The Moment We’re In
“AI is a transformative technology that grows gradually, then arrives suddenly.”
— Reid Hoffman
That sudden moment is here. AI has moved from the sidelines to the center of how work gets done, no longer a futuristic idea, but an active force in everyday business. It’s drafting emails, generating reports, routing tickets, and booking business travel. And it’s not slowing down. Mary Meeker, the influential analyst known for identifying market inflection points, confirms this shift in her 2025 AI Trends report:
“Seems like change is happening faster than ever? Yes, it is.”
She points to over 800 million weekly users of generative AI tools and notes that more than 90% of Fortune 500 companies are already deploying AI in real workflows. AI is no longer experimental. It’s operational. Yet in many organizations, AI is still being implemented through a technology-first lens, with decisions guided by what the tools can do, not by what people or businesses actually need. This can lead to:
- Fragmented experiences across teams and functions
- Overlapping tools that create confusion, not clarity
- More complexity in workflows, not less
Because AI doesn’t just automate, it introduces a paradox of choice.
With more options, more outputs, and more autonomy, it’s easy for teams to lose clarity on what truly matters. And in the absence of intention, friction and fragmentation can take root. This is where design plays a defining role. Design brings focus. It centers AI in human goals, values, and relationships. It helps organizations make sense of complexity, unlock alignment, and act with purpose.
And in this moment, design leaders have a unique opportunity, not just to shape how AI is built, but to influence what it’s built for.
Why It Matters
AI is changing how organizations operate. But without clarity and intention, it risks introducing friction instead of flow.
Design turns complexity into connection. This is a moment for design leaders to step into the room, meet the business where it is, and move the plot forward.
Meeting the Moment
We’ve established the urgency. Now let’s talk about the opportunity. As AI moves from generating content to taking autonomous action, what’s called agentic AI, the design challenges grow more nuanced and more critical.
We’re not just designing prompts. We’re shaping behaviors, decisions, and entire workflows. We’re not just building tools, we’re creating intelligent collaborators. This is where design leaders can step in and lead with clarity. Design has always helped teams navigate complexity and surface human needs. In this age of agentic AI, that role becomes even more critical, not just to design individual features, but to shape how systems behave, earn trust, and deliver business value.
In the pages ahead, we offer three ways design and business leaders can meet this moment:
- Insight: How to identify the right problems through a design thinking lens
- Impact: How to shape agentic AI with intention, using the SHAPE framework
- Influence: How to lead with the right mindset through times of change, with the CLEAR model
This is more than a playbook. It’s a path forward to help leaders align around purpose, build responsibly, and lead with imagination.
Let’s begin with insight.
Why it Matters
This moment presents a powerful opportunity for design leaders, to not just contribute, but to guide. In a time when AI is reshaping how work happens, design can shape it with intention.
This article offers three ways to meet the moment: identifying the right problems, shaping agentic AI through thoughtful systems, and leading with clarity and imagination.
Insight: Solving the right problem
To meet this moment, we need more than to solve problems; we need to find the right problems to solve, that will have the most business impact.
Design Thinking gives us the discipline to pause before we accelerate. It offers a structured way to uncover unmet needs, align on purpose, and build with clarity. And as agentic AI systems take on more complex, autonomous roles in workflows, this mindset becomes even more essential.
Designers already have the tools to navigate this space: end-to-end journey mapping, service design, and participatory research allow us to see across silos, uncover friction, and understand how work really gets done. These tools aren’t just for UX, they’re critical for orchestrating agent behavior in ways that support human goals, not undermine them.
Here’s how the Design Thinking process maps to the challenges of designing agentic systems:
1. Listening for Signals (Problem Space)
We begin by listening, not just to end users, but to stakeholders across the organization: business leads, technologists, compliance partners, and frontline staff. We look for patterns of friction: invisible handoffs, repeated low-value tasks, workarounds that point to broken systems.
This is also the moment to translate business priorities, like cost reduction or operational speed, into human-centered opportunities.
2. Framing for Purpose (Concept Space)
From insight to clarity. We synthesize what we’ve heard and prioritize use cases based on two key dimensions:
- Complex vs. simple: How nuanced is the task?
- Large vs. small scale: How often and broadly is it used?
Simple, high-scale tasks are often the best place to begin. They create quick wins that build trust and momentum. Over time, chaining smaller wins can unlock more intelligent and adaptive workflows.
3. Prototyping for Trust (Solution Space)
We test early and often. Because agentic AI doesn’t operate in isolation, we design in context: embedded in tools, shaped by organizational flows, and experienced by teams.
We surface edge cases, validate assumptions, and ensure we’re designing not just for function, but for trust.
Why It Matters
AI initiatives often struggle not because the technology fails, but because the solution doesn’t align with real business needs.
Design helps teams clarify objectives, reduce friction, and accelerate time-to-value. Bringing design into AI strategy early drives smarter investments and more effective outcomes.
Design Thinking sets the stage, but as these systems grow in autonomy, we need a design language that can guide them at scale. That’s where the SHAPE framework comes in.
