You’ve probably heard the parable of the two wolves—one representing our darker instincts, the other our better nature. The idea is that the wolf you feed is the one that thrives. But in the context of innovation, I’ve come to believe the real challenge isn’t choosing which wolf to feed, but learning how to nurture both.
One wolf represents divergent thinking—the mind that runs wild with unrestrained, endless possibilities. The other is convergent thinking—the disciplined, focused mind that brings clarity and execution to those ideas. I navigate this tension daily, both in my work as a strategist and in my personal artistic journey.
Many years after attending art school, I decided to pursue master draftsmanship through the traditional atelier model, specifically using the Grand Central Atelier (GCA) methods. For those unfamiliar, this fine art method emphasizes precision, observation from life, and classical technique—a practice that might seem far removed from digital strategy, but in truth, there are clear parallels.
Much like the early stages of an innovation project, my artwork begins with a period of divergent thinking. I allow myself to explore the subject freely, not knowing exactly where it will lead. This was evident in my project American Music (inspired by the Violent Femmes song of the same name). The concept for the painting revealed itself to me slowly, much like finding a melody within the noise. At this stage, the possibilities are limitless.
Then comes a necessary shift—the Convergent Wolf steps in. Once I’ve found my direction, I move into a precise, focused mode, where hours of labor go into perfecting the details. In art, this means refining tones, adjusting shadows, and capturing the nuances that bring a piece to life. In my professional work, this shift happens when concepts move from imagination to execution—when user research, data analysis, and strategic planning turn abstract notions into real-world outcomes.
This is an inherent struggle for most of us. We either live unrestrained in the divergent mode, endlessly exploring ideas without bringing them to fruition, or we cling to the predictability of convergent thinking, trying to control ideas before they’ve had a chance to evolve. But through both my professional and artistic practices, I’ve learned that true innovation only happens when you encourage both wolves.
In every project I lead, whether crafting a product roadmap or refining a strategic vision, the tension between creativity and pragmatism is always present. I work with teams to embrace this dynamic—encouraging unbounded ideas from the wild Divergent Wolf, which may seem unfocused at first but reveal valuable insights into where things could go. Then, we shift to the wise Convergent Wolf, refining those ideas together into something actionable and concrete. When we get it right, the two modes feed off each other, creating something greater than either could alone.
Balancing divergent and convergent thinking isn’t just a business strategy—it’s essential for achieving meaningful outcomes in any field. Without divergence, you miss the creative breakthroughs that lead to innovation, and without convergence, those ideas will never see the light of day. This is also why companies can’t ‘research’ their way into innovative products—while research helps to ground ideas in reality, it’s the creative leaps and visionary thinking beyond the data that produce the most valuable results. Successful products emerge when companies balance research with creativity, execution, and the willingness to explore what’s possible beyond what’s known.
“This is also why companies can’t ‘research’ their way into innovative products“
Research is invaluable for understanding user needs and market trends, but it’s inherently backward-looking. Groundbreaking innovation comes from generating new possibilities through divergent thinking—creative leaps that can’t always be predicted by data. Progress requires a balance between research, creativity, and disciplined execution, fueled by human intuition and vision to push boundaries and create transformative products.
This same balance applies to mastering any craft, whether in business or art. Masters don’t simply copy what they see; they interpret, with deep understanding and intention. The journey begins with the spark of an idea and the freedom to explore, but it soon transitions into discipline and focus. Each brushstroke, each adjustment to tone, represents hours of dedicated work, yet all of it stems from the initial creative freedom. Sometimes, honing in on the details sparks unexpected discoveries—where the Divergent and Convergent Wolves meet once again, harmoniously collaborating in new and unexpected ways.
So, which wolf do I feed? Both. Because in the end, it’s not about choosing between creativity and execution—it’s about the dance between them. And that’s where real innovation—and mastery—happens.
Like to learn more about divergent and convergent thinking in technology innovation? Check out the following materials:
The Synergy of Diverge and Converge in Design Thinking – Voltage Control: How divergent and convergent thinking are essential in the design thinking process, offering practical tips on how to integrate these approaches into innovative projects.
