- AI Marketing
AI in marketing: How to use it, tools, and best practices
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What is AI marketing?
What if you could anticipate customer needs long before they even realize it themselves? That, among many things, is the promise of artificial intelligence (AI) in marketing — an emerging field where AI tools and solutions are used to deliver hyper-personalized experiences tailored to each individual.
Millions of data points are harnessed in real time to help businesses be more agile and competitive. AI improves on traditional marketing processes (such as data analytics and content generation) and uses technology to automate the most time- and labor-intensive parts, reducing risk of errors while allowing for more innovations.
In just a few short years, AI has completely transformed modern marketing. A study revealed that nearly 90% of marketers consider AI a necessity to stay competitive and meet customer expectations(opens in a new tab or window). Under AI marketing, marketers enhance and automate processes through technologies like generative AI (GenAI), machine learning, predictive analytics, and natural language processing among others, greatly expanding the scale of their work and quality of results. In fact, a 2025 report found that 93% of CMOs and 83% of marketing teams globally using GenAI tools are seeing a measurable return on investment (ROI)(opens in a new tab or window).
How does AI marketing work?
AI marketing follows many of the same stages as traditional marketing (namely awareness, consideration, and conversion), but automates many of these processes through self-learning algorithms.
Under AI-driven marketing, AI tools like machine learning models are used to track, consolidate, and analyze data like consumer behavior, customer relationship management (CRM) records, and external signals (e.g., social sentiment, market trends, and real world news) as well as derive business insights. These would be used to back other marketing initiatives, such as scoring customer intent, predicting churn, and optimizing creative content and campaigns. AI engines support omnichannel marketing efforts by stitching together data from different touchpoints to help form a seamless, cohesive customer experience (CX). Finally, data from these interactions are fed back into AI systems, allowing models to refine and improve future performance.
How AI is changing the marketing landscape
AI is pushing the boundaries of traditional marketing, giving marketers a chance to do more meaningful work for less effort. Tasks that once took an entire team days or weeks (e.g., content creation and campaign execution) can now be finished in a matter of hours, or even minutes, by one expert. Processes that could only be done on a limited scale (e.g., forecasting or customer analysis) can now reach granular precision thanks to automation.
Below are some of the biggest ways AI has transformed marketing:
Personalization at scale
It’s virtually impossible to manually track every single action a customer does throughout their buyer journey, much less create personalized experiences for every single one of them. Automation makes it possible for AI to track and analyze enough customer behavior to create a profile and make tailored recommendations. As the technology evolved, so too did the scale it could reach, with algorithms now evolving in real time for millions of users.
Recommendation engines allow personalization on unprecedented levels. These tools are capable of tracking every user’s browsing and purchasing activity (within what is legally allowed) and analyze their sentiment to deliver a truly tailor-fitted experience. This covers everything from the next piece of content the users see to what advertisements they receive, even on other platforms.
Amazon and Netflix are prime examples of hyper-personalization success. Amazon’s recommendation engine uses AI to suggest products(opens in a new tab or window) based on each customer’s browsing and purchase history, helping shoppers discover relevant items and enhancing the overall CX. Netflix estimates that 80% of their content consumption is driven entirely by personalized suggestions(opens in a new tab or window), influencing not just what viewers watch but also what shows and movies become popular.
Predictive analytics
The ability to process vast amounts of data and make sufficiently accurate predictions in an increasingly volatile environment is one of the biggest benefits of AI technologies. Machine learning models are able to ingest, analyze, and extract insights from millions of data points, at a speed and scale that humans cannot manually.
AI-driven predictive analytics happens in real time, not retroactively. In the past, analysts would have to wait for reports to be published before making forecasts, with very limited abilities to factor in any emerging developments. On the other hand, AI can be connected to a number of primary sources and process the data as they come in. This allows teams to predict outcomes in minutes rather than waiting for quarterly reports and to address potential roadblocks before they materialize. For example, teams can make changes to unsatisfying customer experiences before they drive away buyers, scale back offers that would likely lead to losses, and prevent other business decisions that could hurt the bottom line later.
