How Much ROI Can Generative Artificial Intelligence Really Drive for Enterprise?

Before we can have a reasonable discussion around leveraging artificial intelligence for enterprise, we need to know something foundational — how worthwhile is it really? With the security concerns, erosion of employee experience, and the possibility of losing out on competitive advantages, is generative AI worth considering for your organization to adopt? Is it worth uprooting old processes and streamlining new ones? What are the right ways to bring this into our business without scaring employees that their job will be lost or the customer experience will erode at the hands of a bot?

While those are critical questions, the answers aren’t cut and dry. Major decisions, such as using generative AI, aren’t ones someone can answer with a yes or no response. It requires strategy. It requires intentionality. And it requires problem-solving that delves below the surface via a framework for using artificial intelligence in enterprise.

So, is generative AI a juice worth the squeeze? Chances are, yes. All signs point to us being squarely at an inflection point on the S Curve of Growth with this new technology. Trying to beat generative AI is likely a losing battle. Instead, we believe now is the time to join it, albeit strategically.

The need to innovate isn’t anything new, either. We’ve seen these moments in history before. Blockbuster vs. Netflix. Toys ‘R Us and Borders vs. Amazon. MySpace vs. Facebook. Blackberry vs. iPhone. The list of companies that failed to innovate and ultimately paid the price for it continues. What each of these iconic demises and innovations alike have in common is this — the organizations that embraced emerging technology strategically and through the lens of opportunity saw the most significant gains.

Artificial intelligence in enterprise is that same emergent technology offering an opportunity to stay relevant while pushing up the S Curve of Growth. How? By leveraging the technology thoughtfully and intentionally to become a thought leader. By taking past investments in your business, marketing, and operations and pairing them up with AI for potential massive future gains.

One company innovating at the forefront of this emerging technology is Qualtrics, which just launched a new operating system now fully enabled with AI.

Qualtrics has long been an organization’s top platform for gathering feedback and experience-based data. With the market for customer experience management analytical tools expected to grow significantly over the next decade, Qualtrics is getting ahead and infusing new technology to help spur its success. In July 2023, the organization announced the release of XM/os2, the next generation of their operating system fully enabled with AI. With the $500 million investment they’ve earmarked for AI innovation over the next four years, it’s safe to assume that this new operating system is only just the beginning of how the company plans to leverage generative AI for its enterprise.

The company’s interest in emerging technology isn’t what’s compelling about this technology. It’s how they’ve deployed generative AI to offer their clients a more robust experience.

The new operating system doesn’t simply connect generative AI sources like ChatGPT to its design. Instead, it takes the 3.5 billion conversations and interactions it captures yearly. It amplifies the analysis for their clients so that organizations can act faster, make more money, and do so through the lens of customer empathy. By taking sentiment data from call centers, chat logs, survey responses, social media posts, and more, organizations can deploy faster, more personalized experiences to their employees and customers on the backend. This is just one approach organizations can take past investments in data and research and deploy them for future gains through stickier experiences that improve customer acquisition cost (CAC) and lifetime value (LTV) ratios.

While this investment seems promising, it’s important to remember that generating an output of suggestions based on customer and employee feedback is only the first step toward seeing ROI from generative AI.

Thomas Ramsoy conducted a study on generative AI that illustrated this point well. He wanted to see how effective generative AI outputs could be for speeding up something as important as product packaging. Here’s what he got when he asked prompted AI for “beautiful packaging for cereal, frost color, cantaloupe color, heather color, sage color,–v 4.”

Aside from the obvious lack of proper product names, AI did a pretty good job at delivering visually appealing product packaging. But, Ramsoy didn’t want to take this output at face value, especially given how new generative AI technology is. Smart man.

To test the caliber of the output, Ramsoy analyzed each of these designs using a secondary type of artificial intelligence — predictive artificial intelligence called Neurons Predict.

As you can see here, Neuron Predict added a layer of insight on top of the generative AI tool. That insight used predictive data to break down which packaging would be the clearest, most engaging, and most beneficial to the brand’s image. This is so powerful because it takes a massive amount of information, such as that which can be gleaned from tools like Qualtrics and heat mapping tools, and then processes it very quickly. Rather than spend weeks or months deciding on new product packaging, teams can move faster, speeding process handoffs. In addition, AI allows teams to make decisions with more intelligence by considering a wide range of factors and whittling them down into more digestible options.

