"Try to try it, now."

Beer brand Heineken reminds us that artificial has nothing on the real thing. In April 2022, it launched its new Silver beverage in the Metaverse—a beer you can't taste.

We agree that some things are irreplaceable by objects in the digital realm, but artificial intelligence (AI) is making huge strides in marketing and advertising.

For the past few years, AI has been optimizing ads, running chatbots, and automating many marketing initiatives for increased productivity. But with the recent hype around generative AI and other platforms that have surfaced, marketers and designers are more interested in this technology than ever before.

Understanding the role of AI in marketing

AI is revolutionizing the way you do marketing.

In the simplest sense, AI is a collection of technologies that mimic human intelligence—they learn from experiences, adapt to new inputs, and even perform tasks that normally require human intellect.

It's not just about big data or machine learning—AI marketing encompasses a range of technologies and techniques that can automate and improve digital marketing efforts. While big data and machine learning are critical for AI, they aren't the only components. Let's break this down.

Big data

This refers to the large volumes of structured and unstructured data that inundate a business daily. In marketing, you'd use big data to gain insights about customer behaviors, trends, and preferences.

Big data forms the basis for machine learning (ML) algorithms to learn and make predictions.

Machine Learning (ML)

Algorithms learn from data and improve their performance over time. ML can be used in marketing to predict customer behavior, personalize content, or optimize pricing strategies.

Natural Language Processing (NLP)

Computers can use AI to interact with human language. NLP enables voice search optimization, text-based sentiment analysis, chatbot interactions, and more.

Deep learning

A type of ML, deep learning mimics the workings of the human brain to process data. It's helpful for image and speech recognition. It also helps detect patterns and make predictions in marketing.

Reinforcement learning

This is an area of machine learning where an agent (the AI) learns to behave in an environment by performing actions and seeing the results. Much in the same way that we learn.

An example of reinforcement learning being used in marketing is programmatic advertising. AI can determine your best bidding strategies using reinforcement learning.

Computer vision

Computers can use AI to see and understand digital images and videos. It's used in image recognition and can be important in graphic design and social media, where visual content is key.

Computer vision is a subfield of AI that enables computers to interpret and analyze visual information through ML models and convolutional neural networks (CNNs). This technology identifies and classifies objects in digital images and videos, enabling computers to respond accordingly.

Examples of computer vision activities include:

  • Image segmentation
  • Object detection
  • Facial recognition
  • Edge detection
  • Pattern detection
  • Image classification
  • Feature matching

Generative AI

This involves AI that can create content, from writing text to crafting images or videos. You can use generative AI to write code, summarize data, create marketing content, or design assets.

AI encompasses a range of technologies that all work together to enhance and automate marketing efforts. Understanding this broad range of applications is key to maximizing the potential of AI in your marketing strategy.

You can use AI tools to predict behaviors, personalize experiences, automate tasks, and drive decision-making.

We’ve placed our list of 27 technologies into seven categories, namely:

  • Graphic design and content marketing
  • Social media and influencer marketing
  • Customer experience
  • Consumer intelligence
  • Advertising and pricing
  • Lead management
  • Digital marketing productivity

Let's look at the digital marketing processes that can be AI-optimized to improve your marketing team's workflows.

Graphic design and content marketing

  • Image creation
  • Content generation
  • Voice search optimization
  • SEO optimization
  • Video analytics

1. Image creation

You've probably dabbled in image generation using one of the available Generative Adversarial Networks (GANs), such as MidJourney, or GPT-3-based platforms like DALL-E. These big data models turn natural language (written or spoken prompts) into realistic or stylized images, depending on the platform and settings you use.

Design platforms are also adding AI-powered image-editing tools, such as Linearity Curve's Auto Trace and Background Removal features.

The Auto Trace feature uses ML to automatically detect lines and shapes in raster images and turn them into editable vector graphics. This is perfect for when you want to add sketches on paper or photographs into digital designs and scale them.

Background Removal relies on a large dataset to detect objects in images and “cut them out.” This feature’s AI algorithm is designed to detect the edges of the main subject in a raster image and leave the background transparent.

AI image recognition technologies analyze consumer behavior and preferences to understand their needs better. This helps marketers quickly identify objects in images and videos to deliver more relevant and polished visual content.

