A B2B marketing strategy in 2026 needs to do more than produce leads. It must help a buying group recognise a costly problem, understand the available choices and feel confident enough to act. That process rarely happens in one channel or one session.

The framework below connects positioning, demand creation, sales support and measurement. It is designed for a practical planning session, not a presentation that disappears into a folder.

Begin with the commercial target

Start by naming the result the business needs. Revenue is too broad on its own. Define the market, offer, time period and customer action. A useful target might be to create a set number of qualified opportunities for a particular service among UK professional firms during two quarters.

Write down the financial assumptions behind that target. Include average deal value, close rate, sales cycle and current pipeline. Those figures reveal how much demand is required and whether the goal is plausible.

Define the account and the buying group

An ideal customer profile describes the organisation most likely to benefit. It should cover sector, size, operating model, buying trigger and signs of poor fit. Avoid a broad description such as “growing companies”. It gives the team nothing useful to find or reject.

Then map the people involved. A budget owner, daily user, technical reviewer and senior sponsor may care about different risks. Interview sales colleagues and recent customers to record the questions each person asks. Those questions are stronger content inputs than invented personas.

Build positioning around a costly tension

Good positioning makes the status quo feel less comfortable. State the problem, its commercial consequence, the change you offer and the evidence supporting that change. Keep the wording plain enough for a salesperson to use in a call.

Test the message with customers and lost prospects. Ask what they understood, what they doubted and which claim mattered. A B2B marketing strategy should treat positioning as a working hypothesis, not a sentence approved once and protected forever.

Separate demand creation from demand capture

Demand capture helps people already looking for an answer. Search pages, comparison content, review material and focused landing pages belong here. Demand creation reaches suitable buyers before they type a product category into Google. Research-led articles, events, partnerships, video and expert social content can do that job.

Give both areas a budget. Teams often fund only the visible final click, then wonder why the pipeline weakens several months later. Early activity should be judged through account engagement, direct traffic, branded search, event response and sales conversations as well as form fills.

Give each channel one clear role

A channel plan becomes easier to manage when every channel has a defined job. LinkedIn might distribute expert ideas and reach named accounts. Search may capture active demand. Email can support consideration. Webinars may help buyers assess risk and meet the people behind the offer.

Do not copy the same asset everywhere. Start with one useful idea, then shape it for the behaviour of each channel. A detailed report can become a short executive summary, a salesperson’s talking point, a focused landing page and a set of social observations.

Connect marketing and sales around evidence

Agree what makes an account worth sales attention. Page visits alone are weak evidence. A stronger signal might combine account fit, repeat engagement, a high-intent action and a known buying trigger.

Hold a short review every fortnight. Marketing should bring audience questions, campaign response and content gaps. Sales should bring objection patterns, deal movement and lead quality. Record decisions and owners. Otherwise the meeting becomes polite reporting with no effect on the next campaign.

Use a small measurement hierarchy

Place commercial outcomes at the top: qualified pipeline, revenue, deal velocity and customer value. Beneath them, use diagnostic measures such as account engagement, conversion by stage, cost per qualified opportunity and sales acceptance. Reach and clicks remain useful for diagnosing delivery, but they should not headline the report.

Compare performance by segment and message. An average can hide that one audience is responding while another drains budget. Add short notes explaining what changed and what the team will do next.

A 90-day B2B marketing strategy plan

1. Weeks 1 to 3: confirm the commercial target, analyse the pipeline and interview customers and sales staff.
2. Weeks 4 to 6: define the account profile, buying group, positioning and message evidence.
3. Weeks 7 to 9: build one demand-creation campaign and one demand-capture route.
4. Weeks 10 to 12: launch, review account response and change the weakest assumption.

For the measurement layer, read Marketing Measurement Strategy: KPIs That Guide Decisions. Teams adding assisted research and creative testing can also use the 90-day AI marketing plan.

The test is simple. A useful B2B marketing strategy helps the team decide which market to pursue, what to say, where to appear and what evidence justifies the next investment.

A content marketing strategy should turn audience insight into a repeatable publishing system that supports a commercial decision. It is not an editorial calendar filled with topics because Tuesday needs a post.

The strongest plans answer four questions: whose decision are we helping, what useful idea can we own, how will the right person find it and what should happen next?

Choose the decision your content should support

Begin with one audience and one meaningful decision. A property buyer comparing developments needs different evidence from a marketing director choosing an agency. Write the decision in plain language, then list the doubts that delay it.