Unleash the Unexpected for Radical Innovation – MIT Sloan Management Review: Explores how radical innovation often emerges from unexpected ideas, highlighting the importance of environments that encourage divergent thinking and creativity. (May require subscription)
The Design of Everyday Things – Don Norman: A classic UX book, essential for understanding design thinking, usability, and balancing creativity with practical application in product design.
About the Author Dorothy is a digital strategist and researcher who helps companies turn big ideas into real-world innovations. Outside of work, she is applying this same balance of creativity and focus to her current pursuit of master draftsmanship.
AI discussions span everything from utopian promises to technical deep dives. As someone whose career has centered on observing how humans interact with technology, I’ve witnessed how our systems shape—and are shaped by—our lives. Recently, as a strategist and researcher, I’ve been more deeply exploring generative AI’s potential.
One realization stands out: We urgently need human-centered design (HCD) and established user-centered methods to steer AI’s broader societal impact. By adapting UX principles and embedding them into the foundations of these models, we can limit further entrenching existing divides, biases, and superficial interactions. This article isn’t a technical exploration of generative AI; it’s an open invitation to rethink how we might design it with greater intention.
It’s tempting to believe that AI could be humanity’s next great leap forward—a technology that will profoundly and ethically enhance our lives. In a perfect world, AI would evolve through global cooperation, with governments and organizations collaborating to set agreed-upon boundaries in our long-terminterdependence with this technology.
Let’s face it, that’s not going to happen.
AI is already deeply enmeshed in profit-driven models, and the commercial forces pushing AI’s rapid development are unlikely to yield a blue-sky vision of AI as a mission-driven, human-first global endeavor. We live in a world where technology will be monetized as quickly as possible, even if it’s still half-baked, and the long-term consequences still need to be considered.
So, what do we do? Our challenge becomes how to guide generative AI’s development without totally abandoning human values while acknowledging its commercialization.
Given this reality, we start by changing the discourse around AI to embrace its potential and risks instead of sensationalizing them. As we develop these systems, we must ensure their training captures and nurtures the best aspects of who we are. To do that, we must create a common framework for what these “best aspects” are while acknowledging that this will include a wide range of relative truths.
Exploratory research asks big questions. Are we taking necessary steps to ensure AI is optimized to enhance humanity’s richness and complexity, or are we reducing ourselves to commodified data points that reflect only the most sensationalized aspects of our behavior? This question should be at the forefront to avoid repeating mistakes.
Digital’s Complex Legacy
From my early work in broadband development to today, I’ve watched well-intended, beneficial advancements also lead to long-term, harmful consequences. Much like current promises about AI, broadband was initially seen as a democratizing force—a tool to empower and connect. High-minded ideals and a utopian vision drove the people who built these services. I know as I was one of those people. I’ve struggled with the pride of being part of an exciting time in our technological history and disillusionment with how some aspects have turned out. As commercial interests took over, particularly with adopting the ad-driven model, the Internet’s original promise was transformed.
Many of the unintended consequences we are dealing with now were foreseen by many, but it didn’t matter; the dot-com bubble burst created an existential crisis for the industry. It forced profit-or-perish decisions that shaped much of today’s Internet, turning hopeful optimism into a commercial necessity. The same unchecked optimism and as much doomsday prediction now surround AI. Might we use AI development as an opportunity to address the negative aspects of our past technological choices head-on?
Due to how broadband shifted to survive, generative AI has been given a complex inheritance. Profit-optimized content has been the foundation for its simulated understanding of human interaction.
Shallow Data In, Shallow Interactions Out
Too often, technology has forced us to adapt to its limitations rather than expand ours. An attempt to address this was in early advocacy for natural language interfaces—aligning systems with how people organically communicate and making interactions easier rather than forcing users to conform to rigid, efficiency-driven workflows. AI should be no different, especially given its potential to be deeply woven into our daily lives.
Today, most AI systems are built on data from platforms like Google, Facebook, and TikTok. These platforms prioritize engagement, rewarding the most attention-grabbing content, not necessarily the most meaningful. While vast and seemingly comprehensive, it represents inaccurate and incomplete versions of ourselves. Consider for a moment those sources. Do they reflect our goals as a species?
