Finding the right lead is often the first challenge for any sales team.

Before a conversation begins, someone must identify potential organisations, assess relevance, find contact information, and prepare outreach details.

At Apni Pathshala, the Sales Team handled this process manually.

Every day, team members searched the internet, created different search queries, explored organisation websites, checked whether they were relevant prospects, collected contact details, and maintained lead records.

The challenge wasn’t just the time it took.

The bigger challenge was consistently finding fresh and unique leads.

As manual searching continued, a large percentage of discovered leads became repetitive. The Sales Team spent valuable time searching for organisations instead of focusing more on conversations, qualification, and outreach.

To solve this challenge, Apni Pathshala’s Software Team built an automated lead discovery workflow using Apna Bot and AnySearch.

The result was a shift from:

Sales searching for leads manually

to:

AI discovering potential leads and delivering them to Sales every day.

The Challenge Behind Manual Lead Discovery

Lead generation requires continuous research.

The Sales Team needed to discover organisations that could potentially collaborate with Apni Pathshala.

The manual workflow involved:

• Creating relevant search queries
• Searching Google and other platforms
• Reviewing search results
• Visiting organisation websites
• Understanding organisation activities
• Checking whether they matched the required criteria
• Finding contact details
• Recording lead information

The process worked, but it required repeated human effort.

One of the biggest challenges was lead freshness.

Approximately 50–70% of manually discovered leads were repetitive.

This meant that as the team continued searching, they increasingly found organisations they had already discovered instead of finding new opportunities.

The Sales Team faced three major problems:

• High manual research effort
• Difficulty finding fresh leads
• Limited scalability of the process

Lead generation itself requires a continuous process of identifying, attracting, and qualifying potential prospects. Modern sales workflows face similar challenges, focusing on improving lead quality and reducing manual research effort. HubSpot’s Lead Generation Guide

The Old Lead Research Process

Before automation, lead discovery depended heavily on individual team members.

The workflow looked like:

Think of a search query

↓

Search internet

↓

Review results

↓

Research organisation

↓

Find contact information

↓

Record lead details

↓

Repeat the process

The Sales Team could manually research approximately 10 leads in one hour.

Depending on workload, they could research around 20–30 leads per day.

However, creating a larger database became difficult.

A list of approximately 700–1,000 leads could require more than 70 hours of manual research effort.

The limitation was clear:

More leads required more manual searching time.

How Apna Bot Changed Lead Discovery

The Software Team created an automated workflow using Apna Bot and AnySearch.

The goal was not simply to make searching faster.

The goal was to remove repetitive searching from the Sales Team’s daily workflow.

The automation works through a simple process:

Apna Bot decides what to search

↓

AnySearch discovers information from the internet

↓

Relevant organisation data is collected

↓

Information is structured automatically

↓

Lead list is prepared

↓

Sales Team receives leads through email

The automation runs every night.

When the Sales Team starts the day, they already have a prepared list of potential organisations to review and contact.

The system collects information such as:

• Organisation name
• Website
• Contact person
• Email
• Phone details

AI-powered sales automation is increasingly used to reduce repetitive sales activities and help teams spend more time on relationship-building. Salesforce: AI in Sales

The Sales Team no longer needs to start every morning by searching for the next lead.

Before vs After Apna Bot

Before Apna Bot

• Manual internet research
• Manual search query creation
• Repeated organisation discovery
• Manual data collection
• Sales team spending time on research

After Apna Bot

• Automated nightly research
• AI-assisted discovery
• Structured lead information
• Daily email delivery
• Sales team focusing more on outreach

The biggest workflow change is simple:

Before: Sales searched for leads.

After: Sales received leads and worked on them.

Measurable Impact

The initial implementation created measurable operational improvements.

Daily Lead Generation

The system currently generates approximately:

50–100 leads per day

This creates a continuous pipeline of potential organisations for the Sales Team.

Reduction in Manual Searching

The Sales Team estimates that manual lead searching has been reduced by approximately:

70–80%

This means the team spends significantly less time on repetitive internet research.

Research Effort Saved

Based on the previous workflow, generating larger lead lists manually required significant effort.

At relevant lead volumes, the automation is estimated to avoid:

70+ hours of manual research effort per week

This is an estimated avoided effort based on the previous manual process and current lead volume.

The key impact is not just saving hours.

It lets the Sales Team spend more time on activities that require human interaction, such as evaluating opportunities and starting conversations.

McKinsey research also highlights how AI can transform sales operations by improving productivity and letting sales teams focus on higher-value activities. McKinsey: AI in Sales Transformation

The Human Work Was Not Removed

Automation did not replace the Sales Team.

Instead, it removed the repetitive discovery layer.

Earlier:

Search → Research → Validate → Collect → Outreach

Now:

Automated discovery → Structured leads → Sales validation → Outreach

The Sales Team still decides:

• Which leads are relevant
• Which organisations should be contacted
• How conversations should move forward

AI handles repetitive information gathering.

Humans handle decision-making.

Early Learning From the Experiment

This case study demonstrates an important principle:

The highest-value automation is not always the most complex technology.

It is the automation that removes a repeated operational bottleneck.

The Sales Team already knew how to research leads.

The problem was that research:

• Required continuous time
• Produced repetitive results
• Was difficult to scale
• Reduced time available for outreach

By moving lead discovery into an automated workflow, Apni Pathshala created a system where information gathering happens in the background while the team focuses on higher-value sales activities.

Current Status and Future Opportunity

Current status:

• System: Apna Bot + AnySearch
• Users: Sales Team
• Execution: Automated nightly workflow
• Delivery: Daily email
• Output: 50–100 leads/day
• Manual search reduction: Estimated 70–80%

The next opportunity is to measure business outcomes beyond research efficiency:

• Qualified lead rate
• Contact success rate
• Outreach response rate
• Meetings generated
• Conversion rate

The goal is to understand not only how much research time was saved, but also the business value the automated lead pipeline created.

The future of AI at work is increasingly focused on combining automation with human decision-making. Organisations are exploring how AI can support employees by reducing repetitive tasks and improving operational efficiency.

World Economic Forum: Artificial Intelligence and Future of Work

Continue Exploring Apni Pathshala’s AI Innovations

Apna Bot shows how AI can remove repetitive operational work and help teams focus on higher-value activities.

This approach is also being explored in education workflows.

Read our previous case study:  How Apni Pathshala Is Making Personalised Curriculum Creation Faster With AI

Explore how Apni Pathshala is using AI across different workflows to create better systems for teams and better learning experiences for students.

You can also explore more about Apni Pathshala’s learning ecosystem: 

Frequently Asked Questions

1. How is AI helping Apni Pathshala create personalised learning experiences? 

Ans. Apni Pathshala uses AI to create personalised curriculum plans based on each learner’s goals, needs, and learning pace, making learning more relevant and structured. 

2. How is artificial intelligence changing the way organisations work? 

Ans. AI helps organisations automate repetitive tasks, improve decision-making, and let teams focus on higher-value work. 

3. How is AI helping sales teams improve productivity? 

Ans. AI helps sales teams automate repetitive tasks, improve lead discovery, and focus more on customer conversations and high-value activities. 

4. Why is a strong lead generation process important for sales growth? 

Ans. A structured lead generation process helps businesses discover potential customers, build a consistent sales pipeline, and improve outreach efficiency.