Reframe and elevate your design impact
Designers are often judged not just by what they create — but by how convincingly they present it.
This project began with a simple question: Why do designers — despite strong storytelling skills — still struggle to gain influence in strategic conversations? And how might AI help them frame ideas in business terms and respond with confidence when challenged?
What is PitchPivot
PitchPivot helps designers translate design thinking into business language. It reframes ideas, highlights impact, and prepares you for tough questions — so your work lands with clarity and confidence.
Goal 1 — Bridge the gap
Translate design rationale into measurable business impact.
Goal 2 — Build confidence
Present ideas clearly and anticipate tough stakeholder questions.
Goal 3 — Elevate influence
Strengthen design’s voice in strategic discussions and decision-making.
The Problem — When Great Design Doesn’t Land
Even strong, well-crafted design work can lose power when presented to non-designers. Many designers struggle to communicate their ideas in business terms — speaking in usability, accessibility, and craft, while stakeholders expect numbers, strategy, and measurable value.
This gap makes it difficult for designers to gain influence in strategic discussions. It often leads to frustration: ideas are misunderstood, dismissed, or never fully implemented. The result is a recurring tension between design intent and business priorities — one that limits both design impact and confidence.
Crickets & Pushback
Why This Matters — A Mature Discipline Still Missing a Seat at the Table
Product design is a proven growth driver — yet it still struggles to gain influence in strategic decisions. Despite a $200 billion market, only 13 % of companies have a UX leader at the executive level. Design-led businesses grow 32 % faster, while poor UX contributes to 65 % of failed digital products.
These numbers reveal a persistent gap between design impact and design influence. Designers create measurable value but often lack the visibility and vocabulary to express it. That’s the gap PitchPivot aims to close — helping designers communicate their impact with the clarity and confidence needed to earn that seat at the table.
sources: designrush, flynn, mckinsey
Design impact vs. Design influence
The Process — Exploring AI in the Design Workflow
I created this project during the 5-week “AI for Designers” course (September–October 2025) by Patrizia Reiners, exploring how AI can enhance the creative and strategic process. The goal was to explore how AI could actively support designers — from early idea framing to communicating impact.
Each week followed a design-sprint rhythm: from research insights to AI-powered prototyping and testing. Tools like Lovable, NotebookLM, and ChatGPT supported each stage — from clustering insights and shaping prompts to visualizing flows and refining tone.
The process was an experiment in collaboration — using AI not just as a tool, but as a design partner that challenged assumptions and accelerated iteration.
Exploration — Finding the Opportunity Gap
At first, my focus was on what “presenting” means for designers — especially as high-stakes presentations often felt the most stressful and the most decisive for how their work gets implemented. I analyzed tools with AI integrations that assist with slide creation, storytelling, and even public-speaking coaching.
Many tools already addressed common pain points — from “blank page” templates to rehearsal coaches that analyze delivery in real time. I needed to identify where real opportunity gaps existed and ground my assumptions through further research.
The Turning Point: What User Research Revealed
To understand where the biggest pain points lie, I asked designers about the most stressful parts of presenting their work. Through five interviews, I learned that the main challenge isn’t making presentations — it’s communicating design decisions to non-designers.
Most said they present more informally than formally, often sharing work directly in tools like Figma or Notion. Their frustration wasn’t the act of presenting itself, but not having enough influence to move their ideas forward — watching strong concepts get misunderstood or dismissed.
Across roles and company sizes, a shared challenge emerged — even experienced designers struggle to show measurable impact and gain stakeholder buy-in.
Influence — A Hot Topic Across Design Communities
Secondary research shows how often this topic appears across design communities — from LinkedIn discussions to UX research reports. Even experienced designers struggle to show measurable impact and cite limited stakeholder buy-in as a top challenge. As UX shifts from intuition to business strategy, designers are expected to link their work to outcomes like conversion and retention — not just craft beautiful interfaces.
This shift is redefining the role: AI tools amplify strategic UX work, and influence now depends as much on communication as on execution.