Marketing automation
Business never sleeps with AI. Marketing engines can run around the clock, automating everything from simple communications (like texting appointment reminders or sending thank-you emails) to complex campaign optimizations (such as purchasing and placing digital ads through programmatic advertising).
AI has evolved enough to be on par, or even surpass, human decision-making, especially when it comes to analytics. For instance, AI-powered marketing automation tools can now determine the true optimal times for sending emails or pushing out messages based on internal and external customer behavior data. It can also adapt message content in real time based on user interaction, such as in cases of smart chatbots and other responsive technologies.
Creative workflows
The manual creative process can be slow and laborious, especially when resources are limited. In the past, one of the biggest barriers to the creative process was the lack of writing and design experts who understood the requirements well enough to produce effective assets. In some organizations, a single marketing team may oversee the work of multiple offices(opens in a new tab or window), maintaining consistent brand standards and quality across every output.
AI helps close the gap between limited creative resources and the growing demand for content. GenAI, in particular, has made it easier for anyone to produce strong creative work with just a few simple prompts. Nearly 90% of marketers say they use GenAI tools to work faster and simplify their creative process(opens in a new tab or window).
Customer journey optimization
With the vast majority (71%) of consumers now expecting personalized interactions(opens in a new tab or window), businesses may be hard pressed to meet their needs with traditional, manual methods alone. Combining the various capabilities of AI solutions — analysis, recommendation engines, content generation, etc. — is essential in keeping up with evolving consumer needs. This lets brands truly understand each and every customer and deliver an experience that perfectly matches their preferences.
The benefits of AI in marketing become even more pronounced with the fact that customer journeys are often scattered across multiple platforms, not all of which the business has control over. AI makes omnichannel marketing possible, so brands can provide a unified and consistent experience no matter where the buyer interacts with them. AI marketing tools deepen customer engagement, creating a truly memorable experience where customer needs are anticipated and fulfilled before they’re even expressed.
The future of AI in marketing
As AI technologies mature, marketers are optimistic about their potential — especially to save time on repetitive tasks and focus on more strategic work(opens in a new tab or window). Already, 85% of marketers report an increase in productivity after using AI(opens in a new tab or window), which supports PwC’s prediction of “20 to 30% gains in productivity, speed to market, and revenue.”(opens in a new tab or window)
With AI, marketers can move away from time-consuming manual production work. They can take on high-level strategic tasks like creating better content at a faster rate, personalizing even more meaningfully, and scaling their initiatives without sacrificing brand identity and ethical responsibilities.
Agentic AI
Large language and diffusion models are increasingly moving away from simple prompt-based work to become more automated and brand aware, essentially functioning as a “creative partner.” Marketers can train GenAI models on their brand’s unique aesthetic and voice, allowing AI to generate novel content ideas and campaign concepts that remain fully aligned with the core brand identity.
Autonomy is becoming a defining characteristic of next-generation AI. In this future, advanced agentic systems may learn aspects of a brand to execute increasingly complex decisions with minimal human intervention(opens in a new tab or window). This moves us closer to environments where a single platform can bridge ideation, execution, and optimization.
Real-time CX personalization
Advancements to user activity trackers and recommendation engines will allow AI marketing solutions to collect even more granular data at faster rates. Every click, swipe, and sentiment cue will be tallied to get the most accurate picture of user interest, which will guide everything from the messaging tone of voice to the actual product or service recommendations.
Even now, personalized website content can increase revenue by as much as 40%(opens in a new tab or window). AI-driven hyper-personalization will continue to move away from basic demographic targeting(opens in a new tab or window), using machine learning to process vast quantities of user data, identify subtle patterns, and deliver the most relevant content, advertisements, or product recommendations in real time.
