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Advantages and Disadvantages of Using AI in Marketing

  • Post By: Faisal Mustafa
  • Published: July 20, 2025
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Shortly after its launch as an integrable feature for digital tools, Artificial Intelligence (AI) has revolutionized traditional marketing strategies. Extending its basic applications for segmenting customers by their preferential patterns, AI is now automating key marketing endeavours: analyzing data, predicting behavioural shifts, suggesting campaign models, locating lucrative ad space, and coordinating between related disciplines. 

The prevalent inclusion of AI is gradually putting an indelible mark on the marketing landscape. But it also has its definitive drawbacks. For inquisitive practitioners, here is an exploration into the advantages and disadvantages of using AI in marketing.

What Is AI in Marketing?

AI is simply a term that refers to any functionality of a tool that uses advanced algorithms to analyze and process data in response to a given set of instructions. The primary aim behind these tools is to cut through a massive clutter of data to get to the essential ones and harness them to achieve a personalized result. Common use cases involve: finding relational data patterns, training machines to give more user-centric responses, generating intent-oriented content, and renovating customer experience.

The unexpected level of proficiency possible to reach by AI implementations has turned them into the fastest spreading trend in almost every industry today. Marketing, a field that is fundamentally dependent on collecting, organizing, and analyzing data on different phases of a user’s journey, is among the most benefited departments with these intelligent abilities. As denoted by an IBM Institute for Business Value study, about 70% organizations believe that competitiveness will be determined by a business’s AI compatibility, which is an implication of the bliss of AI in marketing.

Integrations of AI in Marketing: Historical Evolution

The progression of AI in the marketing realm, starting in 1950, slowly moved through the inventions of new capacities, unveiling new possibilities. Till now, its advancement has gone through three major transitional phases. 

Early Phase of AI Inventions

The duration from 1950 to 1960 can be marked for the advent of the early generations of AI machines targeted towards demographic data mining and purchase pattern analysis. The industry was first familiarized with technologies, like linear programming, game theory, and decision trees. Their success in precise decision-making paved the path to the next generation of more capable instruments, mostly powered by features like neural networks and clustering algorithms.

Introduction of Recommendation Engines

In 1990, the invention of the Internet drew the general attention of the business world toward digital space. Within a few decades, about 2.7 million e-commerce sites, competing with each other to establish a dominating presence, called for the invention of digital marketing tools, like web analytics, search behaviour analysis, and AI algorithms. They also came up with recommendation systems that used machine learning algorithms to suggest products based on a buyer’s purchase history. 

Modern Era of Deep Learning Machines

The late twentieth century put effort into reimagining digital marketing strategies. Google, one of the giants in the game, started to let users place ads on the web spaces. To better optimize such ad campaigns and get a positional advantage, it brought Google Ad that can automate ads and their bidding and targeting processes for better visibility and efficiency.

After 2010, a new dimension has been added to the digital realm with a new variant of AI toolsets with deep learning engines working under the hood. Machines have become smarter, having artificial brains that can learn from and adapt to various usage patterns.

Running on such machines and using text processing models, like NLP, various automated content generators have made the creation of multimedia content and deeper research effortless.

Key Advantages of AI in Marketing

The benefits of artificial intelligence in marketing are far-reaching. Getting a good hold on them will lead to more confident inclusion of automation and analysis in a project. Here are a few notable ones described through the explanations of a few inherent technologies:

Microscopic Personalization

AI applications have made it possible to personalize every marketing funnel step at a resonating level like never before. Landing and product pages are now more relatable, content is more empathic, and finding and targeting easily pursuable audiences seems to shun unnecessary complications. Personalization with AI in marketing happens at diverse levels:

Segmentation: AI-powered customer segmentation leverages sophisticated algorithms to probe a customer’s hobbies, web activities, social media presence, and communications to classify them with their sentiments, attitudes, needs, and wishes.

Advertising: Precise segmentations lead to more rewarding ad placements, also termed programmatic advertising. With the segmented user persona used as the blueprint for planning, crafting, and showcasing ads with the help of specific AI models, conversion becomes more generous. 

Pricing: Dynamic pricing algorithms let marketers judge current rates of a product against its demand, reviews, consumer expectations, and competitors’ insights. The outcome is a pricing model that realigns itself, centering on the shifts in buying patterns, success rates, and retain records.

