How to use AI in marketing: A complete guide for 2026

Key takeaways

• Supermetrics’ 2026 Marketing Data Report surveyed 435 marketers across brands and agencies globally, including the US, UK, Germany, Australia, and Singapore.
• The report found that 80% feel pressure to adopt AI, while only 6% have fully embedded it and 52% do not own their data strategy.
• In a Supermetrics customer case study, The Economist reported an 80% reduction in its data-collection workload after centralizing and standardizing marketing data.
• A useful pilot defines one task, the data it needs, a baseline or control, and a person who reviews the result.
• Start with one workflow you already measure; scale only when it beats the baseline.

AI is already part of many marketing teams’ day-to-day work. It can draft a subject line, flag an unusual change in campaign results, or help a team make sense of data spread across platforms. The harder question is whether that help actually improves the outcome.

There’s a gap between trying AI and making it useful. The 2026 Marketing Data Report found that 80% of marketers feel pressure to adopt it, yet only 6% have fully embedded it in their workflows. You don’t need to automate everything at once. Pick a job you can measure, give the tool the right data, and see whether the result is better than your current process.

This guide covers the main use cases, the data each needs, and a four-step way to test one workflow before you scale it.

What is AI marketing, and what can it (actually) do?

AI marketing uses automated systems to complete defined marketing tasks, analyze performance, predict outcomes, or carry out workflows. Drafting an email, finding an anomaly, forecasting conversions, and completing a sequence of tasks call for different tools and checks.

Generative AI creates text, images, audio, or video from instructions and examples. A large language model (LLM), for instance, can draft copy or summarize a report. Analytical AI finds patterns or anomalies in existing data. Predictive AI estimates a future outcome from historical data. Agentic AI completes a sequence of tasks within the tools, permissions, and approval points it receives.

AI works best on a part of the job with a clear input, output, and way to review the result. It can:

• Collect data, build reports, and make campaign adjustments within rules the team sets.
• Surface performance changes for a marketer to investigate.
• Adapt messages using suitable customer signals.
• Apply a defined workflow across a larger audience without repeating every manual step.

The data foundation matters. Supermetrics connects, manages, analyzes, and activates marketing data so a team can use it in reporting and AI workflows. Marketers still decide what the results mean and what to do next.

How to use AI in marketing: Use cases and strategies

Marketing teams can use AI across the customer journey, from lifecycle messages and creative testing to analytics and search. Start with the workflow and the result you want to measure, then choose a tool.

How to use AI for lifecycle marketing at scale

Lifecycle marketing uses customer behavior to select the next message or action. AI can process more interactions than a person can review one by one, while the team decides which signals and messages are appropriate.

• A product-page visit can trigger a follow-up message.
• An abandoned cart can trigger a reminder or relevant recommendation.
• A completed purchase can inform related-product recommendations.
• A support interaction can change which message the customer receives next.

A running-shoe retailer, for example, might follow a product-page visit with an email about related accessories. The message should make sense to the recipient and use information they knowingly shared. Personalization that reveals an unexpected inference can feel intrusive even if the inference is accurate. For more guidance, check out our guide to personalized marketing.

Netflix offers a prime example of AI in lifecycle marketing. Its recommendation engine analyzes every user’s interactions each day, serving them up with highly tailored content that keeps them engaged and loyal. If you’ve been watching a lot of sci-fi films on Netflix, you’ll get recommendations for other sci-fi shows and movies that might pique your interest.

How to use AI in email marketing

AI can help email teams create variants, select audiences, and compare results against a chosen metric. For example, test a subject line, send time, or message structure while a marketer checks the offer and copy.

Litmus’ 2025 State of Email findings, based on nearly 500 marketing professionals worldwide, found that 35% of companies see a $10–$36 return for every $1 spent on email. Another 30% see $36–$50, while 5% see more than $50. Those figures describe overall email returns; they are not the lift from using AI.

AI email workflows can:

• Build personalized content from customer data that the team can use for the campaign.
• Produce subject-line variants for a test.
• Adapt calls to action for an audience segment.
• Compare send times against click or conversion results.

Culture Kings uses Klaviyo to segment audiences and generate subject lines. Your result will depend on the audience, offer, test design, and metric you choose before launch.

