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

Teams that have figured out how to use AI in marketing are realizing massive gains in efficiency, scalability, and adaptability. However, our 2026 Marketing Data Report shows that only 6% of marketers have fully embedded AI tools into their workflows. What’s more, 52% of marketing teams don’t own their data strategy, which stalls AI use cases and reduces ROI.

Great marketing starts with great data, and the same holds true for implementing AI. To get the most out of AI, you need a unified data foundation and agentic workflows that turn insight into action. Supermetrics serves as that foundation, bringing all of your data together into one platform so you can manage, analyze, and act on it.

In this post, you’ll learn what AI can do for marketers across various workflows, how to prepare your data for AI, and the best practices for implementing AI into your broader marketing strategy.

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

AI in marketing refers to the use of automated systems to execute common marketing workflows. Marketers use different types of AI for different workflows. For instance, they may use generative AI to produce content, analytical AI to score leads, and agentic AI to optimize campaigns based on predictive analytics.

The goal of using AI is to support marketers, not replace them. Augmenting human intuition with AI lets professionals speed up the most time-consuming aspects of their work, support decision-making, and deliver value at scale faster. Some examples of this in action include:

• Automation of repetitive tasks: AI saves time by handling data collection, reporting, and campaign optimizations.
• Data-driven decision-making: AI enables real-time campaign adjustments based on predictive analytics, so humans don’t have to spend hours bogged down in analysis to make a single decision.
• Hyper-personalization: AI tailors content to individual user behavior, allowing marketers to optimize engagement with far less manual effort.
• Scalability: AI allows brands to target larger audiences without increasing workload.

The key to success when using AI in marketing is ensuring that your automated systems can reference clean, centralized marketing data and execute on every task or query with full context.

How to use AI in digital marketing: Use cases and strategies

AI now touches every stage of marketing, from triggering a lifecycle email to reallocating ad budget mid-campaign.

How to use AI for lifecycle marketing at scale

AI is enabling teams to track and support customers throughout their journey at scale. Rather than having teams of humans sit down and track potentially millions of separate interactions, AI can automatically analyze and act on behavioral data, delivering the right message at the right moment to every customer. Examples include:

• Page visits: AI can flag when a new user visits a product or service page and trigger a follow-up action. For instance, if a customer visits a page about running shoes, AI can automatically send an email introducing them to the running accessories your company offers.
• Carts abandoned: If a customer leaves items in their cart without purchasing, AI systems might send a reminder notification or display alternatives with better reviews or lower prices.
• Purchases made: AI can track each customer's purchases and personalize product recommendations accordingly. For example, if a customer has bought a laptop from your store, AI can recommend related accessories such as mice, monitors, and keyboards.

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

Email remains one of the highest ROI marketing channels. According to Litmus’ 2025 State of Email Report, 35% of marketing leaders receive between $10 and $36 for every $1 spent. A further 30% receive $36–$50, and 5% receive more than $50.

But to receive that ROI, you need emails that truly resonate with your customers, prompting action to interact with your brand. This is where AI tools like Klaviyo and Persado help. Examples of how AI helps with email marketing include:

• Building personalized content targeted to specific audiences by referencing customer data
• Writing out stronger subject lines to optimize the clickthrough rate for each email
• Structuring content and CTAs to speak directly to customers’ interests and prompt them to take action, like visiting your website or a specific product page
• Optimizing send time, so recipients get emails at times when clickthrough and engagement are most likely

Culture Kings, for instance, uses Klaviyo to segment their target audiences and generate optimized subject lines.

How to use AI for ad campaign management

AI is embedded in every major ad platform, and it's changing how companies approach digital advertising. Google Ads, Meta Ads, and other major advertising platforms now provide AI tools that can automate various aspects of ad campaign management, including:

• Placements: AI optimizes ad placements based on behavioral data, refining them until they reach the most engaged audience or segment.
• Bidding: AI tools adjust spending in real time based on user-allocated budgets, conversion probability, competitiveness, and historical patterns.
• CTA and Landing Page Optimization: Users input a mix of different possible CTAs and landing page URLs, and AI implements the top-performing options based on real-time signals.

AI-powered ad tools are excellent for automating repetitive tasks, and virtually anyone can access them with limited technical skills. However, you should still analyze campaign performance to prevent AI from making inaccurate budget decisions or targeting the wrong audience.

How to use AI in content marketing

Creating high-quality, engaging content is often a time-consuming, mentally taxing task. AI marketing tools can help streamline content production by writing first drafts for blog posts, generating social media captions, drafting up ad variations for A/B testing, creating videos, and more.

While AI can’t replace human creativity, it can be a powerful assistant that speeds up brainstorming and content generation. You must always keep humans in the loop to review and edit content for accuracy, nuance, originality, and alignment with brand standards and marketing goals. Writers, designers, and their companies own responsibility for quality and accuracy, not AI tools.

