How Implementing AI Is Reshaping the Customer and Employee Experience

Born Digital, Episode 7 Recap

Artificial intelligence (AI) is driving a transformative impact on customer experience, with 54% of organizations having improved efficiency and reduced costs after implementing AI.¹ Let’s start with the business impact. We’re already seeing some brands using AI to personalize experiences, something that customers expect more and more today. At the same time, AI allows brands to gather data, analyze it, and glean insights—all in real time. Predictive and prescriptive analytics allow for better support, guidance, and recommendations for next best actions for your customers. And it aids in optimizing customer journeys, especially through journey orchestration.  

Ultimately, it can all be wrapped up and summarized as a more personalized experience for the customer—or as the experience the customer expects. 

But what about business acceleration? How does implementing AI accelerate business growth and help you stay competitive in today’s fast-paced business environment? Just some of the ways include driving innovation and R&D efforts, automating processes, reducing costs, assisting with better (real-time or proactive) decision-making, improving efficiency, increasing productivity, optimizing infrastructure, and improving customer interactions. All of which can be summed up as a better employee experience and a better customer experience. 

In addition, AI can help you analyze customer data and create personalized marketing campaigns, identify new sales opportunities, and improve sales processes. Having the ability to work with optimized pricing, improved demand forecasting, stronger sales support, and better data-driven decision-making helps businesses attract more customers, drive conversion, and increase conversion rates.  

In our latest episodes of Born Digital, we went straight to the experts to dive deeper into all of that. During the first of a two-part series, which is all about generative AI and how it’s reshaping the way we do business, I spoke with: 

• Dan Berenholz, Senior Practice Lead, Americas Partner Solutions Consulting, Adobe 
• Colin O’Neill, Senior Director of Design, Concentrix 
  

As both marketing and customer expectations evolve, marketers are increasingly beginning to take advantage of generative AI in order to ensure their outputs are more relevant, more personal, and more effective—and to assist the customer throughout their journey. They’ve got a variety of use cases, including (but not limited to): 

• Content creation and copywriting 
• Email marketing – personalization and optimization  
• Social media marketing – content creation and sentiment analysis 
• SEO optimization 
• Chatbots and customer support, including advisor coaching 
• Creative design – graphic and image creation 
• A/B testing and optimization 
• Market research 
• Journey optimization
  

With AI assisting or doing all of that, what’s the impact on employees? That’s a fair question and a major concern for many. There’s this fear factor that “AI will take my job.” 

Companies are facing a demand for more content that’s personalized, and they need a lot of data to personalize the content, the messaging, and the experience overall. That means that, increasingly, there’s a need for employees to have that skillset required to interact with generative AI. It doesn’t sound hard to type something into a prompt to get the result you want, but it does require a specific skillset to ask the right questions to get the right output. Dan shared that it’s referred to as “prompt engineering,” and companies are hiring people with this skill. 

Ultimately, what I heard was that this means good things for employees. Automating menial and repetitive tasks frees employees to focus on the more important work. They’ll be more productive and efficient, learn new skills, and have growth and development opportunities—and opportunities to add more value. 

Later in this episode, we dug deep into the not-so-basic building blocks of a generative AI strategy. By the way, Concentrix has put together a great guide on how to take your generative AI strategy from vision to innovation. In our conversation, we talked about some of these items, and more. 

Where to start? Dan said there are three easy steps; identify the following: 

1. What are your business objectives? What can generative AI do to make those objectives achievable?  
2. Who is the subject matter expert? Or will you have a center of excellence? Assign people to learn as much as they can. 
3. What kind of technology do you have? Is there a roadmap for that technology to incorporate AI? If not, will you need new technologies/licenses to achieve your objectives?

Data is at the heart of designing and delivering a great experience. And, clearly, it’s at the root of any successful AI strategy. I asked Dan and Colin what kinds of data you should be thinking about.  

Colin shared that it’s almost any type of data: text, images, audio, video, operational data, performance data, etc. But brands have struggled with that. They’ve got tons of data but not enough knowledge and intelligence is gleaned from that. The beauty of AI is that it allows you to not only do that, but to also create connectivity across all data stores and sources, helping you translate data to knowledge in a more personalized and rapid way than ever before. 

Finally, when it comes to the customer experience (in this case, it’s both customers and employees), we should always be asking about products: what problems does that solve for me? According to Colin, there are six types of problems generative AI solves for businesses: 

1. Scale: helps handle large data sets 
2. Innovation: when it’s woven into products, helps create space for ideation and for synthesizing different inputs into your processes 
3. Knowledge access: improves access and creates context 
4. Efficiency: provides powerful, high quality results quickly 
5. Personalization at scale: delivers personalized experiences to individuals 
6. Right infrastructures for empathy and ethics: makes you think about that

It’s great for problem-solving and for considering different approaches to issues. It will validate solutions and synthesize scenarios—and there’s a lot of value in that. But it’s important to remember that you need the right mindset and framework to be successful when implementing AI. 

Keep in mind that I just skimmed the surface of what we talked about in this episode. I refer to the two episodes combined as a masterclass on generative AI. There’s so much more detail in these episodes! Be sure to catch this first episode—the second will follow shortly.