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Eliminating Platform Fragmentation with an AI Chat Interface to Enhance Sales Processes

IntelliAssist streamlines access to information, eliminating the need to search across multiple platforms, which boosts productivity and improves the overall sales process

10,000+

Users

6+

Services in one Interface

85

SUS Score

Details, such as names, workflows, and interfaces, have been modified to maintain confidentiality

Research

Initial Design

Usability Findings

Evaluation Metrics

Research

Initial Design

Usability Findings

Evaluation Metrics

Context

Sales reps waste valuable time searching for information across multiple platforms. IntelliAssist, our AI-powered chatbot, solves this by providing instant answers to their questions, boosting productivity and streamlining the sales process.

What

An AI-powered chatbot, IntelliAssist, that provides instant answers to questions and streamlines access to information

What

An AI-powered chatbot, IntelliAssist, that provides instant answers to questions and streamlines access to information

Why

Employees currently navigate multiple platforms to access similar information, hindering productivity.

Why

Employees currently navigate multiple platforms to access similar information, hindering productivity.

Who

Sales representatives, data analysts, data engineers, project managers, executives, and managers

Who

Sales representatives, data analysts, data engineers, project managers, executives, and managers

Where

Integrated into a user-friendly chatbot interface within the existing company website

Where

Integrated into a user-friendly chatbot interface within the existing company website

Business Goals

🚀 Improve sales productivity by providing quick access to relevant information.

💬 Enhance the user experience through a conversational interface and streamlined workflows

🔄 Integrate with existing systems for seamless access to order details and customer data

🔎 Provide instant, accurate answers to users about Dell process management

Users Share Their Experiences to Shape IntelliAssist

We conducted in-depth interviews with five Dell employees representing a diverse range of roles, including sales representatives, data analysts, and project managers. The interviews aimed to understand their daily workflows, challenges, and pain points related to accessing information and completing tasks efficiently

Fragmented Information Ecosystem Hinders User Efficiency

Users across departments reported difficulties accessing information due to its fragmented presence across various platforms. This constant switching between systems led to frustration and hampered productivity

"Navigating between so many different platforms to find the information I need is a constant time sink and a major source of frustration." – Sales Representative

Fragmented Information Ecosystem Hinders User Efficiency

Users across departments reported difficulties accessing information due to its fragmented presence across various platforms. This constant switching between systems led to frustration and hampered productivity

"Navigating between so many different platforms to find the information I need is a constant time sink and a major source of frustration." – Sales Representative

Fragmented Information Ecosystem Hinders User Efficiency

Users across departments reported difficulties accessing information due to its fragmented presence across various platforms. This constant switching between systems led to frustration and hampered productivity

"Navigating between so many different platforms to find the information I need is a constant time sink and a major source of frustration." – Sales Representative

Complex and Confusing Processes

Many users reported difficulties understanding complex Dell processes, such as order management, troubleshooting procedures, or policy guidelines. This lack of clarity often led to delays, errors, and the need to seek help from colleagues or support teams

"Some of our internal processes are so complicated that it's hard to know where to start or who to ask for help." – Sales Representative

Complex and Confusing Processes

Many users reported difficulties understanding complex Dell processes, such as order management, troubleshooting procedures, or policy guidelines. This lack of clarity often led to delays, errors, and the need to seek help from colleagues or support teams

"Some of our internal processes are so complicated that it's hard to know where to start or who to ask for help." – Sales Representative

Complex and Confusing Processes

Many users reported difficulties understanding complex Dell processes, such as order management, troubleshooting procedures, or policy guidelines. This lack of clarity often led to delays, errors, and the need to seek help from colleagues or support teams

"Some of our internal processes are so complicated that it's hard to know where to start or who to ask for help." – Sales Representative

Time-Consuming Manual Searches

Locating specific information within Dell's vast knowledge base and internal wikis was a time-consuming process. Users often relied on keyword searches, which didn't always yield accurate results, or had to sift through lengthy documents manually

"It can take me hours to find the right information, especially when I'm looking for something very specific or technical." – Project Manager

Time-Consuming Manual Searches

Locating specific information within Dell's vast knowledge base and internal wikis was a time-consuming process. Users often relied on keyword searches, which didn't always yield accurate results, or had to sift through lengthy documents manually

"It can take me hours to find the right information, especially when I'm looking for something very specific or technical." – Project Manager

Time-Consuming Manual Searches

Locating specific information within Dell's vast knowledge base and internal wikis was a time-consuming process. Users often relied on keyword searches, which didn't always yield accurate results, or had to sift through lengthy documents manually

"It can take me hours to find the right information, especially when I'm looking for something very specific or technical." – Project Manager

Desire for Self-Service Support

Users consistently conveyed a strong desire for efficient self-service solutions that would enable them to independently and rapidly find answers and resolve issues, minimizing the need to rely on colleagues or support teams for assistance. Preference for autonomy and control over problem-solving

"I would love to have a tool that could instantly answer my questions, especially when I'm working on a tight deadline." – Data Engineer

Desire for Self-Service Support

Users consistently conveyed a strong desire for efficient self-service solutions that would enable them to independently and rapidly find answers and resolve issues, minimizing the need to rely on colleagues or support teams for assistance. Preference for autonomy and control over problem-solving

"I would love to have a tool that could instantly answer my questions, especially when I'm working on a tight deadline." – Data Engineer

Desire for Self-Service Support

Users consistently conveyed a strong desire for efficient self-service solutions that would enable them to independently and rapidly find answers and resolve issues, minimizing the need to rely on colleagues or support teams for assistance. Preference for autonomy and control over problem-solving

"I would love to have a tool that could instantly answer my questions, especially when I'm working on a tight deadline." – Data Engineer

Lean research Synthesizing Insights: Identifying User Needs and Pain Points to Guide the Design Process

In addition to interviews, I conducted contextual inquiries, observing users as they interacted with the INC in their natural work environments. This allowed me to gain valuable insights into their actual workflows, identify specific areas of friction, and understand how the platform's design was impacting their productivity.