Yes, the use cases for generative AI and predictive AI as helpful counterparts are vast. Still, deciding where it fits into your organization and how to allow teams to use it, and use it effectively, is a whole other matter.

The Age of Generative Artificial Intelligence in Enterprise Requires Experience Management

We’re teetering a fine line when it comes to generative artificial intelligence and experience management for enterprise. On one side of the beam is the hesitation, path of obsolescence, stagnation, and full stop of all AI usage. On the other is a full lean into this new technology, uprooting old processes and platforms in exchange for the promises of new AI tools. The line between the two feels thin and unsteady, yet the C-Suite and Directors are dancing delicately while trying to navigate the murky waters below each side.

As you maneuver the same tightrope in your organization, you’re encouraged to turn your attention in one direction — relevancy. How can you remain relevant in today’s market by leaning into experience management with and without generative AI? How can your people, platforms, processes, and products remain relevant, delivering a more sublime experience to your employees and customers?

With all of the discussions around generative AI in enterprise today and the implications that will follow regarding experience management, having a keen understanding of what’s happening in your team and your buyer’s worlds is critical. Without it, you will have a significantly harder time knowing where to lean and how to navigate these complex decisions for your organization.

Mixed Emotions Influence the Employee Experience

To kickstart the conversation around experience management in an age of generative AI, we’re starting at a seemingly unexpected spot — youremployee experience. Too often, organizations make kneejerk reactions to what customers say they want and what headlines say customers demand today from businesses.

Making a more intelligent decision with legs that will hold your organization steady over the coming years requires first looking internally at your team’s wants and needs. These people are steering the everyday decisions, working daily with your organization’s platforms, and navigating the complexities of process handoffs and iterations.

Many teams we’ve heard from feel the weight of generative AI. The C-Suite and Directors we’ve talked to recently have noticed and are paying extra attention to how this technology has started to impact their experience management strategies. Fear has crept in as headlines like the ones seen here are more abundant.

There’s a palpable panic among team members about the possibility of losing jobs to AI. A recent survey commissioned by Microsoft found that 49% of employees worry they will lose their jobs to AI. Another study on the economic potential of generative AI by McKinsey and Company found that generative AI could absorb or automate 60 to 70 percent of employees’ time today, which could mean a distinct shift in work activities and job expectations over the next decade. These statistics do not bode well for how generative AI influences experience management strategies in the workplace.

In addition to these headlines about AI stealing jobs, other headlines about the continued layoffs have caused nail-biting, specifically in the tech industry.

One of the core reasons for these mass tech layoffs is the macroeconomic environment, lowered budgets, and, yes, AI. AI tech layoff reports have increased as more companies navigate the murky waters ahead. Many companies are also placing a hiring freeze until they can determine which roles can be done by AI and which roles require a human touch.

Despite these changes, it’s not all doom and gloom. Columbia Business School professor Dan Wang said:

”AI, as far as I see it, doesn’t necessarily replace humans, but rather enhances the work of humans.”

Sure, AI can feel like a threat, but to many in the workforce, it also feels like an opportunity. With all of the promises of this new technology comes a giant question mark around how jobs will evolve. For some organizations, the answer to assuaging these fears is to show teams that their jobs are safe by temporarily banning all AI from the workplace. After all, if it won’t be allowed in, there’s no threat to job replacement, right? Not quite.

Although the threat of AI stealing jobs is real, more people surveyed by Microsoft said they hoped that AI could help with their growing workloads instead. 76% of respondents said they hoped the technology could help with administrative work, 79% with analysis, and 73% with creative work. This automation could have a distinct improvement on the employee’s experience. 64% of employees say they struggle to have the time and energy to do their job, which has caused 60% of leaders to say they’re feeling the effects of that productivity hurdle in the form of fewer innovative or breakthrough ideas.

There are mixed emotions regarding how and where generative AI for enterprise gets deployed. While some employees fear it, others embrace it. Knowing when and how to implement AI platforms to smooth the employee’s experience requires a framework, which we’ll cover soon. Before we do, let’s look at the other side of this experience management (XM) equation — the customer’s experience.