Connect that decision to a business result. It may be a qualified enquiry, event registration, sales conversation or product trial. The result determines which content matters and which activity is simply keeping the team busy.

Collect real audience questions

Use search terms, customer calls, chat transcripts, social comments, sales objections and support queries. Group the questions by intent: learning, comparing, proving, deciding and using. The pattern often reveals gaps that a keyword tool misses.

Look for emotional friction as well as factual need. Buyers may understand the product and still fear choosing badly. Content can reduce that risk through demonstrations, named experts, process explanations and honest limitations.

Create a point of view before a format

A familiar topic can still be valuable if the organisation has an original observation. Ask what the team has learned through real work, what common advice it rejects and which evidence supports its position.

Build a short editorial thesis around that answer. For example: “Marketing reporting should recommend a decision, not display every available number.” That idea can guide several pieces without repeating the same article.

Use a topic structure people can follow

Organise content around a small number of themes linked to the offer. Each theme should contain an authoritative guide, supporting answers, proof and a conversion route. Link related pages so readers and search systems can understand the relationship.

A simple theme may include:

• A main guide explaining the problem and method.
• Short articles answering specific questions.
• A case study showing the method in practice.
• A checklist or tool that helps the reader act.
• A service page explaining when professional help makes sense.

Plan production around source material

Set a monthly source session with the people closest to customers and delivery. Record an interview, review recent questions and examine a completed piece of work. One good source session can support an article, short video, email, sales note and several social posts.

AI can help organise notes, identify repeated themes and prepare controlled variations. A person should still choose the argument, verify facts, protect confidential material and approve the final voice.

Design distribution before publishing

Organic search may take time. Pair it with direct distribution from the start. Decide which colleagues will share the idea, which email segment needs it, which partner could add a useful perspective and whether paid promotion is justified.

Use the language of the channel. A LinkedIn post needs a clear observation and room for discussion. Email needs a reason to open now. A search page needs a direct answer and enough evidence to deserve attention.

Build a visible conversion route

Every article needs a sensible next step. Match it to reader intent. An early-stage visitor may want a related guide or checklist. A person comparing suppliers may need a case study, process page or short consultation.

Avoid forcing every reader into the same form. The content marketing strategy should create several small routes towards trust, then show which route contributes to qualified action.

Measure usefulness, not publishing volume

Track qualified visits, engaged reading, return visits, assisted conversions, enquiries and sales use. Review results by theme and audience. Ten posts that attract the wrong visitors are less valuable than one page used in real buying conversations.

Ask sales teams which content helped a deal, which objections remain unanswered and where prospects misunderstood the offer. Add those answers to the monthly editorial review.

A 12-week content marketing strategy

1. Weeks 1 to 2: choose the audience decision and gather real questions.
2. Weeks 3 to 4: define the editorial thesis, themes and conversion routes.
3. Weeks 5 to 8: produce one main guide, one proof piece and four supporting answers.
4. Weeks 9 to 10: distribute through search, email, social, partners and sales.
5. Weeks 11 to 12: review qualified response and improve the weakest route.

For search-specific guidance, read AI Search Optimisation: How Brands Get Cited. For channel planning, continue with the 12-week social media marketing strategy.

A social media marketing strategy needs a reason to exist beyond posting regularly. It should define the audience, the behaviour the brand wants to influence and the role social media can realistically play in that change.

This 12-week plan gives teams enough structure to learn without turning every post into a committee project. It works for professional services, education, property, hospitality and other organisations where trust matters before conversion.

Set one primary objective

Choose the main job for the quarter. It may be increasing qualified awareness among a named audience, supporting a launch, building an expert reputation or creating conversations for sales. More than one outcome can occur, but one should guide priorities.

Translate the objective into observable behaviour. “Build awareness” is vague. “Increase profile visits and qualified enquiries from London property professionals” gives the team a clearer target.

Define the audience situation

Describe what the audience is doing, thinking or delaying when they encounter the brand. Record their questions, pressures, vocabulary and sources of trust. Use comments, customer calls, competitor discussions and search queries as evidence.

Do not treat a demographic description as insight. Age and job title tell you little about why somebody pauses, shares or contacts a business. The situation behind the behaviour is more useful.

Give content four distinct roles

A balanced plan usually needs more than promotional posts. Assign each idea one role:

• Recognition: name a familiar problem or ambition.
• Authority: teach something specific through real experience.
• Proof: show outcomes, process, people or customer evidence.
• Action: invite a useful next step with a clear reason.

