LinkedIn estimates 65% of skills needed for a job will change by 2030 largely because of AI. How can we get today’s designers and future workers ready for this change? What skills do we need to develop? What mindset do we need to nurture?
This is the third part of a series investigating the potential transformative impact of artificial intelligence on product design.
Trends and AI impact on design – Exploring AI-driven trends and their impact on design
The evolution of design practice – What is this means for design and designers
Future-proofing designers – The essential skills and mindset designers need in the age of AI
AI may change what skills a designer needs, but that doesn’t mean we should abandon what has served us well for many years. The key question is where can designers provide differentiated value?
Understanding customer needs: Being able to understand which workflows or jobs cause the most pain and would benefit from AI optimization. Being able to guide the team on when and how to intersect that workflow in the best way.
Generating ideas and concepts: Generating ideas and mashing up different concepts in new and unique ways
Prototyping and validating ideas: Experimenting with sample data sets or outputs to see which has the most potential value before committing major resources.
Helping customers become comfortable trying AI tools: The first elevator installed in 1857 got shut down because customers refused to use it. Helping customers understand how to use AI and increasing their comfort will be important to adoption, as well as navigating identity threat.
Shaping the results the AI delivers: If your AI tool is doing analysis and generates results, how do you take action on them? What confirmation do you need? What do you need to see to help you evaluate how much confidence you should have in the result?
Supporting efforts in ethics, trust, and inclusion: Leveraging our empathy and understanding of people to guide decisions
A lot of what is needed, we already have, or it’s similar to skills we have, and some of it is new. But we aren’t alone in needing to learn and develop new skills; our leaders and our colleagues in product, engineering, sales, marketing, HR, and finance are all trying to figure this out too. The most important thing is to get started and work on making regular progress.
Skill set
“Communication is now a technical skill in addition to a human skill” – Jounalist and author Charles Duhigg
Recognizing that we have a lot of valuable skills and practices that translate to the AI age is encouraging. There are three areas we should pay particular attention to. I’ve added a fourth area that while important it’s one most schools, training programs, and design managers have generally failed in developing.
Prompting
Prompting is an area where the existing skills of designers can be extended to great impact. Instead of using our tools and artifacts to create, we can create with words. This section describes the building blocks of a prompt as task description, role, boundaries, context, specific requirements, and reasoning.
Visualization and imagination: Imagining what we want the AI to create in rich detail is a core part of our skill set that is needed to get the most out of AI. Think about a movie director on a big sci-fi movie; they need to envision the world at the smallest detail and the big picture. That clear definition allows all the artisans on the crew to create and bring that vision to life.
Direction and feedback:That vision needs to be communicated to the AI, including what you want, how you want it put together, and what guardrails you want to place. Meaningful feedback (as opposed to vague discontent, which is the worst) will advance you quickly towards your goals. Using the movie director example again, this director is going to need to shape the output and make sure it all fits together.
Technical
We don’t need to be engineers, our goal is to add differentiated value, but there is value in being able to understand what we are working with, what’s easy and hard, and how to get the most out of it.
AI, ML, and data science basics: Artificial Intelligence (AI) is like giving a computer a brain that allows it to think and learn from experiences, making smart decisions. Machine Learning (ML) is a part of AI where the computer learns from lots of data, improving its tasks over time without being directly programmed, while Data Science involves analyzing and interpreting complex data to help make informed decisions, using statistics, ML, and data analysis techniques.
Data literacy: Knowing what data you have, how it’s organized, how it’s related to other data or interacts with third-party data sources. You also may need to understand what data and methodology are important to the industry you are working with and what methodology you are using to benchmark results or how the data is weighted. Knowing this helps you decide what to show, how to contextualize it, organize it, and what options you want to give the customer to slice it.
Governance & Ethics
The responsibility that comes with our creative power cannot be overstated.
Ethical imperative: Being aware of and addressing issues like bias, ethics, and inclusion ensures our designs contribute positively to society.
Bias mitigation: Recognizing how biases in data can lead to unfair outcomes and how to mitigate this.
Data privacy: Privacy concerns related to data collection and processing.
Responsible AI: Ethical design principles to ensure AI systems are used responsibly.
Business
These skills are important to helping designers participate in conversations and decisions at higher levels, and most designers are missing them.
Acumen & Strategy: Grasping the bigger picture of how our designs fit within an organization’s goals is key. It’s about aligning our creative efforts with the strategic direction of the business.
Storytelling: The ability to compellingly articulate our vision and the value of our designs is what convinces others to believe in and back our ideas.
By embracing and refining these skills, we not only enhance our professional toolbox but also ensure our relevance and impact.
Mindset
A co-worker once told me: “change is opportunity.” This idea has helped me a lot, especially when things at work shift around, and they shift around a lot.
Our mindset is the way we think and feel about things every day, big or small. Gary Klein says that our mindset helps us make sense of what’s happening around us. It points us to what’s important, helps us figure out our goals, and shows us how to get there. For the AI age, we need to cultivate a mindset of:
Adaptability: Being ready and able to change
Resilience: Being strong enough to face tough times and bounce back
Learning: Always being curious and willing to try new things, even if they might not work out.
Community: Accepting responsibility for our impact on others and committing
The work of Carol Dweck has shown us that our mindset can be changed and can have a powerful impact on performance. It starts with recognizing and understanding how you think about your abilities and intelligence and reframing them to emphasize embracing challenges, persisting through obstacles, learning from criticism, and finding lessons and inspiration in the success of others.
What this means for managers and leaders
McKinsey’s Global Talent Leader, Bryan Hancock, believes that AI offers an opportunity to completely reimagine the management layer of your organization. Instead of managing tasks and doing administrative tasks and sitting in endless meetings, AI will free up their time so they can focus on developing people.
Managers will need to rethink how they develop talent in the world of AI. Many designers start out working on projects that are pretty well defined by a lead designer or an artifact (template, design system, UI kit), then later they start to create with less direction and can start to take on bigger projects. Along the way, they learn about principles, fundamentals, and approaches from the people who critique their work.
“It’s exciting to see that gyroscope develop over the years – and it does take years… the reason that the gyroscope is so powerful is that, if students have it, then they can look at their own work – maybe not immediately, but, perhaps a week later and they can say ‘Oh, damn. This is terrible.'” – Professor and author of The Reflective Practioner Donald Schön
I read this quote in graduate school, and it’s stayed with me for years. My biggest concern is helping to develop this gyroscope when AI is doing a lot of the teaching. This is further complicated when most of the coaching is over a screen instead of a tap on the shoulders.
What I’ve learned in more than 10 years of working with people all over the world is that it’s possible, but it takes commitment and a deliberate approach. I create a lot of templates and do working sessions to rightly define what we’re doing and getting to a good outline or some kind of design artifact.
Conclusion
This will be a bumpy road to travel; we’ll get lost, we’ll have some trouble, and we can get to a great place, but only if we get started.
“For those who have that skill set (communication) that felt like you were on the outside looking in…now is your moment” – Open AI Ambassador Abran Maldonado
Image: DALL·E experiment
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