How to use AI for ad campaign management

AI can adjust ad placements, bids, creative combinations, and landing-page options within advertiser-set rules. Marketing teams should monitor spend and conversion quality, because a platform may optimize its selected metric without accounting for margin, inventory, or wider business context.

• Ad platforms can adjust placements using campaign and audience signals.
• Automated bidding can adjust bids within the budget and rules the advertiser sets.
• Platforms can test calls to action and landing pages against a campaign metric.
• Marketers can analyze campaign performance to see whether those changes improved business results.

In Stream’s ChatGPT Ads case study, a June 5–August 5, 2026 test with approximately $10,000 in media spend per test group found that more than 70% of ChatGPT Ads clicks became website sessions, compared with below 10% across the other channels tested. Stream reported roughly 2x return on ad spend for its developer-skills campaign. These are Stream’s internal results from one test; the comparison channels are unnamed and results may vary. Use your own baseline or control before increasing spend.

How to use AI in content marketing

AI can draft a blog outline, generate social and ad variations, summarize source material, or help produce video and image assets. That gives writers and designers more material to work with and more time to shape the final piece.

A useful workflow starts with source material and a clear brief. A model creates the first draft; an editor verifies claims and originality; and the brand owner checks tone, terminology, usage rights, and fit with the marketing goal. Claude can support writing tasks, while tools such as Synthesia and Canva assist with video or image production. The review should match the asset and where it will appear.

Measure whether the workflow saves editing time, lowers production cost, increases the number of useful variations, or improves campaign performance. More drafts alone do not mean better content.

How to use AI in marketing analytics

AI can compare channel performance, flag anomalies, summarize changes, and forecast a defined outcome when it has suitable marketing data. It can cut preparation time, but a correct number does not automatically produce a correct explanation of why performance changed.

In a manual workflow, a marketer exports figures from separate platforms, aligns dates, currencies, attribution settings, and metric definitions, then looks for the story. A connected workflow prepares those figures for repeated questions. The team can ask for a channel comparison or anomaly check and spend more time testing the explanation against context such as a promotion, tracking change, or stock constraint.

Supermetrics connects and standardizes marketing data for analysis. Its native Insights Agent can analyze connected data and explain performance changes. Teams can also connect Supermetrics data to ChatGPT, Claude, and Gemini Enterprise for conversations with their marketing data. Whichever tool they use, marketers should check the source figures and interpretation before acting on a recommendation.

In a Supermetrics case study, The Economist reported an 80% reduction in its data-collection workload after centralizing and standardizing data. The same foundation can support strategic methods such as marketing mix modeling; the method should follow the question the team needs to answer.

How to use AI for predictive analytics and lead scoring

Predictive AI estimates a future outcome from historical data. Lead scoring uses that approach to rank leads against a defined conversion event. A lead score is an estimate of one buyer’s likelihood to convert, based on the model’s data and target definition.

A model might use past conversions, account attributes, and appropriate engagement signals. Sales and marketing teams should compare its rankings with actual outcomes after deployment and document where first-hand account knowledge changes a decision. Missing or inconsistent CRM data can distort the scores.

In the Vattenfall case study, personalized, behavior-based customer journeys were associated with a 3% increase in conversion rate. Journey orchestration and lead qualification were associated with a 4.2% increase in average contract value. Those results should not be attributed to lead scoring alone.

How to use AI in SEO and GEO

AI can help with search research, content analysis, internal-link suggestions, and technical checks. Search engine optimization (SEO) improves visibility in search results; generative engine optimization (GEO) aims to make content easier for AI answer systems to retrieve and cite.

• Group keyword candidates by shared intent with automated keyword research.
• Compare a site’s coverage with competitor topics and suggest gaps for editorial review.
• Suggest internal links from an existing content library.
• Flag crawl, Core Web Vitals, and structured-data issues.

Semrush and Ahrefs can support these jobs. A marketer still needs to decide whether a recommendation improves the page for its intended audience. A tool can misread intent, overuse a keyword, or suggest an unhelpful link.

How do you get marketing data ready for AI?

Marketing data is ready for AI when it is available, representative, consistently defined, and suitable for the task. A content pilot and a lead-scoring model need different inputs, so start with the decision the workflow will support.