Review the monthly mix. If every post asks for action without earning trust, response will weaken. If everything teaches but nothing points towards the offer, social activity becomes a free publication with no commercial route.

Build repeatable series

Series make production easier and give audiences a reason to recognise the work. Examples include a weekly objection answer, a short campaign breakdown, a monthly expert conversation or a before-and-after process note.

Set a visual rule and a writing rule for each series. Keep enough consistency to build memory, then vary the topic, format and opening. Templates should support recognition without making every post look mechanically identical.

Use paid media to support proven ideas

Paid and organic activity should share evidence. Test messages through organic discussion, sales conversations and small creative trials. Put budget behind the ideas that attract the intended audience and support a defined next step.

Separate campaign audiences by intent and value. Retargeting, named-account activity and broad reach need different creative and measures. Do not report them as one average.

Treat community management as research

Replies and direct messages reveal questions, objections and language that can improve the next campaign. Set a daily response window, an escalation route and a simple log of useful themes.

A thoughtful reply can carry more trust than another polished graphic. Give the person responding enough information and authority to sound human. Scripts should guide difficult cases, not flatten every conversation.

Test one variable at a time

Choose a clear question for each test. Compare two openings, two formats or two calls to action while keeping the rest reasonably stable. Record the audience, date, spend and outcome so the result can inform later work.

AI tools can prepare variations, subtitle video and group comment themes. Human review should check facts, tone, context, rights and whether the idea deserves publication at all.

Review measures in three levels

Delivery measures show whether the platform distributed the work. Response measures include saves, meaningful comments, profile visits and qualified clicks. Business measures include enquiries, applications, opportunities and revenue influenced.

Report the relationship between the levels. A post with lower reach may still matter if it produces better conversations from the right audience. Add one decision to every weekly report: continue, change, expand or stop.

The 12-week social media marketing strategy

1. Weeks 1 to 2: set the objective, audience situation and baseline.
2. Weeks 3 to 4: define content roles, series and visual rules.
3. Weeks 5 to 6: produce the first content batch and response guide.
4. Weeks 7 to 9: publish, support the best work with paid media and record audience questions.
5. Weeks 10 to 11: repeat the strongest series and test one weak point.
6. Week 12: review business response and write the next quarter’s decision.

Pair this plan with a clear content marketing strategy and the KPI hierarchy in Marketing Measurement Strategy.

A marketing measurement strategy should tell a team what to fund, change or stop. A dashboard full of numbers is not a strategy. It becomes useful only when the measures connect campaign activity to a commercial question.

The method below creates a small hierarchy of outcomes, diagnostic measures and delivery signals. It also makes uncertainty visible, which is more honest than pretending one platform can explain the entire customer journey.

Write the decision before choosing the KPI

Begin with the question the report must answer. It might be whether to increase investment in a channel, which audience to prioritise or whether a campaign changed qualified demand. The decision sets the time period, comparison and data required.

Assign an owner. A report without a person responsible for the next choice becomes background noise, however polished it looks.

Create a three-level metric hierarchy

At the top, use business outcomes such as qualified pipeline, revenue, customer value, applications or sales velocity. These measures show whether the work contributed to the organisation’s aim.

The second level contains diagnostic measures. Conversion by stage, cost per qualified opportunity, sales acceptance, repeat visits and customer acquisition cost can explain why an outcome changed. Delivery measures such as impressions, reach, clicks and video completion sit at the third level. They help diagnose channel mechanics.

Do not remove delivery measures. Put them in their proper place. Reach matters when distribution fails; it does not prove commercial success.

Set definitions before collecting data

Write a plain definition for every KPI. State the source, owner, calculation, reporting frequency and known limitation. Agree what counts as a qualified lead or engaged visit before a campaign begins.

Definitions prevent two teams from using the same label for different events. They also make changes to tracking easier to spot. Keep a short measurement dictionary beside the report.

Use attribution carefully

Attribution models divide credit across recorded touchpoints. They do not observe every conversation, offline influence or private share. Platform reports also tend to view performance through their own boundaries.

Use attribution as one piece of evidence. Compare first-party analytics, CRM stages, sales feedback, customer questions and direct traffic. When a conclusion depends heavily on one model, show how the answer changes under another reasonable view.

Add experiments where the decision is expensive

Experiments can test whether activity caused an outcome rather than merely appearing before it. Useful methods include controlled geographic tests, audience holdouts, message tests and phased launches. The design should match the decision and available volume.