2. Content generation

Now capable of generating creative and engaging content, AI is transforming the content marketing landscape. Generative AI uses ML to create content tailored to your target audience's interests. This includes blog posts, social media updates, product descriptions, and more.

82% of marketers said that generative AI has impacted how they plan to create content in 2023, according to a survey by HubSpot.

Marketers cite time-saving, improved content quality, and personalization as the top benefits of using AI for content generation.

The same study found that 43% of marketers who already use generative AI copywriting tools find it helpful when creating emails, and 54% say it’s very effective.

With the help of AI, marketers can quickly optimize content for maximum performance across different marketing channels and devices, such as social media management on mobile. You can also use generative AI to turn podcasts or videos into blog articles.

3. Voice search optimization

Reaching customers on voice-enabled devices such as Amazon Echo and Google Home requires voice recognition technology. An often-overlooked benefit of voice search is that it also offers greater user accessibility for those with various levels of visual, physical, or learning impairment.

Visual search optimization involves optimizing content to match natural language queries, using conversational keywords, and creating concise answers that are easy to understand.

It's also key to ensure your company's website is optimized for voice search. You can do this by giving it a clear structure and creating content with frequently asked questions (FAQs) and topics your target audience is searching for.

46% of meaningful customer experiences with brands are digital, according to a study by Gartner.

Digital voice assistants like Amazon's Alexa, Microsoft's Cortana, and Apple's Siri allow customers to interact with brands and products through NLP. AI can help marketers create conversational experiences that feel more human, making them more engaging for consumers.

AI is helping optimize content for voice search, as voice search queries tend to be longer and more complex than traditional text searches. And it helps marketers create content optimized for both text and voice search, ensuring maximum visibility on Search Engine Results Pages (SERPs).

4. SEO optimization

With the help of AI, content marketers can quickly identify the most relevant search terms and keywords to target for maximum visibility in organic search engine results. Your prompts determine the level of quality. But with practice and trial and error, this technology can become an effective search engine optimization (SEO) content tool in your toolkit.

AI can also analyze your backlink profile, content quality, and website usability to ensure successful SEO campaigns.

5. Video analytics

Computer vision technologies can analyze videos, providing marketers with insights into how people interact with their brands. AI video analytics breaks down digital frames pixel by pixel to recognize objects and understand the content. It detects movement and can recognize patterns and themes.

You can use this technology to find image-based brand mentions or even counterfeit branded content and products online.

Video footage analytics can also reveal details about store foot traffic (and the most popular aisles), customer demographics, and queue wait times.

Social media and influencer marketing

  • Social listening
  • Influencer marketing

6. Social listening

Tracking and analyzing conversations about your business or industry across social media platforms can be invaluable for improving customer experience. AI-powered social listening tools quickly scan vast amounts of data and identify any brand mentions. They analyze the information and unearth actionable insights, automatically categorizing various types of posts for you.

Using sentiment analysis, AI can detect whether the conversation is positive or negative and respond on behalf of your brand. These features enhance customer relationship management (CRM) by providing insights into their needs and preferences. They also enable accurate trend tracking and predictions.

7. Influencer marketing

By analyzing social media data, AI-powered tools can identify the influencers most likely to resonate with your audience. These tools can also track return on investment (ROI), which is often hard to do with influencer marketing.

Can you imagine how much time you'll save using AI to match your brand with its most relevant influencers? And it takes the guesswork out of whether their posts will really reach your target audience.

Customer experience

  • Personalization
  • Personalized website experience
  • Augmented Reality (AR) experiences
  • Customer retention

8. Personalization

Marketers can create individualized customer experiences by analyzing and using customer data. This includes tailoring messages according to each person’s interests, settings, purchase history, location, etc., which results in higher engagement rates and conversions.

60% of consumers say they would become repeat customers if they received a personalized shopping experience, according to Exploding Topics.

Personalization has become increasingly important in recent years as customers expect a tailored experience when interacting with brands online. With AI, personalization isn’t just possible—it’s easy.

For instance, an AI can potentially recover a sale by automatically sending a personalized email reminder if customers leave items in their cart without checking out. These automation capabilities improve customer engagement rates and efficiency.

AI is revolutionizing email marketing. You can use AI-powered marketing tools to analyze past email performance, trigger workflows after specific actions, and personalize content to audience segments.

You can also use AI to generate high-performing, personalized email subject lines based on previous data and research.

