Over six months, I set out to test design thinking’s relevance in tech by talking to the people doing the work—across multiple functions and levels. This report is what I learned.
Executive Summary
This research synthesizes insights from over 30 qualitative interviews with professionals across design, product management, sales, marketing, and transformation. It aims to understand the current state of innovation practices, design thinking maturity, and the evolving impact of AI across sectors and organizational layers.
Three major themes emerge from the findings:
- Persistent Pain Points: Organizations face entrenched barriers such as siloed structures, misaligned incentives, superficial engagement with user research, and leadership resistance to data-driven change. Human-centered practices like design thinking are often misunderstood, inconsistently applied, or marginalized.
- Emerging Insights: Despite these challenges, many interviewees see value in design thinking as a mindset for strategic alignment, cross-functional collaboration, and user-centered innovation, particularly when championed by senior leadership. AI is viewed with both excitement and apprehension, seen as a potential amplifier of insights but also a driver of hasty, optics-driven experimentation.
- Strategic Opportunities: There is a clear need for more coherent integration of design thinking across roles and functions, more iterative feedback loops that connect user needs to business outcomes, and deeper executive engagement with human-centered and AI-enhanced practices. These opportunities point toward a future where design thinking and AI are not isolated initiatives but integral to how organizations learn, adapt, and grow.
This report outlines a path forward for embedding these practices into organizational DNA, offering both tactical recommendations and strategic provocations to reshape how value is defined and delivered.
Introduction
This document synthesizes insights from over 30 qualitative interviews with professionals across design, product management, sales, marketing, and transformation. It aims to understand the current state of innovation practices, design thinking maturity, and the evolving impact of AI across sectors and organizational layers.
Methodology
Over 30 semi-structured interviews were conducted with professionals spanning various functions, including design, product management, marketing, sales, transformation, and customer experience. The study employed a qualitative approach, emphasizing narrative depth, reflective insight, and thematic synthesis.
Participant Profile:
Industries Represented:
Healthcare, financial services, education, enterprise software, telecommunications, and government services.
Company Types:
Interviewees came from startups, mid-sized firms (e.g., Genesys, IntelePeer, Keyloop), and large enterprises (e.g., Google, Microsoft, Oracle, IBM, Expedia Group).
Geography:
The majority of participants were based in North America and Western Europe, with a smaller representation from the Middle East and South Asia. The concentration in the western hemisphere reflects the researcher’s professional network and design community connections.
Seniority and Role Distribution:
Interviewees ranged from C-level executives and VPs to directors, senior managers, and individual contributors. Many held hybrid roles (e.g., product and strategy, design and transformation), and several had cross-functional influence or enterprise-wide mandates. This mix offered a layered view of both strategic direction and operational constraints.
Context
Design thinking (DT) is broadly defined as a human-centered methodology for problem solving that blends empathy, ideation, rapid prototyping, and iterative learning. While it is widely known, its adoption varies significantly across sectors and organizational layers. The research explores how DT is understood, practiced, resisted, and reinterpreted in different contexts, often revealing a gap between intention and execution.
The researcher’s visibility in the design community may have attracted participants predisposed to be familiar or curious about DT, but interviews also included critical perspectives, particularly from those who experienced friction between DT ideals and real-world business demands.
Pain Points
The interviews surfaced ten persistent pain points experienced across roles and industries. Each is paired here with an insight that contextualizes the issue, and a direct quote from participants to preserve the voice of the field.
1. Siloed Organizational Structures
Pain Point:
Teams are structured around internal functions, not customer journeys, leading to fractured ownership and experience.
Insight:
Legacy silos prevent unified delivery and make accountability ambiguous.
“There is no one coherent org necessary to deliver a service outcome to a customer.”
2. Lack of Executive Alignment and Sponsorship
Pain Point:
Initiatives lose momentum without visible senior support.
Insight:
Leaders may endorse in principle but fail to operationalize support through action or resources.
“Unless there is a senior exec championing it, it will get lost.”
3. Superficial Adoption of Design Thinking
Pain Point:
DT is seen as a trendy label, not a deep practice.
Insight:
DT is often introduced without sufficient context, training, or leadership modeling.
“Design thinking isn’t what I’m hearing from SVPs.”
4. Misaligned Metrics and Measurement
Pain Point:
Performance is measured by proxy rather than real outcomes.
Insight:
Organizations measure what’s easy to quantify, not what matters most to users.