Some AI proponents believe techniques like cross-domain learning and transfer learning—where AI systems are trained on data from multiple domains—can mitigate biases and data gaps. These techniques can improve AIs’ ability to handle more complex tasks. However, these technology-focused solutions are band-aids unlikely to address the underlying design failure: the data itself.
One of my favorite tech dad jokes came from a colleague at a systems integrator: “How did God create the universe in 7 days? There was no legacy system.”
I’m passionate about technology, but we must be pragmatic in its implementation, and the devil is in the details. Surrounded by the hype of AI, it’s easy for tech leaders to forget: Legacy systems have taught us once systems are implemented, they tend to persist, flaws and all. Retooling from scratch is costly and time-consuming, meaning the foundations of today’s AI systems will continue to shape tomorrow’s world. Without a focused effort to integrate context-rich, qualitative information, we risk building AI systems that fail to enrich our lives, limit how we engage with the world, and codify narrow representations of ourselves well into our children’s futures.
We’ve Already Adapted to Technology’s Distorted Lens
Seemingly insignificant behaviors can reveal profound insights that broad datasets miss. Hesitations, pauses, or other external behavioral changes could indicate a point in a workflow that inherently requires a higher cognitive load or a genuine barrier to completing a task—big data, which typically only captures outcomes, fails to detect this. There is an even more concerning fact: users often adapt to poorly designed system experiences and believe their struggles stem from their inadequacies rather than the technology itself.
We’ve watched technology move beyond being a tool we adapt to in small ways into a force that shapes our culture in significant ways we don’t control, eroding our collective feeling of agency. Narratives pushed by the algorithms behind these platforms don’t just diminish our senses of self-worth and reality but also encourage consumerism, impulsivity, and emotional reactivity. As AI systems inherit data from these platforms, they reinforce these interactions, codifying hyper-consumerism, performative relationships, and attention manipulation.
Our lived experiences—unique, messy, and deeply meaningful—are being flattened, standardized, and spit back at us in increasingly distorted forms. Not only are we facing a loss of biodiversity but also of human diversity, and it’s unfolding on our watch.
Correcting Course
At the heart of AI’s current development lies a mismatch between its commercial incentives and society’s broader needs. The race to deploy AI to boost stockholder confidence undermines its real potential. Yet, we can correct course, making conscious choices to align AI’s evolution with our better angels.
Pivoting to a Long-Term Investment Model
We should adopt a long-term investment model that encourages careful consideration before releasing AI technologies to the public instead of using us as experiments and training fodder. It’s tempting to think that if we need more data, we can simply allow users to provide it in real-time. However, this data is a feedback loop of simplified tasks and interactions, and I question the ethics of this approach.
Companies can still find a win-win. Jeff Bezos’s well-known approach to Amazon is a powerful example of this kind of long-term investment thinking. From the outset, Bezos emphasized long-term infrastructure over immediate profits, understanding that the company could grow exponentially by building the necessary foundation.
Ethical Walled Gardens
Another crucial component of redirecting AI’s future is the concept of ethical walled gardens. “Walled gardens” often mean monopolistic ecosystems controlled by tech giants like Google or Facebook, but an ethical walled garden can have different purposes. Secure, gated spaces where clear moral principles and safeguards govern AI development and deployment.
Privacy-focused laws, such as the European Union’s General Data Protection Regulation (GDPR), help protect user privacy and create secure spaces for data collection. However, they will not inherently address the quality or completeness of AI training data. While these frameworks safeguard data from exploitation, they must be paired with strategic efforts to improve the collected data.
The Value of Qualitative Data
Addressing AI’s inherited blind spots to build better systems indicates investing heavily in purpose-generated qualitative data such as ethnographic studies, contextual observations, and curated sociological findings. Valuable insights can come from methods that capture real-life, context-rich interactions beyond the direct influence of consumerism. Armed with this goal-focused content, AI can begin to internalize the diverse and frequently counterintuitive ways humans engage with the world, providing essential balance.
To ensure AI systems are trained on meaningful data, we should continue to optimize them to incorporate and use unstructured data more effectively. This will require AI models that can contextualize human behavior. Some research suggests that combining big data with smaller, qualitative datasets—often called data triangulation—can help AI systems better reflect the richness of our experiences.