Heinz, for instance, led a campaign in which they asked various AI models to draw ketchup, and many of the outputs resembled a Heinz bottle. The point was to validate Heinz’s dominance by illustrating that the brand itself is virtually synonymous with ketchup.

LLMs like Claude are useful for supporting written content creation, while dedicated AI tools like Synthesia and Canva automate graphic content creation, such as videos and images.

How to use AI in marketing analytics

AI-powered analytics tools turn raw marketing analytics into actionable insights, allowing marketers to measure performance, predict trends, and make data-driven decisions. With these tools, pulling insights from millions of metrics sitting in a data warehouse no longer takes hours or days of manual time.

Supermetrics, for instance, unifies marketing data from 150+ data sources (such as Google Ads, HubSpot, and GA4) into a single layer. Once unified, your data can be accessed by both native Supermetrics AI (such as Insights Agent) and external tools like ChatGPT, Claude, and Gemini.

The Economist has used Supermetrics to centralize both its online and offline data, automate data standardization, and implement AI to handle time-consuming tasks like root cause analysis. As a result, the company reduced its data collection workload by 80%, drove significant improvements in ROAS, and enabled quicker responses to market shifts at both the strategic (through marketing mix modeling) and tactical level.

How to use AI for predictive analytics and lead scoring

In marketing, predictive analytics is the use of historical data, machine learning algorithms, and AI to predict future customer behavior with maximum accuracy.

Lead scoring is a common use case for predictive analytics. It trains models on historical conversion data, buyer patterns, and more to score and rank new leads by their likelihood of converting. Vattenfall used Supermetrics to implement behavior-based lead scoring. This contributed to a 3% increase in conversion rate and a 4.2% increase in contract value.

Keep in mind, the accuracy of predictive models reflects the data that they’re trained on. A lead-scoring model that's based on 12 months of incomplete or inaccurate CRM data will certainly perform worse than one with access to years of accurate data. Even if your CRM data is spotless, it’s worth having sales personnel audit lead scores based on their personal knowledge.

How to use AI in SEO and GEO

Using AI in SEO and GEO can help you speed up and catch more details throughout various tasks, such as:

• Keyword research clustering: AI can identify target keywords relevant to your business and automatically group them into categories with shared or similar semantic intent.
• Content gap analysis: Dedicated AI tools can automatically scan your website for underperforming and missing content relative to posts featured on competitor websites, then recommend the topics and content structure you need to compete.
• Internal linking: AI can surface recommendations for internal links in your content based on your existing content library.
• Technical SEO auditing: Specialized platforms can scan your website and automatically surface issues affecting SEO and GEO visibility, such as crawl errors, Core Web Vitals issues, and structured data gaps.

Common AI tools for SEO and GEO include SEMrush and Ahrefs. AI can be a powerful SEO ally, but you should avoid over-reliance on AI-generated recommendations. These tools may suggest keyword stuffing or irrelevant optimizations, harming content quality, user experience, and brand positioning.

How to get your data ready for AI

Before AI can deliver real value, you need to have AI-ready marketing data on hand. To know if your data is truly AI-ready, evaluate it based on the following questions:

• Is your data siloed off in disjointed platforms? If so, AI won’t be able to analyze data in full context — it will only draw conclusions based on data already within the platform, which is likely limited and incomplete.
• Are metric naming conventions standardized across all your data? If you have different naming conventions for the same metrics across platforms and don’t unify them, you’ll likely get meaningless or inconsistent insights when using AI marketing software.
• Are datapoints missing attribution to specific outcomes? Without a clear chain from metrics (like impressions or clickthroughs) to conversion or revenue, AI tools won’t actually be able to determine what works and what doesn’t.

Once you’ve got your data AI-ready, start consolidating your sources. Connect platforms like Google Ads, Meta Ads, and your CRM into a single, unified destination — whether that’s a data warehouse like BigQuery or an automated spreadsheet tool. Consistency and real-time updates are key. Once your data is centralized, enrich it as much as you can to give the AI more context.

With Supermetrics, you can bring data from over 150 sources into a single platform, derive meaningful insights, and make data-driven decisions without needing engineering support.

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

AI marketing strategies work best when implemented in phases rather than as a simultaneous overhaul. With each AI implementation, you should select a narrow starting point, measure the outcomes, and scale only after you have concrete, documented evidence of success. Below is a four-step approach for implementing AI that applies to marketers across the industry, regardless of role or team size.

Step 1: Audit what you are doing manually

List out the most repetitive, time-consuming tasks that your team handles regularly, and order them in terms of time spent. The 5–10 items at the top of your list are likely the most natural candidates for AI automation. This is especially true for tasks that involve finding patterns in existing data or applying a consistent set of rules to a recurring decision.

Tasks that require original creative judgment, first-hand knowledge of the relationship context for specific client accounts, or novel problem-solving are best kept human-led. However, AI can still add significant value by supporting the analysis and production that feed into those tasks.

Step 2: Pick a use case and test it

Start with a single, low-risk AI use case with measurable outcomes. Taking a slow, one-step-at-a-time approach to AI implementation lets you iron out issues or inefficiencies before deploying it at full scale.