“Everywhere you go, people are measuring the wrong thing.”
5. Short-Termism Driven by Revenue Goals
Pain Point:
Quarterly targets trump long-term innovation.
Insight:
Financial pressures lead to decision-making that prioritizes financial gain over user impact.
“Layoffs were purely for IPO.”
6. Difficulty Translating User Insights into Action
Pain Point:
Research findings are disconnected from decisions.
Insight:
Research is often seen as supportive rather than strategic, making it easy to sideline.
“They felt they already knew what customers want.”
7. Leadership Resistance to Data and Change
Pain Point:
Leaders ignore or undermine data that contradicts expectations.
Insight:
Confirmation bias and internal politics inhibit evidence-based decision-making.
“Even when people know the data is right, they resist it.”
8. Design Seen as Cosmetic
Pain Point:
Design is undervalued as a strategic asset.
Insight:
Design’s role is often confined to late-stage refinement rather than early-stage framing.
“Design was treated as buttons and screens.”
9. Operational Inertia and Bureaucracy
Pain Point:
Layers of process and approval stifle momentum.
Insight:
Matrix structures create confusion over roles and dilute ownership.
“Huge bureaucracy and intradepartment dependencies.”
10. Collaboration Fatigue and Misuse of Tools
Pain Point:
Tools and rituals often waste time without achieving alignment.
Insight:
The prevalence of meetings and digital tools often replaces, rather than enhances, collaboration.
“Collaboration is just talking; alignment is often missing.”
Pain Points by Function
While the top 10 pain points emerged across roles, their manifestation and impact varied by function. This section highlights function-specific challenges and identifies key pain points that span multiple departments, revealing systemic patterns of dysfunction and opportunities.
Design
Design leaders and practitioners often find themselves advocating for a more strategic role in organizations that still equate design with visual polish. The function is routinely marginalized, and its outputs undervalued.
Perception Problem
Design is still widely misunderstood as aesthetics or UI enhancements.
“People still don’t understand what design is.”
Research Deprioritized
User research roles often lack institutional power, despite decisions being made without clear problem framing.
“Executives can't answer the question: what's the problem, for whom, and what does success look like?”
Integration Struggles
Coordination with engineering is difficult, especially in globally distributed teams.
“It’s like mixing oil and water. Getting empathy into engineering teams across cultures is tough.”
Performative Design Thinking
Design Thinking is inconsistently applied and often scapegoated when outcomes fall short.
“Design thinking was blamed.”
Cross-Functional Pattern: These challenges reflect broader themes of design marginalization and research-action disconnect, which also appear in product, transformation, and CX roles.
Product Management
Product managers operate under intense pressure from all sides—expected to lead strategically but often constrained by outdated systems, short-term incentives, and fragmented vision.
Agile-Corporate Clash
Agile methodologies often conflict with fiscal-year planning and rigid budget structures.
“Corporate structures inhibit agile or iterative development.”
No Roadmap Owner
Vision fragmentation results from temporary teams and a lack of long-term ownership.
“There’s noone owning a full roadmap.”
Stakeholder Misalignment
Sales and PM functions are often at odds due to differing incentives.
“Sales wants to sell what they know, even if the product strategy has moved on.”
Execution Burden
PMs are overwhelmed by tactical delivery, reducing time for long-term planning.
“Product managers are like ‘X-wings’ going into the Death Star trench—stay on target.”
Cross-Functional Pattern: PM pain points echo the design and transformation functions in terms of strategic misalignment, ownership gaps, and “short-termism.”
Transformation & Other Functions
Professionals in Transformation, Marketing, CX, and HR described systemic challenges related to ownership, alignment, and cultural buy-in—issues often overlooked in traditional digital strategy narratives.
Lack of Ownership Post-Transformation
Transformation teams often carry the weight with no clear handoff.
“Teams wait for us to do it all and won’t take over.”
Leadership Incoherence
Strategic misalignment and political maneuvering derail momentum.
“None of the leadership shares the same point of view.”
CX is Undervalued
Even when cost savings and impact are proven, CX is vulnerable to cuts.
“We saved $1M in revenue and still got cut.”
Marketing Cost Spiral
Media costs rise while engagement and returns diminish, prompting a shift toward omnichannel branding.
“We're paying 3x what we did six years ago.”