Write the hypothesis and success threshold before seeing the result. A small change may be statistically uncertain but commercially useful, while a tiny statistically clear change may not justify the cost.

Build a report around change and choice

Start each report with three lines: what changed, why the team thinks it changed and what should happen next. Place charts and channel detail beneath that explanation.

Use annotations for campaign launches, price changes, tracking breaks and external events. A line moving upwards is not insight until the team understands the likely cause and the quality of the evidence.

Match the review rhythm to the measure

Delivery issues may need daily attention during a launch. Creative and audience signals can be reviewed weekly. Pipeline quality and revenue often need a monthly or quarterly view because buyers take time to act.

Do not force every KPI into a weekly meeting. Short-term movement can provoke unnecessary changes to work that needs time. Set an expected response period for each level of the hierarchy.

Use AI for analysis, not invented certainty

AI can group comments, flag unusual changes and prepare a first reporting summary. Give it controlled data, explain the metric definitions and require the analyst to verify every claim.

Never present generated numbers as live client evidence. Mockups should carry a visible illustrative label. Where data is incomplete, state the gap and the assumption used.

A marketing measurement strategy checklist

• Name the commercial decision and its owner.
• Select one or two business outcomes.
• Add diagnostic measures that explain movement.
• Keep delivery signals in a supporting layer.
• Define every KPI, source and limitation.
• Use experiments for high-cost decisions.
• End each report with a choice and an owner.

For a wider planning model, read B2B Marketing Strategy 2026. Teams applying assisted analysis can use the controls in Human-Led AI Marketing Strategy.

AI marketing trends in 2026 are changing more than the tools inside a marketer’s browser. Search behaviour, creative production, media buying and measurement are shifting at the same time. The brands that respond well will not be the ones producing the most machine-made content. They will be the ones using better evidence, stronger creative judgement and clearer controls.

Below are seven changes already visible in the market, followed by practical steps for marketing teams.

1. Search is becoming a conversation

People are asking longer and more specific questions. Google reported in May 2026 that the average query in AI Mode was three times longer than a traditional search query. The same update said more than one in six US searches used voice or images, while image searches were growing quickly.

Keyword lists still matter, but they no longer tell the full story. Marketers need to understand the situation behind a query: what the person is comparing, what is making them hesitate and what evidence would help them decide.

What to do: collect real customer questions from calls, comments, search terms and sales teams. Build useful pages around those questions, with direct answers and clear supporting evidence.

2. Original sources gain more value

AI answers need material they can cite. Google has added more visible source links, preferred sources and signals for original reporting inside its AI search experiences. Generic summaries have little reason to earn a reference when dozens of sites say the same thing.

First-hand material now carries extra weight. Good examples include named case studies, original data, tested methods, expert commentary and photographs showing real work.

What to do: create fewer empty explainers. Publish work only your organisation could have produced, then support it with authorship, dates, sources and a clear editorial point of view.

3. Creative volume becomes a testing problem

Generative tools can produce more images, copy routes and short videos than any team could review carefully. Google said advertisers created almost 70 million assets with Gemini in AI Max and Performance Max during the final quarter of 2025. More production is not automatically better marketing.

The hard work moves upstream and downstream. Teams need a sharper brief before generation, then a disciplined testing plan after it. Without those controls, extra assets merely create noise.

What to do: define the audience tension, brand boundaries and test question before making variants. Judge each route against one goal, not personal taste.

4. AI-assisted ads become more conversational

Google announced new ad formats for AI Search in May 2026, including experiences designed to answer detailed product questions. Ads are moving closer to guided consideration, especially where customers need confidence before acting.

Static promotional claims will struggle in that setting. Brands need useful product information, proof, comparisons and answers that remain accurate when adapted to a specific customer question.

What to do: improve product feeds, service pages and FAQ material. Treat factual content as part of campaign production, not an afterthought handed to the web team.

5. Measurement returns to business outcomes

Attribution has always been imperfect. Fragmented journeys, privacy changes and AI-led discovery make last-click reporting even less persuasive. IAB’s 2026 State of Data report focuses on incrementality, attribution and marketing mix modelling as teams rethink how performance is measured.

A useful report should help someone choose what to fund, change or stop. Large dashboards often fail because they describe activity without answering that decision.

What to do: agree the business question first. Combine platform reporting with experiments, sales quality, customer value and wider demand signals.

