Organizations are increasingly aware that leveraging artificial intelligence (AI) is essential to stay competitive. Yet for many, a critical question remains unanswered: How should we use AI? The answer might not lie in AI itself but in understanding how to use AI tools to improve the metrics that matter.

Too often, businesses face “analysis paralysis” with AI, hesitating on where to start and, as a result, missing practical opportunities for improvement. The solution? Start with the needle you want to move — the specific business metric that can drive your success. Whether it’s reducing costs, increasing revenue or enhancing customer satisfaction, it’s crucial to define your target KPI.

Practical AI capabilities embedded within an orchestration platform can illuminate a path to value, without requiring a team of data scientists. Let’s look at how focusing on core business goals, instead of adopting AI for AI’s sake, can open doors to strategic and measurable customer experience (CX) success.

Define the Business Metric: Start with the Problem

AI isn’t a magic solution looking for a problem to solve. It’s simply a new tool that, when applied thoughtfully, can drive targeted improvement. Begin by answering the essential question: What metric do you want to improve?

Companies have long measured KPIs, such as customer satisfaction (CSAT), Net Promoter Score (NPS), first-contact resolution (FCR), conversion rates and cost efficiency. These indicators are already engrained in the company’s goals and CX strategy.

The focus, then, should be less on understanding AI’s complex algorithms and the latest large language model (LLM) trends, but rather on leveraging proven solutions that use the right AI approach for the job that impact these important KPIs.

The real value of AI lies in its ability to solve the same age-old business problems in a faster, smarter and more effective way. In most cases, businesses won’t need AI specialists to see the benefits; AI solutions are built into platforms and are designed to be operational with minimal overhead.

Here’s how practical applications of AI can help you meet specific goals and help move the needle.

Cost Savings and Efficiency: Conversational AI Can Streamline Operations

If the business’ KPI needle is pointed to cost savings and operational efficiency, the practical path to success often lies in automating repetitive and low-value interactions. The objective isn’t necessarily to achieve full automation, but to reduce friction and empower customers with self-service options that are simple and effective.

In this age, where it seems that nearly everything wears the “AI” label, conversational AI can be overlooked and seen as an older AI technology. In reality, most companies have only begun to scratch the surface on the business value that conversational AI brings.

Built for purpose, it can easily handle repetitive and well-defined customer interactions, such as checking an order status, updating account information, scheduling or changing an appointment, or changing a password. By combining conversational AI capabilities with the power of LLMs, we’re now able to fulfill more complex, multi-intent interactions.

To see immediate value, start with simpler, higher-volume customer intents, as automation has the potential to not only reduce costs but also improve customer satisfaction by meeting customer needs on-demand. Key benefits of conversational AI:

  • Reduces agent workload: By handling routine queries, conversational AI frees up agents to focus on complex issues.
  • Supports customer preferences: Many customers prefer self-service options for straightforward issues.
  • Enhances efficiency: Increased FCR and decreased average handling time (AHT) can significantly reduce operational costs.

Consider a retail company that receives thousands of customer inquiries about order tracking. By using conversational AI to field these queries, the company can reduce call volumes and shorten wait times, driving efficiency gains without sacrificing customer satisfaction.

Revenue Growth: Machine Learning for Real-Time Customer Engagement

When the goal is to drive revenue growth, AI can play a critical role by identifying and capitalizing on key moments in the customer journey. Businesses often lose revenue opportunities when customers drop off or disengage at crucial points. AI has the power to detect these signals and provide timely interventions.

  • Track and analyze behaviors: Solutions using machine learning models can be used to track and analyze customer behaviors that correlate with purchasing decisions and to pair sales representatives that are most likely to close a sale with a specific customer.
  • Personalize messaging: Machine learning can trigger targeted prompts based on a customer’s likelihood of purchasing, delivering offers or information relevant to their needs. When the likelihood of a successful sale or business outcome begins to decrease, an AI-driven intervention can re-engage the customer.
  • Intelligent routing: Machine learning is also used to match customers with agents who have the highest probability of securing a sale or resolving an issue, which can increase sales conversion and retention rates. By harnessing machine learning, companies can optimize customer interactions for sales, retention or up-sell opportunities at moments when these engagements are most likely to succeed. This ensures that AI’s impact directly aligns with revenue-growth goals.

Customer Satisfaction: Improving Agent Efficiency Through Generative AI

Many companies aim to improve customer satisfaction and, in turn, their NPS and CSAT scores. A major contributor to customer dissatisfaction is inefficient or slow service, often due to agents spending a large portion of their time searching for information and summarizing interactions.

Generative AI is a valuable tool for enhancing agent efficiency by assisting with real-time information retrieval and content generation. A few ways it impacts customer satisfaction are:

  • Quick access to information: Generative AI can provide agents with summaries of previous interactions, product and service information, and knowledge base articles, which can save valuable time.
  • Automates after call work: Summarizing conversations, drafting follow-up messages and accurately categorizing interaction reasons become faster with generative AI. This allows agents to focus on engaging the customer rather than on administrative work.
  • Reduced transfers and escalations: By automatically empowering agents with contextual information at their fingertips, generative AI enables agents to handle a wider array of interaction types with confidence. This can increase FCR rates and reduce the need to transfer interactions to more tenured agents or other departments.

By harnessing multiple AI approaches to address common business challenges and improve important KPIs, we see a reduction in the time agents spend on administrative tasks. This can allow them to increase their focus on delivering a personalized and efficient experience. A positive customer experience not only drives satisfaction but also increases brand loyalty and repeat business.

AI Benefits by the Numbers

In practice, AI applications have shown measurable improvements across different KPIs. Here are a few compelling statistics that illustrate the impact of aligning AI with specific business goals:

Operational efficiency and customer satisfaction: Virgin Atlantic uses Genesys Cloud AI as a key differentiator in its CX strategy. The company has reported a 15% handle-time reduction from leveraging AI-powered routing and a 29% increase in queries resolved without an advisor after introducing conversational AI-powered voicebots and chatbots. In addition to these operational efficiency improvements, it has seen a 25-point improvement in customer satisfaction.

Revenue growth: IONOS, a global web hosting and cloud computing provider, has leveraged the machine learning capabilities within the Genesys Cloud™ platform to predict the best time to engage with customers. Increasing chat acceptance rates by engaging with prospects at the right time has led an increase in revenue for IONOS. Following a 10-point increase in chat acceptance rate, they’ve seen a 68% increase in sales conversion rates, which has led to a 29% increase in revenue per customer visit.

The Value of AI Is in the End Goal

Using AI strategically isn’t about applying the latest technology just because it’s popular; it’s about starting with the end goal. Ask yourself: What KPI is most important? From there, AI is an indispensable, increasingly flexible tool at your disposal to achieve your business objectives more rapidly.

It will often require multiple AI techniques in a given solution but rest assured that these techniques are becoming standard solutions and are safely and ethically built into the platform.

Conversational AI, machine learning and generative AI can each drive meaningful improvement but only when applied in service of clearly defined business goals. When you define the business metrics that matter, AI’s role becomes a natural extension of your strategy and solution. By focusing on your KPIs and aligning AI to these practical opportunities, the path to value is clear.

Ready to transform your CX with AI? Read “How to build a business case for AI” for guidance on where to focus your AI efforts to achieve results and set you on the path to transformation.