Cross-Functional Pattern: The themes of leadership misalignment, handover failures, and proof not being enough for resource commitment also appear in product and design interviews, suggesting a systemic undervaluing of human-centered functions unless directly tied to revenue.
Pain Point Thematic Maps
Thematic Maps are a tool to help leaders and transformation teams diagnose where key challenges are rooted—whether in culture, structure, strategy, or day-to-day operations. By distinguishing between issues that require mindset shifts and those that call for structural redesigns, these maps enable more targeted and effective interventions.
Cultural: Resistance to Change
These are behavioral and mindset-level issues embedded in the organizational culture. They influence how people perceive new ideas, collaborate across functions, and engage with methods like design thinking or agile.
- Resistance to Change:
Many leaders and teams are skeptical of new methodologies, particularly when they challenge established power structures or personal expertise. Even when data or user feedback suggests a better path, there’s often inertia or pushback. - Performative Buy-In:
Design thinking and other innovation efforts are sometimes adopted superficially—used for branding or workshops, but without being deeply integrated into how decisions are made or how teams operate
Structural: Silos, Lack of Process Ownership
These issues relate to how organizations are architected: the formal and informal lines of authority, team configurations, and ownership models that shape workflows.
- Silos:
Teams are structured in ways that limit visibility and collaboration across departments. These silos hinder holistic service delivery and complicate coordinated decision-making. - Lack of Process Ownership:
Transformation and innovation initiatives often have unclear owners. Handoffs between departments are fuzzy, and temporary teams dissolve without continuity.
Strategic: Short-Termism, KPI Misalignment
These challenges are rooted in how organizations prioritize and evaluate success. They often reflect disconnects between stated values (e.g., innovation, user-centricity) and what is rewarded.
- Short-Termism:
Organizations are driven by quarterly goals, IPO narratives, or other short-term financial targets that undercut long-term value creation. - KPI Misalignment:
The metrics used to gauge progress often focus on efficiency or output rather than outcomes that matter to users or reflect true impact.
Operational: Bureaucratic Drag, Collaboration Breakdown
These issues manifest in day-to-day workflows and execution, affecting the speed, quality, and consistency of output.
- Bureaucratic Drag:
Excessive layers of approval, process rigidity, and matrixed governance slow down innovation and increase friction. - Collaboration Breakdown:
Despite a proliferation of tools and rituals, real alignment and shared ownership are often missing. Meetings abound, but clarity and commitment lag.
Desired Outcomes
These Desired Outcomes (across roles and organizations) reflect what interviewees shared they hope to achieve. While not always explicitly stated, interviewees often referenced improved application of design thinking, innovation practices, and AI.
1. Iterative Product Delivery and Faster Feedback Loops
Many participants emphasized the need to get working prototypes or updated versions of products into users’ hands more frequently, even in regulated or hardware-intensive sectors.
“We need 3–4 iterations per year in the hands of customers.”
2. Executive Sponsorship and Strategic Alignment
Interviewees repeatedly expressed the need for consistent executive engagement, not just approval, but active and ongoing involvement in innovation initiatives.
“Unless it’s championed, it fades.”
3. Embedded Human-Centered Practices Across Functions
There’s a clear desire to move beyond siloed DT use in design teams toward broader organizational adoption, including product, engineering, marketing, and operations.
“It should be part of the business strategy.”
4. Tangible Proof of Value
Practitioners want to demonstrate the business impact of DT and human-centered approaches through measurable, defensible outcomes, whether in cost savings, retention, or user satisfaction.
“We need to show this isn’t a flash in the pan.”
5. Improved Collaboration and Decision-Making Infrastructure
Interviewees want to move from fragmented, performative meetings to meaningful, cross-functional collaboration with clear accountability and shared goals.
“From ‘them and us’ to ‘we.’”
6. Strategic Use of AI to Enhance, Not Replace, Human Insight
There’s interest in using AI to support user research, cluster insights, and automate repetitive tasks, provided it is grounded in user value and ethical design.
“If you’re good at writing briefs, you have a future writing AI prompts.”
7. Clear Ownership and Continuity in Transformation Initiatives
Transformation leaders want their work to be continued and scaled rather than abandoned post-launch. Continuity and ownership are recurring concerns.
“We were ready to roll off, but they didn’t want to take it over.”
8. Cultural Shift Toward Learning and Experimentation
Several interviewees called for a shift away from perfectionism and fear of failure toward a culture that supports small bets, fast learning, and iterative growth.