Check four conditions:

1. Availability: Can the tool access the data needed for the question? A single platform gives it only part of a cross-channel picture.
2. Definitions: Do dates, currencies, and metrics mean the same thing across sources?
3. Outcomes: Can you connect campaign activity to the conversion, revenue event, or other result you chose?
4. Access: Does the workflow have permission to use those inputs, and can the team revoke it?

A reporting summary needs consistent dates and metric definitions. A lead-scoring model also needs representative historical outcomes and a clear conversion label. Document missing periods, tracking changes, and known gaps before testing.

The AI-ready marketing data process often involves connecting the necessary sources to a common destination, such as a warehouse or reporting environment, then standardizing them before comparing metrics. Supermetrics can move the sources a workflow needs into analysis destinations. More data does not always make a better model; irrelevant inputs can add noise, cost, or privacy risk. The AI Readiness Quiz can help identify preparation gaps.

What three steps do you need to complete before you start?

1. Choose one task your team already does regularly.
2. Set one business or workflow metric and record the current baseline.
3. Confirm access to data that is suitable for the task.
4. Name the person who will review, approve, or override the output.

The aim is a documented pilot with a task inventory, a measured result, connected inputs, and a decision to scale, revise, or stop.

How to use AI in your marketing strategy: Where to start

Use four steps: rank recurring work, test one measurable workflow, connect the required data, and decide whether to scale. Keep the test narrow enough that you can tell what changed.

1. Audit manual work: List tasks your team repeats each week or month. Record time spent, decision risk, data requirements, and review effort, then rank them. Report preparation, anomaly detection, and subject-line drafting often have stable inputs and a clear output. Keep strategy, relationship context, and novel creative decisions human-led.
   Output: a ranked task list with one pilot candidate.
   
2. Run one measured pilot: Choose a use case such as subject-line testing or a monthly campaign summary. Record the current completion time, error rate, conversion rate, or another relevant baseline, and define success before changing the workflow. For a paid-media test, a treatment-and-control design may be useful: Google Ads Conversion Lift compares exposed and unexposed groups to estimate incremental outcomes. A controlled test will not fit every task. Record the prompt, inputs, tool settings, reviewer, test period, and result.
   Output: a pilot result measured against the predefined baseline or control.
   
3. Connect only the required data: Map the inputs in your marketing tech stack. A cross-channel summary may need ad spend, web conversions, and revenue data; an email-copy test may need only the brief and brand guidance. Check metric definitions and document permissions, retention terms, and who can revoke access.
   Output: a documented set of connected inputs and data checks.
   
4. Make a scale-or-stop decision: Compare the result with the metric you selected. Record time saved, performance change, review effort, errors, and conditions that may have affected the outcome. A missed target calls for a revision or a stop; a successful pilot can expand with monitoring and review.
   Output: a written decision to scale, revise, or stop.

What are the best practices for using AI in marketing?

Define the task, limit its data and permissions, test the output, assign a reviewer, and monitor performance after launch. The level of oversight should reflect the consequences of an error, especially when a workflow affects customers, ad spend, or public claims.

• Review personalization for surprise, sensitivity, consent, and relevance before sending a message.
• Test models for recurring errors or biased outcomes in their actual use context.
• Verify facts, calculations, source use, and brand voice before publishing.
• Review data retention and AI-training terms and the requirements that apply to the use case.
• Document who can approve, override, pause, or revoke the workflow.
• Monitor live results as models, source data, and campaigns change.
• Record the tool version, inputs, reviewer, output, and final decision.

The voluntary NIST AI Risk Management Framework recommends examining data suitability, human oversight, privacy, fairness, testing, and monitoring. These practices help manage risk, but the team must still assess the legal and platform requirements for its use case. A budget change, customer-facing claim, audience decision, or lead score merits more scrutiny than an internal first draft.

What is the best way to start using AI in marketing?

Choose one repetitive, measurable workflow and test it with the data it needs. Keep a person responsible for the output. Compare the result with your baseline, then scale only if it meets the goal set before the pilot.

That turns a broad AI ambition into a task list, a measured result, and a clear decision. It also shows when a tool creates more review work than it saves. Marketing intelligence helps connect performance data with business decisions, while Supermetrics AI can work with that data foundation. Ready to test a workflow with your own marketing data? Sign up for free.

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