Generative Qualitative Research for AI
Qualitative data is used today to augment and refine AI models; this is not new. However, this unstructured data (an estimated 80% of all content available) isn’t purpose-built for the role. As discussed, it is subject to the same biases and blind spots as other data. Qualitative data requires significant human effort to clean, code, and curate. A partial solution currently being explored uses a hybrid model, where researchers use AI tools to help codify the data. The importance of human oversight cannot be overstated in this effort.
A complementary and amplifying approach to mining our unstructured data treasure trove is to conduct targeted generative studies that can be more heavily weighted to assist AI models in deciphering the raw data they consume. Rigorous methodologies and research objectives based on collectively defined goals can ensure that specific concepts and dynamics the AI model is trained on are conveyed in a way that requires less human oversight in the long term.
A framework for generative research for AI model training can and should be defined. Methods used in traditional software systems today can provide insight. Foundational principles for current user research are one-on-one interviews (vs. focus groups) and a heavy focus on observational and contextual methods. When properly structured, implemented, and analyzed by people with expertise in human-centered design, this type of research can be highly directional and capture a wide range of targeted, nuanced insights. The results of these studies, codified with the help of AI and weighted by humans, could provide robust training benchmarks.
As we shape the future of AI, let’s move beyond technological optimism or doomism, roll up our sleeves, and work to address our industry’s complex legacy. The time for developing mutually defined goals for human-centered design in AI is now. We have a choice: allow this technology to evolve only to reflect what’s commercially viable or intervene to ensure AI represents us authentically. Answers lie in investing in richer data, focusing on human experience, and refusing to let AI reinforce a flaw that has plagued our digital spaces for decades.
Would you like to learn more about human-centered design and AI? Check out the following materials:
What is Human-Centered AI (HCAI)? – Interaction Design Foundation: Learn more about HCAI and its ethical foundations in AI development.
About the Author Dorothy is a digital strategist and researcher who works with companies to blend human-centered research with emerging technologies to navigate complex challenges.
How can a trusted financial institution like Schwab innovate in the rapidly evolving world of cryptocurrency while maintaining its credibility and regulatory alignment?
As Schwab’s leadership grappled with how to approach the crypto market, they were facing substantial outflows, losing hundreds of millions of dollars each month as investors turned to competitors for crypto investments. They needed a strategy that would recapture these investors while preserving their reputation for trustworthiness and regulatory adherence. I led research for an initiative within Schwab’s Innovation Accelerator to explore whether, how, and to what extent they should enter the crypto space. Through in-depth market analysis, investor research, and strategic ideation, we developed a path forward to provide Schwab with a competitive edge without undermining the brand’s core values.
In collaboration with the Innovation Accelerator team, I provided crucial decision-making insights, revealing the mindsets of both existing Schwab clients investing in crypto elsewhere and crypto-focused investors not yet using Schwab. Our evaluation of key competitors such as Fidelity and Robinhood informed a risk-reward analysis for various entry strategies. Beyond a direct crypto offering, we identified a valuable opportunity for Schwab to enhance its position by providing advanced decision-making tools—offering investors clarity while aligning with Schwab’s trusted brand image.
Results Snapshot:
Key Market Opportunity Uncovered: Our research identified a major opportunity for Schwab to provide advanced decision-making tools for crypto-curious investors, bridging the gap between the brand’s trusted reputation and the growing interest in cryptocurrency.
Data-Driven Insights for Executive Decision-Making: Provided Schwab leadership with comprehensive data on crypto investor mindsets, including both existing clients investing in crypto elsewhere and new investor segments. This data was crucial for evaluating market entry options.
Competitive Analysis for Strategic Differentiation: Offered an in-depth comparison of competitors like Fidelity and Robinhood, highlighting the risks and rewards of various entry strategies. Schwab’s competitive edge was tied to a differentiated platform focused on credible, robust decision-making tools.
Positioning Schwab for Strategic Growth: Our work provided a comprehensive strategy that positioned Schwab as a forward-thinking leader in the crypto space, offering a path to maintain brand trust while exploring new market opportunities.
