As the whole field of product design is headed toward a pivotal transformation, product design leaders need to proactively explore (and answer) significant strategic and practical questions such as:

• What are the concrete advantages AI augmentation brings to product design?
• How (much) do these tools change decision-making processes and the role of product designers?
• What are the key skills needed by tomorrow’s design leaders, and how will their organizations best combine the powers of humans and machines?

This is how design leaders can thrive in the coming age of all-encompassing AI augmentation.

A CASE STUDY OF AN AI-AUGMENTED PRODUCT DESIGNER

The director of product design is preparing for an important strategic innovation meeting with SVPs and the CEO. There will be three main options regarding new product openings for the coming two quarters.

Two of them are building on top of the existing product features; the third option is a true wild card that leverages some of the latest technological developments. Simulations on customer behavior and business effects have been run with two more conservative options, but the company has never done anything like the third before.

Based on past data, the AI-powered design assistant comes through with quite convincing KPI (key performance indicator) predictions. For the third option, there’s no historical data to help predict customer behavior or business impact. But it could be a real innovative game changer for the whole company expanding the business beyond its current sector and markets.

Choosing that would require purposeful risk-taking and mindful yet fast-paced creative iteration. This is a call to be made in the meeting, but the director’s AI assistant draws a blank on providing further analysis, only spewing out generic comments about the innovation opportunity. They delete that particular section in the presentation.

Also, the director needs to think about the team structure, budget, and capacity. The latest UI assistant models have been providing great results very fast, so the innovation team will be a combination of humans and specialized AIs to be trained for this particular project.

However, the latest UI assistants come with a hefty price on computing, including its environmental effect. Importantly, the team needs to have true proactive outside-the-box thinking and strong collaboration among designers, data scientists, and business leaders. How can this be ensured, and the team be set for success in their innovation work?

HOW TO SEE THE BIG PICTURE

The current generative AI models lack true causal understanding and thus are unable to fully grasp the Why behind their computational activities. The emerging, more sophisticated AI models still won’t come up with their own final purpose even though they start to have a more in-depth grasp of their applications.

Product designers will be at the core of finding and defining the purpose and direction for AI-powered products and AI-augmented organizations both from the customer and business perspective.

As more and more information and data are synthesized by more advanced AI assistants, design leaders need to be able to clearly see the big picture to answer the critical Why questions before jumping into What, How, Who, and When.

Lead product designers need to focus on driving organizational development that empowers such collaboration and streamlined decision-making at scale. Leading product design will move away from making decisions about particular visuals and UI elements and will increasingly require a deep understanding of how dynamic customer experiences are created by the combination of user interfaces and interactions, AI and data, and how they impact the customer and their physical and digital ecosystems.

As a result, long-term scenario-based strategic design thinking, building on top of deep business acumen, will play an even bigger role in deciding the key problems to be solved, how they should be solved, and who would be solving them.

In a world of data abundance, the competitive advantage won’t just be based on data and AI models, but rather on how relevant data can be turned into true insights and knowledge that then drive effective decision-making and impact-focused actions.

In addition to developing a lead product designer’s AI and data literacy, it becomes even more critical to develop one’s own voice and standpoint to be able to make fitting decisions based on all the available fast-updating information. At the same time, values will be playing an even bigger role for design leaders as humanity and our planet will be facing serious challenges in the coming years.

PRODUCT DESIGNERS LEADING HUMANS AND AIS

In the future, product design and development teams will consist of humans and AIs alike. The composition of the team depends on the strategic direction and operative task at hand.

5 KEY THINGS LEAD PRODUCT DESIGNERS NEED TO DO TO WORK EFFECTIVELY WITH AI

Explore and understand the dynamics of the emerging AI-powered digital ecosystems to create a customer experience vision that ensures that AI technologies are designed, built, and applied ethically and sustainably. This allows you as a design leader to effectively drive the decision-making around the purpose and usage of advanced AI technologies in design processes, organizational development, and customer experiences.

Enable your organization and teams to embrace AI-powered change through an iterative scenario-based product design strategy that drives goal-oriented exploration and execution, as well as pragmatic use and effective adaptation of AI. Facilitate your teams to proactively explore and experiment with new AI tools, methods, and solutions to drive continuous learning and iteration. As a lead product designer, you need to pragmatically lead change in fast-moving ecosystems. To do that, ensure that your team and its processes and tools are set up in a way that supports the creation of healthy and productive working practices and environments in which humans and AIs collaborate in alignment with multidisciplinary teams.

Understand the workings of intelligent machines—their superpowers as well as their weaknesses—to be able to combine the best of human intelligence with the best of machines. Understanding the internal workings of AI “thought processes” will play a role in a similar way to understanding human psychology. AIs don’t get tired, and can work around the clock if needed. The design leader needs to ensure that human time and attention are used in the most effective way to empower creativity and innovation, rather than trying to duplicate the more production-focused AI efforts. Importantly, this allows you to ensure that the collaboration between humans and AIs systematically develops both human and AI abilities and capabilities. For example, in the above scenario, the UI assistant models can continuously learn from human designers (and customers) and incorporate their input into the further development of the models.

Recognize key drivers and champions for emerging AI-augmented design practices to lead cross-functional organizational transformation. To solidify the new direction and to champion multidisciplinary AI-augmented approach, your design organizations will likely need new job roles such as “AI designer,” “CX prompt designer,” or “head of AI CX,” who will as part of their core design skill set deeply understand AI models, machine learning, and data-driven methods, thus being able to connect these areas with real human needs, customer problems, solution directions, and business opportunities.

Similarly, as human designers move (completely) away from being tool experts and turn into more holistic design practitioners, you need to level up existing teams and hire new talent that can systematically drive organizational transformation.

Build and develop truly multidisciplinary design teams in which AI-augmented designers, data scientists, engineers, user researchers, psychologists, and business experts work together to realize the AI-powered CX vision and drive innovation.

A deep understanding of different aspects of human behavior including human psychology and physiology as well as the environmental effects of product design need to become part of the design organization’s core DNA.

At the same time, a multidisciplinary approach is integral to ensure that your organization can recognize and proactively act on emerging risks and developments, and simultaneously keep innovating ahead of the curve. For example, biased data or AI model regression can lead to deteriorating customer experience if not spotted as early as possible through multidisciplinary effort.

Or, as another example, in the era of increasingly faster development cycles, design organizations need to be able to ensure that their solutions, experiences, and content stand out from the masses, especially as the generative AIs can make certain AI-generated visual styles, UI/UX solutions spaces, or content types popular overnight, potentially leading into a design monoculture.

Today’s lead product designers need to take prompt actions (pun not intended) to ensure that their organizations proactively level up in their ability to understand and effectively leverage AI-augmented design tooling. Leaders need to make certain that their teams, practices, and workflows (importantly, including iterative exploration of new emerging AI methods) are set up in the right way by building truly multidisciplinary teams that are empowered to drive human-centric design in the digital ecosystem that will be moving at an unprecedented pace.

That will allow product design to take a direction-defining role in effectively shaping how tomorrow’s digital realities are designed and built for the lasting benefit of humanity and our planet.