6. Agentic commerce shortens the path to action

AI systems are beginning to help people compare, select and buy with fewer separate steps. Google introduced the Universal Commerce Protocol and new shopping features in 2026 as part of this move towards agent-assisted purchasing.

For marketers, discoverability depends on accurate product data and a clear offer. If price, availability, specifications or policies are difficult to interpret, an automated assistant has less confidence in recommending the brand.

What to do: audit product and service data. Use consistent names, current prices, detailed attributes and plain policies across the website and merchant feeds.

7. Trust becomes visible

In July 2026, Google announced more disclosure features for ads created or altered with generative AI. The direction is clear. Audiences, platforms and regulators expect greater clarity about synthetic media and the data behind automated decisions.

Trust is not a paragraph hidden in the footer. It appears in accurate claims, clear approvals, sensible data use and the ability to explain why a piece of content exists.

What to do: create a short AI marketing policy covering data, copyright, review, disclosure and prohibited uses. Train the people applying it.

A practical AI marketing checklist for 2026

• Map longer customer questions, not only short keywords.
• Invest in original evidence and named expertise.
• Set creative rules before generating variants.
• Improve structured product and service information.
• Measure decisions and commercial outcomes.
• Record where AI is used and who approves the result.

Sources and further reading

Google: How AI Mode is changing search
Google: Commercial experiences in 2026
IAB: State of Data 2026

The winning habit is simple: use machines for speed, then apply human judgement where consequence, context and taste matter most.

AI search optimisation is the practice of making your content useful, clear and trustworthy enough to appear in conversational search results and cited AI answers. It builds on good SEO rather than replacing it. Technical access, relevant pages and authority still matter. The difference is that people now ask longer questions and expect a direct, supported response.

Google said in May 2026 that the average AI Mode query was three times longer than a traditional search query. Marketers should plan content around decisions and follow-up questions, not a single keyword repeated across a page.

What AI search optimisation changes

Traditional SEO often begins with a phrase and a results page. AI search begins with a problem that may contain several constraints. A person might ask for the best approach, compare options, refine the request and then look for proof.

Your content needs to survive that journey. A strong page should state what it covers, answer the main question early and make the evidence easy to identify.

Write answers that can stand alone

Place a concise answer directly below a descriptive heading. Definitions, steps and comparisons should make sense without a long introduction. This helps readers scan the page and gives search systems a clear passage to understand.

Do not strip out personality. A direct answer can still carry a point of view. The aim is clarity, not blandness.

Publish material worth citing

Google’s 2026 Search updates place greater emphasis on original content, trusted sources and visible links from AI responses. A rewritten summary of other summaries offers little value. Original evidence is far more useful.

Useful source material includes:

• Named case studies with dates and outcomes.
• Original survey results with sample details.
• Expert commentary linked to a real author profile.
• Photographs, examples or demonstrations from real work.
• A tested method that explains what succeeded and what failed.

Make authorship clear

A reader should be able to see who wrote the article, why that person knows the subject and when the material was reviewed. Add a useful author page, a publication date and a modified date where the content changes.

For a professional portfolio, connect articles to case studies and career experience. Expertise becomes more credible when a reader can trace it to actual work.

Build topic groups, not isolated posts

One article cannot answer every related question. Create a focused set of pages around a clear subject, then link them naturally. An article about AI marketing trends might link to a practical implementation plan, a measurement guide and a case study.

Use descriptive link text. “Read our AI marketing strategy guide” tells people and search systems more than “click here”.

Use structured data carefully

Article structured data can identify the headline, author, dates and main image. Organisation and Person markup can clarify the publisher and author. Markup must reflect content visible on the page. It should not invent awards, reviews or claims.

Structured data is a label, not a shortcut. Weak content does not become authoritative because a script calls it an article.

Give images and video a proper role

More searches now begin with voice and images. Visuals should explain or prove something. Use descriptive file names, accurate alt text, surrounding context and captions where they add value.

A diagram showing a method, a campaign example or an original product demonstration is stronger than a decorative stock image placed between paragraphs.

Protect technical foundations

Search systems need to access and interpret the page. Keep important content in the HTML, use a logical heading order and avoid hiding the core answer behind interaction. Pages should load quickly and work well on mobile devices.

Check canonical URLs, indexation, internal links and XML sitemaps. AI search has not removed these basics. Frankly, no amount of fashionable terminology repairs a page that cannot be crawled.

