AI is undeniably transforming the landscape of design as well. Digital product design teams working or starting to experiment with AI and the decision-makers leading them ask themselves the question more and more often: is it worth investing in human-led design in the future? We don’t see this as just a black-or-white choice but a nuanced question that requires diligent exploration. In this post, we give you an intro into the Design Automation Scale (DAS), a robust, forward-thinking framework we designed to help you strike the perfect balance between AI automation and human creativity in the design process.
AI can help designers to deliver a functional prototype within tight timeframes, optimize and allocate their limited resources effectively, rapidly iterate on designs, conduct thorough user research and validation in rapidly evolving markets and integrate complex technical components and functionalities. All this without compromising on quality and while maintaining coherence and consistency throughout the process.
Does it sound awesome? It might but let’s face it: it’s a bit more complicated than just buying an AI tool for these tasks and letting it solve all of your problems. AI is not a standalone fix. Instead, it works alongside designers as a collaborative tool. But how to leverage the capabilities of AI and integrate it effectively into the process?
Designers first need to identify where AI can improve efficiency and creativity in the process and then set clear objectives for AI-driven solutions and select the right tools. And this is where the Design Automation Scale (DAS) comes in. We crafted this guiding framework to offer a comprehensive, structured and systematic approach to rigorously evaluate the suitability of AI across different design tasks, either to replace human workforce for some tasks or to boost the performance of designers in the team.
For the DAS, we developed the following critical dimensions to give each aspect a score ranging from 1 to 5.
SIMPLICITY: How easy is the task?
ACCESSIBILITY: Is the data accessible and of good quality?
AVAILABILITY: Is there a suitable AI solution available for this task?
SECURITY: Is the data secure and legally compliant?
ETHICS: Are ethics clear around the data and the use of AI solutions?
From these 5 values, we provide the average point to give you a quantitative measure of the suitability of AI: 1 = the task requires a high level of human creativity, empathy and expertise, while 5 = it can be effectively automated and streamlined with AI. To make it a bit more tangible,let's dive into the results when it comes to the visuals of our MVP - we're talking logos, branding, and all that eye-catching design goodness.
While more designers are adopting AI tools like DeepArt, Looka, Uizard or Aikiu Studio potentially reducing costs, our experiments show that human expertise and oversight remain essential for aesthetic appeal, brand consistency, and user engagement. One of the biggest weaknesses of these tools currently is that AI-generated designs can differ in terms of consistency and quality. You might find yourself investing more time examining and improving AI-generated assets than if you had designed them from scratch yourself.
Additionally, ethical considerations must be addressed here as well, as AI-generated visuals should align with organizational values and resonate with target audiences. The reality is, these AI tools still fall short when it comes to fully meeting unique client requests and corporate preferences. They're getting better, but they're not quite there yet. To sum up, visual elements indicate a moderate suitability for AI utilization in this phase, hence its DAS score of 3.8.
While we believe that the DAS scores are universally applicable, it’s always important to consider the specific needs and resources of the project, the various attributes of the problem and factor all those when outsourcing certain tasks to AI tools. It’s also important to not just classify these design tasks in general along the five dimensions listed above, but also to look behind the particular third-party AI tools considered for those tasks and examine the same aspects for these tools.
The emergence of the AI shift is inevitable, but we don’t know how and on what timescale it will change design work. For now, we think of AI powered solutions as complementary tools augmenting our capabilities, rather than solutions fully replacing designers. It may very well be that in a few years we won’t consider AI to be a distinct feature or product at all, as it becomes more and more omnipresent, just like electricity or cell service. However, till then it is the decision-makers’ and designers’ role to strategically integrate AI into their workflows and adapt these strategies according to advancements in AI technology and design practices over time.
To find out how DAS assign scores for the other steps of creating an MVP and to dive deeper into how to harness the potential of AI to enhance productivity and foster innovation in design, read our latest white paper “The Design Automation Scale – Optimize AI-Human Collaboration for the Future”.
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