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Research details

Lean research Synthesizing Insights: Identifying User Needs and Pain Points to Guide the Design Process

In addition to interviews, I conducted contextual inquiries, observing users as they interacted with the INC in their natural work environments. This allowed me to gain valuable insights into their actual workflows, identify specific areas of friction, and understand how the platform's design was impacting their productivity.

Show more

Initial Design

Research-Driven Design

Iterative Prototyping

Data-Informed Refinement

Choosing Interface Styles

Clarity Over Hidden Complexity

I converged on option B after balancing user feedback, brand guidelines, and technical constraints

Employees needed quick answers, not more clicks. Design 2 put sample questions and tools front and center, cutting the guesswork of how to use the AI. Tests showed users adopted it faster, and the familiar Dell branding built trust, which is essential for a company-wide tool

Option A

Familiar First Impression (other chat bots)

Clear Visual Hierarchy

Lower discoverability of sample questions (click to reveal)

Higher Interaction Cost

Option B

Instant discoverability of sample questions

Brand consistency with Dell signature colors

Dense layout

Poor reading pattern

🚨 Balancing Business "wants" with User Guidance

The business loved the yellow tile for sample questions—so we kept it! Turns out, that pop of color isn’t just fun; it guides attention to key actions without breaking trust in Dell’s brand. A small compromise for big clarity

Chat Interface

Quick-start prompts guide users to explore AI features effortlessly. The clean layout cuts clutter, making it easy to ask questions naturally

Quick-start prompts guide users to explore AI features effortlessly. The clean layout cuts clutter, making it easy to ask questions naturally

Streamlined Responses

It’s like a conversation with two voices—yours and the AI’s—each color-coded so you never lose track. Simple, clear

It’s like a conversation with two voices—yours and the AI’s—each color-coded so you never lose track. Simple, clear

Prioritizing Clarity & Scannability for Order Details

The order details response template prioritizes clarity and scannability, presenting crucial information upfront with clear visual cues. This streamlined design allows users to assess order status quickly, review relevant details, track progress, and access further information as needed, creating a seamless and efficient customer experience.

The order details response template prioritizes clarity and scannability, presenting crucial information upfront with clear visual cues. This streamlined design allows users to assess order status quickly, review relevant details, track progress, and access further information as needed, creating a seamless and efficient customer experience.

Verified Answers with Source Transparency

IntelliAssist's responses prioritize accuracy and trust by providing verified answers along with their sources. This lets users quickly validate information, fostering confidence in the AI. By integrating knowledge base articles and support resources, IntelliAssist empowers users to explore further, enhancing the user experience through transparency and self-directed learning

IntelliAssist's responses prioritize accuracy and trust by providing verified answers along with their sources. This lets users quickly validate information, fostering confidence in the AI. By integrating knowledge base articles and support resources, IntelliAssist empowers users to explore further, enhancing the user experience through transparency and self-directed learning

Evaluation Metrics

User Satisfaction (Early Adoption)

System Usability Scale

Sentiment analysis

The pilot group rated the assistant 78/100 — praised "intuitive guidance" and reduced first-time anxiety


Preliminary data from initial launch phase

Workflow Efficiency Gains

Task log analysis

SME interviews

82% task completion rate for core workflows (e.g., order details), saving users ~3.2 minutes per task vs. manual searches


Sampled 2 weeks of post-launch data.

Self-Service Momentum

Escalation rate tracking

58% of inquiries resolved without human support; repeat users hit 72% self-resolution.


Early indicator of learnability and trust.

Increased Decision Confidence

Pre/post-launch surveys

Behavioral telemetry

68% of users reported higher confidence in task outcomes when using IntelliAssist

Measured across 18 observed workflows

Navigating Real-World Constraints

⏳Tight Deadlines for Impact

Accelerated timelines prioritized MVP delivery over exploratory research

Focused on high-impact, SME-backed workflows (e.g., order status checks) to align with stakeholder goals

🔒Resource & Access Limitations

Restricted access to cross-departmental users and historical data due to compliance policies

Leveraged sales-team SMEs as proxies for edge cases and validated designs against existing analytics

🌫️Ambiguous Requirements

Early-stage AI capabilities lacked clear technical boundaries, creating design ambiguities

Adopted iterative prototyping to align engineering feasibility with user needs

While these limitations narrowed initial scope, they forced sharper prioritization of features that delivered immediate ROI for sales teams. Future phases will expand validation to address gaps in:

  1. Cross-role adoption (engineering, support)

  2. Long-term behavior patterns (90-day retention)

While these limitations narrowed initial scope, they forced sharper prioritization of features that delivered immediate ROI for sales teams. Future phases will expand validation to address gaps in:

  1. Cross-role adoption (engineering, support)

  2. Long-term behavior patterns (90-day retention)

While these limitations narrowed initial scope, they forced sharper prioritization of features that delivered immediate ROI for sales teams. Future phases will expand validation to address gaps in:

  1. Cross-role adoption (engineering, support)

  2. Long-term behavior patterns (90-day retention)

While these limitations narrowed initial scope, they forced sharper prioritization of features that delivered immediate ROI for sales teams. Future phases will expand validation to address gaps in:

  1. Cross-role adoption (engineering, support)

  2. Long-term behavior patterns (90-day retention)

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