I originally wrote today’s post for Concentrix. It recently appeared on their site. This is a slightly modified version.
In a recent study, Capgemini found that 60% of customer experience leaders expect artificial intelligence (AI) to have a “‘transformative” or “significant” impact on the experience. They also found that 96% of executives interviewed believe that AI is not a disruptor but an accelerator.
Artificial intelligence IS driving a transformative impact on customer experience, with 54% of organizations having improved efficiency and reduced costs after implementing AI.
Business Impact of AI
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, I believe that can all be wrapped up and summarized as a more personalized experience for the customer – or as the experience the customer expects.
Business Acceleration Due to AI
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.
I’m reading into all of that: 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.
Born Digital Episode 7 and 8
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: Sr. Practice Lead, Americas Partner Solutions Consulting, Adobe
- Colin O’Neill: Sr. 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 agent coaching
- Creative design – graphic and image creation
- A/B testing and optimization
- Market research
- Journey optimization
AI and the Employee Experience
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 – content that is personalized – and they need a lot of data to personalize the content, the messaging, and the experience overall. That means that, increasingly, there is 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 Berenholz shared that it’s referred to as “prompt engineering,” and companies are hiring people with this skill. McKinsey defines it as “the practice of designing inputs for generative AI tools that will produce optimal results.”
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.
Building Blocks of Generative AI Strategy
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:
- What are your business objectives? What can generative AI do to make those objectives achievable?
- Who is the subject matter expert? Or will you have a center of excellence? Assign people to learn as much as they can.
- 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 is at the root of any successful AI strategy. I asked Dan and Colin what kinds of data you should be thinking about.
Types of Data Needed
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.
Problems That AI Solves
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? So, I asked what problems generative AI solves for businesses. Colin said there are six types:
- Scale: helps handle large data sets
- Innovation: when it’s woven into products, helps create space for ideation and for synthesizing different inputs into your processes
- Knowledge access: improves access and creates context
- Efficiency: powerful, high quality results quickly
- Personalization at scale: delivers personalized experiences to individuals
- 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.
In Closing
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 has been recorded and is coming soon!
Here’s an important distinction about tasks and jobs…
Like with all technological revolutions …I believe that there will be far greater jobs on the other side of this, and that the jobs of today will get better.” …GPT-4 and other systems like it are good at doing tasks, not jobs. ~ Sam Altman
Image courtesy of Pixabay