28% of marketers use generative AI to create personalized emails, according to the HubSpot survey mentioned earlier.

We expect this percentage to increase as more designers adopt marketing AI tools.

The same study by HubSpot also revealed that marketers using generative AI reported saving more than three hours of work on each piece of marketing content. This means they have more time to focus on the strategic aspects of their role.

9. Personalized website experience

AI can help customize your website experience for each visitor, serving up personalized content and buying experiences tailored to their preferences. This can ensure users get the most relevant information, which will be more engaging.

Website personalization includes product recommendations, custom content, and a unique user interface.

It provides positive browsing and shopping experiences and increases the likelihood of customers buying from your brand.

10. Augmented Reality (AR) experiences

Augmented Reality (AR) experiences powered by AI can help bring products and services to life for customers. This helps marketers stand out from the crowd and deepen customer engagement.

AI algorithms can analyze a user's behavior, preferences, and context to create highly personalized AR experiences. For example, a retail app might use AR to show how a piece of furniture would look in the customer's living room, and AI could tailor the products displayed based on their browsing and purchasing history.

Brands are starting to use AI-powered AR ads that users can interact with. These ads might allow users to virtually try on products, like sunglasses or makeup, or explore a 3D model of a product from all angles.

This increases engagement and boosts conversion rates as users can see how a product would look on them or in their space before purchasing.

11. Customer retention

Closely connected to marketing and sales strategies, customer retention campaigns focus on customer satisfaction and constantly improving their experiences. AI can help marketers predict customer behavior and proactively address potential issues.

For example, AI can analyze the customer journey to identify where customers may be encountering problems or becoming frustrated. It can then decide how to respond or alert you to take action.

Consumer intelligence

  • Predictive Analytics
  • Customer Segmentation
  • Behavioral Tracking
  • Sentiment Analysis
  • Churn Prediction

12. Predictive analytics

Predicting customer behavior enables marketers to create more targeted and successful campaigns. Predictive analytics uses historical data, statistical algorithms, and AI processing to identify possible future outcomes based on past patterns. Think of it as your data analytics assistant.

It can help optimize marketing campaigns, personalize content, and provide accurate reporting. Predictive analytics can be used to predict customer engagement points, track "drop-off points," do profile scoring, and build custom models.

This can help you anticipate customer actions and optimize your marketing efforts.

For example, analyzing behavioral data and mapping customer journeys on ecommerce websites enables marketers to predict when customers will likely convert. They can use these predictive analytics to tailor their digital marketing strategies accordingly.

This means you can identify potential leads and prioritize those that are most likely to convert. Personalized content can be created based on customer behavior models, targeting the right audience at the right time.

AI can determine the content to be delivered based on affinity analysis, response modeling, and churn analysis reporting methods. Affinity analysis digs into big data and finds insights. Response modeling classifies the insights to find out which customers will respond best to the marketing campaign. Churn analysis looks at the results to make predictions about customer churn.

13. Customer segmentation

Tagging customers into groups based on shared characteristics is called customer segmentation. These characteristics could be demographics, location, lead source, buying behaviors, email open rate, or any other valuable actions they've taken.

With the help of AI, you can analyze customer data to identify patterns and automatically create segments that are more effective for targeted marketing.

AI-enabled segmentation tools can also adjust segments in real-time based on changes in customer behavior, enabling more personalized experiences. This helps you create campaigns that are more likely to convert and maximize ROI.

14. Behavioral tracking

AI-powered tools can track how customers interact with your website and analyze their location, device, and more. It collects data points from different sources of customer interactions to provide a unified omnichannel experience.

This helps you tailor the customer journey for maximum engagement.

15. Sentiment analysis

Beyond social listening, AI can automatically analyze any user-generated content (UGC) to determine the sentiment behind it. Businesses integrate AI-powered sentiment trackers to quickly scan large amounts of user data and accurately identify entities (or situations) and emotions such as joy, anger, or frustration.

Customer sentiment analysis helps you gain deeper insights into how your customers feel about your products and services, so you can better tailor your marketing messages.

16. Churn prediction

Through ML, AI can analyze customer data and identify patterns that indicate a customer might be at risk of churning. This helps marketers identify potential churn risks before they become bigger problems.

For example, a decrease in product usage or a negative customer service interaction might signal that a customer is becoming dissatisfied.

