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Pre implementation KPI

50%

Low User Readiness Index
Business Impact: Resistance to adoption could delay implementation timelines and reduce ROI.Headline

60%

High Non-Compliance Rate
Business Impact: Inconsistent compliance exposes the organization to regulatory risks and customer dissatisfaction.

20 min per call

Extended Manual Audit Time
Business Impact: Inefficient processes lead to higher operational costs and slower feedback loops.

60%

Low Willingness to Adopt
Business Impact: Resistance at managerial levels could stall deployment and affect overall system adoption rates.

75%

Inaccurate Violation Detection Rate
Business Impact: Misidentifications result in undetected violations or false positives, leading to potential customer and compliance risks.

7+ Days

Inefficient Feedback Mechanism
Business Impact: Delayed feedback hinders performance improvement and impacts customer satisfaction scores.

Project Oppourtunity

AI Zero Tolerance Policy (ZTP) Call Audit project presented a transformative opportunity for the business

Revenue Enhancement Through Automation:

Overall ROI :

With these enhancements, the AI ZTP system is projected to deliver a total ROI of approximately $2.25 million annually, making it a highly impactful investment that leverages cutting-edge AI technology to drive significant business value.

References

Deloitte Insights: "AI in Customer Service: Trends and Predictions" Deloitte.com
Gartner Research: "The Impact of AI on Customer Service Efficiency" Gartner.com
Forrester Research: "The Rise of AI in Call Centers" Forrester.com
McKinsey & Company: "How AI is Shaping the Future of Customer Service" McKinsey.com
Journal of Artificial Intelligence Research: "Contextual Sentiment Analysis in Customer Service" Jair.org
Harvard Business Review: "Reducing Bias in AI-Driven Sentiment Analysis" HBR.org
PwC: "AI and Compliance: Enhancing Call Center Evaluations" Pwc.com
MIT Sloan Management Review: "The Role of AI in Enforcing Zero Tolerance Policies" Mitsloan.mit.edu