For instance, you could start with a simple task, like writing email subject lines for A/B testing in a single campaign or generating a monthly report for a single account. It’s critical to define what success looks like with specific metrics, document your outcomes, and compare outcomes with precedent to illustrate the specific value AI adds.

Step 3: Connect your data sources

Most AI tools produce better outputs when they have access to cross-channel data for full context, rather than narrow data from one platform. When AI can see web analytics from GA4, ad performance from Google Ads and Meta, and email engagement from Klaviyo at the same time, it identifies patterns that a tool working with just one of those datasets can’t detect.

Supermetrics makes this possible, ensuring that data from 150+ sources in your tech stack connects to LLMs and native AI agents. You can query chat tools with plain-English questions and receive trustworthy answers and informed recommendations supported by your entire dataset.

Step 4: Scale what works

After your pilot test, evaluate performance against the metrics you defined before you started. For instance, if you were testing how much time you could save by involving AI in writing first-draft email content, compare the average completion time while using AI to the average completion time when done entirely manually.

Scaling AI in marketing is an ongoing process, not a one-time change. Successful implementation depends on scaling up what has been proven to work. If AI didn’t succeed by your metrics in one area, perhaps try a different approach or two for your tests. If it just doesn’t work for you, it’s not worth implementing.

AI marketing best practices

AI can yield significant advantages when used correctly, but mismanagement can introduce risks that marketers must be aware of at all times. To compound advantages and limit the potential for issues, consider the following seven best practices:

• Consider the effects of overpersonalization. When AI drafts content meant for customers, check to ensure it doesn’t include anything they’d be surprised you know — it may create a misunderstanding or a rupture in trust. AI should produce outputs based on signals customers have consciously and knowingly shared, such as purchase history, stated preferences, or page visits.
• Be aware of bias in AI models. LLMs learn from historical data and may encode biases that can lead to inaccurate judgments when performing tasks like lead scoring and audience segmentation. Make sure humans validate decisions before they’re finalized.
• Review outputs for quality and brand voice. Generative AI tools can generally produce fluent first drafts, but they may contain factual errors, hallucinations, and tone or language that drifts from brand guidelines. Keep humans in the loop to review every AI-assisted piece before pushing it live.
• Prioritize data privacy and security. Your first-party data is a protected asset. Before connecting your data to any tool, review its data retention policies, AI training agreements, and compliance with data security regulations. Platforms that don’t adequately protect user data may leak proprietary information, thereby reducing or nullifying critical competitive advantages.
• Understand compliance requirements. Map compliance requirements and relevant regulations before scaling any AI use cases. Bills like the GDPR, CCPA, and other emerging AI-specific regulations across borders impose distinct requirements on AI usage.
• Be transparent with your audience. Transparency about how AI is used in personalization is both a legal obligation (in many cases) and a trust signal. Implement notices and opt-outs wherever you intend to use AI for data collection, analysis, and activation.
• Make time for human oversight. There is no replacement for human expertise and judgment. It’s worth keeping humans in the loop for all AI-assisted workflows and decisions, even those with low stakes. Repeated low-stakes errors can add up to become a serious problem if left unaddressed.

Transform your strategies with marketing AI

Learning how to use AI in marketing is crucial for maintaining a competitive edge. AI is transforming how marketers strategize, create, and engage with audiences. Those using it effectively are realizing gains in efficiency and accuracy across their workflows, from lifecycle and email marketing to analytics and lead scoring.

Teams are using generative AI to draft deeply personalized content, analytical AI to pull useful marketing intelligence and predict outcomes, and agentic AI to automatically execute tasks that would otherwise require manual effort.

Supermetrics is the engine that makes implementing AI in marketing possible, with proven success across more than 200,000 companies. Supermetrics lets marketing teams centralize and standardize data from over 150 sources on a single, AI-embedded platform. With that, it helps teams move from passive reporting to impactful, data-driven decision-making.

Discover how to implement AI across your marketing workflows with a free trial of Supermetrics.

FAQs

What are the best AI tools for marketing in 2026?

Supermetrics is one of the best AI tools for marketing because it lets you connect, manage, analyze, and activate data from over 150 sources on a single platform.

Other strong options for marketing teams include Claude for copywriting, SEMrush for SEO analysis and content optimization, and Canva for graphic design.

Can AI replace marketing teams?

No, AI cannot replace marketing teams. AI can handle time-consuming, repetitive tasks, but it cannot replace human ingenuity in strategy, creativity, and brand judgment. The best marketers treat AI as a capable assistant rather than an all-in-one replacement.

How does data quality affect AI marketing performance?

AI models produce outputs based on the quality of the data they receive. If your data is siloed, outdated, or of otherwise poor quality, those issues will be reflected in your AI outputs. To ensure that your AI outputs are more reliable, connect all your data sources into a single pipeline so AI can reference the full context rather than a limited picture.