Mixed Emotions Around Using Enterprise Generative Artificial Intelligence Can Impact the Customer Experience

Imagine this: You’re flying home from a business trip the day before Thanksgiving. On the way to the airport, you get a text message that says your flight was canceled. Thoughts of missing out on a feast with your family instantly put weight on your chest. You’re traveling on one of the year’s busiest weekends and desperately need help getting yourself on the next flight out. In this instance, would you rather wait to talk to an employee or have a bot work magic to rebook you on the soonest flight available, even if that means adding an extra stop along the way?

The knee jerk reaction to that question would be the latter. After all, isn’t that the beauty of living in an age where AI can help with tasks such as these? To scale customer service and improve the customer’s experience? In some instances, the answer is a resounding yes. However, there’s still work to be done to reach that point where customer service can become scalable, contrary to where we are now, where customer support times are directly tied to how many hands you have on deck and those individuals’ responses and resolution times.

We’ve been building robots for a long time, but only recently has the quality of this technology improved to a level where it can replace customer support teams and offer faster, more dynamic service. While AI as an algorithm can be faster than human thinking, it cannot still connect and emote. Those emotions? They’re tied within the human brain to the core of their experience.

In every interaction, two core parts of the brain are stimulated — the emotional response center, the limbic system, and the logical response center, the neocortex. The human brain taps into each of these areas to decide which brands to work with, trust, and praise or criticize publicly by reconciling past experiences and emotions, as well as predicting future emotions and experiences. Understanding this human behavior is core to understanding where and how AI can fit into the customer’s experience. Emotions and human connection are key for going deeper than leveraging a bot to find a solution.

Perhaps this lack of human connection or emotional response has caused some hesitation among consumers regarding generative AI specifically. That human experience isn’t just for major life events, such as missed family memories around the Thanksgiving dinner table. It can even be felt in seemingly mundane situations, such as placing a fast food order in the drive-thru.

For the consumer, drive-thrus are inherently low on the human connection index. You don’t have to exit your vehicle. You don’t have to look someone in the eye when placing your order. Heck, you could wear your pajamas and still be considered acceptably dressed for the situation. However, for the employee, 10 things are going on at once. Sure, they’re taking the order, but they’re also being tasked with accepting payment, clarifying past orders, fulfilling orders, and yes, still acting warmly, welcomingly, and within a reasonable timeframe when the customers arrive.

Wouldn’t it seem logical to add a bot into the mix to get something like an order of burgers and fries? The customer would have their order taken, while the employee would get one thing taken off their plate. Not quite, according to a Forbes article that outlines how consumers feel about having AI take your order, even in the drive-thru.

When it comes to having AI step into an employee’s role, myriad underlying elements must be considered.

• Is the business obligated to inform the customer when conversing with AI?
• How will AI approach upselling, and will it go through the roof?
• What if the generative AI flies off into wonderland?
• Will customers try to trick the AI or needlessly waste time with it?
• How will generative AI handle hangry people?
• Could customer accents and other issues hinder generative AI?
• Will AI use in fast-food exacerbate obesity or overweight issues associated with fast food?
• Is AI a sentient being?

That last question is critical. Can AI perceive and feel something? In short, the answer is yes. How people interact with generative AI shapes how it perceives the intent of the interaction and feels its way toward its response. This is why, as mentioned earlier, Mo Gawdat emphasizes how we speak to the bots we interact with. The better we “raise” these bots to be kind, caring, and empathetic, the more the bots will treat our questions and inputs with that same care and empathetic lens.

Before AI can be incorporated into a setting as seemingly straightforward as a fast-food drive-thru, the customer experience must be considered. How consumers understand, interact with, and feel about a generative AI tool taking their order can be the difference between them choosing one restaurant over another. What feels grassroots is not. Competition at a corporate level depends on these decisions.

Fast food isn’t the only industry where these types of questions arise, either. There are loads of other scenarios where the customer experience could be negatively affected by infusing AI into the mix for the sake of productivity, efficiency, or cost savings by the enterprise.

Although artificial intelligence has existed since the 1940s, as showcased above, how organizations strive to adopt it is new. That leaves a lot of room for things to go awry when integrating these platforms in a way that directly impacts the overall customer and employee experiences.

Continue reading about the framework we use in shaping these employee and customer experiences in the full blog post on generative AI for enterprise here.