Adopting AI for customer experience (CX) is no longer a matter of if but when and how: 45% of customer support teams already use AI, while Gartner projects that 80% of customer service companies will use generative AI (GenAI) by 2025.  

Businesses should know, however, that an AI-powered CX solution is only as effective as the people who implement and operate it. Many companies struggle with AI adoption despite the best intentions and significant investments. In retail, for instance, the lack of in-house expertise remains the biggest challenge for 41% of surveyed e-commerce leaders in the UK. For more than 50% of customer engagement professionals, tech stack implementation and skills gaps hinder them from fully adopting AI. 

A critical yet often overlooked factor is AI maturity — how deeply AI is integrated into the organization and how prepared employees are to use it. At its core lies the value that AI creates for the organization, encompassing quantitative and qualitative outcomes such as productivity gains, revenue growth, and stronger customer satisfaction. The true measure of success lies not only in how AI transforms the business’s competitive positioning, but also in how it optimizes internal processes and empowers the workforce. This is vital, because your employee experience directly affects the customer experience your business delivers.  

Here are five success factors that can be your business’s starting point to achieving AI maturity and your workforce’s readiness to turn AI tools for CX into lasting customer loyalty: 

AI integration and utilization rate 

How this reflects AI maturity: This entails embedding AI into daily operations and enabling employees to use it frequently. It indicates how integral AI is in enhancing decision-making, improving efficiency, and driving better outcomes. It shows that employees trust it as a valuable tool in their work. 

Your starting points: Conduct a comprehensive audit of your business processes across departments. Identify which processes involve AI, such as automated customer support or analytics, and track how often these tools are employed. Use system logs, usage statistics, and employee feedback. Calculate the proportion of AI-integrated processes against the total number of processes to see AI’s penetration in daily operations. 

An acceptable utilization rate depends on where your organization is in its AI journey. For example, 59% of enterprises considered early adopters focused on high-impact areas as test cases for broader adoption or on developing their in-house tool. Benchmarks also vary by industry. For instance, tech companies and financial institutions, which are heavily data-driven, might achieve higher utilization rates. The complexity and function of the AI tools also play a role. Simple automation tools might reach high utilization quickly, while more complex systems, such as those used for decision-making, might take longer to adopt fully. 

Your next steps: If utilization is lower than expected, first identify the root cause. Is it due to insufficient training, or perhaps a lack of relevance to specific tasks? Engage with your team through surveys or discussions to understand their challenges. Based on these insights, tailor your approach by offering targeted training, adjusting AI tools for CX to better align with workflows, or revisiting its implementation to ensure it meets your organizational goals. 

Employee engagement and AI proficiency 

How this reflects AI maturity: In CX operations, high proficiency levels ensure that employees have the skills needed to use AI-powered tools to improve how they deliver customer service. Active engagement indicates that employees see AI’s value. Proficiency and engagement demonstrate that AI is effectively driving improvements in their work processes and in customer interactions. In a recent study, human agents using conversational AI and AI-enabled “Agent Assist” tools gained a 14% boost in productivity. Average net promoter scores improved, while monthly agent attrition dropped by 9%. 

It's not just about the business, too. In a 2024 Microsoft report, 90% of surveyed employees who are using AI at work said it helps them save time, be more creative, and enjoy their work more. 

Your starting points: Track training completion rates through your learning management system. Evaluate proficiency through certifications and practical evaluations. Monitoring usage frequency — how often and for how long are AI tools being used? Also, gather data on participation in AI-driven projects and feedback submission.  

Your next steps: Reassess your training programs. Are they effectively equipping employees with the necessary skills, or are there gaps? Consider offering refresher courses, hands-on workshops, or personalized training sessions. To boost engagement, ensure that AI tools are user-friendly and aligned with daily tasks. Involve employees in customizing AI tools to increase their buy-in. Clear communication about the benefits of AI, combined with incentives for active participation, can foster more consistent and meaningful engagement.  

Boosting employees’ AI proficiency is also about leadership stepping up to empower their teams to embrace new technologies, cultivate continuous learning, and equip them with the tools to seamlessly integrate AI into their daily work. Yet, according to the same Microsoft report, despite widespread optimism among employees, only 39% of global workers using AI have received any training from their employers. 

AI system accuracy and reliability 

How this reflects AI maturity: When AI solutions consistently deliver accurate and reliable results, employees are more likely to use it regularly and effectively. It also enables them to translate AI’s outputs into actionable insights, increase the success rate of AI-driven tasks, and improve the extent to which employees act on AI-enhanced predictions and recommendations. According to a McKinsey report, only 32% of companies that have adopted GenAI are actively addressing the risks of inaccuracies in their systems. 

