Artificial Intelligence (AI) has undergone significant advancements over the past decade. These developments have led to an expansion and diversification of AI applications across various job functions and industries. This trend is particularly impactful for customer experience (CX) solutions, as each industry has unique expectations and comfort levels with AI tools. The distinctiveness of these solutions arises not only from the evolution of AI models but also from how these models operate. Unlike a phone system or a CRM that can be used similarly across different businesses, AI tools for support agents need to be tailored to specific use cases and industries.
Navigating AI Solutions for CX Optimization
For CX leaders, exploring AI options for optimizing customer experience can be challenging. The investment in generative AI tools for customer support must yield quick returns, and choosing the wrong solution could result in frustrated employees and customers. So, how can we differentiate solutions for CX optimization with AI? As the market rapidly expands, new solutions emerge daily. Some are merely superficial layers on top of public AI models, while others offer robust, customizable solutions combining multiple models to ensure success. Buyers must be prepared to ask the right questions to identify vendors that can genuinely enhance support agent performance with AI.
Identifying Your Use Cases
AI tools for support agents address various problems. It’s crucial to focus on the specific issues you need to solve, rather than broadly asking what AI can do for you. Start by identifying problems such as customer churn, low productivity, agent or customer frustration, and strategic challenges.
Example: A large e-commerce company might face high customer churn due to delayed response times during peak shopping seasons. Implementing conversational AI can streamline interactions, provide instant responses, and manage high volumes effectively.
In the market, you’ll encounter terms like:
- Workflow Automation: Streamlining repetitive tasks to improve efficiency.
- Conversational AI: Enabling natural, real-time interactions with customers.
- Ticket Categorization: Organizing and prioritizing support tickets for better handling.
- Sentiment Analysis: Analyzing customer emotions to improve service quality.
- Action Recommendations: Suggesting next steps for agents based on historical data.
- Intelligent Support Operations: Providing insights to enhance support processes.
- Automated Quality Assessments or Compliance: Ensuring service quality and adherence to regulations.
These tools often promise to “supercharge support agents with AI,” “optimize operations,” “increase productivity,” and “reduce manual effort.” As a purchaser, it’s essential to rise above the marketing jargon and ask, “What problems do we currently face, and what will they be as we scale and evolve?”
Finding AI Solutions for CX
AI-driven CX optimization can take various forms. Consider these questions to identify the problems that generative AI tools can solve:
- Are your ticket workflows complex and require data from multiple apps or weeks of training?:
- Solution: Workflow automation can improve support agent performance by integrating various systems and automating routine tasks.
- Example: A healthcare provider can use workflow automation to streamline patient appointment scheduling, reducing administrative burden and improving patient experience.
- Do your customers feel responses are too slow or need 24/7 availability?
- Solution: Conversational AI can provide instant, round-the-clock customer support.
- Example: An online retailer can deploy conversational AI to handle customer inquiries about order status and returns, providing quick responses without human intervention.
- Do you have the insights to improve processes and products based on customer feedback and ticket data?
- Solution: Intelligent Support Operations can provide valuable insights for continuous improvement.
- Example: A software company can use intelligent support operations to analyze customer feedback and identify common issues, leading to targeted product enhancements.
- Is ticket routing or categorization hindering your ability to make staffing decisions and delaying responses?
- Solution: Ensure your tickets are well-categorized and integrated into a workforce management tool.
- Example: A telecom company can use advanced ticket categorization to prioritize urgent service outages over routine billing inquiries, ensuring faster resolution of critical issues.
- Is the problem related to customer or agent access to knowledge?
- Solution: This might be an issue for action recommendations, conversational AI, or workflow automation.
- Example: A financial services firm can implement action recommendations to guide agents in providing accurate investment advice, improving service quality.
- Do you need your customers to self-serve?
- Solution: Customer-facing workflow automation or conversational AI can deliver great results.
- Example: A utility company can offer a self-service portal where customers can report outages or pay bills, reducing the load on call centers.
- What demographics are you serving, and through which channels do they access support?
- Solution: Differentiate between phone AI systems and chat/email systems, and choose those preferred by your customers.
- Example: A travel agency serving a tech-savvy clientele can prioritize chat and email support, while another serving an older demographic may focus on phone support.
Asking the Difficult Questions
Once you have an idea of the solutions you want, it’s crucial to dig deeper into the specifics of each tool and the company’s direction. Here are some key questions to ask:
- What AI model(s) do you use?
- Insight: Not all models are equal. Depending on your needs, a publicly available model like ChatGPT might suffice, or a proprietary model might be more suitable.
- Example: A global enterprise might prefer a proprietary AI model trained on industry-specific data for more accurate and relevant responses.
- How is the tool trained?
- Insight: Understand the data requirements and the level of cleanliness needed, as this can impact manual preparation time.
- Example: A retailer with a vast and varied product catalog may need a tool that can handle extensive and diverse training data to provide accurate product recommendations.
- What manual effort is needed to train the model or build actions based on its training?
- Insight: Avoid vendors who claim their tool works “out of the box.” CX optimization with AI requires some level of training.
- Example: A financial institution may need significant manual effort to train the AI on regulatory compliance data to ensure accurate and lawful responses.
- Is any of our company data sent to a publicly available model or third parties?
- Insight: This is crucial for security and privacy concerns.
- Example: A healthcare provider must ensure patient data is not shared with third parties, adhering to strict confidentiality and data protection laws.
- How do we address errors or hallucinations in the model’s responses?
- Insight: Ensure there is a mechanism to correct inaccuracies.
- Example: An e-commerce platform should have a process to quickly correct any misleading product information generated by the AI.
- What tools do you integrate with, and are there specific license requirements?
- Insight: Confirm compatibility with your tech stack and be aware of any additional costs for required licenses.
- Example: A SaaS company should verify that the AI tool integrates seamlessly with their CRM and customer support platforms.
- What is the cost model?
- Insight: Understand the pricing structure, including platform fees, user fees, usage fees, and per resolution costs.
- Example: A startup may prefer a flexible pricing model that scales with their growth, avoiding hefty upfront costs.
- What are the capabilities versus features?
- Insight: Ensure that the model’s capabilities are fully implemented and tested to avoid overpromising and under delivering.
- Example: A startup may prefer a flexible pricing model that scales with their growth, avoiding hefty upfront costs.
Differentiating AI Solutions for CX
AI solutions for CX will play a crucial role in the coming years. It is essential to develop a strategy that includes how these tools can enhance your services. However, with the hype surrounding AI, it is easy to get caught up in the excitement rather than focusing on functionality. Differentiating solutions is a necessary step in procuring tools that will genuinely supercharge support agents with AI and deliver tangible improvements in customer experience.
Conclusion
In conclusion, the rapid evolution of AI has created a wealth of opportunities for enhancing customer experience. By carefully identifying your use cases, asking the right questions, and focusing on functionality over hype, you can select AI solutions that truly address your needs. This strategic approach will help you harness the power of AI to optimize operations, increase productivity, and ultimately deliver a superior customer experience.
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