AIOPS.D

AIOPS.D is a first-of-its-kind business and subscription offering designed to help clients quickly and easily implement AI-fueled autonomous business processes across industries and functions. Organizations are increasingly turning to microservices to quickly build, test and deploy cloud-native applications

With user-centered approach, the goals was to create an intuitive interface for effortless management while incorporating gamification.

Overview

Role

Product Stratergy

3 Months

Tablet

Web Application

Product Manager

Product Owner

Figma

Miro

Product and UI /UX

Platform

Teams

Duration

Tools

Problem Statement

Large organizations often struggle with disconnected systems, repetitive manual tasks, and slow decision-making due to scattered data across multiple platforms. Business teams spend too much time fixing errors, managing exceptions, and coordinating across ERPs, which leads to inefficiencies and higher risks. Without smart automation, critical operations become time-consuming and error-prone. There was a clear need for a unified, intelligent platform to streamline processes, reduce manual effort, and drive smarter, faster business operations.

Research & Discovery

We conducted in-depth interviews with key stakeholders and clients to understand their pain points, workflow challenges, and expectations from the AIOPS.D™ platform. These sessions included product owners, business analysts, operations leads, and end users from various functions. To validate our findings at scale, we also shared a detailed survey with all clients to gather broader feedback on usability, feature gaps, and overall satisfaction. The combined insights from interviews and survey responses helped us prioritize key problem areas and shape a user-centered redesign strategy focused on automation, clarity, and operational efficiency.

Ideation

We held design thinking workshops with cross-functional teams (product managers, developers, AI specialists, and end-users) to brainstorm solutions. Key ideas included

A centralized dashboard to provide a holistic view of all ERP functions.
Guided workflows powered by AI to suggest next steps and automate repetitive tasks.
Contextual recommendations embedded within workflows to reduce decision fatigue.
Role-based interfaces to cater to different user needs ( procurement vs. finance).

we ideated an AI bot that simplifies and automates key tasks. Using AIOPS.D’s AI/ML capabilities, the bot proactively suggests creating a purchase order by analyzing inventory shortages and budget constraints, recommending top suppliers based on cost, delivery speed, and past performance (e.g., “Order 1,000 backpacks from Supplier A for $10/unit, 5-day delivery”). It automates approvals for orders under predefined thresholds, flags pricing errors in real-time, and pulls data from ERPs like SAP or Oracle via natural language queries (e.g., “Show backpack orders in Q3”). The bot also provides vendor performance insights, such as delay risks, and guides supplier onboarding with compliance templates. Delivered via chat, voice (iOS/Android), or email alerts, it ensures simplicity, cuts booking time by up to 40%, and maintains a single source of truth, enabling efficient, autonomous procurement.

User Flows & Wireframes

Mapping user flows for key tasks

Procurement: Supplier onboarding, purchase order creation, approval workflows.

Billing: Invoice generation, payment tracking, error detection.

Finance: Financial reporting, budget forecasting, compliance checks.

Vendor Management: Vendor performance tracking, contract renewals.

Technical Constraints

The platform needed to integrate with diverse ERP systems (SAP, Oracle, Microsoft Dynamics) without duplicating data.

AI/ML models required real-time data processing for recommendations without latency.
Scalability to support organizations of varying sizes and industries.

Interview Insights

Manual Overrides Common - Users don’t trust the bot for high-value decisions.

No Decision Transparency - Bot actions lack rationale

Fragmented Workflow - Users switch between AIOPS.D™ and ERP for full process tracking.
Lack of Notifications - Missed approvals due to no smart alerts or nudges.

Rigid Approval Flows - Same workflow used for all categories/amounts inefficient.

Overview

AIOPS.D™ is an AI-fueled platform with a growing portfolio of business processes configurable to the needs of any organization across a wide range of industries and ERP functions. AIOPS.D automates a wide range of business functions including procurement, billing, finance, and vendor management.

Streamline day-to-day operations, reduce risk, reimagine work, and unlock the value of autonomous business operations with AIOPS.D microsolutions.

Self-learning and self-healing AI/ML capabilities that help capture human inputs.
Guided workflow and real-time recommendations.
Single source of truth remains with the core/source ERP(s) with no duplication via multiple data lakes.
Efficiencies are transferred to manage the business by exception. Achieve new heights of operational performance.
Simple. Perform critical business functions using a single platform that reaches disparate ERP systems, driving simplicity in business operations and related productivity through connectivity and completeness.
Efficient. Orchestration of end-to-end business processes creates further efficiency by using trained AI/ML models to perform comprehensive tasks.
Autonomous. Streamlining of day-to-day operations enables reduced risk and reimaged work to unlock the value of autonomous business operations.

Creating Style Guide

Post approval started with Style guide and defining the components

Hi-fidelity Designs

Speak to our Users and gather Insights

Organized a session with our users to better understand how this existing process is assisting them and what it needs, all while studying the real-world pain spots that slow them down in their daily Tasks. This session was conducted with five managers and Technical team leads from different locations around the world.

Re-prioritising features and plan the roadmap

From the workshop with users, I gathered a lot of new insights that were not identified before. A list of critical needs was identified. The experience outcomes were updated with the latest needs.

what did I do next ?