Your starting points: Monitor indicators like error rates, prediction accuracy, and resolution success rates. Aim for accuracy rates of 90% or above in predictive tasks and minimal error rates. AI tools for CX should also deliver reliable results across different scenarios. Compare these against internal and industry benchmarks to assess performance. Utilize testing environments to evaluate AI under various conditions. Track the number of AI-generated insights and compare them with how often they are acted upon. 

Your next steps: Check if your AI systems aren’t meeting standards. This could involve retraining models with higher-quality data, refining algorithms, or strengthening validation processes. More often than not, inaccuracies and inconsistencies in data are the culprits behind an underperforming AI tool. Properly labeled datasets ensure that the tool learns from accurate and representative data. Regular system updates and tests are also essential to maintaining performance. If AI-based insights aren’t being utilized as expected, consider enhancing the clarity and relevance of AI recommendations. Aligning AI’s outputs more closely with business needs or providing further training on how to apply AI-generated insights can also improve utilization. 

Time to AI adoption 

How this reflects AI maturity: This involves the time it takes for AI tools for CX to transition from proof of concept and deployment to regular use. It includes the phases of initial learning, trial periods, and full integration. Faster adoption times indicate that employees are comfortable with the technology and that effective training and support systems are in place. Conversely, longer adoption times reveal barriers, such as resistance to change, inadequate training, or technical challenges.  

Your starting points: Record the deployment of each AI tool. Monitor employee usage over time to determine how long it takes for the tool to become a regular part of daily operations. Use system logs and adoption tracking tools to gather data on how frequently the AI tool is used.  

Consider the development timeline as well. According to a 2024 Gartner survey of businesses in the US, the UK, and Germany, leaders reported that it takes an average of eight months to move AI projects from prototype to production. In a 2024 survey of professionals in the US and the UK, only 7% of companies achieved “maturity” in AI per their benchmark, while 41% are still engaged in pilot projects and small-scale experiments. 

Your next steps: Pinpoint the root causes if adoption is taking longer than planned. Are there gaps in training? Is there a lack of clarity in communicating the benefits of AI tools? Are there technical issues preventing them from using it? Did you figure development time into the equation? Assess whether the AI tool itself might need adjustments. The long-term goal is to streamline the adoption process to reduce the time it takes for AI tools to become integral to daily operations. 

AI scalability  

How this reflects AI maturity: This is the capability of your AI-powered CX solutions to expand, including their capacity to handle increased loads, adapt to different use cases, and be adopted by a larger user base. Scalability demonstrates that AI tools for CX are designed to evolve alongside the organization’s needs. 

Your starting points: Evaluate the performance of AI systems during and after their rollout:  

• How quickly the system can be implemented in new areas 
• The ability to maintain accuracy, response time, and reliability under increased load 
• Whether additional resources or customizations are needed 

Your next steps: Scalability is a perennial challenge. In fact, 56% of surveyed companies said they lack the proper infrastructure to support their desired AI workloads. The issues include outdated or legacy systems and the ability to set up, scale, and manage AI infrastructure on demand.  

To alleviate these challenges, start by identifying the bottlenecks. These could include technical limitations like insufficient data storage or processing power, gaps in human expertise, or inefficiencies in operational processes. Consider upgrading your infrastructure to support larger AI deployments, refining data management practices, and investing in training to empower your team to fully utilize AI. Ensure that your AI-powered CX solutions are flexible enough to adapt to various and future use cases. 

In AI for CX, employee experience is customer experience 

AI maturity goes beyond simply having the latest tech stack or state-of-the-art infrastructure. Even the most advanced tools can become obsolete if the people using them aren’t fully prepared. Achieving AI maturity means technology and human expertise working together to transform technical capabilities into meaningful customer experiences.  

To help companies dispel misconceptions about AI and realize its potential to their CX, TDCX has developed an AI maturity program. This program takes a structured approach to assess the company’s capabilities across critical areas, including leadership and vision, AI proficiency, organizational structure, process maturity, technical infrastructure, and data security and privacy. TDCX provides companies with a detailed analysis of their position relative to industry best practices, enabling them to strategically advance their AI initiatives. 

As you establish your business’s maturity in AI for CX, understanding their intersection is vital. Join TDCX Talks: Powerful CX in the Age of AI on October 10, 2024, at Marina Bay Sands, Singapore, to explore how leading organizations are using AI to enhance their CX strategies. This event brings together industry experts to discuss practical approaches for accelerating maturity in AI and translating it into business growth.