Agentic AI workflow | B2B SaaS

Feature | 0 to 1

Challenge ๐ŸŽฏ

HR, IT, and finance teams spend most of their day resolving repetitive tickets that don't need their expertise.

Goal ๐Ÿ“”

Design a system where HR can define their process in plain language, and an agent executes it โ€” freeing up HR admins' time for the complex work that actually needs them.

Results ๐Ÿ’น

70% of action tickets auto-resolved.

Let's dive in

Overview โญ•๏ธ

Leena AI

Leena AI is a pioneer in Agentic AI Colleagues for enterprises - voice-enabled, personified AI coworkers prebuilt for IT, HR, Finance, and Procurement. They automate complex workflows across existing systems like Workday, ServiceNow, and SAP.

Agentic AI workflows

Designed both halves of an Agentic HR platform: a no-code builder that lets HR admins describe a process in plain language, watch it decompose into a structured workflow, test it against real data, and launch it live โ€” no engineering required ( Part 1 )
and an assistant that executes multi-system requests for employees with full transparency and reversibility. ( Part 2 )

Timeline

12 weeks ( with design system )

NDA

In compliance with NDA agreements, I have shared only the brief process.

My role

ยท I led the design end-to-end
ยท Research
ยท Interface design

Team

ยท Collaborated with PM, understanding requirements and brainstorming ideas
ยท Worked with engineers checking feasibility
ยท Ran QA review pass before release

Understanding the "Problem"โ‰๏ธ ( part 1 )

The problem

  • The back office work is manual and repetitive

  • HR. IT, finance spend most of their day on work that doesn't need their expertise

  • Eg. HR is spending their day answering "how many leaves do I have left", "can you send me my payslip"

The problem depth 1

  • A single employee touches multiple systems

  • One request from one employee requires HR to go into Workday then Service Now, then email, then SAP and then back to email

  • A human being is manually carrying information between systems that don't talk to each other

The problem depth 2

  • Tickets pile up and never fully resolve

  • 70% of tickets are resolved by humans doing work that didn't need a human

The problem depth 3

  • Existing chatbot only answer questions

  • They don't do anything

  • A chatbot could tell about a policy, holiday list etc.

  • Chatbot couldn't apply the leave and update the workday

The problem depth 4

  • Building automation requires engineering, which takes too long

  • By the time it was built something had already changed

About users ๐Ÿ‘จโ€๐Ÿ‘ฉโ€๐Ÿ‘งโ€๐Ÿ‘ฆ

Research ๐Ÿ”

User interview insights

Define ๐Ÿ”

Point of view

Define ๐Ÿ”

The idea that hit both users most directly

Solution ๐Ÿ”

Approach/direction 1

Approach/direction 2

Approach/direction 3

Iterated on approach 3

IA ๐Ÿ”

User flow

User journey visualisation

User flow 1

User flow 2

Thinking ๐Ÿ”

"UI" Flow ๐Ÿ”

First time HR admin discovers AI Prebuilt processes

Business "impact" ๐Ÿ’ฐ

-70%

Reduction in manual HR agent tickets for action-based tasks

10 mins

From plain language description to a live running process

0 code

Required to configure and launch a process

My "Learnings"

    1. Designing for AI requires communication at every step to build trust

      When AI executes consequential actions, transparency isn't optional โ€” every step needs to be visible to the employee so they can trust what's happening and why.

    1. Every interaction matters โ€” even the tiniest ones

    In an AI interface, interactions carry the experience. What happens between steps is just as important as the steps themselves.

    1. Every design decision is high stakes โ€” I traced each one back to the employee

    This assistant touches payroll, tax, and insurance for thousands of employees. Every decision carried real consequences, so I kept coming back to one question: what does the employee actually need to feel confident here?

ยฉ Richa Srivastava 2026