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The Future Of Design: Human-Powered Or AI-Driven?

In this article, Keima Kai provides a brief history of AI in web design, explores its current implications for creativity, and offers suggestions for how web designers can stay ahead of the curve.

For years, reports have been warning of technology taking away jobs, particularly in fields like food preparation, truck driving, and warehouse operations. These jobs are often considered “blue-collar” and involve repetitive manual labor. However, many in the creative community believed their careers were immune to automation. After all, a designer’s craft is anything but monotonous. While computers can crunch numbers quickly, how are they going to be able to design?

Then something surprising happened: Artificial intelligence (AI) made inroads into the design. In product design, Mattel is using AI technology for product design. In interior design, designers are creating mockups with AI that can detect floors, walls, and furniture and change them. In graphic design, Nestle used an AI-retouched Vermeer painting in marketing to sell one of its yogurt brands. Advertising agency BBDO is experimenting with producing materials with Stable Diffusion.

But how about fields with a distinctly defined medium, like web design? Turning the focus to web design, this article will provide a brief history of AI in web design, explore its current implications for creativity, and offer suggestions for how web designers can stay ahead of the curve.

The Road Leading Here #

AI’s capabilities outlined are a result of development dating fifty years ago and have rapidly accelerated in recent years with advanced computation models, additional training data that goes into improving the models, and improved computing power to run the models.

In 1950, Alan Turing, known as the Father of modern computer science, asked the famous question: Can machines think? Research began by attempting to teach machines human knowledge with declarative rules, which eventually proved to be difficult given the many implicit rules in our daily lives.

In the 90s, the above knowledge-feeding approach transitioned to a data-driven approach. Scientists began creating programs for computers to learn from large amounts of data with neural network architectures, much as how a human brain functions. This shift accelerated progress, producing breakthroughs, including IBM’s Deep Blue beating the world champion at chess in 1997, and Google Brain’s deep neural network learning to discover and categorize objects.

Recently, advancements in neural network model sophistication, data availability, and computing power further accelerated machines’ capabilities. In 2014, Ian Goodfellow created the first generative adversarial neural network, which allowed machines to generate new data with the same statistics as the original data set. This discovery readies the stage for AI models like DALL·E 2StableDiffusion, and MidJourney in 2022, which demonstrate original creations abilities outlined at the beginning of the article.

Next, we will explore the implications of these technologies for web designers.

Today’s Implications #

Today, designers and clients typically go through six stages together before arriving at a new website. The term “client” is used loosely and can refer to inter-departmental teams working on in-house websites or the individual responsible for building a website on their own.

  • Forming
    The designer works with the client to assess the context for a website design.
  • Defining
    The designer extracts the complete set of requirements and drafts a project plan to meet expectations.
  • Ideating
    The designer generates tailored ideas meeting the requirements.
  • Socializing
    The designer presents the ideas to the client and supports in choosing one to proceed.
  • Implementing
    The designer creates high-fidelity designs, which are then turned into code for deploying.

In order to better understand the impact of AI, we will break down the five stages of the web design process and examine the specific activities involved. Using the latest academic research and deployment examples, we will assess AI’s theoretical capabilities to perform activities in each stage. Our team will also create a webpage with AI technologies that everyone has access to today and compare it with the manual process for a practical perspective.


Forming calls for the designer to inquire about the unique instance, explore ambiguous perspectives, and ignite stakeholder enthusiasm.

  • Inquires unique instance: Undemonstrated capacity.
    When taking on a new client, it’s crucial to evaluate their unique context and determine whether web design is the right solution to meet their business goals. However, current AI models often struggle with analyzing subjects that aren’t included in their training data sets. With it being impossible to pre-collect comprehensive data on every business, it’s clear that current AI models lack the ability to be inquisitive about each unique instance.
  • Explores ambiguous perspectives: Undemonstrated capacity.
    At the beginning of the engagement, it is essential to consider multiple perspectives and use that information to guide exploration. For example, a designer might learn about the emotional roots of a client’s brand and use that knowledge to inform the website redesign. While AI models from institutions like MIT and Microsoft have shown early promise in recognizing abstract concepts and understanding emotions, they still lack the ability to fully adopt human perspectives. As a recent article from Harvard Business Review pointed out, empathy is still a key missing ingredient in today’s AI models.
  • Ignites stakeholder enthusiasm: Undemonstrated capacity.
    In order to set up a project for success, both the client and designer must be enthusiastic and committed to seeing it through to completion. While AI has shown potential in creating copy that resonates with consumers and motivates them to make a purchase, it remains unproven when it comes to sparking motivation for long-term business engagements that require sustained effort and input.

The AI Experiment

In preparation for a product launch, our designers evaluated the different launch approaches and decided to build a landing page. They intuitively decided to focus on nostalgic emotions because of the emotional connection many designers have with their tools. The team worked closely with product managers to get them excited.

For the purpose of this article, the design team also attempted to use AI for the same tasks. General conversational models like ChatGPT were unable to diagnose a website’s necessity for us and only offered generic advice. When it came to generating early directions, models mostly produced results that skewed towards functional differentiation, failing to consider empathy and emotions that could make designers and stakeholders enthusiastic.


Defining calls for the designer to collect detailed requirements, set expectations, and draft a project plan.

  • Collects requirements: Theoretical capacity
    To ensure that all detailed requirements are collected, clients should be encouraged to verbalize their needs in terms of technical specifications, page count, and launch dates. AI models are now capable of performing these requirement-collection tasks. Thanks to examples of human exchanges fed to the models, Natural Language Processing (NLP) and Natural Language Understanding (NLU) have enabled AI to parse, understand, and respond to inputs. One of the latest models, OpenAI’s ChatGPT, can ask for additional context, answer follow-up questions, and reject inappropriate requests. AI models are already being deployed for customer service and have shown positive results in terms of trust and satisfaction.
  • Aligns expectations: Theoretical capacity
    The client and designer should align on criteria such as acceptance standards and future communication schedules. To help facilitate this alignment, AI models are now capable of handling negotiations autonomously. In academia, research from Meta (formerly Facebook) shows how AI models can use simulation and prediction to complete negotiations on their own. In the business world, companies like Pactum are helping global retailers secure the best possible terms in B2B purchases with their proprietary AI models.
  • Drafts project plan: Theoretical capacity
    To ensure that a project stays on track, it’s important for the designer to establish milestones and deadlines. AI models are now capable of estimating task durations and sequencing activities in a project. In 2017, researchers demonstrated the use of a machine learning algorithm called Support Vector Machine for accurate forecasting of project timelines. Further research has also established the use of Artificial Neural Networks for defining task relationships and creating work breakdown structure (WBS) charts.

The AI Experiment

Designers collected requirements from the product team using a tried-and-true questionnaire. The landing page needs to match the product launch date, so the teams chatted about the scope. After some frustrating back-and-forth where both teams accused the other of not having a clue, they finally came to a mutual agreement on a project plan.

Designers tried the same with ChatGPT. Designers have AI role-play as the design team to collect requirements from the product team. AI performed admirably, even inspiring the team to add new items to their questionnaire. Designers then asked it to create a project plan while feeding it the same concerns received from the product team. Though the designers did not expect to use the produced schedule as-is, as factors like the team’s current workload were not considered, they still thought it performed reasonably well.

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