Predictive Analytics

Attracting consumers, not only with what they are searching for in real time, but also with a targeted wishlist, is not an exhausting process anymore. Predictive analytics in marketing goes beyond superficial analysis of market biases. Rather, they combine data models, statistics strategies, and machine learning to churn historical data and produce a future projection. The process can be characterized by three phases:

  • Data Accumulation: Smart algorithms, designed to scavenge across complicated web networks and go in-depth while discerning one user from another by their online activities, pile up relevant data.
  • Deciphering and Evaluation: At the second stage, the collected data are handed over to particular AI models. A systematic reckoning process looks for repetitions and patterns in the data. Finally, the findings, making use of solicited and unsolicited pre-defined data sets, lead to a clear resolution of user behaviours and help predict future actions.
  • Adaptations: The future market is always drifting. So are the data captured by AI models for a particular business, product, or service. AI models detect gaps between historical data and change decisions and adapt accordingly. 

Task Automations

One of the most transformative characteristics of AI models is their ability to automate routine tasks. The resulting comfort and saved time let professionals engage in more creative parts of a project. They can be more invested in brainstorming, ideating, solving problems, and strategizing to stand out. Common automation scenarios may include:

  • Generating outlines and insights essential for content planning and creation
  • Monitoring visiting consumer interactions to build a more targeted approach
  • Writing and sending emails to the consumers to inform them about their order status, and to update them on more relevant buys
  • Greet new customers and respond to their queries

Dynamic Content Generation

AI-integrated websites can generate and show personalized content based on user segments. The practice is growing in popularity as such dynamic content can reflect in visitors’ intent better than usual static alternatives. Imagine a product page that alters the shapes and content of the CTAs, headlines, search results, and pop-ups based on device types, locations, and visiting hours. Optimal implementations of such functionalities can lead to:

  • Custom emails and newsletter creations, where subject lines, marketed products, and content bodies are structured to appeal to certain groups
  • Creative ad designs with suggestions for different units like images, headlines, and CTAs
  • Real-time product description and inventory updates after each purchase
  • Social medial posts with auto-suggested hashtags and captions highlighting the context

Consumer Service

Businesses can ensure lower revert rates by attending to customers with buying intent. Automated replies providing solutions and support for various queries and issues create a bigger chance of satisfaction. The widespread implementations of NLP-based chatbots exemplify how online platforms are leaning towards AI applications in their day-to-day pre-sale and after-sales services. Common practices involve:

  • Detecting and reaching customers by their recent impressions on a post
  • Inviting them to the official website or shopping portal
  • Consulting on product qualities, prices, and redirecting to more suitable buys
  • Providing guidance through the ordering, checkout, and shipping processes
  • Explaining refund and return policies and providing addresses of local support points

Search Engine Optimization

Google, the owner of the world’s most popular search engine, has devised several machine learning and advanced neural networking algorithms to figure out query intent and present the most relevant content. Search engine optimizers work on strategies to design content most noticeable by search engine crawlers. From keyword research and engaging content creation to link building, executing every part of such strategies has become quicker and more efficient with AI technologies, like predictive SEO, and various generative, tracking, and analysis models. Now it’s effortless to:

  • Get AI-enabled marketing insights
  • Build data-driven marketing strategies
  • Semantic analysis and clustering of keywords
  • Rapid content creation and update
  • Sort out higher potential link-building opportunities
  • Content analysis for balanced on-page SEO executions

Disadvantages of Using AI in Marketing

Despite its explicit beneficial impact on the digital marketing industry, AI has created confusion among practitioners. The frequent irrelevance and misput data surfaced by AI analysis tools still puts their blind usage into question. Here are some major challenges that still need a considerate approach before relying on these innovations.

Upskilling Necessity: AI models' smartness largely depends on their users' efficiency. Amateur workers are at risk of missing out, necessitating an instant upskilling effort.

Generalized Approach: Deep learning and machine learning algorithms are at their natal phases. Regardless of its vastness, the solicited data doesn’t offer much room for working outside the box. The unsolicited data are comparatively slow collectibles that need time to be assimilated. That means inefficient and lingering self-training, and therefore, a lack of novelty in the output.

Ethical issues with AI marketing: Using generative models has still not addressed copyright and sustainability concerns. In addition, the increasing fear of AI taking jobs, leading to mass unemployment, is gradually demonstrating itself.

