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Predictions For AI And The Future Of Product Design


DATE: 15 May, 2023

10 Ways AI Can Improve Workflows

Like most, I've been curious about how AI will affect my day-to-day life. But tbh, the idea of AI potentially taking over my job does make me a little nervous.

As a digital product designer, a lot of what I do could be (and should be) automated. Tasks like briefs, engineering hand-offs, and QA could all be streamlined with AI. However, the quality of the output depends heavily on the quality of the input. That's why, in my opinion:

“We'll still need people with a trained eye and deep subject matter expertise to guide and train AI models for specific tasks.”

Currently, tools such as Chat GPT and Stable Diffusion (Mid journey/Dalle) are primarily productivity tools, with potential for future developments. With that being said, let's assume that we will all still have our jobs this time next year and focus on how we as designers can maximise the use of these tools to improve our workflows and increase efficiency, ultimately allowing more time for problem-solving and creativity.

10 ways AI can improve workflows and increase efficiency, now and in the future:

1. Insight Gathering

Insight gathering is a critical phase in the product design process, as it provides the necessary understanding of user needs and pain points, trends, and behaviours from a qualitative and quantitative perspective. Insights that then go on to influence product roadmaps, prioritise initiatives, and affect the way we think about problem-solving and eventually define solutions. 

The biggest opportunity I see with AI in this area is the ability to impart unbiased insights in real-time. Whether it's through the use of predictive analytics or some other means, AI could identify patterns in user behaviour, make recommendations based on data, and streamline the time it currently takes to gather and interpret insights by 10 fold, helping designers make better, more informed decisions as they design.

2. Automated Design Tasks

Automate, automate, automate. Automation has already impacted various parts of the design workflow, from automated resizing and file exports to AI-assisted colour palettes and font suggestions.

With the advancements in AI technology, we can expect even more tasks to be automated in the future, such as…

  • Brief creation (i.e. isolating specific problems to solve related to goals)
  • Estimating project scope and timelines more accurately based on complexity and productivity
  • Generating starter kits complete with an automatic assignment of design systems and flows
  • Setting up a project with engineering which contains all the parameters and boiler-plates required to streamline development
  • Generating presentation slides that build themselves as you work through the solutions in preparation for stakeholder reviews

Ultimately reducing repetition and improving efficiencies at every twist and turn throughout the design process, and freeing up designers to focus on more complex design challenges, creativity, and problem-solving.

3. Design Systems

Design systems have become an increasingly popular approach to streamlining the design process, ensuring consistency, and speeding up production. I've been a huge fan since the inception of Airbnb's DLS. But with all the talk about design systems, they are inherently flawed if:

  • They're not maintained, 
  • They're not codified, and 
  • The design system is out of sync with production. 

With AI, the potential to create and maintain design systems could be enhanced even further. AI could analyse the usage patterns of design system components, identify inconsistencies, and recommend updates or new components based on user needs, business and team requirements, or shifts in market trends. Additionally, AI could assist with creating and managing design tokens, automating the conversion of designs to code, generating design documentation on the fly, and updating production automatically to ensure everything is in sync. Magic!

4. Use-Case (Flow) Generation

Use-case (flow) generation is another area where AI can help improve the design process. As designers, we often spend a lot of time mapping out user flows, wireframes, and prototypes to ensure a seamless experience for the end user. However, this can be a time-consuming and iterative process, with multiple rounds of feedback and revisions. AI can assist with this by analysing user behaviour and generating potential use cases and flows automatically based on conventional wisdom, ease of use, and limited friction.

By leveraging AI to generate commonly used, tried and tested use cases, designers can focus on refining and iterating on the most promising flows rather than starting from scratch each time. For example, there's no need to redesign a consumer login flow every time you create a new product, or for that matter a payment flow, a booking flow, or a customer feedback flow.

5. Content & Tone

Content and tone play a crucial role in communicating the brand message and values to the user. Traditionally, this is a task for copywriters and content strategists, who work closely with designers to ensure the overall user experience aligns with the brand vision. However, with the help of AI, we could see significant improvements in the content and tone of digital products. 

Natural Language Processing (NLP) and other AI-powered tools can analyse user feedback and engagement data to determine the most effective language and tone to use in different contexts. AI could also be trained on a company's brand voice and messaging guidelines, allowing designers and content creators to maintain a consistent tone throughout the product.

6. Collaboration

Collaboration is a critical component of any design process, and it's no different when it comes to incorporating AI. As AI becomes more prevalent in design workflows, collaboration will become increasingly important between designers and engineers, as well as between designers and AI. Designers will need to work closely with AI systems to ensure that the output aligns with the design intent and meets the project's goals. Additionally, designers will need to collaborate with engineers to ensure that any AI-generated designs are feasible and can be implemented within the product's technical constraints. 

AI Co-creation (COAi™) is an exciting concept, and we are already seeing some promising Figma plugins that leverage GPT4+ technology to generate UI designs based on prompts. For example, the “DesignLingo” plugin generates design terminology to help designers write more effective design briefs. The “Design Me” plugin generates design concepts based on keywords and adjectives, providing designers with a starting point for their designs.

