4 key elements of building an AI business case

Powering your customer experience (CX) technology with artificial intelligence (AI) is no longer a nice-to-have. AI is essential for delivering the personalization and contextual relevance consumers expect from service interactions.??

But even CX professionals who are bullish on using AI to support their customer and employee experiences don’t always know where to begin — or how they can best measure its value. To do that, you need to follow a proven methodology for building an AI business case.?

A typical business case for adopting traditional technologies or solutions is based on straightforward metrics. Whereas a business case for AI needs to encompass the unique aspects of the technology, including its data dependencies, learning capabilities and impact on processes. It also needs to consider AI ethics.?

Here are four key elements of building a business case for AI in customer and employee experience.?

1. Get consensus on your AI strategy??

The first step to building an AI business case is to create consensus. And that starts with knowing your primary stakeholders’ needs and objectives.??

?These include their goals for anticipated business benefits, their targets for items such as a new technology’s total cost of ownership, and financial expectations like savings from operational efficiencies.???

?It’s also essential to understand those stakeholders’ concerns so you’re prepared to handle any objections. Gaining this insight will help facilitate the communication and collaboration you’ll need to bridge organizational silos and rally cross-functional teams around shared goals.?

2. Prioritize by use case?

There are countless ways to use AI to support the customer and employee experience. It’s vital to understand which use cases will deliver the most value to your organization immediately — and which will drive long-term benefits.??

Use this information not only to prioritize your AI projects, but also to select pilot projects that will deliver quick wins. And to identify ones that will give teams the confidence to tackle larger projects to facilitate long-term transformation.??

Plan for a centralized knowledge hub that will provide the relevant data you need to support your various use cases and related projects. It also needs to provide consistency and support data quality efforts.?

3. Determine success measures??

Implementing AI can benefit the customer and employee experience in multiple ways. So, calculating the value of AI means looking at the results it brings from multiple perspectives.??

For example, time saved by automating a process like call summarization can positively affect individual agents’ performance metrics. And that leads to better team productivity and shorter wait times for customers.?

But looking at immediate gains is just the start. Consider long-term success measures, as well. For instance, using AI to automate multiple manual tasks agents typically perform during each interaction could cumulatively lead to performance gains so significant you won’t have to hire as many agents. And that reduces costs. Additionally, consider non-financial benefits, such as increased customer and employee satisfaction, as interactions are resolved more efficiently.?

4. Show the value??

Setting success measures will demonstrate key benefits of implementing AI in each of your prioritized use cases. And that’s important, but it’s just the baseline.??

Combining them to show the bigger picture of the short- and long-term potential creates a compelling story that shows AI’s true value to your organization. Adding innovation and continual optimization to that story will make an even more persuasive argument for implementing AI.?

Read “Telling the CX transformation story: How to build your business case for AI” to dive deeper into each of the four elements of our AI business case methodology.?