Impact: SHAPE AI Agents with intention
As agentic AI becomes more capable, not just generating suggestions, but completing tasks, the role of design becomes even more critical. It’s not enough for these systems to work. They must work in a way that’s intuitive, trustworthy, and deeply human-centered. To support that, we developed a simple yet powerful design framework, SHAPE, made up of five core principles that guide how we bring AI to life in the workplace.
S – Seamless UX
Designers are trained to zoom out before zooming in. And with AI, this couldn’t be more important. Instead of focusing on individual features or chatbots, we need to look at the entire end-to-end journey. Where does the solution fit? Where can it actually reduce friction? This holistic view helps us find the right intervention points where AI adds value without adding complexity. The goal is to create experiences that feel natural and coherent, not like stitching AI onto an existing process as an afterthought.
H – Human in the Loop
AI can spot patterns, synthesize data, and move quickly. But in regulated industries such as healthcare and financials, where the stakes are high and decisions carry weight, the final call still needs to rest with people. That’s why we build systems that blend automation with human oversight, not just for compliance, but because human judgment, empathy, and accountability matter. Think of AI as an advisor or an assistant, not the final decision-maker, especially when there’s a premium on accuracy, context, or ethical nuance. In those moments, human discernment isn’t just helpful, it’s essential.
A – Accountability
Trust is the currency of adoption. If a user asks, “Why did the AI do that?”, they should get a clear, honest answer. That’s what makes transparency and explainability non-negotiable. We need to design interfaces that show how decisions were made, what data was used, and what logic the system followed. This builds confidence, not just in the tool, but in the organization behind it. While agents may integrate systems and automate actions, it is ultimately humans who are accountable to customers, to regulators, and to the values the organization stands for. Design must make that accountability visible and actionable.
P – Patterns
As agentic systems become more widespread, design consistency becomes crucial. We can’t afford for every team to reinvent the wheel. This is where a design system becomes more than a library, it becomes a strategic asset. A well-crafted system introduces coherence in a fragmented landscape of tech experimentation, ensuring that diverse agents behave in familiar, intuitive ways: how they show progress, signal reasoning, or ask for input. Especially as interfaces become more dynamic and AI-driven, a shared design language becomes the connective tissue that holds the experience together, making innovation feel not just possible, but seamless.
E – Ease the Cognitive Load
AI is meant to help, not distract, interrupt, or overwhelm. We need to design interactions that respect the user’s mental focus, delivering just enough information at the right time. Think of AI as a very smart intern: it can do a lot, but it needs direction, and it shouldn’t pester you every five minutes. Reducing noise is just as important as adding intelligence.
When you apply SHAPE, agentic AI stops feeling like a tool built for the business and starts feeling like something built for the people using it. It gives us a practical foundation to design AI in a way that supports not just efficiency, but empathy, clarity, and creativity.
Why It Matters
Strategic alignment is what separates successful pilots from scalable platforms. SHAPE provides a repeatable model for prioritizing use cases, de-risking experimentation, and building momentum. It helps teams move from isolated wins to integrated, enterprise-wide value.
Influence: Lead with a CLEAR Mindset
Frameworks help us build. But mindset shapes how we lead, especially in a space that’s still evolving.
The CLEAR mindset offers a guide for design and business leaders alike, helping us stay grounded, adaptive, and purposeful as we shape the future of work.
C – Curiosity Over Control
We don’t have all the answers. But curiosity helps us ask better questions, and discover unexpected possibilities. In an era of rapid experimentation and fast-moving AI developments, control may no longer be possible, or even desirable. Rigid plans can quickly become outdated. Curiosity keeps us open, agile, and better prepared to learn from what’s unfolding in real time.
L – Learning as Leadership
Model a learning mindset. Share what you’re figuring out. Make room for reflection and iteration. That’s how teams grow.
E – Ecosystem Thinking
No design exists in isolation. AI decisions ripple through teams, systems, and outcomes. See the whole map, not just your corner.
A – Always Human-Centered
Keep real people, not just personas, at the core. Design not just for speed or savings, but for clarity, connection, and confidence.
R – Responsible Imagination
Imagination drives innovation. Responsibility gives it direction. Together, they help us dream wisely, and build what matters.
Why It Matters
AI can introduce ambiguity in decisions, roles, and outcomes. The CLEAR mindset equips leaders to navigate change with clarity, confidence, and purpose.
It’s how organizations build alignment across functions and move faster with less friction.
Closing Thoughts: Moving the Plot Forward
AI is evolving quickly. But the outcomes we shape today, in systems, in strategy, in culture, will define how we work for years to come. This is a critical moment. One that calls on design leaders to be in the room where AI decisions are made, to speak the language of the business, and to move the plot forward in a way that centers both performance and purpose.
With frameworks like SHAPE and mindsets like CLEAR, we can lead with clarity and confidence. Let’s stay curious. Let’s stay thoughtful. Let’s stay clear.
And let’s design the future, together.
Acknowledgments: We’d like to thank Purvi Shah and Martin Granström for their thoughtful review and feedback.
