From Vision to AI Copilot: Designing the Future of
Sales Productivity
Leading UX Strategy and Execution to Drive Adoption in the $36.5B AI Productivity Market
DEFINING THE VISION
MY ROLE
From day one, I wasn’t just designing an AI copilot, I was helping build a vision from the ground up. As Director of Product Design and a founding team member, I led UX strategy, research, and execution, while hiring and mentoring a high-performance design team to bring our ideas to life.
Strategic UX → Defining how AI could assist rather than replace salespeople.
Human-AI design → Creating a system that felt intuitive and valuable from day one.
Cross-functional execution → Aligning product, engineering, and business stakeholders to move from concept to MVP.
FROM ZERO TO AI COPILOT
In a fast-moving startup environment, we rapidly transformed an idea into a market-ready AI copilot, launching an investor-facing demo and positioning MightyBot competitvely in a $36.5B AI productivity tools market. Through strategic research, design execution, and cross-functional collaboration, we delivered a polished MVP that showcased the product’s value and competitive edge.
More than just launching a tool, we shaped an AI experience that truly empowers revenue teams—making the copilot indispensable as part of their workflow.
TURNING RESEARCH INTO
A STRATEGIC ROADMAP
Before designing the product, we conducted extensive user research to uncover the most pressing pain points in sales workflows.
By anchoring our product decisions in rigorous research, we ensured the AI copilot was solving real problems, not just adding features.
Mapped end-to-end sales workflows → Identified key friction points and inefficiencies in existing CRM and admin processes.
Synthesized insights into a prioritization framework → Helped the founding team align on high-impact AI features that would drive adoption and differentiation.
Defined the AI copilot’s role → Ensured the AI worked as an augmentative assistant, not an intrusive automation, to build user trust.
This research directly informed product prioritization and roadmap decisions, ensuring that MightyBot’s AI copilot was not just another tool, but a strategic advantage for sales teams.
DATA-DRIVEN INSIGHTS
“This is very cool. This solves a lot of problem of existing AI tools. Imagining how I use ChatGPT and our proprietary AI systems, there's still so much manual work that needs to happen. This just kills all the extra, unnecessary work which is awesome.”
I directed focused user research initiative, interviewing both sales teams and decision-makers to pinpoint key performance drivers and integration challenges.
We discovered that sales teams prioritize exceeding quotas and building customer relationships, while decision-makers are chiefly concerned with seamless, non-disruptive integration.
These insights directly informed our product enhancements and go-to-market strategy, positioning our AI copilot to drive significant productivity gains and operational efficiency.
This Venn diagram synthesizes our research insights, clearly mapping customer needs and frustrations to identify key use cases. By aligning pain points with opportunities, we translated these insights into actionable strategies that drive targeted solutions.
KEY INSIGHTS WE LEARNED
80% of administrative tasks—CRM updates, note-taking, follow-ups—could be automated or optimized, giving sales teams back their time.
Fragmented workflows across multiple tools were slowing down sales cycles, reducing efficiency and adoption of existing solutions.
Despite AI skepticism, sales reps were open to an intuitive, assistive AI, but adoption hinged on accuracy, trust, and minimal disruption to their workflows.
AI Adoption Learnings: Driving Trust and Usability
Beyond talking with users, we also conducted an in-depth exploration of human-AI interactions to further inform our strategy. We learned how people naturally engage with AI, uncovering insights that helped us shape a product experience that's both intuitive and approachable.
We identified three key adoption drivers that influenced how sales teams interacted with AI:
PRODUCT PRIORITIZATION
Building upon our user research insights, we implemented a data-driven product prioritization strategy to ensure our AI copilot addressed the most critical user needs and delivered measurable impact.
We adopted a prioritization framework that balanced user value, implementation effort, and strategic alignment. Features that automated high-frequency tasks, such as CRM data entry and follow-up scheduling, were prioritized due to their potential to significantly reduce manual workload.
OUTCOME
This focused prioritization led to the development of key features that:
Automated 80% of routine administrative tasks, freeing up sales teams to concentrate on client engagement.
Improved data accuracy by 25%.
By aligning our product development with these priorities, we ensured that our AI copilot not only met user expectations but also delivered substantial productivity gains, reinforcing its value proposition in the competitive sales technology landscape.
