clear.

Here is one way to write it, for instance.

Across twelve internal-exercise themes run over the month after the training, the average time to produce a first draft per theme fell from 180 minutes to 70.

In this way, it is important to attach the comparison conditions to a figure.

When AI produces a figure, always confirm “what is the basis for that figure.” Figures whose basis cannot be confirmed are not used in published material. Simply enforcing this rule lowers the risk.

Don’t lose your brand character

AI’s text, while polished, tends towards generic expression that could apply to any company.

When using it in a marketing team, how to preserve brand character becomes important.

To that end, you need to hand AI information such as the following.

• Your company’s turns of phrase
• Expressions to avoid
• Your stance towards customers
• Points of differentiation from competitors
• Pitches that drew a good response in the past
• Words used on the sales floor
• Questions that actually came up from customers

The more you entrust to AI, the more clearly people need to hold the brand’s standards.

I regard brand guidelines as showing not only “design and notation rules” but “the attitude with which you face customers.” It is important to arrange prompts and reference materials so that this attitude is reflected in AI’s output.

Don’t take security and information handling lightly

A marketing team may handle customer information, deal information, ad data, competitor comparisons and undisclosed service materials.

When entering these into generative AI, handling in line with internal rules is required.

The following information in particular needs care.

• Personal names
• Email addresses
• Combinations of company name and contract details
• Undisclosed pricing information
• Per-customer deal status
• Information covered by an NDA
• Business strategy not yet made public

In training, it is important to cover not only “what may be entered” but “how to judge when in doubt.”

As a principle for when in doubt, I recommend three: “don’t put it in,” “mask it,” “check with the person responsible.” AI use is meant to raise speed, not to rush your judgement.