AI can identify specific triggers associated with churn and even estimate when the churn might happen. If a particular customer is predicted to churn, you may opt to send them a personalized offer or reach out to them directly to address any issues.

One of the key benefits of AI is its ability to learn and adapt over time. A churn prediction model will become more accurate and sophisticated as it collects more data. It'll improve its predictions and help your business retain more customers.

Advertising and pricing

  • Programmatic advertising
  • Dynamic pricing
  • Omnichannel marketing
  • Location-based campaigns

17. Programmatic advertising

Marketers use this technology to target relevant audiences more accurately and efficiently than ever before. Programmatic advertising has been around for a while but now uses AI to automatically buy and place ads based on a range of data points. These data points include spending patterns, demographics, and location.

By examining past performance, programmatic ad buying offers a greater return on investment (ROI) by optimizing ad campaigns in real-time.

It allows advertisers to automate bidding processes so they don’t have to adjust bids manually every time there are changes in demand or competition levels.

With ML algorithms constantly analyzing user behavior patterns, ad targeting becomes more precise—resulting in higher clickthrough rates (CTR) at lower cost-per-click (CPC) values. Learn more about how Google Ads incorporates AI in ad generation and placement.

18. Dynamic pricing

Deciding on pricing is always a tricky process, because there are so many factors to take into account. You also want to create personalized pricing for customers based on their specific needs and budgets. This often requires many meetings, calls, and manual quote adjustments.

AI-driven dynamic pricing can help businesses optimize their offering across different channels, products, and geographies.

By analyzing customer behavior, AI algorithms can recommend prices that match demand. Dynamic pricing also considers external factors such as market conditions, competitors’ prices, and the time of day to ensure you’re always offering competitive prices without losing out on revenue.

19. Omnichannel marketing

AI-powered tools allow you to create campaigns tailored to each customer across multiple channels. This ensures maximum reach and engagement wherever they interact with your brand.

Omnichannel marketing involves reaching customers across various platforms, from websites and emails to mobile apps and social media. AI can analyze data from multiple channels to understand customer preferences and behaviors to personalize content and offers for each customer on each platform.

It can also analyze past customer behavior across channels to predict future behavior, helping you anticipate customer needs.

For example, it might predict what kind of product a customer will likely be interested in next or which channel they'll use for their next interaction.

20. Location-based campaigns

The first step in a location-based campaign is gathering location data. This data can come from multiple sources, such as GPS data from a user's smartphone, IP addresses, or Wi-Fi networks. AI can help gather and analyze this data in real-time.

Once location data is collected, AI algorithms can identify patterns, trends, or important insights.

For example, it may identify areas where many potential customers are or times of day when users are most active in certain locations.

One of the major advantages of location-based campaigns is the ability to engage with users in real-time. AI can automate this process, sending personalized notifications or content to users based on their location at any given moment. If a user walks by a store, an AI system could automatically send them a discount offer for that store.

AI can also use location data for predictive analytics. It can predict where a user will likely go based on their location history and then send personalized content or offers based on that prediction.

Lead management

  • Chatbots and virtual assistants
  • A/B testing
  • Demand forecasting
  • Lead scoring
  • Fraud detection

21. Chatbots and virtual assistants

AI-powered chatbots and virtual assistants are becoming integral to customer service and marketing strategies. They can handle various tasks, from answering FAQs to guiding users through a website or application.

With the ability to work 24/7, they offer immediate, consistent, and personalized interactions, enhancing customer experience and freeing up human agents to handle more complex queries. AI chatbots can also be used for lead generation, customer support, and sales conversations.

IBM reports that using chatbots can reduce customer service costs by as much as 30%.

22. A/B testing

Digital marketing teams have long used split testing or A/B testing to find high-performing copy and design combinations that deliver the most conversions. A/B testing is also a great way to test different variations of campaigns and landing pages to see which ones perform best.

AI is now being used to quickly analyze large datasets to identify the variables most likely to contribute to success. This gives you the most viable options for conducting A/B tests with positive results.

23. Demand forecasting

Businesses can create accurate forecasts of future demand through AI-powered tools such as predictive analytics. This helps them plan ahead to ensure they’re meeting customer needs while maximizing profits.