Creative productions
AI is poised to cut back weeks of intense production work into mere hours. Generative models trained on brand guidelines, past sample works, and approval workflows can automate versioning, localization, and even layout decisions. This makes it simpler than ever before to create on-brand templates for a wide range of assets, including complex videos and multimedia materials.
As these technologies advance, the role of writers and designers will also evolve. They’ll take on more managerial and strategic responsibilities. In effect, they’ll act like creative directors who guide and curate content rather than crafting every piece themselves.
How to use AI in marketing
A 2025 study by S&P Global Market Intelligence found that 42% of companies have scrapped most of their AI initiatives(opens in a new tab or window). Whether it’s due to poor quality data, outdated tech stacks, or a lack of follow-through, the core reason is the same: these projects are built on a shaky foundation — a lack of strategy.
Applying AI in marketing isn’t about using the most advanced tools everywhere. To prevent costly mistakes and AI-fatigued customers(opens in a new tab or window), there are a number of things to consider before integrating AI into your marketing initiatives — if it is even needed at all.
Before purchasing an AI solution, there must first be an actual problem to address. Clearly identify your objective. Then, connect it to measurable goals. Remember to make your target as specific as possible. It’ll be easier to determine whether AI is truly needed or if the issue can be solved with existing tools.
Got a high churn rate? Your SMART goal could be to make the buying experience more seamless and enjoyable, thereby boosting your e-commerce platform’s performance.
AI relies heavily on first-party data(opens in a new tab or window), which is the information your company directly collects from customers and operations. If the data you gather is inconsistent, inaccurate, incomplete, or poorly maintained, it can distort insights and undermine the accuracy of your AI systems(opens in a new tab or window). Reliable, well-maintained first-party data (like web analytics, transaction logs, and purchase records) is essential for building accurate models and supporting future analysis.
After you’ve determined your marketing objective and established your data groundwork, look for platforms that truly fit your team’s needs. Pick tools that work smoothly with your current systems before expanding into more advanced solutions.
Connect every marketing goal to an AI tool that solves a real problem. For instance, predictive analytics suites could help resolve issues with churn or lifetime-value scoring. Personalization engines could serve real-time “segment-of-one” offers. GenAI tools could help boost creative capabilities for smaller teams.
Take your time comparing options. Test different products. Talk to vendors. Use free trials before making a final decision.
The most advanced AI tools won’t make an impact if your team isn’t confident using them. After confirming that a tool works well with your current systems, roll it out in stages instead of all at once. This gradual approach helps your teams adjust and prevents disruptions to established workflows.
Host short demos or hands-on sessions. Use real examples to show how AI can reveal useful insights and make everyday work more efficient.
Keep checking how AI is performing for your team and your overall goals. Start small by testing easy-to-track routine tasks like creating documents or performing administrative duties. Track results such as accuracy, turnaround time, and efficiency to see where AI is having the biggest impact.
For more advanced tools like AI-powered CRMs or predictive analytics systems, launch a pilot group. Set up the tool with real data, give it time to adjust, and compare results with your existing process before expanding.
Use what you learn to fine-tune your tools and workflows. Feed customer feedback and past marketing reports into your AI systems to help them improve over time. Most importantly, give both your team and your tools enough time to adapt before making any major changes.
AI in marketing best practices
Without the right strategies and guardrails, AI marketing could fall far short of its purported potential. While there is no one-size-fits-all strategy for AI adoption and implementation in marketing, there are methods worth following to make the most of your AI investment.
Ensure data privacy and compliance
AI is powered entirely by data, so adopting a privacy-by-design framework from day one goes a long way in strengthening your foundation.(opens in a new tab or window) Know all relevant regulations governing AI, data use, and data protection. Stay up-to-date with emerging policies, especially now, as the law is still catching up to the technology.
It’s better to err on the side of caution by limiting data collection only to what you truly need. Likewise, strengthen internal policies and systems on data handling and familiarize yourself with relevant regulations such as GDPR, HIPAA, CCPA, and more. If necessary, onboard an expert to oversee your compliance.
