DIGITAL TWIN
The Future of Productivity is here & It starts with ARYA
PROJECT BRIEF
This case study explores the application of an AI-powered Digital Twin designed to enhance the productivity, efficiency, and overall performance of Sales Managers (SMs) at Piramal Finance. The Digital Twin acts as a proactive, intelligent assistant that helps SMs manage their daily tasks, build stronger relationships, and achieve their sales targets more effectively.
MY ROLE
User Research
Storyboarding
Video Creation
TOOLS
RunwayML, Synq.io, ElevenLabs, ChatGPT, Sora & After Effects
TIMELINE
2 Months
GOAL
Our goal was to create a concept for a Smart AI Assistant – Arya – that proactively helps Sales Managers plan their day and automates low-value tasks like scheduling meetings, taking notes, and setting reminders, to help them meet their monthly targets and improve job satisfaction.
A 'VAGUE' PROBLEM STATEMENT
Create an AI concept for the Smart Assistant-ARYA which can improve Sales Manager's performance and help them in day-to-day tasks.
USER PAIN-POINTS
By synthesizing observations and feedback, we identified the key challenges faced by Sales Managers in their daily workflows:
Reactive vs. Proactive Planning: Days are driven by urgent, inbound requests instead of a clear strategy, making it hard to focus on high‑value activities.
Information Overload: Remembering every client’s and channel partner’s details (past business, complaints, personal notes) creates a heavy cognitive burden.
Administrative Burden: Manually logging meeting notes, scheduling follow‑ups, and tracking tasks takes up valuable selling time.
Communication Bottlenecks: Delays occur when SMs are unavailable (e.g., driving) to resolve urgent queries from internal teams, stalling critical processes like loan approvals.
REDEFINED PROBLEM STATEMENT
"How might we create an intelligent 'co-pilot' for our sales team that automates daily planning and administrative tasks, so they can focus on activities that drive revenue?"
GUIDING PRINCIPLES
To begin, I explored academic and industry literature on intelligent agents to understand how different types of AI assistants operate. This research revealed two primary models:
1.
Reactive Agents - Conventional Assistants
Reactive AI agents respond only to direct user inputs or immediate events, without memory or understanding of past interactions. They are easy to design and work well in simple, predictable scenarios, offering fast and reliable responses. However, they struggle to adapt to new or unexpected situations beyond their predefined rules.
AI understood
the command
AI understands
user's command
AI Agent waits for the input
(voice, text, image)
User Input Received
Provide the result based on memory data
AI could not understand
Request a clarification
Check Memory
If Found
If couldn't
find
2.
Proactive Agents - Context-Aware Assistants
Proactive AI agents are intelligent systems that go beyond just reacting—they anticipate future needs and take action based on predictions and user goals. Unlike reactive agents, they use memory and learning to make smarter decisions over time. However, proactive systems are complex to design, require large amounts of data and computational resources, and may perform poorly if the initial data quality is low.
No Action
Required
User input required?
Wait for user action
Constantly Analyse Environment
Predict Possible user need or task
Automatically take action
Update memory and context
Action
Required
Nudge user through notification
User takes action?
Update memory and context based on the user's input
3.
Hybrid Agents - Digital Twin
We adopted a hybrid AI agent model—blending reactive and proactive intelligence—to create a more effective assistant. This approach will allow the system to respond instantly to user commands while also anticipating needs and automating routine tasks. As a result, it is expected to reduce manual effort, improve task completion speed, and provide seamless, always-on support for Sales Managers—making the assistant feel like a true partner in their day-to-day work.
SOLUTION OUTLINE - THE DIGITAL TWIN
Based on these principles, the proposed solution is an AI-Powered Digital Twin—a hyper-personalized, voice-enabled assistant that functions as an extension of the Sales Manager.
The Digital Twin leverages AI to understand the SM's context, goals, and even their communication style, allowing it to act autonomously on their behalf. It integrates with various data sources (Channel Partner Database, customer database, location data, performance dashboards, communication apps) to provide a seamless and intelligent experience.
KEY FEATURES AND VALUE PROPOSITION
Proactive Daily Planning: The Twin analyzes performance data, targets, location, and task urgency to create an optimized daily schedule.
Intelligent Meeting Briefings: Before meetings, it provides a concise summary of the relationship history, business value, and any outstanding issues with the client or partner.
Automated Note-Taking & Task Management: The Twin listens to meeting debriefs, automatically logs notes, and creates and tracks follow-up tasks for itself or the SM.
Autonomous Query Resolution: It can communicate with internal teams in the SM's voice and style to resolve operational issues (like missing documents) without direct intervention, keeping processes moving.
Real-time Performance Coaching: The Twin provides on-demand updates on leaderboard rankings and offers data-driven suggestions on how to improve performance and achieve goals.
Seamless Multimodal Interaction: The SM can interact with the Twin via voice commands and receive updates through mobile notifications, creating a hands-free experience when needed.