Measure the right signals

Traffic alone may not show the full effect of AI search. Watch branded searches, qualified enquiries, assisted conversions, citations, referral patterns and engagement with high-intent pages.

Review articles quarterly. Update facts, strengthen weak sections and add new first-hand evidence. Keep the original publication date and show a modified date when the change is material.

AI search optimisation checklist

1. Choose one decision or customer question per page.
2. Answer it directly near the top.
3. Add original evidence and a named author.
4. Link to related pages using descriptive wording.
5. Use accurate Article and Person structured data.
6. Optimise useful images with descriptive alt text.
7. Review technical access and mobile performance.
8. Refresh the page when facts or products change.

Sources and further reading

Google: How people are using AI Mode
Google: New source links in AI Search
Google: Original content and preferred sources

An AI marketing strategy should begin with a business problem, not a subscription. Teams often buy several tools, produce more material and then discover that approvals, data quality and measurement remain exactly as difficult as before.

This 90-day plan keeps the work grounded. It uses AI where speed and pattern recognition help, while people retain control over the brief, claims, brand voice and final decision.

Days 1 to 15: choose the right problem

Start with work that is repetitive, slow or difficult to analyse. Good early use cases are research summaries, content adaptation, campaign reporting and controlled creative variation.

Write each use case as a testable statement. For example: “Can assisted analysis reduce weekly reporting time while preserving accuracy?” This is stronger than “use AI for reporting” because it defines the result and the condition.

Create a simple risk screen

• What customer or employee data enters the tool?
• Could the output make a factual, legal or financial claim?
• Does the work contain copyrighted or confidential material?
• Who reviews the result before publication?

Days 16 to 30: prepare evidence and brand rules

AI output reflects the material and direction it receives. Gather approved product facts, customer questions, past campaign results and examples of writing that genuinely sound like the brand.

Create a short brand instruction covering audience, tone, prohibited claims, spelling, product names and words the organisation avoids. Keep it specific. “Sound premium” is vague. “Use short sentences, avoid exaggerated promises and explain technical terms on first use” gives the system and the reviewer something concrete.

Set a baseline

Record current time, cost and quality before changing the process. If a campaign report takes six hours and contains two corrections on average, those figures provide a fair comparison later.

Days 31 to 60: run two controlled pilots

Select two use cases with different types of value. One might save time, while another improves the number or quality of creative routes tested.

A useful pilot has a small scope, a named owner and an existing process to compare against. Keep the human review step visible. If nobody can explain why an output was approved, the process is not ready to expand.

Pilot one: audience research

Use AI to group search terms, survey answers or anonymised call notes into themes. A strategist should review the groups, identify missing context and turn the useful findings into a brief.

Pilot two: creative variation

Start with one approved proposition. Produce several visual or copy routes against defined channel needs, then test a small number. Record which prompt, source material and approval produced each final asset.

Days 61 to 75: connect work to measurement

Google’s January 2026 guidance for advertisers places emphasis on higher-level strategy, audience discovery across platforms and creative quality. The practical lesson is not to automate everything. It is to connect automation to a meaningful performance question.

Review pilot results using four measures:

• Time: did the process reduce hours or delays?
• Quality: did reviewers accept more work with fewer corrections?
• Performance: did the campaign improve the agreed commercial measure?
• Risk: were there data, accuracy, brand or rights concerns?

Days 76 to 90: decide what earns a permanent place

Keep the parts that produced a clear benefit. Change or stop the rest. A failed pilot is useful if it prevents a poor process becoming routine.

Document the approved workflow in one page. State the inputs, tool, owner, review step, record kept and measure checked. Review it every quarter because models, platform rules and team needs change.

Where people should remain in control

Human review is most valuable where context and consequence are high. People should retain the final say over positioning, sensitive audience choices, public claims, legal approval, crisis communication and any use of personal data.

Machines are good at producing options. A good marketer decides which option deserves to exist.

A 90-day AI marketing strategy checklist

1. Select two defined problems with measurable baselines.
2. Set rules for data, claims, rights and approval.
3. Prepare accurate source material and brand examples.
4. Run small pilots against the existing process.
5. Measure time, quality, performance and risk.
6. Document only the workflows that earn continued use.

Source and further reading

Google Ads: Three AI strategies for marketing in 2026
Google Marketing Live 2026 announcements

The point of an AI marketing strategy is not to make the team look modern. It is to improve the work without giving up judgement, accountability or a recognisable voice.