“You either build something cool or something useful. Preferably both.”
The Role of AI
Based on the interview transcripts, approximately 30% discussed AI in depth, concerning their roles and organizational challenges. These discussions spanned a variety of business functions.
1. Strategic Vagueness and Executive Pressure
Many organizations are investing in AI under leadership pressure to “do something AI-related,” often without a clear user need or business case.
“Boards ask: What are we doing for AI?”
2. AI as a Buzzword, Not a Tool for Value
Several interviewees noted that AI is being adopted more for signaling innovation than for delivering real user outcomes.
3. Loss of Control by Design and Research
Design and research teams reported being sidelined as data science gained priority.
“Data scientists gained the upper hand.”
4. Lack of Process Maturity
AI development was described as chaotic, with one participant comparing it to “building a jet and a runway at the same time.”
5. Time-to-Value Pressure
AI initiatives often begin with openness but quickly shift toward outcome pressure without allowing time for necessary experimentation and feedback loops.
Emerging Themes in AI Strategy
Theme
Early-stage exploration
Perceived vs. actual value
Design + AI tension
Narrative control
Observation
Many companies are still experimenting with AI, unsure how to integrate it meaningfully.
AI is often overhyped in strategy decks but underutilized in execution.
As AI grows in influence, design is sometimes deprioritized unless it demonstrates quantifiable value.
Some teams are reframing AI initiatives as user-centered to align better with org values and metrics.
Integrating Design Thinking and AI
What does it mean to combine DT and AI?
Opportunities for Human-Centered AI
The integration of DT and AI would be a strategic convergence of empathetic, user-centered problem solving with data-driven, scalable decision-making. Together, they represent a dual engine of innovation:
Design Thinking: Human insight, iterative learning, and co-creation frameworks.
AI: Analytical horsepower, pattern recognition, and automation at scale.
This combination allows organizations to move beyond reactive problem-solving toward proactive, adaptive transformation rooted in both empathy and evidence.
- Bridging the Strategy-Execution Gap
- Humanizing Technology
- Amplifying Human Capability
- Scalable Insight, Local Relevance
Design Thinking helps define the right problems to solve; AI accelerates testing, synthesis, and iteration, turning insights into actionable outputs more rapidly.
As AI becomes pervasive, Design Thinking ensures its applications remain ethical, inclusive, and aligned to real-world contexts, not just business logic.
AI frees time from routine tasks, enabling more space for creative, collaborative work guided by design thinking principles.
AI clusters and synthesizes data at scale; DT ensures that what emerges is locally meaningful and actionable for specific user groups.
Design Thinking & Innovation Maturity Model
Design thinking doesn’t succeed on its methods alone. It scales through leadership, mindsets, and integration into how organizations operate. This model maps the progression from isolated experiments to an enterprise-wide discipline that informs strategy, accelerates learning, and drives transformation. As AI amplifies the ability to synthesize insight and execute at scale, design thinking becomes a critical lever for navigating complexity and shaping competitive advantage.
Maturity
Stage
1: Ad Hoc
Culture &
Mindset
Awareness of DT exists, but often stigmatized or misunderstood.
Execution
Practices
Used inconsistently
or in isolated
workshops.
Leadership
Engagement
Little to no sponsorship;
seen as tactical.
Metrics & Value
Realization
Anecdotal; no alignment to outcomes.
Strategic
Opportunities
Clarify language
and reframe DT
to reduce stigma.
2: Emerging
DT is seen as useful, but not a priority under time pressure.
Champions pilot efforts, often without support; burnout risk is high.
Mid-level leaders
are curious but inconsistent in support.
Early tracking of process metrics (e.g., number of sessions).
Support and protect DT champions; build training for cross-functional teams.
3: Operationalized
Growing understanding
of DT as strategic when embedded in team rituals.
Integrated into agile planning and feedback loops.
Some executive engagement in initiatives.
Tracked against business proxy metrics
Formalize DT in rituals; expand beyond design teams to product, sales, and ops.
4: Embedded
DT is a shared language across departments; rituals are well-established.
Used to align teams and iterate both products and internal workflows.
Executives co-create with teams using DT.
Outcomes linked to KPIs: time-to-market, retention, revenue.
Use DT to co-create and visualize strategy; tie activities to measurable outcomes.
5: Strategic
DT mindset embedded in culture; continuous experimentation and learning are valued.
