Transformative Discovery: Integrating Coaching Principles for Project Success

The Human-Centered Approach to Discovery

At the core of effective discovery work lies the importance of coaching when gathering requirements. Over time, I’ve realized that meaningful insights rarely emerge from rigid templates or formal interviews; instead, they arise through genuine conversations where people feel supported enough to pause, think deeply, and express what they need.

Often, an initial request such as “We need a dashboard,” or “Can you shorten this workflow?” uncovers more fundamental issues like decision-making, team alignment, confidence, or communication barriers. By approaching discovery with a coaching mindset, we can reveal these underlying concerns rather than just addressing superficial symptoms. If you’ve ever experienced a discovery session that seemed more like coaching than interviewing, you’ll recognize the value of intentionally cultivating this dynamic.

Reflecting on my recent years of interviews, I’ve noticed a shift, they increasingly resemble coaching sessions. Initially, I thought I was merely “collecting requirements,” but over time, it became clear I was guiding people in clarifying their actual needs. Rather than just recording their requests, I was facilitating their thinking.

In early design meetings, users typically begin with basic asks: “We want a dashboard,” “Can you make this workflow shorter,” “Can we have a button that does X?” These are useful starting points, but they seldom tell the whole story. When I consciously adopt a coaching approach, slowing down, listening attentively, and posing thoughtful questions, the dialogue changes dramatically. At that moment, our focus shifts beyond the user interface into deeper topics: friction, decision-making processes, confidence, accountability, ambiguity, and the human elements hidden beneath feature requests.

Many professionals who have spent decades in their roles rarely get the chance to reflect on the patterns shaping their daily work. So, when I ask something as straightforward as, “What’s the hardest part about planning next season?” the answer often reveals gaps and bottlenecks behind the scenes, rather than issues with the software itself. These stories simply don’t surface during standard meetings.

Uncovering Deeper Insights through Curiosity and Coaching

Curiosity allows us to explore areas untouched by process charts and requirement documents. Prioritizing the individual over the process exposes context that’s invisible on paper, like emotional burden, workplace politics, quiet worries, workarounds, and shared tribal knowledge. Coaching fosters an environment where all these factors come to light, transforming them into valuable material for design decisions.

I used to think the better I got at systems, the less I’d need to do this. But it turned out the opposite is true. The better the system, the more human the conversations become. Coaching is almost like a bridge, helping people cross from “I think I need this feature” to “Here’s what I’m actually trying to solve.”

Active Listening and Guided Curiosity

Active listening forms the core of my approach, ensuring I deeply understand not just participants’ words but the meaning behind them. I reflect statements back — such as, “So it sounds like the challenge isn’t entering the data, it’s aligning on which data to trust, right?” — to confirm genuine understanding. This often transforms technical discussions into conversations about alignment, ownership, or governance.

A key tool is the “Five Whys” technique, which I use as a guide for curiosity rather than a rigid checklist. If someone requests better notifications, I’ll probe: “Why is that important?” and follow with questions like, “Why is it hard to notice things right now?” or, “What happens when you miss something?” By the fourth or fifth ‘why,’ the conversation surfaces underlying factors such as workload, confidence, or fear of missing out, revealing emotional and operational triggers beneath the initial request.

In workplaces, these deeper issues often connect to organizational culture. For example, a request for faster workflows sometimes indicates a real need for predictability or reduced chaos, rooted in communication or authority structures rather than the system itself. Recognizing these patterns enables more effective design decisions by addressing root causes instead of just symptoms.

Intentional silence is another valuable technique. After asking a question, I resist filling the pause, giving participants space to think and speak freely. This silence often prompts unfiltered insights, especially when someone is on the verge of articulating something new. Allowing this space helps participants trust and own their insights, leading to more meaningful outcomes.

Future-Focused Exploration and Empowering Language

I also employ future-anchoring questions like, “Imagine it’s six months after launch — what does success look like for you?” or, “If the system made your job easier in one specific way, what would that be?” These help participants shift from immediate concerns to aspirational thinking, revealing priorities such as autonomy or coordination that guide design principles.

Tone and language are critical for psychological safety. I aim to make discovery feel inviting, often assuring participants, “There’s no wrong answer here,” or encouraging them to think out loud. When people use absolutes — “We always have to redo this,” “No one ever gives us the right information” — it signals where they feel stuck. I gently challenge these constraints by asking, “What might need to change for that to be different?” This opens possibilities and helps distinguish between real and internalized limitations. Coaching-based discovery is key to uncovering and addressing these constraints for lasting change.

Reflections and Takeaways

Coaching Tools as Foundational Practice

Initially, I viewed coaching tools as separate from implementation work, and more of an optional soft skill than a crucial element. Over time, my outlook changed: I saw these tools as fundamental to successful outcomes. I noticed that the best results happened when participants truly took ownership of the insights we discovered together. That sense of ownership was strongest when the understanding came from them, even with my guidance. Insights gained this way tend to last longer and have a greater impact.

