AI-Powered Design: Key Stages of AI Design Process

Today's issue is delivered by Agata R?czewska , Innovation Client Partner & UX Expert at Netguru

AI's role in the design and innovation process is not about replacing humans but rather serving as a supportive co-pilot.?

By assisting with specific tasks while still under human oversight and direction, AI transforms the design process into a powerful tool within a complex framework. This enhances efficiency in targeted areas.

Last week, I shared with you an overview of design thinking AI. Today, I’ll talk a little bit more about the key stages of the AI design process and top challenges of incorporating AI into the journey.

Key stages of the AI design process

Data collection and analysis

Some hypotheses suggest that traditional research is becoming unnecessary due to the vast amount of data available. AI can predict user needs and behaviors, essentially performing the entire discovery phase for us.

Using AI for data collection allows us to automate tedious tasks, such as transcribing user interviews, or integrate case-specific insights by analyzing data from other projects.

Discovery can also focus on competitive analysis, evaluating the strengths and weaknesses of competitors. It should explore viable technologies, assessing their potential to be integrated in ways that address and enhance user needs.

Model training and development

AI tools might operate on training data which doesn’t ideally reflect your go-to user personas or the target market. So, it’s important to ‘calibrate’ it through experimentation and comparing AI and human output. If you see that the results don’t fully meet your expectations, then you’ll need to feed it with the right data so it can learn and improve over time.

AI-driven ideation and prototyping

Here, we provide initial ideas which the AI can then develop further. Currently, AI can automatically generate a set of wireframes for multiple ideas, resilient enough for testing, particularly in the early stages of idea generation.

When faced with choices, such as deciding between 'A' or 'B', we've traditionally used sketch prototypes, pen and paper, or user stories for testing. Now, you can either transform your hand-drawn ideas into digital designs using tools like Sketch2Code, or even generate digital, low-fidelity prototypes fully with AI.

Continuous testing and improvement

You can use AI tools to conduct A/B tests, allowing designers to compare the performance of different design variations and identify which elements best achieve user goals. Additionally, designers can optimize designs for accessibility, tailoring products to meet the needs of users with varying abilities.

Addressing key challenges in design thinking AI

Let’s be honest: we’re a long way from being sure all ethical considerations of implementing AI in design are properly addressed. But we’re moving closer each day. August 2024 saw the EU’s AI Act enter into force and while it does present challenges in terms of enforcement, it addresses design-related issues such as identifying and labeling of AI-generated content.??

Eliminating bias

The reason AI systems are biased is because they are trained on data created by humans. And the reason why we operate on stereotypes is because our brains wouldn’t be able to function without them due to the amount of everyday stimuli.

When we recognize that both humans and AI are biased, we can leverage AI to our advantage. We can train it on diverse datasets making sure they’re representative of our target audience, eliminating a partial approach as much as possible. Secondly, though bias is there in the database, we can prompt the AI correctly so that it is aware of it and tries to work around it.

Furthermore, you must run regular audits.

Securing buy-in for AI output

I think one of the biggest challenges with AI is securing stakeholder buy-in. Even when it offers objective data, decision makers want to hear from a human before making a decision. What I suggest is organizing workshops with stakeholders to show AI capabilities and gather their input. It’s also worth involving them in prototyping sessions, so they can see AI in action and share feedback.

Involving stakeholders in the design process is the only way to make sure that they understand, trust, and support the decisions that the team makes.

Ethical considerations

The long-term potential commercial applications of AI remain uncertain. It’s not just about what the technology is capable of – it’s primarily about meeting legal and ethical requirements.

A good example is our collaboration with Neuronest, where we were tasked with building an app for Alzheimer’s Patient Management. The project raised a lot of ethical and design questions, such as: “How do we deal with consent?”. “Do we inform the users that we use AI?”. “And if we do, how do we inform them?”

The dilemmas we’ve faced while building a healthcare app show just how careful we must be when using AI tools.

What I recommend when you experience a similar situation is to put transparency forward. Clearly communicate to users when and how you’re going to use AI and explain the AI’s decisions.

Also, to secure your organization from a legal standpoint, remember to obtain users’ explicit consent before you collect and analyze their data.

Do reach out in case you have comments around the topic!

Best,

Agata?