Instead of utilizing manual operations and spreadsheets, you can employ demand forecasting AI to check inventory levels, optimize stocking procedures, and predict demand so you can keep up with supply.

24. Lead scoring

AI can analyze a vast amount of data and identify complex patterns much more efficiently and accurately than humans can. You can use it to quickly analyze large datasets and accurately identify potential customers.

This data-driven approach prioritizes the marketing-qualified leads (MQLs) most likely to convert and ensures your company's sales team spends their efforts on the most promising prospects.

25. Fraud detection

At the heart of AI's power in fraud detection is its ability to recognize patterns and anomalies. AI systems are trained to understand what normal transactions or behaviors look like.

When a transaction or behavior deviates from these patterns, AI can flag it for further investigation. This could include anything from unusual purchase patterns to suspicious login activity.

AI can use past data on fraudulent activities to predict future fraud.

ML algorithms analyze historical fraud data and learn to identify indicators of fraudulent behavior. This means it can help you spot potential fraud before it happens, saving the costs of loss and time spent on the post-fraud investigation.

Digital marketing productivity

  • Automated campaign optimization
  • Marketing productivity tools

26. Automated campaign optimization

Many marketing platforms now offer campaign automation. The addition of AI analysis means campaign performance can drastically improve. This includes metrics like CTR, conversion rates, engagement, and more.

By analyzing campaign data as it comes in, AI can quickly identify what's working and what's not and adjust accordingly.

It can identify which types of content, messaging, or targeting strategies are likely to yield the best results based on past campaign performance. Moreover, AI can run multivariate A/B tests on digital marketing materials, which is more complex and time-consuming to handle manually.

Automating the testing and optimizing of marketing campaigns helps reduce costs while improving your campaign ROI.

27. Marketing productivity tools

Remember Clippy from the old Windows 97 operating system? Although it wasn't an AI, we think most of us were excited about the idea of having a virtual assistant.

Computer software and online productivity platforms have provided ways for marketers to organize and manage tasks better, offering automation tools and workflows. With AI-enhanced features, your team productivity can skyrocket.

Some AI marketing productivity tools can automatically record and transcribe meetings, summarizing identified pain points and actions to take.

AI can also automatically create tasks, analyze each task's attributes (such as deadline, importance, duration, and even your working habits) and prioritize your tasks effectively.

This ensures you're focusing on the most important tasks first. It can measure your workload, track the progress of tasks, and provide updates and alerts when your team's running behind schedule.

With marketing AI, huge amounts of data can now be processed, analyzed, and interpreted faster and more accurately. This holds many advantages for marketers: AI can uncover new insights about your customers, helping you understand them better. And it can use historical data to predict future behavior.

Marketing AI is more than programmed automation. Many of your routine marketing tasks can be enhanced and adapted to changing inputs. You can improve the quality of your work, save time, and open up your schedule to focus more on strategic planning.

The future of AI in marketing and design

We’re excited about the future of AI in marketing and design. As AI platforms and integrated tools become more sophisticated and accessible, marketers will increasingly look for new ways to engage and convert their audiences.

Let's explore some emerging trends and how they might further transform your role as a marketing designer.

More advanced personalization

As AI becomes more sophisticated, expect to see even greater personalization in marketing design. AI will continue to refine its understanding of individual customer behavior, leading to more tailored and effective marketing materials.

AI and AR/VR integration

Augmented Reality (VR) and Virtual Reality (VR) technologies are becoming increasingly prevalent in marketing. AI will play a crucial role in these technologies, from creating immersive, personalized AR/VR experiences to analyzing how users interact with these experiences to optimize them continually.

Increased use of generative design

AI's ability to create unique designs based on set parameters (generative design) will continue growing, pushing the boundaries of creativity and helping you produce designs more efficiently.

Design platforms like Linearity Curve (formerly Vectornator) will continue to innovate by integrating AI into their software.

AI-driven User Experience (UX) design

By analyzing user behavior and feedback, AI will help create more intuitive, user-friendly designs that improve engagement and conversion rates.

Tracking and optimizing the customer journey across digital and physical experiences will become seamless.

Emotion AI

Also known as affective computing, Emotion AI is an area of AI that's gaining traction. It involves AI systems recognizing and responding to human emotions.

In marketing design, Emotion AI could be used to understand how different design elements evoke emotional responses in users and use this information to create more impactful designs.