My approach to discovery has evolved significantly over time. Initially, I viewed discovery as a process focused on extracting insights from users. More recently, it has transitioned into facilitating users’ own self-discovery, enabling them to articulate intuitions and knowledge that may have previously been unexpressed. This progression from a transactional checklist to a collaborative and transformative meaning-making practice has had a substantial impact on my design methodology.

Efficiency through Early Alignment and Clarity

Contrary to prevailing assumptions, coaching-based discovery does not impede project timelines. Although it demands greater initial investment of time, the resulting enhanced alignment and mutual understanding often expedite progress. Early engagement in substantive discussions enables teams to minimize rework, clarify decision-making processes, and avoid misinterpretations, which can ultimately result in projects being completed ahead of schedule due to unified objectives.

Efficiency is driven by clarity. When users feel acknowledged and their perspectives are incorporated, their level of engagement and willingness to collaborate increases. The trust established during these interactions persists throughout testing, feedback, and rollout stages, mitigating many subsequent problems that typically occur when user requirements are not considered from the outset.

Strong Implementation Questions Are Strong Coaching Questions

At their core, effective implementation questions are essentially strong coaching questions. These are fuelled by curiosity, maintain a non-judgmental tone, and aim to empower others. Instead of guiding someone toward a set answer, such questions encourage individuals to uncover their own insights about the work.

Regardless of the type of discovery — be it design, implementation, or workflow — insight comes from those directly involved. Coaching goes beyond mere technique; it represents a mindset based on the belief that people already hold valuable wisdom. The coach’s job is to help draw out this knowledge, using thoughtful questions.

A key moment in coaching-based discovery happens when someone has a sudden realization, saying things like, “I’ve never thought about it that way,” or “Now I understand why this keeps happening.” These moments are where improvements in design and implementation begin.

Such realizations act as anchors throughout a project. When team members shift their understanding, these breakthroughs can be revisited during times of complexity or tough decisions, providing direction as a “north star” to keep teams aligned.

Coaching is not just a resource, it should be demonstrated in everyday interactions. As teams experience its benefits, they often adopt coaching practices with each other, leading to genuine transformation that extends past individual projects and influences wider workplace culture.

Ultimately, the real value of this work lies not just in the solutions themselves, but in the conversations that reshape how people engage with their work.

Case Study: Designing an AI-Driven Product with Strategic Ownership

“In complex AI systems, clarity is not a bonus—it’s the core feature. Product Designers must lead the charge, not just in how things look, but in how they think and work.”

Project Overview

In this case study, we examine a team that has recognized the potential of Machine Learning (ML) and Artificial Intelligence (AI) to refine and enhance a longstanding methodology for forecasting product order volumes. By leveraging AI and ML, the team can achieve more precise ordering based on those forecasts, while also gaining the capability to monitor market prices and receive insights on how to adjust orders to minimize costs. Historically, this team has relied on Excel for manual and meticulous user input, maintaining continuous communication among members and adhering to a process honed over several decades. While this approach has been effective, they have now realized that integrating AI and ML can significantly enhance their workflow by handling larger datasets at a faster pace and generating profound insights aimed at maximizing efficiency, reducing costs, and driving business growth.  

Role of the Product Designer

The Product Designer in this role helped initiate a significant project aimed at transforming a long-standing, Excel-based process into a web-based application. This endeavor required not only a user-centered design approach but also an understanding of how AI and ML can be applied effectively to realize the project goals and meet the users needs.

To achieve this, the Product Designer addressed the following considerations:

  • What processes were being followed?
  • How did users use Excel for data entry and forecasting?
  • Which identified business processes needed to be maintained, which could be enhanced with AI, and which could be replaced entirely?
  • Where could AI and ML introduce efficiencies and savings, and provide valuable insights?
  • Were the identified AI efficiencies and insights aligned with user expectations?
  • How would the design help users provide inputs easily, and act upon AI-generated outputs and insights?
  • How would the design help users enhance their work efficiency and enable them to focus on more strategic and human-centric tasks?

At this stage, and before any of the AI models and algorithms were developed, the Product Designer assumed a strategic role in defining the foundational framework for the data the AI models would use and the outputs and insights they would generate for user consumption and action.

