The cost of AI adoption in graphic design


Mar 12, 2024

Updated Mar 12, 2024

Graphic design plays a pivotal role in capturing an audience's attention and conveying powerful messages. With the rise of artificial intelligence (AI), the realm of graphic design has witnessed significant transformations. However, one crucial aspect that businesses need to consider is the cost associated with AI adoption and deployment, particularly for marketing purposes. Let’s explore the various factors that contribute to the cost of incorporating AI into graphic design for marketing campaigns.

Initial investment

Implementing AI in graphic design for marketing necessitates an initial investment. This includes acquiring AI software, tools, and technologies that align with specific business requirements. The cost of these resources can vary depending on the complexity and sophistication of the AI solutions chosen.
When it comes to creating banners that align with brands' guidelines, AI tools can be invaluable in providing full editing flexibility and ensuring uniqueness in images, copy, patterns, and shapes. While having all these capabilities in one tool may not be feasible, there are a variety of AI tools available that address these challenges individually or in combination. Let's explore a few such tools:


Canva is a versatile design platform that incorporates AI to simplify and streamline the banner creation process. It offers a wide range of templates, that might be a limitation for big brands that are not ready to use publicly available common templates.

Adobe Photoshop

Adobe Photoshop, a longstanding industry-standard tool, offers advanced editing capabilities for creating visually appealing banners. It provides a wide array of AI-powered features like content-aware fill, smart objects, and pattern generation. While Photoshop allows for comprehensive editing flexibility, creating unique images, copy, patterns, and shapes may require combining AI features with manual design techniques.
It could become an ideal solution, though it has production automation limitations. Designers have to scale and resize their creatives manually and animate them manually as well using other tools.

As the range of AI tools for graphic design continues to expand, it becomes evident that assessing the scope of work lies within the responsibility of the creative and marketing teams. With a plethora of options available, these teams can carefully evaluate the requirements and challenges at hand, enabling them to choose the perfect combination of tools to meet their unique needs. It can vary significantly. The cost can range from initially offering free access with limited capabilities for individual use, allowing users to experience the benefits of automation, to potentially reaching thousands of dollars per team member for a single tool. When considering the overall cost, it's important to factor in the number of seats required and the number of tools that need to be onboarded, as this can significantly impact the total expenses.

Data collection and preparation

AI algorithms rely on vast amounts of data to learn and make informed decisions. Collecting and preparing data for AI-driven graphic design campaigns can be a time-consuming and resource-intensive task. Businesses may need to allocate resources to curate and preprocess data, ensuring its quality and relevance to the marketing goals.

The availability and nature of data are crucial factors that significantly impact the cost of training an AI model. The complexity of the data influences the level of training required, meaning that more complex data incurs higher training costs. For instance, training models and operating an AI that heavily relies on images entails substantially greater expenses compared to one that solely utilizes and produces text.

Moreover, both the quality and quantity of data play significant roles in the cost considerations. Larger datasets require more processing power, thus increasing the expenses. Additionally, low-quality data may not yield optimal results, necessitating the use of more sophisticated models.

The challenge with AI in the field of design arises from the scarcity and high cost of obtaining usable data, which can lead tool creators to continually rely on the same datasets. However, this practice poses a significant risk as it can introduce bias into the algorithms that guide AI. Although the information within an AI solution is what gives it its remarkable capabilities, it also presents a substantial danger. At a minimum, this repetitive use of data will eventually erode the uniqueness and originality of the resulting designs.

I suggest reading an article on that delves into the various types of neural networks employed in design and the specific input methods utilized in each case.

Training and development

AI models require training to understand the intricacies of graphic design and marketing. This may involve hiring AI specialists or investing in employee training programs to upskill existing designers. The cost associated with training and development varies depending on the complexity of the AI models and the expertise required.

Historically, businesses would typically have to employ data scientists, machine learning engineers, and software developers in order to develop and deploy AI technologies.

Based on the current market data, a compact AI development team can incur technology development expenses that exceed $400,000 annually for a business:

Data scientist in the United States
Machine learning engineer in the United States
Software engineer in the United States

Integration and customization

Integrating AI into existing graphic design workflows and marketing systems may require customization and fine-tuning. This process ensures that AI aligns seamlessly with the specific requirements of marketing campaigns. The costs associated with integration and customization depend on the level of complexity and the extent of modifications needed.

Maintenance and upgrades

AI systems require ongoing maintenance and periodic upgrades to ensure optimal performance. This involves monitoring the AI algorithms, addressing any issues that arise, and staying up-to-date with the latest advancements in AI technology. Businesses need to allocate resources for regular maintenance and upgrades to maximize the benefits of AI adoption.

AI systems, including ChatGPT, Claude, or Bard, necessitate continuous upgrades to maintain optimal performance. It is crucial to monitor the quality of the results regularly. As seen from our experience at Viewst, the output of ChatGPT can undergo significant changes within a short period of time, sometimes leading to negative dynamics. Therefore, ongoing monitoring and improvements are essential to ensure consistent and positive outcomes.

Legal risks

As businesses embrace the benefits of artificial intelligence (AI) systems, it is crucial to fully understand and address the legal risks involved. AI adoption brings about unique challenges and potential legal implications that must be carefully navigated to ensure compliance, protect intellectual property, and mitigate potential liabilities.

AI systems can generate creative works, including designs, art, music, and written content. Determining ownership and protecting intellectual property rights associated with AI-generated works can be complex. Clear agreements and contracts should be established to address ownership, licensing, and usage rights to safeguard against infringement claims and disputes.


In conclusion, the adoption of AI in graphic design for marketing campaigns comes with costs that businesses need to carefully consider. The initial investment in AI software, tools, and technologies, along with ongoing maintenance and periodic upgrades, can accumulate significant expenses. Integration and customization may also require additional resources, depending on the complexity of the existing workflows and systems.

Despite the costs involved, the potential benefits and return on investment (ROI) of AI adoption in graphic design can be tremendous. AI tools offer improved efficiency, flexibility, and automation, allowing businesses to speed up go-to-market and scale visually appealing designs that align with their marketing goals. However, it is crucial to conduct thorough research to evaluate the goals and available tools to ensure a positive ROI and make informed decisions.

As the field of AI in graphic design continues to evolve, it is essential for businesses to stay updated with the latest advancements and trends. By carefully considering the costs and benefits, businesses can harness the power of AI to enhance their graphic design capabilities and achieve impactful marketing results.

Founder, CEO at Viewst
Founder, CEO at Viewst
Founder, CEO at Viewst

Victoria is the CEO at Viewst. She is a serial entrepreneur and startup founder. She worked in Investment Banking for 9 years as international funds sales, trader, and portfolio manager. Then she decided to switch to her own startup. In 2017 Victoria founded Profit Button (a new kind of rich media banners), the project has grown to 8 countries on 3 continents in 2 years. In 2019 she founded Viewst startup. The company now has clients from 43 countries, including the USA, Canada, England, France, Brazil, Kenya, Indonesia, etc.

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