Looking for more insights? Use this download form to access my collection of detailed case studies, showcasing impactful results across industries. Discover how I’ve tackled complex challenges with innovative solutions.
How do you increase user trust in an automated system they don’t understand?
Indeed Enterprise was facing a growing problem. Employer clients were increasingly frustrated with the lack of transparency in a recently implemented automated payment allocation algorithm. The behind-the-scenes process distributed funds to job postings based on market conditions and other metrics. While well-intended to help employers maximize the value of Indeed’s advanced job market advertising model, users didn’t trust the system. Many employers bypassed the algorithm entirely, manually allocating funds per job, which led to inefficiencies, misaligned priorities, and mounting dissatisfaction.
As Senior UX Researcher, I led research for a redesign initiative to address these concerns, focusing on creating a more transparent, user-centered solution. Through in-depth user research and iterative prototyping, we developed a system that empowered employers with greater control over job postings’ budget allocation. The new solution not only increased user engagement and trust in the algorithm, but also shaped the product roadmap for future enterprise clients.
Results Snapshot:
Higher User Engagement: Clients who adopted the new workflow reported greater confidence and efficiency in managing job priorities and budget allocation, resulting in better overall platform satisfaction.
Increased Trust in Algorithm: The added transparency and control features significantly improved trust in the automated system, leading to higher adoption rates.
Strategic Product Influence: The success of this project directly shaped the future direction of Enterprise product strategy, influencing key roadmap decisions.
Model for User-Centered Design: The initiative was widely recognized internally as a benchmark for applying user-centered design principles, setting a new standard for the organization.
Looking for more insights? Use this download form to access my collection of detailed case studies, showcasing impactful results across industries. Discover how I’ve tackled complex challenges with innovative solutions.
Can design thinking methodologies uncover new opportunities for patient engagement while ensuring innovation readiness for senior teams?
In an effort to engage patients more deeply with the Merck brand and explore new digital opportunities, I led a design thinking initiative focused on chronic illness research. The project aimed to mitigate the risk of Merck’s pharmaceuticals losing exclusivity to generics by identifying digital solutions that would enhance patient engagement. This initiative was also part of a broader digital transformation to build digital acumen across the organization. The challenge was twofold: develop patient-centered digital solutions while guiding senior team members to embrace new methodologies and strategies.
As a design thinking coach, I introduced the team to the Design Thinking model, guiding senior product and marketing leaders through iterative research processes. Together, we uncovered actionable insights into patient behaviors and attitudes, enabling Merck to explore new health-tech solutions that aligned with patient expectations and extended the value of their pharmaceutical offerings.
Results Snapshot:
Identified Health-Tech Opportunities: The research uncovered areas where Merck could introduce digital health solutions that aligned with the needs of chronic illness patients and physicians, extending their engagement beyond traditional pharmaceuticals.
Leadership Empowerment with New Methodologies: Senior market researchers and product leaders gained hands-on experience with design thinking and new digital research methods, which helped them adopt more innovative approaches across other initiatives.
Paved the Way for Future Digital Initiatives: The project laid the groundwork for future patient engagement strategies, positioning Merck to explore further digital transformations in the healthcare space.
Looking for more insights? Use this download form to access my collection of detailed case studies, showcasing impactful results across industries. Discover how I’ve tackled complex challenges with innovative solutions.
How can Wawa introduce new dinner options while ensuring an intuitive, high-quality ordering experience for customers in both established and new markets?
As Wawa expanded into new markets and introduced a dinner menu, the company faced unique challenges: maintaining their reputation for high-quality, made-to-order food, and overcoming the negative connotations associated with traditional gas station fare. Additionally, Wawa needed to enhance the functionality of their self-serve kiosks and mobile app to support these new dinner options, while making the ordering experience smoother and more intuitive for users.
I was engaged as a UX Research Consultant, drawing on my expertise in customer research and menu taxonomy to help Wawa tackle these issues. Our goal was to refine the kiosk and mobile app experience, using qualitative and quantitative methods to gather insights into customer behavior, and ultimately improve both satisfaction and order completion rates.
Results Snapshot
Optimized Menu Taxonomy: Streamlined the ordering process by revising the dinner menu structure and improving overall navigation.