AI in Different Marketing Channels

Today’s consumers roam across numerous virtual platforms for entertainment or to build social or professional networks. It’s rationally struggling for marketers, whose primary aim is to turn as many heads as possible toward their proposition, to coordinate between all these platforms. For them, AI is more than just a solution. 

Tracking customers’ footsteps, reaching them at peak hours, and sending a custom message don’t take 24/7 monitoring and shuffling between different apps and websites anymore. With tools like AI trackers, lead generators, and social media optimizers, automating multi-channel marketing has come to the doorstep. Trained marketing experts, by mindful usage of these tools, can:

  • Get actionable insights from historical data stored in all social media
  • Hack into consumer intelligence mapped from their lifestyle data, like check-ins and achievements
  • Design ad campaigns from a single platform and pre-schedule their show times
  • Some AI tools let users track a range of multi-channel marketing KPIs, like counts of impressions, reaches, mentions, traffic, and so on.
  • Get inspiration for captions, hashtags, and creative content creations 

Future Outlook: Should You Embrace AI in Marketing?

Only a few years have passed since the world realized how impactful AI can be. Its influential benchmark is only to rise in the upcoming days. The wonder of automation has captured the attention of big investors and tech aficionados. Billions of dollars are thrown away to give motion to the ongoing upgrades of all aspects of the technology. Experts are predicting an imminent future where AI will be able to:

  • Sympathize with consumer sentiments and offer more resonating solutions
  • Personalization will take a more individualistic approach compared to today’s group-wise segmentations
  • Immersive technologies, like AR and VR, will make virtual product showcasing more realistic
  • Automated communications through different channels will break the barrier of generalized and pre-formulated responds
  • Predictive features will be more accurate in their suggestions and forecasting the next market trend, being able to process even a greater amount of data
  • Collected data from multiple channels will pass the reliability tests, inviting broader integrations
  • Voice and visual searches will be more habitual and in need of optimized content

Overcome the challenges of AI in marketing with VISER X

VISER X, the leading digital marketing and SEO agency in Bangladesh, is pioneering the integration of AI advancements in the local and international marketing landscape. Our team of professionals doesn’t trip while maintaining a balance between AI and Human touch. Brands can make themselves more in line with their customers’ needs and desires by utilizing their personal data, managed and analyzed by our experts. Reaching us can be the next big and right decision for your business, if you want:

  • Full-fledged digital persona creations validating audience sentiments
  • Cross-screen and multi-channel visibility boosted by precise AI-targeted ads
  • Strategies that strike a balance between automation and human customization
  • Creative approach in building a unique brand awareness beyond generic solutions
  • Automated A/B testing to ensure performance and competitive advantage
  • Dynamic content and UI experience by leveraging smart algorithms

Conclusion

Marketing automation using AI is here to stay. The possibilities with AI technologies have skyrocketed general expectations, driving an industry-wide shift in consumer segmentations, analysis of personal data, campaign planning, content ideation, SEO realignment, sorting authoritative backlinks, multi-platform ad management, and every single activity associated with an all-encompassing digital marketing strategy. 

While personalization at scale has been the centralized practice of AI integrations, irresponsible algorithmic bias in marketing can damage credibility and ultimately reputation. Involving experts from trusted digital marketing agencies, like VISER X, can ensure practical AI adoption

FAQs

Is AI marketing better than traditional methods?

AI is the next stage of the marketing game. Strategies and approaches have more outbound potential with added AI power than without it. Comparing AI with usual practices puts the whole scenario in the wrong perspective. Instead, their thoughtful merger can yield the best result.

Can small businesses benefit from AI in marketing?

Earning latency of small businesses greatly depends on how they can produce more using fewer resources without risking quality. The convenience of automating repeated tasks and hectic analysis saves workers’ time, enhances productivity, reduces costs, and boosts income opportunities. 

What are the risks of AI-generated content?

Content sourced from AI generators follows a bleak, plain, and robotic tone. The style is too generic to be used for commercial purposes. The charm and hooks are hard to get without putting seriousness at stake. Moreover, factual data and suggestions are often irrelevant and demand a deeper search.

How do you choose the right AI marketing tool?

AI agents and assistants come in varied types. Prevalent examples must include content generators, like Jasper and Copy.ai, email automations, like Mailchimp and ActiveCampaign AI, and digital analytics, like HubSpot and Hootsuite AI. The right tool is the one that serves your purpose.

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