When choosing your AI marketing tools, check the providers’ data privacy and compliance frameworks. Some products are purpose-built to align with industry regulations, while others are made by developers who have deep industry familiarity and a track record of working with businesses in the sector. This may be your best bet when it comes to finding AI solutions that truly protect your data.
Balance automation with human creativity
These days, algorithms can generate copy, images, and even videos in seconds, but audiences still recognize and reward authentic human nuance. There is also an emerging problem with AI “cannibalizing” itself; so prevalent has AI-generated content become that AI models are now beginning to train on them(opens in a new tab or window), leading to progressively lower-quality content.
Then there is the issue with AI fatigue. The proliferation of AI has begun to make interactions feel impersonal and mechanical, so much so that a fully disclosed chatbot could see purchase intent plummet by 79% once people realize there is no real human on the other end of the line(opens in a new tab or window). Brands that truly want to connect with customers must maintain a human touch.
For example, create drafts with AI but ensure that someone from your team reviews it for tone and sensitivity. Automate as much of the customer journey as possible, but have a real representative at the ready should your customer need help.
Align AI with cross-department goals
Marketing does not operate in a vacuum. Map every AI initiative to its adjacent teams — sales, product, finance, IT, etc. — and ensure effective collaboration among all involved. Align goals, strategies, and tools with one another.
For instance, if the sales team uses an AI-powered CRM platform, the marketing, product, and customer success teams should be using the same tool or at least have sufficient access to its data. That way, they can see what strategies are being used to nurture customers and how well these strategies are performing. Other departments can make similar adjustments and maintain alignment across the board.
Continuously monitor AI outputs
No model should be “set and forget.” It’s vital to consistently track performance quality, monitor bias and drift, and schedule ongoing retraining or prompt-library refreshes. AI is designed to learn continuously, and if its training dataset has become stale or obsolete, it will affect the tool’s future outputs. For this, a dedicated AI committee must be assigned to keep track of the tools’ performances and catch potential problems before they materialize.
Canva makes AI marketing simple
See how Canva has empowered organizations by streamlining workflows and boosting efficiency, all while keeping things secure.
"Canva is an invaluable tool for our business. Canva enables our team to scale best practices when it comes to visual communications across the company."
Frequently asked questions
Canva Enterprise(opens in a new tab or window) is an all-in-one design platform where organizations can create, collaborate, and publish visual content securely and sustainably at scale. Its suite of creative solutions, including graphic design tools, video editors, and AI-powered content generators, are built to meet the design needs of larger businesses who may struggle to do so using traditional options.
A core part of Canva’s offering is automating the more cumbersome aspects of the design process, such as creating assets, adhering to brand guidelines, and coordinating with multiple stakeholders. On a large scale, even minor production delays can snowball into major roadblocks. This is what Canva Enterprise aims to prevent with its centralized Brand Kit(opens in a new tab or window), customizable brand-approved asset templates, and seamless, secure collaboration functions among others.
Unlike other design platforms, Canva Enterprise is tailored for the needs and challenges of large organizations. It has built-in features and functionalities that are unavailable (or, at least, severely limited) on other solutions such as its AI-powered tools(opens in a new tab or window), real-time collaboration between users, content management, and more.
Beyond that, Canva is comparably more accessible than other platforms. It is available online, so users do not have to download any software. It has an intuitive interface that anyone can figure out easily, allowing more users to quickly work on centralized assets and pre-approved templates. It also offers scalable and flexible packages; businesses can avail only of what they need and adjust their subscription over time.
Canva offers different plans and pricing depending on your organization’s size. Canva Teams(opens in a new tab or window) works for small groups of at least three people, ranging from startups with a handful of members to small-to-medium enterprises with limited design needs.
Meanwhile, Canva Enterprise is made for large organizations — typically involving hundreds or thousands of employees from varying departments. It has all the functions that come with a Teams plan, but more advanced brand control, security, user permissions, and administrative features across the entire organization.