By adopting a product owner’s mindset and taking on a strategic role in determining the user requirements as they pertained to the AI models, the Product Designer shaped the product through the following actions:

  • Identifying key stakeholders through close collaboration with the project manager
  • Gaining an understanding of the current business process and the value the project proposes to achieve
  • Developing interview scripts designed to deepen understanding of:
    • The current business process and how Excel is used to generate forecasts
    • The ideal product vision and how it aligns with the user needs and expectations, particularly as it applies to the use of AI and ML
    • How the product could add value to users’ day-to-day work
  • Scheduling and facilitating interviews with stakeholders.
  • Maintaining an openness to any additional insights gained during the interviews such as including additional stakeholders, and exploring other areas of the business as necessary.
  • Collecting and synthesizing feedback from the stakeholder interviews.
  • Establishing a framework based on the feedback analysis for user personas, user journeys and user flows.

These activities enabled the Product Designer to acquire deep insight into the current business process, the stakeholders and users and their roles, and most importantly, insights into the AI and ML user needs and what outputs would be expected .

Research & Discovery

Using the insights gained from stakeholder interviews, the Product Designer was able to:

  • Identify user personas based on the roles and users discussed during the interviews.
  • Conduct additional workshops to further refine the identified user personas.
  • Discover new opportunities for additional user personas not previously identified and develop them further.
  • Develop user journeys for key user personas or those with the most critical needs.
  • Create user flows that outline the application’s essential features, associated screens, inputs, and outputs.
  • Refine user flows, enhance identified features, and ascertain any missing components and data.
  • Develop an information architecture (IA) aimed at providing easy and intuitive navigation that prioritized productivity and ease of navigation between various sections of the application.

The outcome of this process was a well-defined product roadmap and vision that provided a clear framework for technical teams. Data scientists, engineers, back-end and front-end developers could use this roadmap, along with the user needs and technical requirements identified, to begin designing and developing AI models. This structured approach ensured that the AI models were not only functional but also optimized to enhance the user experience.

Designing with Clarity and Logic

The research and discovery phase provided essential insights, enabling the Product Designer to conceptualize how the application would meet users’ needs and allow the AI models to generate the necessary outcomes. The user flow diagrams established an information architecture that formed the basis for the features the application offered and the overall user experience. With the information architecture now established, wireframes and mockups were created to facilitate discussions with users regarding the design direction.

  • Wireframes and mockups enabled stakeholders and end users to understand how input is provided into the AI models and what the output would look like.
  • This stage was crucial in the product design process as it helped establish the foundation for robust AI models that would directly meet the users’ needs.
  • The wireframes and mockups were refined based on feedback from users and stakeholders through recurring reviews and workshops.

Wireframes, mockups, and user flows helped the Product Designer build a prototype that:

  • Assisted data scientists, engineers, front-end, and back-end developers in visualizing user input and the generated output in the application.
  • Assisted data scientists and engineers in understanding the requirements for data input and output processing, and design algorithms to meet these requirements.
  • Illustrated the detailed interactions necessary to enable users to calibrate their inputs into AI models.
  • Facilitated collaboration between back-end and front-end developers with data scientists and engineers to design and build APIs that support the flow of data and insights from AI models.
  • Allowed running usability testing sessions with end users to validate the design and iterate based on the feedback received.

The development of a prototype represented a significant milestone and underscored the strategic role of the Product Designer played in guiding stakeholders and users through discovery sessions, workshops and design reviews. The Product Designer’s efforts in understanding data input and output requirements, and visualizing them clearly as part of a prototype, enabled technical teams to design AI models and algorithms that met those requirement. This hybrid mindset adopted by the Product Designer, functioning as an intermediary between business strategy and technical execution, was pivotal in fostering collaboration among product, engineering, and data teams, ensuring a clear understanding of the product vision and roadmap.

To drive the success of this AI-enabled product, the Product Designer delivered a strategic and structured design process that included:

  • User personas, journeys, flows, and an information architecture that defined core behaviours and ensured the experience aligned with user needs.
  • Interactive prototypes, refined through multiple rounds of usability testing and stakeholder input.
  • Detailed interactions clearly outlining user inputs and the data required to support model performance.
  • Insight-driven visualizations, which shaped how AI outputs were presented and guided model design.
  • An end-to-end product roadmap, mapping the full product vision while enabling the extraction of an MVP and a plan for iterative, future releases.

Lessons Learned

In this case study, the product designer started their work by identifying users’ needs and pain points related to an existing business process. Stakeholders and users wanted to explore how AI and ML could introduce savings and efficiencies into their business.

The designer’s responsibilities included identifying and analyzing the problem, and integrating ML and AI solutions through extensive collaboration with stakeholders, product managers, engineers, and data teams. This collaboration was crucial for the designer to articulate the product vision using a user-centered approach while also providing comprehensive insights into the data engineering efforts needed to optimize AI model outcomes.

Designing and developing AI and ML models for a product is a time and resource-intensive process. Therefore, it is essential for organizations to ensure these resources and efforts are invested in a manner that maximizes benefits and potential gains. In this case study, the Product Designer’s role was vital in establishing the product vision and roadmap, and in helping the various project teams understand and acheive this vision.