These insights — from interviews and online research — shifted my focus from improving presentation delivery to reframing design reasoning from the start — helping designers express impact early, anticipate questions, and stand by their choices with confidence.
Public posts by Artiom Dashinsky and femke.design, shown here for educational and research context.
Prototyping — From Insight to Interaction
Early prototyping revealed AI’s real potential. With the help of NotebookLM (AI feature opportunities), ChatGPT (prompting), and FigJam (user flows), I quickly created several flows to understand where AI could add the most value.
However, it was only after pasting my first structured prompt into Lovable that I could truly test and understand the implications of this potential. I also realized how complex and wide-ranging these three features could become — which led to a series of simplifications to start with.
I decided to remove the initial login wall to make the first entry point more accessible and testable. For future releases, the vision was to make the tool increasingly personalized — learning from each designer’s behavior and adapting its guidance over time.
First Lovable prototype: clarified where AI feedback could best support designers — prioritizing logic and usefulness over visuals.
Early flow mapping how Impact Framing and Pushback Pivot connect within the prototype
Narrowing the Concept — Bringing MVP Focus
With a clearer sense of what AI could realistically do, I narrowed the concept to a functional MVP focused on Impact Framing and Pushback Pivot — the two most interrelated features.
This phase centered on two main goals: refining AI-generated answers (improving persona questions to better reflect each stakeholder’s role) and developing the tool’s visual branding and UI. I had fun implementing hover states and micro-animations — and was impressed by how easily these could be realized in Lovable.
This was the most rewarding part of the process, as I could finally experiment with visuals and UI. While editing, however, I also noticed some of Lovable’s limitations — particularly when applying a design system and understanding its structural dependencies.
AI feedback is labeled by source for transparency.
The Two Core Features
Impact Framing — AI analyzes design text and translates UX jargon into clear, business-oriented language, highlighting measurable outcomes so designers can communicate value beyond usability or visuals.
“Impact Framing” state 28.10.25
Pushback Pivot — simulates tough stakeholder questions based on audience type — from executives to PMs. AI prompts designers to practice confident, structured responses and strengthen their reasoning.
“Pushback Pivot” state 28.10.25
Testing & Iteration — Learning from Friction
Once the second iteration was functional, I ran think-aloud sessions with four designers. The concept resonated — most found the AI personas and framing logic accurate — but testing also revealed small friction points that broke the flow.
Some users overlooked the “Generate Example” button, missed the connection between exercises, or weren’t sure when to use the tool in their workflow. These findings informed future improvements aimed at increasing clarity and continuity — improving visibility, showing relationships between features, making content easier to compare side by side, and continuously refining the AI’s answer logic.
Shimmer effect animation to increase its visibility
Reflection & Outcome — What I Learned
This project was my first vibe-coding experiment — an exploration of how creative intuition and AI logic can work together. Using tools like Lovable and NotebookLM was eye-opening: they didn’t just accelerate my process, they revealed new ways of thinking. AI became less of an assistant and more of a collaborative partner, helping me recognize patterns and articulate impact with greater clarity. When guided well, AI doesn’t replace human judgment — it enhances it.
Due to time constraints, I explored only limited UI directions, something I’d expand in future iterations. Early design output can emerge directly from prompting, yet it also demands a more critical layer of testing and iteration. As designers, we must challenge what AI produces — visually, structurally, and conceptually — shaping and reshaping our own thinking in response.
What began as a study of AI presentation tools evolved into a reflection on how designers communicate value. Translating design intent into measurable outcomes required empathy toward users, stakeholders, and the AI itself. Through this process, I also realized how vast and multifaceted UX truly is — with many variations depending on work type, environment, and industry. This diversity is something I’m eager to explore further, to better tailor future iterations of this tool to different contexts and workflows. Looking ahead, I want to refine the prototype visually, enable session saving, and guide AI more precisely — making its reasoning clearer, more transparent, and trustworthy. Above all, I hope this work helps designers trust the process and speak about impact with confidence.
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