Research Questions

  1. What are the primary compliance issues faced by customer service teams?
  2. How do QA teams currently audit calls for zero-tolerance policy violations?
  3. What are the limitations of manual call audits?
  4. How would real-time detection of violations impact agent performance?
  5. What are the most common zero-tolerance policy violations?
  6. How do agents perceive the current feedback mechanism?
  7. What features would QA analysts find most valuable in an AI-powered audit tool?
  8. How can the AI system ensure accuracy in detecting violations?
  9. What are the potential challenges in implementing the AI ZTP system?
  10. How should the system handle false positives/negatives in violation detection?
  11. What is the expected impact of the AI ZTP system on QA team efficiency?
  12. How will the system’s scoring mechanism influence agent behavior?
  13. What role does context play in identifying policy violations?
  14. How can the system support continuous learning and improvement for agents?
  15. What privacy concerns might arise with AI monitoring of calls?
  16. How will the system integrate with existing QA workflows?
  17. What metrics should be tracked to measure the success of the AI ZTP system?
  18. How can the system adapt to evolving zero-tolerance policies?
Aspect Assembly.AI Rev.AI Speechmatics Google
Core Functionality Real-time speech recognition, transcription, and keyword detection. Focus on customizable models for various industries. High-accuracy speech-to-text conversion with support for multiple languages. Offers transcription and speech analytics. Advanced speech recognition and transcription with emphasis on accuracy and language support. Converts text to speech in multiple languages and voices, mainly for voice applications.
Zero Tolerance Policy (ZTP) Compliance Supports customizable keyword detection and alerts for ZTP violations. Allows integration with third-party compliance tools. Provides transcription with potential for keyword spotting, but requires additional customization for ZTP compliance. Allows for custom vocabulary and keyword spotting, can be used for ZTP, but requires configuration. Limited direct support for ZTP; primarily focused on text-to-speech, not detailed compliance monitoring.
Accuracy High accuracy in transcription, especially with industry-specific models. High accuracy, particularly in English, with regular updates to models. Known for high accuracy and real-time processing, supports multiple languages. Very high accuracy in text-to-speech; however, accuracy in transcription when using Google Speech-to-Text API is competitive.
Language Support Supports multiple languages with specialized models for specific use cases. Supports multiple languages, with a strong focus on English. Extensive language support with customization options for accents and dialects. Extensive language support, with voices and dialects across various regions.
Integration Capabilities Easily integrates with existing QA and compliance systems, including ZTP enforcement tools. Offers APIs that integrate with various platforms, but may require additional development for ZTP-specific use cases. Provides APIs for seamless integration with other systems, suitable for customized ZTP workflows. API allows integration, but primarily for TTS use; requires additional tools for full ZTP integration.
Customization Highly customizable models and settings to cater to specific industry needs, including ZTP. Some customization available; more limited compared to competitors in terms of real-time compliance features. Highly customizable with options for adding custom vocabulary, making it adaptable for ZTP. Limited customization options focused on text-to-speech, not tailored for compliance scenarios.
Real-time Capabilities Supports real-time transcription and keyword alerts, useful for immediate ZTP enforcement. Primarily post-call transcription, though real-time processing is available, it’s less focused on immediate compliance. Offers real-time transcription with the ability to flag keywords instantly, suitable for ZTP. Primarily used for text-to-speech, with real-time transcription capabilities available via Google Speech-to-Text API.
Pricing Competitive pricing with tiered options based on usage and features. Tailored for enterprise solutions. Affordable pricing, with options that scale with usage. Slightly more cost-effective for basic transcription needs. Mid-range pricing with options for enterprise-level customizations. Pay-as-you-go model, cost-effective for general use but may require additional tools for comprehensive ZTP audits.
Ease of Use User-friendly with comprehensive documentation and support. Designed for quick integration into QA workflows. Easy to use, with straightforward API documentation. May require more effort for ZTP-specific customization. Intuitive interface with extensive documentation, suitable for teams with technical expertise. Very easy to use, particularly for TTS. More technical expertise required for transcription and compliance integration.
Support and Training Strong support with dedicated resources for enterprise customers, including training for QA teams. Good customer support, but more focused on general use cases rather than specific compliance needs. Comprehensive support with training options, particularly for custom deployments. Extensive support for TTS; transcription support is robust but may lack specialized compliance training.
Use Case Suitability for ZTP Call Audits Excellent fit due to its focus on real-time compliance monitoring and customizable features for QA teams. Suitable for transcription with some potential for ZTP audits, but may need additional customization. Well-suited for ZTP audits with real-time capabilities and high customization. Limited suitability for ZTP audits, better suited for general TTS and transcription tasks with external tools for compliance.
Key Takeaways:
Information Architecture - ​Card Sorting
DASHBOARD OBS ROLES USERS AUDIT SHEET FILTERS AUDIT TRAILS SETTING QUICKLINKS
create Create Create Create Create View Manual
view View View View View AI
User Journey Map
Stage Onboarding Call Preparation Call Interaction Immediate Feedback Post-Call Reflection Continuous Learning Performance Review Adaptation Long-Term Engagement
Agent Goals Understand AI ZTP system and compliance requirements Ensure readiness for compliance during calls Adhere to zero-tolerance policies Receive real-time guidance and address violations Review call performance and identify improvement areas Improve skills and avoid future violations Track overall performance and compliance adherence Implement changes to improve future call compliance Maintain high compliance and continue professional development
Agent Actions Attends training on AI ZTP system and zero-tolerance policies Reviews compliance guidelines and system dashboard Conducts customer call while following compliance guidelines Responds to real-time feedback and adjusts behavior if needed Logs into the system to view performance scores and flagged calls Attends training sessions based on feedback Reviews monthly/quarterly performance summaries Adjusts behavior based on feedback and training Continuously reviews feedback and updates skills
Touchpoints Training modules, System dashboard Compliance guidelines, System dashboard Real-time alerts, Call interface Real-time alerts, Post-call feedback Performance dashboard, Feedback reports Training sessions, Performance reviews Performance summaries, System dashboard Future calls, Ongoing system interaction Regular system interaction, Career development opportunities
pain points Overwhelmed by new system and policy complexities Lack of real-time guidance Distraction from call focus due to real-time alerts Stress from immediate corrective actions Difficulty in understanding AI-generated feedback Repetitive or generic training content Frustration if progress is not reflected in reports Difficulty in breaking old habits Fatigue from continuous monitoring and training
Opportunities Offer interactive and easy-to-understand training sessions Provide quick-access resources and tips within the dashboard Optimize alert system to minimize disruptions Ensure feedback is constructive and clear Simplify feedback presentation and offer actionable insights Customize training content based on specific feedback Offer personalized progress tracking and highlight improvements Provide regular, positive reinforcement and ongoing support Introduce gamification or rewards for sustained high performance
UX Laws
1. Law of Proximity:
Example: In the table, the Ticket/Call ID, OBS, Audio File, and Action columns are aligned closely together. This makes users perceive them as related information within a single row (or audit task). The proximity of these elements helps users understand that they are grouped as part of the same task for a specific Call ID.

2. Law of Common Region:
The entire audit sheet table is enclosed within a bordered container, giving the data in each row a clear boundary. This makes it easier for users to perceive these rows as distinct from other elements of the UI, like the sidebar navigation on the left.

3. Fitts' Law::
The clickable buttons under the Action column, such as "Start" and "Completed", are large and easy to interact with. According to Fitts' Law, larger buttons close to where the user needs to click reduce the time it takes to perform an action, improving usability.

4. Law of Prägnanz (Simplicity):
The overall layout is clean and simple, avoiding clutter. The uniform table layout and minimalistic sidebar navigation make it easier for users to scan and understand the data. This aligns with the Law of Prägnanz, which suggests that users will interpret the layout in its simplest form.

5. Law of Similarity: 
In the Action column, buttons like "Start" and "Completed" have the same shape and style but different colors, 
helping users distinguish between tasks that are in progress or completed. The consistent styling of buttons within the column helps users easily identify them as action items.

6. Law of Disclosure: The interface follows progressive disclosure principles by showing only the most relevant details in the main table, such as Call ID, OBS, and Audio File. Additional information like the detailed audit or audio playback may become visible only when the user interacts with the "Start" button, keeping the initial view clean and focused on essentials.

These UX laws work together to create a more intuitive and user-friendly interface for managing audit tasks. The design aligns with principles that reduce cognitive load, simplify interactions, and make the interface more efficient for the end user.

Final UI AI Audit - AdminView