Don’t Panic: Why AI FOMO is Overblown

The hype around AI is pervasive, and organizations across industries are investing in what has quickly become a must-have technology. As fast as this new AI arms race has heated up, many organizations are left wondering if they are already late, and that fear of missing out (FOMO) can quickly turn into a panic that they are behind the competition and destined to be on the outside looking in.

Spoiler alert: they are not.

However, it’s not because this is a case of unwarranted hype—adoption of AI technology is growing rapidly—but the sense of “get on board with everything now because the last train is leaving the station” is overplayed. Furthermore, FOMO is never a good reason to do something—especially deploying a cutting-edge technology that is still evolving with new use cases being thought up and brought to market at light speed. Per a recent study, 42% of CIOs do not expect to see positive ROI in their AI investments for at least 2-3 years. The significant time and resource investment in AI is a marathon—not a sprint. So, then, what is an organization to do? Maintain focus, find your center, and execute. Here is how.

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Keep the precedent in mind

First, we must acknowledge that we have seen a version of this movie before. The market gets a disruptive new technology (PC, Dotcom, Smartphone, Big Data, SaaS, Cloud, etc.) that promises to fundamentally shift the way business is done, and organizations immediately react as though everything must go to it right away. Let us look at one of these examples. With the cloud, use cases were wide open, and organizations jumped in headlong, ceding massive amounts of data, control, and even large budgets to a handful of companies with no plan B.

Only later, though, did they realize that these benefits came with some tangible challenges: vendor lock impacting the promise of cheap scalability; concerns about visibility, accessibility, and security of data; ability to integrate with other areas of their organizations; among others. While the wonderful promise of cloud has come to fruition, the industry has also learned that there is no one-size-fits-all approach, and organizations must be deliberate in their strategies.

The key lesson learned from the disruption brought on by cloud technologies was the importance of maintaining focus on the desired outcome before blindly allocating IT resources. Today, the same holds true with AI. As its usage becomes increasingly pervasive, organizations must strike the right balance between exploring its wide-open adoption and concentrating investment on clearly defined projects that will produce tangible and measurable results.

There is a sense of urgency in all organizations wanting to deploy AI, but they must also remain grounded in best practices and lessons learned. In short, yes. They need to get on board, but they need to be realistic and deploy toward a tangible result where ROI is noticeable and impactful —not just a sweeping promise.

Know the tech but maintain focus on outcomes

One of the more recent challenges with determining the correct course of action with AI is differentiating among the variety of AI/ML technologies. For example, while most organizations understand the main differences between traditional AI and Generative AI, they also exist in a host of different flavors and come with a constant stream of new concepts and models. The technology itself can be completely overwhelming, and per the recent CIO survey, less than half (49%) believe their IT departments have “AI ready” technical skills. Confidence in AI readiness wanes even further when considering other areas/functions within the organization such as data and analytics reporting or security infrastructure.

Yet, flashy and exciting as it is, the technology is only part of it—the “what.” Organizations need to maintain their focus on the “why.” It is important to take time to understand the models available, but it is critical to tie them to use cases and outcomes when making decisions about which make sense to implement. Organizations should know their own pain points and areas for improvement, and they can always partner with industry leaders who can help them determine what AI technology to use and where AI can have an immediate positive impact.

Organizations should get started in an area where they will see results—whatever those may be (new revenue streams, call center productivity, impact on employee experience, revamps of painful processes, etc.). Furthermore, the results do not immediately need to have a massive impact on the bottom line to count. They simply need to be positive, tangible, and measurable. Success builds on itself. From there, organizations can build on that early momentum to find more and more use
cases that would benefit from AI, but they must still maintain their focus on their own desired outcomes. This requires blocking out the noise and market FUD about competitors or what some aspirational companies are doing with AI.

Market FUD is especially dangerous when organizations are comparing themselves to their industry peers, as it can be easy to look at competitors and see early examples of their AI “successes.” However, in many cases, these ideas or concepts are the ones they want to share, and organizations are far from the promise of scaling or even implementing AI across their businesses.

Also Read: AiThority Interview with Nicole Janssen, Co-Founder and Co-CEO of AltaML

Conclusion

With the unrelenting hype around AI, it is easy to see how organizations can feel that they need to go all in on the technology right away for fear of being left behind. The reality, though, is that organizations are not launching AI initiatives at any kind of scale—that will
take time.

The smartest ones are not standing still and are finding the specific areas in which they can apply AI to drive a tangible positive result and build on these early successes. Furthermore, they are not navigating this solo. The endless stream of innovations and models are a good reminder to seek the expertise that can help make an AI investment go smoothly. As with every disruptive technology, aligning with the right partner who understands AI from all angles can help make informed decisions on how to go about getting started. From an initial project showing positive results, organizations can set the stage for additional use cases and begin to scale their AI efforts.

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