Readiness Assessment for AI Implementation
in a Contact Centre - Part #2

Critical Areas for Consideration 

The key to unlocking the benefits of artificial intelligence in a way that compliments and improves your version of  your orchestrated customer experience, will depend on the degree and breadth of the Readiness Assessment. AI can be so much more than automating a call type. It can bring Cx, Ex and efficiency gains in almost every aspect of you contact centre including (but not limited to); HR, Quality, Workflow, Call Flow, Scripting, Sentiment identification and proactive prompting, agent assist, WFM (Intraday, forecasting), gamification, performance management etc. 

When assessing your contact centre's readiness to implement AI, consider including the following key areas in the scope.

1.     Current Technology Infrastructure

• Hardware and Software: Evaluate the existing technology stack.

• Network Capacity: Ensure the network infrastructure can handle increased data traffic.

• Integration Capabilities: Check the ability to integrate AI tools with current systems.

2.     Data Management

• Data Quality: Assess the accuracy and completeness of existing data.

• Data Availability: Verify that sufficient historical and real-time data is available.

• Data Privacy and Security: Ensure robust policies and systems are in place to protect sensitive information.

• Data Storage:

  • Storage Location: Identify where data is stored (on-premises, cloud, hybrid).

  • Storage Solutions: Evaluate the storage solutions used (e.g., databases, data lakes, warehouses).

  • Accessibility: Ensure data is easily accessible for AI tools and applications.

  • Backup and Recovery: Review backup and recovery procedures to ensure data integrity.

  • Scalability: Assess the ability of storage solutions to scale with data growth.

  • Compliance: Ensure data storage practices comply with relevant regulations (e.g., GDPR, CCPA).

3.     Workforce Readiness

• Skills and Training: Evaluate current skill levels and training needs.

• Change Management: Assess the organization’s ability to manage change.

• Role Redefinition: Plan for how AI will impact job roles and responsibilities.

4.     Business Processes

• Process Documentation: Ensure all processes are well-documented and standardized.

• Process Optimization: Identify processes that can be improved or automated using AI.

• Customer Interaction Data: Analyze existing customer interaction data to identify patterns and areas where AI can add value.

5.     AI Strategy and Goals

• Clear Objectives: Define specific goals and objectives for AI implementation.

• KPIs and Metrics: Establish key performance indicators and metrics to measure success.

6.     Vendor and Solution Evaluation

• Vendor Capabilities: Assess potential AI vendors.

• Solution Fit: Ensure the chosen AI solutions align with the contact centre’s needs.

• Pilot Programs: Consider running pilot programs to test AI solutions on a small scale.

7.     Regulatory and Compliance Considerations

• Industry Regulations: Ensure AI implementation complies with relevant regulations.

• Ethical AI Practices: Consider ethical implications and ensure AI systems operate fairly and transparently.

8.     Financial Assessment

• Budget and ROI: Evaluate the cost and potential return on investment.

• Funding and Resources: Ensure adequate funding and resources are allocated.

• AI-driven Financial Models: Assess the potential for AI to improve financial modeling, cost predictions, and identifying cost-saving opportunities.

9.     Customer Impact

• Customer Experience: Predict how AI will impact the customer experience.

• Feedback Mechanisms: Implement systems to gather customer feedback on AI-driven interactions.

10.   Scalability and Future-Proofing

• Scalability: Ensure AI solutions can scale with growth.

• Future Trends: Stay informed about emerging AI trends and technologies.

11.   Knowledge Management

• Knowledge Base Structure: Assess organization and quality of the knowledge base.

• Content Accessibility: Evaluate search functionality and user interface.

• Content Management Process: Review creation, update, and review processes.

• Integration with AI: Check compatibility with AI tools.

• User Feedback and Analytics: Implement feedback mechanisms and analyze usage patterns.

• Training and Adoption: Ensure agents are trained and the knowledge base is widely adopted.

• Content Accessibility for AI Training: Ensure content is structured and tagged for AI use.

• Continuous Improvement: Plan for regular updates and improvements.

• Security and Compliance: Ensure proper access controls and compliance with regulations.

12.   Communication and Collaboration Tools

• Tool Integration: Evaluate current communication tools and their integration with AI solutions.

• Collaboration Capabilities: Ensure that tools support efficient collaboration among agents, especially in a remote or hybrid work environment.

13.   Customer Journey Mapping

• Customer Insights: Analyze the customer journey to identify touchpoints where AI can enhance the experience.

• Personalization: Assess how AI can be used to personalize customer interactions based on journey data.

• Real-time Suggestions: Evaluate the ability of AI to track customer behavior in real-time and make relevant suggestions based on their actions and interests.

• Journey Data Integration: Ensure that customer journey data is integrated with AI systems to provide a seamless and personalized experience.

• Predictive Analytics: Utilize AI to predict customer needs and proactively offer solutions or recommendations.

• Omnichannel Experience: Ensure AI can provide a consistent experience across all customer touchpoints, including web, mobile, social media, and in-store.

14.   Cultural Readiness

• AI Acceptance: Gauge the overall acceptance and openness to AI within the organization.

• Leadership Support: Ensure leadership is committed to supporting AI initiatives.

15.   Quality Management

• Quality Assurance Processes: Evaluate existing QA processes for monitoring and improving agent performance.

• Consistency and Standards: Ensure that quality standards are well-defined and consistently applied.

• Feedback and Coaching: Implement systems for providing regular feedback and coaching to agents based on quality assessments.

• Quality Metrics: Define and track metrics related to service quality, such as first call resolution, customer satisfaction scores, and error rates.

• Continuous Improvement: Foster a culture of continuous improvement by regularly reviewing and refining quality management processes.

