Hexaware Positioned as a Visionary in the 2026 Gartner® Magic Quadrant™ for Custom Software Development Services
Hexaware Positioned as a Visionary in the 2026 Gartner® Magic Quadrant™ for Custom Software Development Services
Gartner, Magic Quadrant for Custom Software Development Services, By Jaideep Thyagarajan, Ryan McKinney, Nathan Davie, 7 October 2026. Gartner and Magic Quadrant are trademarks of Gartner, Inc. and/or its affiliates. Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose. This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Hexaware.
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– Generative AI can analyze user feedback and market trends to suggest product roadmap milestones.
– AI-crafted user personas like Eager Emily and Corporate Carl save time versus traditional research.
– Generative AI can automate Agile user stories and acceptance criteria based on the product roadmap.
– ChatGPT generated a phased roadmap for a health center’s mobile app in Hexaware’s example.
In today’s technological landscape, innovation isn’t just an advantage; it’s a requirement. One of the most promising disruptors in this space is Generative AI. But have you ever considered using AI for crafting product roadmaps, creating user personas, or even generating Agile user stories? If not, this blog post is for you.
What the Experts Say
Traditional product roadmapping has often relied on the collation of extensive market research, user feedback, and a series of iterative discussions among the product team. While this approach is still valid, Generative AI can add a layer of data-driven suggestions based on a wider dataset than a human could easily manage.
How it Works
Through machine learning algorithms, AI can analyze user feedback, competitor landscapes, and current market trends. It can then generate roadmap suggestions, identifying possible short-term, mid-term, and long-term goals. Once these milestones are established, product managers can work on refining the generated roadmaps, giving them a head start in the planning phase.
Here is an example expanding on the new healthcare app vision that we started in the previous blog:
Prompt: “Help me create a product roadmap for this product. Please write in the English language.”
ChatGPT:
Certainly! A product roadmap is a strategic document that outlines the vision, direction, and progress of a product over time. For your health center’s mobile application, I’ll draft a high-level product roadmap based on the information provided. Let’s break this down into short-term, mid-term, and long-term goals:
Given the myriad of potential features, prioritization will be key. Here’s a recommendation:
Remember, a roadmap is a dynamic document. It should be revisited and revised as you gather more data, feedback, and as market conditions evolve.
AI can contribute to planning for scalability. If you remember our health center app example, it aimed to grow into mental health features over time. AI can identify similar existing solutions in the market and analyze their scaling timelines to provide a recommended scaling strategy. This is an area that we believe holds significant promise to provide input into user story prioritization, validation, and creation.
Typically, creating user personas involves user interviews, surveys, and market segmentation. However, Generative AI can quickly analyze extensive datasets containing user behaviors, preferences, and demographics to generate user personas. These personas can be used as starting points that are later refined through human interpretation and qualitative research.
For instance, if you aim to create a health app, AI can generate personas that will most likely use your app—like “Eager Emily,” a 24-year-old fitness newbie or “Corporate Carl,” a 35-year-old busy professional. These AI-crafted personas can save enormous amounts of time by offering a comprehensive overview of user types based on real-world data.
Here is my output from asking ChatGPT to help create 5 personas for our healthcare example:
Absolutely, personas are essential in UX design as they help in understanding the users’ needs, experiences, behaviors, and goals. Since your health center’s mobile application aims to offer a highly personalized and community-oriented experience, the following five personas can capture a broad yet nuanced view of your potential user base:
Key Features Relevant for Emily:
Key Features Relevant for Carl:
Key Features Relevant for Maya:
Key Features Relevant for Chris:
Key Features Relevant for Olivia:
While not perfect, I hope you would agree that this content gives product owners and designers a significant head start in defining key user types for this kind of application.
Agile and AI: A Perfect Pair
Agile relies on quick iterations and rapid adaptation to change. AI can complement this approach by automating the generation of Agile user stories and acceptance criteria based on the product roadmap and user personas.
Take a look at this output from asking ChatGPT to create user stories for creating a profile for our healthcare app:
User Story 1: Basic User Profile Creation
As a new user,
I want to be able to create a basic profile with my essential information,
So that I can have personalized experiences in the app.
Acceptance Criteria:
User Story 2: Profile Picture Upload
As a registered user,
I want to upload a profile picture,
So that I can make my profile more personalized.
Acceptance Criteria:
User Story 3: Health History Input
As a registered user,
I want to add details about my health history,
So that the app can offer me more tailored fitness and nutrition plans.
Acceptance Criteria:
User Story 4: Personal Preferences Setting
As a registered user,
I want to set my workout and content preferences,
So that I can receive recommendations that suit my tastes and needs.
Acceptance Criteria:
User Story 5: Profile Privacy Settings
As a registered user,
I want to control who can see my profile and activity,
So that I can maintain my privacy within the community.
Acceptance Criteria:
Each of these user stories can be further broken down into tasks to be completed by your development team. This approach keeps the development process agile, facilitating quick iterations and allowing the team to adapt to changes and feedback effectively.
Generative AI algorithms can review the planned features on the roadmap, consider the needs of each user persona, and then generate a set of user stories with associated acceptance criteria. For example, our AI could generate a user story for “Eager Emily” that would focus on enabling a beginner-friendly fitness journey, complete with acceptance criteria like a guided tour and a selection of beginner workouts.
While Generative AI’s role in product development is still an evolving field, the potential is enormous. Not only can it significantly accelerate the development process, but it can also introduce data-driven methodologies that can vastly improve the final product.
By incorporating AI in crafting product roadmaps, user personas, and Agile user stories, we’re not just keeping up with technological trends; we’re leveraging them to create better, more user-centric products. However, it’s important to remember that the human element—your intuition, empathy, and understanding of your user’s needs—cannot and should not be fully replaced by AI. Consider Generative AI as a tool in your toolbox, one that you can wield to augment your capabilities, rather than replace them.
So, are you ready to let AI guide your next product development cycle? With the rapid advancements in machine learning and AI technologies, this concept is fast becoming our reality.