Business is Realizing the Limitations of Generative AI

In a previous post, I mentioned a statistic that Gartner predicts 39% of organizations will discontinue their generative AI projects by 2025. This forecast is aligned with the usual hype cycle associated with all revolutionary technological advancements, including generative AI.

The “Gartner Hype Cycle” Charts a Path Forward

Garter produces “Hype Cycle” reports that describe the typical progression of emerging technologies. Following the peak of inflated expectations, there is often a phase known as the “Trough of Disillusionment.” According to reports from Gartner, generative AI has advanced quickly through the Hype Cycle. It has moved past the “Peak of Inflated Expectations” and has entered the “Trough of Disillusionment” in 2024. Predictions indicate that generative AI will reach mainstream adoption within two years.

Generative AI’s Current Position

As of 2024, generative AI has rapidly progressed through Gartner’s Hype Cycle, moving beyond the “Peak of Inflated Expectations” and entering the “Trough of Disillusionment”[1] [2]. This transition represents a significant shift in the technology’s maturity and perception, occurring faster than typical for emerging technologies in many sectors[3]. The current positioning indicates that generative AI is moving from a phase of inflated expectations to a more realistic assessment of its capabilities and challenges, setting the stage for refined implementations and practical applications in various industries.

Generative AI and the Gartner Hype Cycle

The rapid progression of generative AI through Gartner’s Hype Cycle is unprecedented, with the technology moving faster than typical for emerging technologies in procurement [1]. By the end of 2024, 73% of procurement leaders are expected to adopt GenAI technology1. This swift advancement has positioned GenAI to reach the “Plateau of Productivity” within two years, indicating an accelerated path to mainstream adoption [2].

As GenAI enters the “Trough of Disillusionment,” organizations shift their focus from the initial excitement surrounding foundation models to practical use cases that drive return on investment [3][2]. This phase is crucial for refining implementations and addressing challenges, particularly in data quality and integrating existing systems. To navigate this stage effectively, companies are advised to launch targeted use-case pilots to identify scalable capabilities and maximize the potential of GenAI in their operations [1][2].

Future Predictions for Generative AI

While generative AI rapidly progresses through the Hype Cycle, Gartner predicts it will reach the “Plateau of Productivity” within two years, signaling a fast track to mainstream adoption[1][2]. This accelerated timeline suggests that organizations have a narrowing window to build competitive advantage through early adoption of GenAI in procurement1. As the technology matures, businesses are expected to focus on refining use cases, addressing implementation challenges, and preparing for broader adoption across various sectors[2][3].

The Current Business Implications of Generative AI

The rapid progression of generative AI through Gartner’s Hype Cycle has significant implications for businesses. As GenAI enters the Trough of Disillusionment, companies face challenges in successful implementations, particularly regarding data quality and integration with existing systems[1][2]. This phase is crucial for organizations to refine their approach and develop practical use cases that drive tangible ROI.

How to Profit Now from Generative AI

This predictable hype cycle intrigues business leaders and executives as it offers insights into human behavior. It strategically allows us to allocate resources during the upcoming “enlightenment” phase, where the focus shifts from wishful thinking to the relatively mundane task and hard work of incorporating these new technologies into existing operations.

To capitalize on GenAI’s potential, you should launch targeted use-case pilots, clarifying which capabilities are scalable within their contexts [3] and that provide customer-facing value. The accelerated timeline towards mainstream adoption means that the window for building competitive advantage through early adoption is narrowing, emphasizing the need for strategic and focused implementation efforts[2][4].

Getting a jump by acting now on prototyping and scaling up these workflows offers the opportunity to build a competitive advantage over those who wait the estimated 2 years for these technologies to become industry-wide.