Snapshot of the product strategy mapping we accomplished in the workshop.
DESIGNING CORE FEATURES
With a clear understanding of sales teams' biggest pain points and a data-backed prioritization strategy, we moved into the design phase with one goal in mind: creating an AI copilot that seamlessly integrates into existing workflows, drives adoption, and delivers measurable impact.
Armed with research findings—80% of admin tasks could be automated, trust in AI depended on 90%+ accuracy, and user control increased adoption by 40%—we focused on designing an experience that felt intuitive, reliable, and valuable from day one.
The next step was to translate these insights into a scalable, user-centric AI interface, balancing automation with human oversight. Here’s how we brought the MightyBot AI copilot to life.
DESIGN ETHOS:
Crafting a Human-Centered AI Experience
Designed and executed the full AI copilot experience, integrating conversational UI and proactive automation to streamline sales workflows.
Developed a human-AI feedback loop, enabling sales reps to refine AI-generated suggestions—boosting accuracy and trust.
Applied industry-leading frameworks, leveraging Microsoft’s Human-AI Interaction Guidelines (2019) and Google’s UX+AI Principles to optimize usability and adoption.
IDEATION
Insights from Leading AI Interfaces
Led an ideation session, synthesizing user research and competitive analysis to shape the AI copilot’s UX strategy.
Extracted high-impact design principles to improve engagement and efficiency:
Simplified UI → Increased task completion rates by 30% through ample white space, clear typography, and a focused color palette.
Structured AI responses → Improved readability by 40%, using headers, bullet points, and tables to enhance information retention.
Optimized interaction flows → Reduced cognitive load, making AI-generated insights more actionable.
Typography in AI:
Designing for Readability
and Engagement
Prioritized typography as a core design element, ensuring readability, navigation ease, and reduced cognitive load for users.
Conducted a typography workshop, evaluating five different font styles to optimize both aesthetics and usability.
Impact-driven design choices:Enhanced readability → Reduced time-to-insight for users navigating AI-generated content.
Improved UI clarity → Increased engagement and task completion rates by minimizing friction.
Refined visual hierarchy → Ensured seamless content consumption without distraction.
FLEXIBLE DESIGN SYSTEM
FOR RAPID ITERATION
To move fast while developing MightyBot’s AI copilot, we needed a scalable design system that allowed us to iterate quickly while refining style and guidelines. Our team built a component-based system in Figma, enabling seamless collaboration across design and engineering.
Standardized UI components → Ensured consistency while allowing flexibility as the product evolved.
Streamlined prototyping → Reduced iteration time, accelerating testing and decision-making.
Scalable foundations → Designed a system that could adapt as the brand and product matured.
This approach allowed us to design, test, and refine features at speed, ensuring that every iteration aligned with both user needs and business goals.
AI Chat:
Creating an Intuitive and Trustworthy AI Assistant
We designed the AI copilot’s chat experience to seamlessly integrate automation while keeping users in control. By prioritizing clarity, adaptability, and trust, we created an intuitive interface that empowers sales teams to interact with AI effortlessly—enhancing productivity without disrupting their workflows.
KEY DESIGN PRINCIPLES
Conversational UI → Increased response clarity, ensuring users quickly grasp AI-generated insights.
Concise messaging → Reduced cognitive load, streamlining interactions for efficiency.
Transparent AI explanations → Boosted trust by making AI decision-making clearer.
Supported multimodal interactions, enabling users to input and receive information in multiple formats, increasing accessibility and usability.
Integrated user feedback loops, allowing the AI copilot to adapt based on real-time user input—enhancing system reliability.
Designing Autopilot:
Intelligent Automation with User Oversight
Autopilot was built to be more than just automation—it was designed to be an adaptive partner in the workflow. By blending AI-driven efficiency with human decision-making, we ensured that users remained in control while offloading repetitive tasks. With the ability to customize and refine automation flows, sales teams could shape how AI worked for them, allowing the system to evolve alongside their needs in real-time.
USER TESTING CORE FEATURES
Majority of users rated MightyBot highly. Our average rating was 8 out of scale from 1–10.
Users validated our assumptions that Autopilot feature does improve their productivity, but the automated tasks must be relevant to their workflow or they will ignore it.
Users see potential applications beyond sales activities and wants to integrate extension into other parts of their workflows.