Ethical and responsible AI

As AI becomes more integrated into marketing and advertising, there is a growing emphasis on using AI ethically and responsibly. This involves ensuring AI systems are transparent, unbiased, and respectful of user privacy.

The future of AI design is to keep the human experience at the center and protect personal information while providing optimized products and services.

Enhanced accessibility

We expect increasing improvement in the accessibility of designs, ensuring that marketing materials are inclusive and can be accessed and understood by people with a wide range of abilities and attention spans.

Dynamic content creation

The ability to create engaging content using AI, from writing copy to producing videos, is at hand. This will enable you to create a wide range of marketing materials more efficiently.

In conclusion, the future of AI in marketing and design is a landscape of exciting opportunities. By staying up-to-date with these trends and understanding how they can enhance your work, you'll be well-equipped to leverage AI as a marketing designer.

Remember, the goal is not to replace human creativity but to augment it. With AI at your side, you'll be able to tap into a new world of creative work.

Next steps for next-gen marketers

AI is here to make your job easier, more efficient, and more effective. By harnessing these powerful tools, you can better understand your customers, automate tedious tasks, and create personalized marketing experiences that resonate.

Now that you know the potential of the various AI applications discussed in this post, it'll help you decide how to integrate them effectively into your marketing strategies.

Mapping out your marketing AI optimization journey:

  1. Make a list or diagram of your team's marketing processes
  2. Add the relevant technology and platforms used at each stage
  3. Put down some rough time estimates for each activity
  4. Ask your team to jot down blockers or frustrations
  5. Review our list of AI marketing technologies and assess which stages could benefit from these
  6. Create a shortlist of AI technologies you and your team would like to try
  7. Research three platforms for each technology. Pay special attention to available integrations with existing software, as this will help streamline your workflows
  8. Choose the platforms that work best
  9. Review your process diagram made in Step 1 and discuss the impact of your chosen marketing AI systems
It's all about enhancing your ability to connect your brand with its audience and deliver compelling, helpful content.

Which AI software should I use?

If you’re ready to jump into the world of marketing AI, here are just a few of our favorite AI tools to get you started.

Adobe Sensei

Sensei is a new part of Adobe’s creative suite — it’s a dependable platform that uses deep learning algorithms to offer a whole host of marketing AI features. These include AI-generated content, customer segmentation, predictive analytics, and campaign optimization across various marketing channels.


Acoustic offers AI-based solutions for marketing campaigns, helping you optimize your marketing efforts based on data and customer profiles. It also allows you to manage and analyze your content, and improve customer experiences. Acoustic is designed to be used by anyone, including marketing teams with no previous AI experience.

Salesforce Einstein

Salesforce is a leading platform for digital marketing services, and it's delving into the world of artificial intelligence with its new tool, Einstein GPT. It integrates seamlessly with Salesforce CRM tools and uses deep learning algorithms to automate your marketing processes. Expect predictive lead scoring, precise customer segmentation, and personalized content recommendations.

Seventh Sense

This platform specializes in enhancing your email marketing campaigns with artificial intelligence techniques. It can integrate with Hubspot or Marketo to improve the efficacy of your email campaigns. This involves optimizing email subject lines, sending out emails at the most effective time for each recipient, and offering suggestions to improve your content in real-time. While we're on the topic, check out this post to learn how you can use images in your email marketing strategy most effectively.

Wrapping up

We've painted a broad picture of the wide range of uses that marketing AI can play for your brand. When used properly, it'll help you find potential customers with ease, reach your active customers more efficiently, improve your customer service offerings, and enhance your digital advertising efforts.

We don't foresee AI completely replacing marketing roles in the future. Instead, AI will help digital marketers make better-informed decisions and optimize tedious, data-based tasks.

AI tools can solve complex problems such as customer inquiries and finding the best-performing combinations of marketing materials and distribution channels. This means you can focus more on creative and strategic work, which skills are declining in marketing and design leadership and other related roles.

Marketers and designers worldwide are empowered to create their best work with the least amount of resources using AI.

Continue to improve your marketing strategy by reading about our favorite marketing software tools, and use AI to boost your brand's identity design with these amazing AI art generators.

Linearity Curve's AI-enhanced Auto Trace and Background Removal features enable you to turn any digital image into a stunning design. And our feature roadmap holds more AI power in store for you.

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