DT + AI used to drive enterprise transformation and design strategy.
DT is embedded in planning cycles and leadership decision-making.
Human-centered metrics embedded in dashboards and OKRs.
Position DT as a core business process; use it to navigate complexity and accelerate innovation with AI.
3 Strategic Opportunities
As organizations experiment with AI, there are also opportunities for AI integration with Design Thinking that can deliver both immediate and long-term value. The following examples highlight how this combined approach can unlock new forms of value across different levels of the organization.
1. Uncovering Hidden Silos and Reframing Organizational Structures
Problem:
Functions like HR, IT, and Real Estate/Facilities are each critical to the employee experience, but they often operate independently with separate goals, budgets, and hierarchies. This fragmented structure creates disjointed and inconsistent experiences for employees, particularly across key moments such as onboarding, adoption of digital tools, and engagement with physical workspaces.
Opportunity:
Redesign the employee experience holistically, integrating both physical and virtual dimensions. AI can be used to map real-time communication, workflow, and mobility patterns, uncovering hidden silos and operational inefficiencies. These insights can then be paired with design thinking practices to co-create new organizational models, processes, and integrated tooling that better support employees throughout their journey. As companies redefine work modalities (in-person, hybrid, remote), introduce AI augmentation, and reconsider the very nature of work itself, this becomes not just an optimization effort, but a strategic lever to enhance employee value and organizational adaptability.
2. Translating User Insights into Actionable Strategy
Problem:
Product teams often receive overwhelming volumes of user feedback (interviews, support tickets, feature requests, etc.) where every issue is presented as a top priority and framed with a proposed solution (e.g., put the button here). This makes it difficult to discern the underlying user needs, prioritize effectively, and translate feedback into actionable strategy without significant manual effort.
Opportunity:
AI can be leveraged to analyze and synthesize large volumes of user input, identifying patterns and surfacing the core needs behind user requests. This foundational analysis enables teams to focus their efforts on targeted research, collaborative sense-making, and iterative prototyping through design thinking practices. Rather than swinging between exhaustive manual analysis or reactive order-taking, this combined approach allows organizations to scale insight generation while maintaining a thoughtful, user-centered process for prioritization and solution design.
3. Automating Tactical Work to Enable Strategic Innovation
Problem:
In large organizations, teams spend significant time on repetitive, low-complexity tasks such as documentation, early-stage research synthesis, and internal knowledge sharing. These activities, while necessary, consume time and resources that could be better spent on high-value, creative problem solving.
Opportunity:
AI can automate many of these tasks: processing data, generating documentation, and standardizing communication artifacts. This frees up teams for more strategic work. A simple example is the use of standardized PowerPoint templates, which enable content creators to focus on substance (rather than formatting), ensuring consistent, trusted outputs for consumers. AI can extend this principle at scale, accelerating the generation and dissemination of information while preserving consistency. This creates space for teams to engage in the deeper, collaborative work that design thinking supports: exploring divergent opportunities, addressing complex problems, and co-creating solutions that require human creativity, judgment, and nuance.
These three examples illustrate just a small sample of how Design Thinking and AI can be combined to address complex organizational challenges. As both capabilities continue to evolve, there is significant opportunity to define additional use cases that extend across functions, industries, and levels of organizational maturity.
Conclusion
The State of Design Thinking in Tech
Design thinking is no longer a new idea in the tech sector, but its implementation and impact remain inconsistent. While awareness is nearly universal, maturity is highly fragmented. The method’s strongest advocates are often overextended, under-supported, and forced to operate within environments that still misunderstand or undervalue its potential.
Across interviews, it became clear that the term “design thinking” itself can hinder adoption. Many practitioners choose alternative framings, such as co-creation, strategic collaboration, or problem framing, to avoid resistance and reposition their work in ways that resonate more clearly with business and technical stakeholders.
Yet, despite these obstacles, design thinking is not obsolete. Rather, it is at a turning point. Its future lies not in clinging to past formulations, but in evolving into a cross-functional, outcome-driven, and AI-enabled practice. Design thinking can no longer be seen as the domain of designers alone. Its true strength lies in being a shared capability that enables organizations to navigate ambiguity, align around real user needs, and adapt at the speed of change.
In a business world being reshaped by artificial intelligence, design thinking offers the human context, ethical grounding, and systemic clarity that technology alone cannot provide.
Together, they can establish a new foundation for responsible and responsive innovation.