The journey of artificial intelligence is not a sprint but a marathon, requiring businesses to adjust their pace accordingly. Companies that attempt to accelerate before mastering the fundamentals will falter, ultimately joining the ranks of those that fail by hastily striving to achieve specific AI milestones. In reality, there is no definitive finish line for AI. Organizations cannot reach a destination and declare they have fully mastered AI. According to McKinsey, 2023 was the breakout year for AI, with approximately 79% of employees indicating some level of engagement with AI. However, the development of breakthrough technologies does not follow a linear path; they experience fluctuations until they become an integral part of the business structure. Most enterprises understand that AI is a marathon rather than a sprint, a fact that should remain firmly in mind.

Using Gartner’s Hype Cycle as an example, almost every emerging technology undergoes the same sequence of stages on the technology maturity curve, with few exceptions. These stages include the Technology Trigger, Peak of Inflated Expectations, Trough of Disillusionment, Slope of Enlightenment, and Plateau of Productivity. In 2023, Gartner classified generative AI explicitly in the second stage: Peak of Inflated Expectations. At this phase, the hype around the technology reaches its zenith. While some companies can seize opportunities and rapidly ascend, the vast majority will experience the Trough of Disillusionment and may not even reach the Plateau of Productivity.

This all indicates that businesses need to approach AI deployment with caution. Although the initial appeal and capabilities of AI are enticing, it is still finding its footing and exploring its limits. This does not mean that companies should avoid AI; rather, they should recognize the importance of setting a sustainable pace, defining clear objectives, and meticulously planning their AI journey. Leadership teams and employees must fully embrace this mindset to ensure data quality and integrity, achieve compliance goals—and this is just the beginning.

By starting on a small scale and setting achievable milestones, companies can leverage AI in a controlled and sustainable manner, ensuring they evolve in tandem with AI technologies rather than outpacing them. Below are some of the most common pitfalls we observed in 2024:

Pitfall 1: AI Leadership

The fact is: without executive support, AI projects will struggle to advance. While employees might independently discover generative AI tools and integrate them into their daily tasks, this can expose the company to data privacy, security, and compliance issues. Regardless of how AI is deployed, executive backing is essential. A lack of interest or overcommitment from leadership is equally perilous.

Take the U.S. health insurance industry as an example. A recent survey by ActiveOps revealed that 70% of operations leaders believe that executives are disinterested in AI investments, creating significant barriers to innovation. Although nearly 80% agree that AI can substantially enhance operational performance, the lack of executive support is becoming a frustrating obstacle to progress.

Where AI is utilized, internal organizational support and executive backing are crucial. Clear communication channels between leadership and AI project teams should be established. Regular updates, transparent progress reports, and discussions about challenges and opportunities will help maintain executive engagement and awareness. When leadership deeply understands the AI journey and its milestones, they are more likely to provide the ongoing support needed to navigate complexities and unforeseen issues.

Pitfall 2: Data Quality and Integrity

Using low-quality data for AI is akin to putting diesel in a gasoline car. You’ll end up with poor performance, damaged components, and high maintenance costs. AI systems rely on vast amounts of data to learn, adapt, and make accurate predictions. If the data input into these systems is flawed, incomplete, misclassified, or biased, the results will inevitably be unreliable. This not only undermines the effectiveness of AI solutions but can also lead to severe distrust in AI capabilities and significant setbacks.

Our research shows that 90% of operations leaders report that extracting insights from their operational data requires too much effort—too much data is scattered across multiple systems and is inconsistent. This is another pitfall companies face when considering AI: their data is not yet ready.

To address this issue and improve data quality, businesses must invest in robust data governance frameworks. This includes establishing clear data standards, ensuring ongoing data cleaning and validation, and implementing systems for continuous data quality monitoring. By creating a single source of truth, organizations can enhance data reliability and accessibility, providing additional benefits for the smooth implementation of AI.

Pitfall 3: AI Literacy

AI is a tool, and tools are only effective when used correctly. The success of AI projects depends not only on the technology but also on the people who use it—people who are currently in short supply. According to Salesforce, nearly two-thirds (60%) of IT professionals believe that a lack of AI skills is the primary barrier to AI deployment. This suggests that companies are not yet prepared for AI and need to address this skills gap before significantly investing in AI technologies.

However, this does not mean extensive hiring is necessary. Companies can implement training programs to enhance the capabilities of existing employees, ensuring they are equipped to use AI effectively. Fostering AI literacy within the organization requires creating an environment that encourages continuous learning—workshops, online courses, and hands-on projects can help demystify AI, making it more accessible to employees at all levels and laying the groundwork for faster deployment and more tangible benefits.

What’s Next?

Successfully adopting artificial intelligence (AI) requires more than just technological investment; it demands a well-paced, strategically clear approach to ensure employee buy-in and executive support. Additionally, companies must possess self-awareness and a realistic understanding of technology's limitations. Despite the high interest in AI and adoption rates reaching historic highs, it is highly likely that the AI bubble will burst before AI self-corrects and evolves into a stable, reliable tool that businesses need. Remember, we are currently at the “Peak of Inflated Expectations,” and the “Trough of Disillusionment” is yet to come. Companies eager to invest in AI can prepare for the impending storm by: cultivating employee skills, establishing AI usage policies, ensuring their data is clean, well-organized, correctly classified, and integrated across the entire organization.