Yekta Bozorgian

Introduction

MidShift set out to transform career growth by integrating mentorship, AI-driven roadmaps, and an LMS

In October 2023, MidShift was set out to accelerate career growth through mentorship and complementary services. However, user research uncovered a significant market gap, leading to the AI-generated roadmap becoming the platform’s core service.

Understanding the Problem

Users Craved Clear Guidance: Why Mentorship Alone Misses the Mark

Career development platforms often left users feeling lost and confused. Mentees found it hard to choose the right mentors, often unsure about how to connect or what to expect. Mentors, on the other hand, struggled to offer tailored advice without spending a lot of time getting to know each mentee’s specific situation. This whole process feels disorganised and ineffective for both sides, which raised this question for us:

Research and Identifying the Need

Interviews and analysis exposed gaps in mentorship, guiding the creation of a user-focused career roadmap.

Our stakeholder interviews revealed key challenges for mid-level professionals seeking efficient paths to senior roles: finding aligned mentors, fitting skill development into busy schedules, and needing flexible career planning. Existing solutions—from traditional education to self-learning platforms—often lack personalisation, sustained feedback, and structured support. These gaps reinforced our vision for an AI-driven mentorship platform that tailors guidance, addresses evolving skill needs, and accelerates professional growth.

Analysing Market & Competitions

Analysis exposed inefficient tools in current platforms, reinforcing the need for a streamlined solution.

Once the direction was set, I conducted a competitive analysis to understand market solutions and their functionality. It revealed that existing mentorship platforms were resource-heavy and lacked efficient mentor-mentee connection tools, highlighting the need for a streamlined solution.

We hypothesised that a career roadmap could address mentees’ uncertainty, reduce mentors’ workload, and create a unique value proposition for MidShift.


Validating Our Assumptions

I interviewed both mentees those seeking career growth and mentors from existing platforms. critical insights emerged:

• Mentees’ Challenges: Mentorship was time-consuming and expensive. They struggled to find the right mentor and spent significant effort explaining their goals, skills, and potential.
• Mentors’ Challenges: Effective mentoring required time-intensive exploration of a mentee’s personality, aspirations, and abilities beyond their resume, making it challenging to provide impactful guidance.

Identifying pain points of persona

Low clarity and support derail progress

Although new mentees often begin with optimism, many quickly lose direction without clear milestones or structured feedback. A lack of tangible progress can make mentees question whether they’re moving closer to—or further from—their career aspirations.

From Insights to Actionable AI Solutions

A key part of this process was aligning business goals with user needs.

We analysed user needs to uncover opportunities, emphasising AI-generated roadmaps and mentorship. By collaborating with stakeholders, ML engineers, and the CTO, we addressed resource constraints and startup challenges. Together, we defined technical requirements, ideated designs, and developed a user-centric MVP that balanced innovation with practical feasibility.

Understanding User Needs and Opportunities for Growth

Once we identified user needs and areas for improvement, we analysed key opportunities to refine the roadmap. We outlined the core services—AI-generated roadmaps and mentorship—along with their features and applications, collaborating closely with stakeholders and developers in several meetings.

Evaluating and Implementing Technical Solutions

To better understand the AI’s capabilities in creating personalised career roadmap, we collaborated with ML engineers and the CTO. We faced challenges such as:

To better understand the AI’s capabilities in creating personalised career roadmap, we collaborated with ML engineers and the CTO. We faced challenges such as:

• Time constraints
• Limited resources
• The complexities of building the AI-generated roadmap neural network.

We developed a comprehensive diagram to ensure feasibility, detailing required inputs, expected outputs, and the overall AI roadmap generation functionality.

Design Ideation and Bringing Ideas to Reality

We brainstormed the roadmap’s visual structure and collaborated with stakeholders, the CTO, and the ML engineer to align technical feasibility with implementation constraints. After evaluating and voting on ideas, we finalized the concept.

Top-voted Ideas Shaping an Effective User Flow and MVP

Using the top-voted ideas, we developed a user flow focusing on enhancing the user experience. The final step was designing the MVP, ensuring it met both user expectations and technical feasibility.

Designing the AI Generated Roadmap MVP

A Minimal Roadmap to Validate the Concept

To bring Midshift’s roadmap generator to life, we prioritised rapid execution to validate the concept and gauge market interest. With limited resources and a dataset of 9,000 resumes, we focused on building a functional prototype that could quickly test user value while balancing constraints and demonstrating feasibility to potential investors.

Objective: Test the hypothesis and assess market interest under tight constraints.

Approach:

• Built a functional prototype to provide a clear, structured career roadmap.
• Analysed user-selected fields, suggested target roles, and generated basic roadmaps based on technical skills.

Trade-offs:

• Limited usability testing and iteration due to resource constraints.
• Delivered a simplified solution aimed at validating the broader vision.

Outcome: Early feedback helped refine our direction while laying the foundation for future iterations.


Promising Launch,Uncovering Gaps

Early Analytics Revealed What Worked and What Didn’t

The marketing team adopted an MVP approach to attract users and gather valuable insights for the AI Career Roadmap Generator. Below is the Google Analytics data we captured right after launching our MVP for the AI Career Roadmap Generator.

Engagement Overview Insights:

• Strong Initial Interest: Users averaged ~2 minutes 33 seconds, showing immediate engagement.
• Engagement Dip: A quick spike followed by a drop indicates some friction or unmet expectations.

Retention Overview Insights:

• Retention Challenge: Only 999 of 3,200 new users returned, revealing significant drop-off.
• Feature Feedback: This gap signals that while some features resonated, others need refinement, prompting further user research.

While the initial engagement metrics were encouraging, it became clear there were several areas needing improvement—some features worked well, others didn’t resonate as we hoped.

To uncover these nuances, we combined quantitative analytics with direct feedback from user interviews and surveys, prompting us to dive into the next iteration.

Gathering Feedback

A static roadmap with limited data fails to retain users

The MVP prototype was launched to achieve two goals: measure user engagement and generate interest from stakeholders for investment. While this approach enabled us to move quickly, we anticipated gaps in usability and personalisation, viewing feedback as a key opportunity to refine the product direction.

Key Learnings:

• Users downloaded their roadmaps but rarely returned, as the static design didn’t adapt to individual needs.
• The AI required more robust data and algorithms to meet user expectations.

These findings underscored the challenges of prioritising speed over depth in validation. While our focus on rapid execution allowed us to gain insights and secure initial interest, it also highlighted the importance of early usability testing and iterative refinement.


“>The Speed Trade-Off: Lessons from Launching an MVP

The decision to launch a minimally viable product rather than an iterative prototype was both a strategic risk and a valuable learning experience. While it allowed us to validate the core idea and attract stakeholder interest, it also highlighted the trade-offs of prioritising speed over deeper user validation.

Shaping the Next Version

Redefining the Roadmap: A More Personalised Experience

The first version validated the concept but lacked flexibility and personalisation. User feedback highlighted key gaps, driving us to rethink how career planning should work. To address these challenges, we expanded our team and redesigned the roadmap to be interactive, intelligent, and adaptive to individual needs.

Key Insights That Drove Change:

• Interactivity: Users needed features to track progress, edit tasks, and adapt goals.
• Mentorship: Better integration was required to match users with mentors at the right stages.
• Resource Access: Users sought curated tools, courses, and content for skill development.