Countless discussions about AI’s transformative potential have taken place over the past two years since ChatGPT’s initial release generated so much excitement. Corporate leaders have been eager to use the technology to reduce operational expenses. Perhaps surprising, though, is that for many leaders, the key metric used to evaluate the success of an AI tool is not the lifetime return on investment (ROI). It’s the speed to ROI.
Amid shrinking risk tolerance and increased revenue pressure, leaders expect investments to drive changes and pay off quickly. At the same time, the hype around AI is dying down, making way for more pragmatic conversations around the return on AI investments.
The Next Phase: Getting Real About Where AI Works
Success in today’s market—where subscriptions are king—relies on how well you keep customers, not how well you acquire them. In most sectors, the market is oversaturated, and many organizations offer similar services of near-identical quality. Add in a decline in customer loyalty, rising expectations and an increased willingness to switch brands, and organizations find themselves with no room for error to keep up with fierce competition. Customer experience (CX) is the factor that determines whether subscription-based organizations thrive or fall short.
In this environment, organizations can compete best by leaning into incremental improvements rather than away from spending. Each and every choice the organization makes must be oriented toward specific, customer-centric goals — even if it costs a bit more at the start. That extends to AI implementation. Organizations have been asking how AI can recoup its cost by using it as a replacement for existing resources. Now, they need to ask how AI can create value for the organization by improving how they work with customers.
The answer is straightforward enough. AI has numerous potential applications that improve CX both directly and indirectly. AI-powered tools can enhance personalization by using customer behavior data to ensure the users see the right message or promotion at the right time. The same data can help guide product development, highlighting gaps in the market that the organization might capitalize on to better serve customers’ needs. They can also make organizations more proactive, helping them anticipate disruptions, activate contingency plans and communicate necessary information to users.
However, this work happens primarily behind the scenes, and it cannot happen overnight.
Want AI at Its Best? Start With ‘Invisible’ Applications
The only way to know for certain whether a back- or front-end use case will yield the results you’re after is to leverage AI’s more discreet, behind-the-scenes capabilities first.
Behind the headlines about instant transformation is AI’s core capability: analysis. Large language models (LLMs) like ChatGPT turned heads for their apparent flexibility, but they perform only one task no matter where they operate. They summarize information. It’s on organizations to make the right information available, and that takes time. Those are two facts that have often been lost in the conversation, and they represent an end to the “quick fix” reputation AI has come to enjoy.
The next era will be defined by the invisible improvements facilitated by AI as organizations build up their technical foundations. Organizations can start with LLMs that help:
- Integrate existing databases and break down silos to provide end-to-end visibility – and the context that comes with it.
- Implement real-time data collection tools to ensure insights are up to date and reflect the most recent trends, patterns and disruptions.
- Expedite reconciliation and management to ensure accuracy and free up workers to focus on higher-level tasks that require a human touch.
Organizational change is the first step to effective implementation and extends to both systems and staff. At this point, leaders should also consider the ways AI deployments might affect staff and work to get ahead of potential obstacles. Developing upskilling and reskilling programs will help ensure staff is ready to work effectively alongside the new technologies. AI itself can help in these efforts—another of its invisible applications. For example, it can highlight individual knowledge gaps based on utilization data. This kind of information can guide training programs to make sure workers have everything they need to thrive.
Once organizations have integrated, accurate and up-to-date records and a staff that understands how and when to use AI, they can add another layer of “invisible” tools. The next wave of solutions should focus on analytics that help cultivate a deep understanding of how the business runs, what customers want and obstacles getting in the way. These solutions build on one another, with each step revealing a new level of insight.
More specifically, descriptive analytics use historical data to identify historical patterns; they tell organizations what happened. Diagnostic analytics use additional data to contextualize what happened, identify causes and highlight the effects of incidents and changes; they tell organizations why things happened the way they did. Predictive analytics use insights from past events to model the impacts of proposed changes and keep tabs on trends; they show organizations what might happen. Prescriptive analytics use all of these outputs to make informed decisions; they tell organizations what to do next.
Though analytics solutions like these may tap into AI’s more advanced capabilities, it’s worth noting that—at first—nearly all these processes happen behind the scenes. Eventually, predictive and prescriptive algorithms may make their way into consumer-facing solutions, but that can only happen once this critical, internal foundation is laid.
As AI’s honeymoon ends, so too will its reputation as a magic fix—but shedding this perception is critical to realizing the technology’s full potential. Leaders who want to make headlines tomorrow with innovative AI applications must first complete this foundational work, which may be a hard pill to swallow amid pressure for faster and faster returns. However, moving toward more holistic, incremental and long-term assessments of AI’s value will enable organizations to expedite returns. This approach gives leaders the tools and time to develop a clear picture of what needs to be fixed, insight into the small changes that will have the biggest impacts and the ability to develop sound strategies that yield returns today without damaging profitability tomorrow.
Pragmatism from End-to-End
Though flashy use cases may entice customers at first glance, and cost-cutting opportunities might catch the eye of corporate leaders, neither is likely to define AI’s impact in the long run. Instead, the technology will become synonymous with behind-the-scenes work that drives tangible improvement at scale.
The end of the honeymoon phase marks the beginning of a more mature relationship with AI, one that requires careful consideration of how it can genuinely enhance customer experiences and drive profitability. Ultimately, the key is to view AI not as a quick fix but as a strategic partner in the pursuit of customer loyalty, satisfying experiences and simple solutions in today’s increasingly complex operations.
In the coming months and years, the organizations that excel will be those that dig deeper, commit to change and recognize AI’s potential as both a short- and long-term investment.