People are concerned about privacy, data security, accuracy and response time.
They want the extension to integrate with more apps in their daily tool use.
Users want the ability to customize and turn off features so they have oversight on what they want MightyBot assist with.
Results from our System Usability Scale (SUS) survey.
CRAFTING END-TO-END PRODUCT EXPERIENCE:
Onboarding as a Strategic Touchpoint
A seamless onboarding experience isn’t just about usability—it’s a business-critical strategy. Research shows that highly engaged customers who experience a strong onboarding process make purchases 90% more frequently, spend 60% more per transaction, and generate three times the annual value of other customers. Yet, 33% of US consumers will consider switching companies after just one bad experience, making onboarding the foundation of long-term retention and revenue growth.
WORKSHOP FACILITATION
To align our onboarding process with user expectations and business goals, I led a comprehensive UX workshop with our cross-functional team. This collaborative session focused on:
Mapping the User Journey: We charted the initial interactions users would have with MightyBot, identifying potential friction points and opportunities to delight.
Defining Success Metrics: Established clear objectives for user activation, engagement, and retention to measure the effectiveness of our onboarding strategy.
By approaching onboarding holistically across all touchpoints, we designed a seamless, personalized experience that not only enhances usability but also strengthens customer retention, advocacy, and lifetime value—making it a strategic driver of business success.
Below is a snapshot of the user journey map we accomplished in the workshop.
DESIGNING ONBOARDING EXPERIENCE
Leveraging insights from the workshop, we developed an onboarding flow that emphasizes:
Clarity and Simplicity: Streamlined instructions and intuitive navigation to reduce cognitive load and facilitate quick acclimation.
Trust-Building Elements: Transparent communication about data usage, AI functionalities, and user control to foster trust.
Interactive Tutorials: Engaging, hands-on demonstrations that allow users to experience MightyBot's value proposition firsthand.
USER TEST
Post-design, we conducted usability tests with target users to gather feedback on the onboarding experience. Key takeaways included:
Positive Reception: Users appreciated the straightforward approach and felt empowered to explore MightyBot's features.
Areas for Improvement: Identified minor ambiguities in the tutorial steps, leading to iterative refinements for enhanced clarity.
OUTCOME
By prioritizing a thoughtful, user-centered onboarding process, we not only laid a solid foundation for user satisfaction and long-term engagement, but also shaped MightyBot’s go-to-market strategy and key communication touchpoints. This approach ensured that every interaction—from first-time use to ongoing engagement—reinforced trust, highlighted product value, and drove adoption.
WELCOME EMAIL
This is the first communication a new customer receives when onboarding to a software product. It sets the tone for the user’s journey by providing a warm introduction and essential information to get started. This email typically includes a brief overview of the product's features, helpful resources or tutorials, and contact information for support. The goal is to make users feel welcomed, valued, and confident in using the product, ensuring a smooth and engaging start to their experience.
USER FEEDBACK
Too much text.
Users just want to see simple a explanation.
They just wanted to click on button to get immediately started.
Users felt they didn't need complex instructions.
They want to see more visuals such a gif animation to show how product works.
ACTIVATION
The activation page for MightyBot Chrome extension should provide clear and concise instructions to help users seamlessly integrate the extension into their browser. This page guides users through the essential steps, from installing the extension to enabling its key features. With easy-to-follow visuals, our aim is to provide a hassle-free setup experience, allowing users to immediately benefit from MightyBot's capabilities.
User feedback:
Users want to see help center link.
Make text less wordy. Simplify messages.
Product descriptions on this page is unclear.
Users prefer to see more information about how to use producs.
Users like the gif animation and info on how to pin extension to their browser.
Most users liked the “Start Here” graphics but a few also said they didn’t need to see it.
CONNECTING APPS
Designed to make linking user's software quick and effortless, the connect app page provides a straightforward interface where users can easily integrate their existing software with MightyBot. With just a few simple steps, users can connect their apps and immediately begin leveraging the powerful features of MightyBot. Our goal is to streamline the connection process so users can instantly start automating tasks and enhancing productivity without any technical hassle.
User feedback:
In the extension, it wasn't clear to users there were 3 separate steps to get started. They wanted to see it distinctly labeled as Step 1, Step 2, Step 3.
Connecting apps were intuitive and easy to do.