Improved User Satisfaction: Real user feedback drove enhancements that boosted customer satisfaction with the kiosk and mobile app experience.
Actionable Insights: Delivered key findings on user preferences, helping Wawa introduce new offerings while maintaining brand consistency across markets.
Looking for more insights? Use this download form to access my collection of detailed case studies, showcasing impactful results across industries. Discover how I’ve tackled complex challenges with innovative solutions.
How do you transform a legacy logistics system into an efficient, user-centered platform without major infrastructure overhauls?
PCSTrac, a carton tracking solution for online retailers, had proven its value in core functionality but was hindered by a cumbersome user interface and workflow inefficiencies. Although technically robust, users often relied on time-consuming workarounds that slowed down operations. My role was to collaborate with senior engineers to bring a user-centered perspective, aligning design improvements with the existing technical constraints of the legacy platform.
Through contextual inquiries, persona development, and usability testing, I led the research that informed both workflow optimizations and interface redesigns. The project resulted in more streamlined processes that significantly improved user satisfaction and system usability. Additionally, the PCSTrac team embraced user-centered design principles, applying these to future product development.
Results Snapshot:
Balanced Technical Constraints and User Needs: Despite the limitations of the legacy system, the user-centered design approach introduced practical solutions that enhanced the user experience without necessitating expensive system overhauls.
Increased Efficiency through Streamlined Workflows: By addressing key workflow bottlenecks, the system allowed users to eliminate redundant manual processes, leading to measurable improvements in operational productivity within 3PL operations.
Improved User Satisfaction and Task Efficiency: The redesigned interface, with a cleaner and more intuitive layout, led to increased satisfaction among users who had previously been frustrated by the outdated interface. This improvement enhanced task efficiency and reduced friction in daily operations.
Looking for more insights? Use this download form to access my collection of detailed case studies, showcasing impactful results across industries. Discover how I’ve tackled complex challenges with innovative solutions.
How did unexpected user insights help Comcast re-engage customers and improve product workflows?
Comcast’s WiFi On Demand service experienced unexpected growth among various user demographics, including low-income users and former subscribers. Originally designed for business travelers, our research revealed that many users viewed the service as an alternative to traditional home internet, especially during life transitions. These insights provided Comcast with an opportunity to re-engage these customers with targeted payment plans, as well as enhance the product’s application interface to address key pain points and workflow inefficiencies.
Through buyer interviews, personas, user journey mapping, and surveys, I led a comprehensive research effort to understand these diverse behaviors and pain points. This informed strategies to re-engage key user segments and improve the interface design, ultimately increasing recurring pass purchases and expanding the service’s reach across underserved demographics.
Results Snapshot:
Re-engaged Former Customers: Comcast developed targeted re-engagement strategies for lapsed customers who had switched to daily passes due to overdue bills. This included customized payment plans aimed at bringing them back into monthly subscription services.
Enhanced Workflow and UI Efficiency: Direct improvements to the product’s user interface and workflow design were made based on the pain points identified during user research. These changes optimized the service’s overall usability and reduced friction in the ordering process.
Increased Revenue from Underserved Demographics: Insights revealed untapped customer segments, leading to increased recurring pass purchases from previously overlooked user groups, including recent movers and transient users.
Looking for more insights? Use this download form to access my collection of detailed case studies, showcasing impactful results across industries. Discover how I’ve tackled complex challenges with innovative solutions.
It’s a common scenario: A company is planning a new product or significant redesign. There have been various discussions about how the product should have a “great user experience” and “focus on the user.” But, there are also conflicting ideas on what a great experience might entail, along with competing priorities for what the product absolutely must do to be successful in the marketplace.
Where to begin? How do you break through the confusion and move towards a clarified product vision? Whether a large established corporation or lean start-up, organizations struggle with progressing from early ideation into clear requirements and a tangible design phase. This webinar will explore ways to leverage user experience design methods in the very early stages of the product life cycle.
This session covers the following:
An overview of practical user research and design planning methods useful for early stage products and redesigns
Strategies for leveraging these methods to refine a product’s vision and ensure features are tied to user goals
Examples of how keeping a focused eye on user needs can help resolve conflicting priorities and promote product team alignment