In the past two years, since ChatGPT was first released, there has been countless and exciting discussions about the transformative potential of AI. Business leaders have been eager to leverage the technology to reduce operating expenses. But perhaps surprisingly, for many leaders, the key metric used to evaluate the success of AI tools is not lifetime return on investment (ROI). This is speed ROI.
Amid reduced risk tolerance and increased revenue pressure, leaders expect investments to drive change and pay off quickly. At the same time, the hype around AI is fading, making way for more pragmatic conversations around the return on investment in AI.
Next stage: Really understanding how AI works
In today’s marketplace where subscriptions are king, success depends on your ability to retain customers, not acquire them. In most industries, the market is oversaturated with many organizations offering similar services of almost identical quality. Combined with declining customer loyalty, rising expectations and an increased willingness to switch brands, organizations find themselves with no room for error to keep up with fierce competition. Customer Experience (CX) Yes this Factors that determine whether a subscription-based organization thrives or fails.
In this environment, organizations can compete best through incremental improvements rather than foregone spending. Every choice an organization makes must be geared toward specific, customer-focused goals—even if it costs a little more initially. This extends to the implementation of artificial intelligence. Organizations have been asking how AI can pay for itself by using it as a replacement for existing resources. Now, they need to ask how artificial intelligence can create Create value for the organization by improving how it works with customers.
The answer is simple. Artificial intelligence has many potential applications that can directly or indirectly improve customer experience. AI-driven tools can enhance personalization by using customer behavior data to ensure users see the right message or promotion at the right time. The same data can help guide product development and highlight gaps in the market that organizations can leverage to better meet customer needs. They can also make organizations more proactive, helping them anticipate outages, initiate contingency plans and communicate necessary information to users.
However, this work mainly happens behind the scenes and cannot be accomplished overnight.
Want artificial intelligence to be at its best? Start with “invisible” apps
The only way to determine whether a back-end or front-end use case will produce the results you want is to first leverage AI’s more discreet behind-the-scenes capabilities.
Behind the headlines about instant transformation lies AI’s core capability: analytics. Large language models (LLMs) such as ChatGPT are notable for their apparent flexibility, but they only perform one task no matter where they are run. They summarize information. Organizations have a responsibility to provide the right information, and this takes time. These two facts are often overlooked in conversations, and they represent the end of the “quick-fix” reputation that AI has enjoyed.
As organizations build their technology foundations, the intangible improvements brought about by AI will define the next era. Organizations can start with an LL.M. to 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 current and reflect the latest trends, patterns and disruptions.
- Accelerate coordination and management to ensure accuracy and allow employees to focus on higher-level tasks that require a human touch.
Organizational change is the first step to effective implementation and extends to systems and people. At this point, leaders should also consider the ways in which AI deployment may impact employees and jobs to overcome potential barriers. Developing upskilling and reskilling programs will help ensure employees are prepared to work effectively with new technology. Artificial intelligence itself can aid in these efforts—another invisible application of artificial intelligence. For example, it can highlight individual knowledge gaps based on utilization data. This information can guide training programs to ensure workers have what they need to thrive.
Once organizations have integrated, accurate and up-to-date records, and employees understand how and when to use AI, they can add another layer of “invisible” tools. The next wave of solutions should focus on analytics to help gain insights into how the business operates, what customers need, and what obstacles are encountered. These solutions build upon each other, revealing new levels of insights at every step.
More specifically, descriptive analytics uses historical data to identify historical patterns; they tell an organization what happened. Diagnostic analytics use additional data to understand what happened, identify causes and highlight the impact of events and changes; they tell the organization why things happened the way they did. Predictive analytics use insights from past events to model the impact of proposed changes and keep an eye on trends; they show organizations what is likely to happen. Prescriptive analytics use all of these outputs to make informed decisions; they tell the organization what to do next.
While such analytics solutions may leverage the more advanced capabilities of artificial intelligence, it’s worth noting that nearly all of this process occurs behind the scenes at first. Eventually, predictive and prescriptive algorithms may find their way into consumer-facing solutions, but that will only happen after this critical internal foundation has been laid.
As AI’s honeymoon period ends, so will its reputation as a magical solution, but moving away from this perception is critical to realizing the technology’s full potential. Leaders who want to make tomorrow’s headlines with innovative AI applications must first complete this groundwork, which can be a difficult pill to swallow amid pressure for faster and faster returns. However, a more comprehensive, incremental and long-term assessment of the value of AI will enable organizations to accelerate returns. This approach gives leaders the tools and time to gain a clear understanding of the problems that need to be solved, insight into the small changes that will have the greatest impact, and the ability to develop sound strategies that generate returns today without compromising profitability tomorrow.
End-to-end pragmatism
While flashy use cases may attract customers at first glance, and cost-cutting opportunities may catch the attention of business leaders, neither is likely to define AI’s impact in the long run. Instead, the technology will become synonymous with the behind-the-scenes work that drives real improvements 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 to truly enhance the customer experience and improve profitability. Ultimately, the key is to view AI not as a stop-gap solution, but as a strategic partner in the pursuit of customer loyalty, satisfying experiences, and simple solutions in today’s increasingly complex operations.
The organizations that will outperform in the coming months and years will be those that dig deep, commit to change, and recognize the potential of AI as a short- and long-term investment.