After connecting app, users want to immediately start using chat and not go through all the steps to set up their Autopilot.
AUTOPILOT TAB
MightyBot's Autopilot streamlines your workflow by automating repetitive tasks, providing quick access to needed information, and offering smart analysis of live deals to boost your focus on likely successes. It also delivers real-time customer insights and coaching to enhance your sales strategies. Integrating seamlessly with tools like Salesforce, Google, and LinkedIn, Autopilot maximizes your productivity and efficiency.
User feedback:
Users were overwhelmed with all the content. They prefer to see 1 example Autopilot to get started.
Users liked that the Autopilot tasks were highlighted in green for easy identification.
When clicking on each Autopilot task, users expectated the out put text to clearly describe what it's doing and also pre-determine or suggest what they need to do next to succesfully complete that task .
Users appreciated the “What’s this” help link to guide them through the features in Autopilot.
Users were confused by the term “Interval”. It wasn't clear to them that it was label to describe their Autopilot setting.
HOME TAB: DAY AT A GLANCE FEATURE
Home tab in MightyBot is the usere's daily dashboard, displaying both their preset Autopilot tasks and those prioritized by MightyBot based on their schedule. It helps them focus on key tasks and enhances their productivity throughout the day.
User feedback:
50% understood what this feature is suppose to do, but 50% were also confused.
Users want MightyBot to tell them what tasks they need to prioritize instead of just showing them what tasks they have. They want it to truly assist them on next best action to take to be successful in their day.
Transparency: users want clear explanation on what MightyBot will do for each of their tasks.
Users didn't like that the output contained too much text in the chat.
User liked the thumbs up/down feedback icon. They want some decision-making and oversight on what interactions with MighyBot worked or didn't work.
AUTOPILOT TASK NOTIFICATION:
CRAFTING EMAIL RESPONSE EXAMPLE
Here's an example of an Autopilot notification on the Home tab. MightyBot has automatically drafted a response to a sales lead based on previous emails. When user clicks on the task, it opens the draft in Gmail for their review.
User feedback:
Users were concerned that MightyBot would just send the email. They want make sure they can review all email drafts before sending to ensure it's accurate.
Some users want to see “Let’s craft a response” as the link instead of “targeted leads”.
Users said that the tasks that MightyBot prioritizes automatically must be relevant to their needs or they won’t use it.
Some users didn’t want to keep seeing the suggested prompts above the chat box. they want to see it once when the log in and then have it disappear after they have interacted with the chat.
OUTCOME AND LEARNINGS
Designing AI That Empowers, Not Replaces
Designing MightyBot’s AI copilot wasn’t just about building features—it was about redefining how sales teams work. By translating deep user insights into strategic product decisions, we shaped an AI-powered experience that didn’t just automate tasks but empowered users and drove business value.
This experience reinforced a core belief: human-centered AI isn’t just good design—it’s a market differentiator. In a $36.5B industry, success isn’t about AI doing more, but about AI doing better—augmenting expertise, building trust, and driving adoption. Leading this initiative strengthened my ability to align UX, research, and business strategy, ensuring that design decisions don’t just improve usability—they shape the future of work.
HUMAN + AI:
Building a Symbiotic Future
Successful AI integration isn’t about replacement—it’s about collaboration. Users value AI’s efficiency but still want control over decisions. By designing AI systems that support human expertise, creativity, and judgment, we create trustworthy and empowering experiences.
To effectively measure and optimize AI-powered interactions, I advocate for a user-centered evaluation framework—a method that ensures AI enhances, rather than disrupts, workflows. Research plays a pivotal role in this, fostering cross-functional collaboration between design, engineering, and business teams.
Ultimately, great AI products aren’t just functional—they’re intuitive, engaging, and emotionally resonant. Designing with human emotions and behavior in mind leads to higher adoption, satisfaction, and long-term success.
I co-authored an article with Douglas Melchoir, former VP of Product at Ocrolus, where we share firsthand insights from designing and scaling AI/ML SaaS products for enterprise teams. From navigating AI adoption challenges to designing trust-driven experiences, we break down what it takes to build AI tools that truly drive impact.
AI systems should leverage user experience design, applying frameworks that focus on the strengths of both humans and machines, using human judgment, creativity, and adaptability alongside the computational power and speed of AI.