“In the future, it's not hard to imagine a world where we input a detailed brief, and the AI generates entire design flows containing up-to-date components. This would make the role of a designer more like a Design Director, co-creating with AI to achieve the desired result.”

However, one of the biggest questions in my mind is whether someone with no design background or experience could co-create designs at the same level as someone who does, with AI. Would it require a “trained eye” or not?

7. Design Review & Approvals

I've experienced both sides of the design approval process — presenting my work to review panels, stakeholders, and CEOs, as well as giving feedback to other designers. While design reviews have many benefits, they often create bottlenecks in the product and engineering workflow, causing frustration for product managers who prioritise speed and MVPs over everything else. Designers have to navigate the tension between advocating for users, high-quality solutions, and systems, while also satisfying the demands of their product teams. 

AI could provide a solution to this challenge — codifying design principles, removing subjectivity from feedback, and aligning stakeholders well in advance of the design review, and in some cases eliminating the need for a review altogether!

8. Engineering Hand-Off & QA

Given all the advances in design tools and systems over the years, I still see designers “red-line” their work in preparation for engineering. Despite all the red-lining, engineers still fail to replicate designs accurately, leading to quality issues, design debt, and, worst of all, unhappy designers! So why, in this day and age, haven't we evolved beyond red-lining, and how can AI play a role? 

If we think about a mature startup and product team made up of the usual combination of engineers, PMs, and product designers (including design systems experts), there should be some predictable workflows and methods applied to building and shipping new products and features. In this world, an AI could act as a “quality assurance guide” across the entire production process. In fact, engineers already have AI systems in place when it comes to coding and code reviews, so training the AI on design systems to interpret mocks and recommend components, design patterns, and interactions, or even construct the scaffolding of a single view or use-flow, doesn't seem too far-fetched. 

A Quality Assurance AI (QAAI™) could save thousands of hours in reviews, reduce the number of errors and bugs being shipped into the wild, improve engineering time (PMs would love this), and overall improve the end-user experience! Win-win.

9. Data Analysis

Data analysis may fall a little outside of the conventional product design workflow, but it does have a meaningful effect on insight gathering. Currently, we devise product roadmaps using a combination of data and UX research, with data playing a more significant role in my experience. Analysing core metrics is a tedious job that involves analysts and product managers rifling through dashboards, extracting key insights from the latest experiments and production builds to then reporting back to the product team.

“What if AI, which is trained on the inner workings of your product, could provide feedback in real-time, along with recommendations, making the analysis process more inclusive for non-technical staff and enabling the entire product team to problem-solve on the fly?”

10. Values & Principles

Quite often, we approach design problems based on personal beliefs, values, principles, or a collective mission. In the corporate world, this plays out at both the macro level (the entity) and micro level (the individual). Ideally, you want both to align to avoid dissent and complications between conflicting values (although I strongly believe that a little dissent goes a long way when it comes to challenging groupthink).

At the entity level, a company will more than likely define a mission that acts as a north star vision (e.g., “Belong Anywhere”), along with a set of core values and principles (e.g., “Be a host”, “Embrace the adventure”, or more notoriously, “Move fast and break things”). These function as a rallying cry as well as a touchstone to help with decision-making when trade-offs need to be made or behaviours are called into question.

What if there was an entity-level AI trained on these specific details of the company mission and its values, which could be deployed (or be ever-present) to guide a company closer to its goals and achieve its stated mission?

How would this affect the way we design or what we'd design?

10 Ways AI Can Improve Workflows (Summarised):

  • Embrace AI for the critical phase of insight gathering
  • Automate design tasks through AI
  • Streamline design processes by using AI to create and maintain design systems
  • Utilise AI for use-case (flow) generation
  • Improve the content and tone of digital products with the help of AI
  • Incorporate collaboration with AI into your work
  • Utilise AI for design reviews and approvals
  • Incorporate AI for engineering hand-off and quality assurance (QA)
  • Streamline data analysis with AI
  • Explore entity-level AI trained on company values and principles

“While the idea of AI potentially taking over jobs can be nerve-wracking, there is no denying that AI has the potential to revolutionise the design process by reducing repetition, improving efficiencies, and allowing more time for problem-solving and creativity.”

IMO the biggest opportunities for AI in design include unbiased real-time insight gathering, automating more design tasks, enhancing design systems to generate ready-to-use tokens and for engineers along with documentation, generating common use-case flows to remove repetition and redundancy, improving content to match a brands voice and tone, and fostering collaboration between design and engineers by making the hand-off process more efficient and accurate eliminating the need for QA. 

While there is still a long way to go, AI is already making significant strides in design. And as a designer that has ridden the many waves of technology over the past 25 years, one thing remains true – a designer's superpower lies in their ability to harness technology to its fullest potential, resulting in better and more impactful products and user experiences.

If you have any other predictions on how AI could affect product design, feel free to reach out. I'd love to collaborate!