16.   Contact Types, Reasons, and Complexity

• Contact Categorization: Identify and categorize the different types of customer contacts.

• Reason Analysis: Analyze the common reasons for customer contacts and how frequently they occur.

• Complexity Assessment: Evaluate the complexity of each contact type to determine which interactions are suitable for AI automation.

• Resolution Pathways: Map out the resolution pathways for different contact types to identify opportunities for AI intervention.

• AI Suitability: Assess which contact types and reasons can be effectively handled by AI, such as through chatbots or virtual assistants, and which require human intervention.

• Agent Assistance: Determine how AI can assist agents with complex interactions by providing real-time information or suggestions.

17.   Workforce Management (WFM)

• Scheduling and Forecasting: Assess the current WFM systems for forecasting call volumes and scheduling staff.

• Real-time Management: Evaluate tools for real-time monitoring and adjustments.

• AI Integration: Determine how AI can optimize workforce management processes, such as predicting call volumes more accurately and optimizing schedules.

18.   Recruiting and Hiring Process

• Candidate Screening: Assess the efficiency of current recruiting processes and the potential for AI to automate candidate screening and shortlisting.

• Skill Matching: Evaluate how AI can be used to match candidates’ skills with job requirements more effectively.

• Onboarding: Consider AI tools that can streamline the onboarding process and provide new hires with necessary training materials.

19.   Reporting and Analytics

• Current Reporting Systems: Evaluate the current reporting tools and their effectiveness in providing actionable insights.

• AI-powered Analytics: Assess the potential for AI to enhance reporting capabilities, providing deeper insights and predictive analytics.

• Customizable Dashboards: Ensure the reporting system allows for customizable dashboards tailored to different user needs.

20.   Gamification

• Current Engagement Strategies: Review existing strategies for agent engagement and motivation.

• AI-enhanced Gamification: Assess how AI can personalize gamification elements to individual agents, increasing engagement and performance.

• Performance Metrics: Use AI to track and analyze performance metrics related to gamification.

21.   Call Guides

• Script Effectiveness: Evaluate the effectiveness of current call guides and scripts.

• Dynamic Scripting: Assess the potential for AI to provide real-time, dynamic scripting based on the context of the call.

• Agent Support: Determine how AI can assist agents by providing real-time suggestions and information during calls.

22.   CRM Systems

• Current CRM Capabilities: Assess the features and functionalities of the current CRM system.

• Data Integration: Ensure the CRM system can integrate with AI tools for data sharing and real-time updates.

• AI Compatibility: Evaluate the CRM’s ability to leverage AI for enhanced customer insights, predictive analytics, and automation.

• User Adoption: Assess how widely and effectively the CRM system is used by agents.

• Customer Data Quality: Verify the quality and consistency of customer data within the CRM.

• AI-driven Personalization: Determine how AI can use CRM data to personalize customer interactions and improve service delivery.

• Scalability: Ensure the CRM system can scale with the growth of AI capabilities.

23.   IVR Systems

• Current IVR Capabilities: Evaluate the features and performance of the current IVR system.

• Call Routing: Assess how well the IVR system routes calls based on customer needs.

• Self-service Options: Determine the effectiveness of existing self-service options and identify areas for improvement with AI.

• Integration with AI: Evaluate how the IVR system can integrate with AI to provide more dynamic and personalized responses.

• Customer Experience: Analyze customer feedback and satisfaction with the current IVR system.

• Scalability and Flexibility: Ensure the IVR system can scale and adapt to new AI functionalities and increased call volumes.

24.   CCaaS Platform Capabilities and Limitations

• Current Capabilities: Evaluate the current capabilities of the Contact Centre as a Service (CCaaS) platform.

• Integration with AI: Assess how well the platform integrates with AI tools and technologies.

• Performance and Reliability: Review the performance and reliability of the platform, including uptime, scalability, and support.

• Limitations: Identify any limitations of the current CCaaS platform that could hinder AI implementation.

• Customization and Flexibility: Determine the platform’s ability to be customized and its flexibility to adapt to new AI requirements.

25.   Email and Chat Channels

• Current Systems: Evaluate the existing email and chat systems for handling customer inquiries.

• Response Time: Assess the average response time and effectiveness of current communication.

• AI Integration: Determine how AI can automate responses to common inquiries and route more complex issues to human agents.

• Natural Language Processing (NLP): Evaluate the use of NLP for understanding and responding to customer queries in email and chat.

• Customer Experience: Analyze the impact of email and chat on overall customer satisfaction.

• Personalization: Assess how AI can personalize email and chat interactions based on customer history and preferences.

• Analytics and Reporting: Implement AI-driven analytics to gain insights into email and chat interactions.

• Security and Compliance: Ensure that AI solutions for email and chat comply with data privacy regulations.

26.   Business Discovery and Opportunities:

• Meet with business leaders across departments and verticals to identify business challenges and opportunities to use AI to solve them.

• Highlight and prioritize the business outcomes to achieve.

27.   AI Model Development and Deployment Assessment:

• Examine the organization's proficiency in developing and deploying AI models using a specified CCaaS solution 

• Provide insights into optimizing the model development life cycle on CCaaS platform.

28.   Roadmap for Implementing AI:

• Identifying business problems and potential solutions leveraging Artificial Intelligence, including recommended implementation budget for client-selected scenarios.

• Prioritize initiatives, considering short-term gains and long-term strategic objectives

• AI driven self service, Agent Assist & omni-channel transformational strategy

Including these areas in your assessment will provide a comprehensive understanding of the contact centre’s readiness to implement AI and help ensure a successful transition.

Eric Young
President
Tele-Centre Assist Inc.
www.telecentreassist.com