🎲 The Hype Cycle and the reality of Generative AI

AI data and trends for business leaders | AI systems series

The Hype Cycle and the Reality of Generative AI | Source: Hype Cycle for Emerging Technologies, Gartner, 2023

Generative AI has undeniably captured the world's imagination. Its capabilities seem almost magical, from crafting realistic images and composing music to generating human-like text. Yet, beneath the surface of this technological marvel lies a more complex reality.  

Gartner has positioned generative AI at the peak of its infamous hype cycle¹.

This suggests that the technology is currently overhyped and on the brink of a disillusionment phase, where its limitations and challenges become more apparent.

▸ Indeed, a staggering 30% of generative AI projects are reportedly abandoned after the proof-of-concept stage, fueling the perception that the technology is struggling to live up to its promise.  

However, is this high failure rate truly exceptional? A more nuanced picture emerges when placed in the context of broader business initiatives.

McKinsey & Company found that most large-scale change programs within organizations fail to achieve their objectives².

Similarly, the Standish Group reported alarmingly low success rates for full-scale IT projects³.  

▸ This raises a critical question: Is the generative AI industry experiencing the same growing pains as other complex technological endeavors, or are unique challenges inherent to this field?

By examining the historical patterns of technology adoption and considering the potentially distorting effects of survivor bias, we can unravel the hype surrounding generative AI and gain a clearer understanding of its potential and limitations.

Let’s try to better understand through facts.

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📌 Insight 1: High failure rates are not unique to GenAI

Despite the apparent similarities, generative AI projects inherently carry higher risks due to the novelty and complexity of the technology, making their failure rates more concerning than those of traditional business initiatives.

• Statistics: 70% of complex, large-scale change programs within businesses fail to reach their goals (McKinsey & Company).

• Numbers: 31% of full-scale IT projects are canceled before completion (Standish Group).

This data indicates that the challenges faced by generative AI projects are part of a broader pattern of difficulties in complex business initiatives.

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📌 Insight 2: The hype cycle might oversimplify technology adoption

This insight challenges the notion that technology adoption follows a predictable pattern and highlights the importance of considering factors beyond hype when evaluating a technology's potential.

• Statistics: 30% of generative AI projects are abandoned after the proof-of-concept stage.

• Numbers: There is no specific number, but the argument suggests that the Gartner Hype Cycle might not accurately represent the complex and nuanced process of technology adoption.

The Gartner Hype Cycle is an essential tool for understanding technology trends, and while it may not perfectly predict the trajectory of every technology, it remains a valuable framework for strategic planning.

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📌 Insight 3: Survivor bias can distort the perception of technology success

While survivor bias is a factor to consider, the overwhelming success stories in generative AI demonstrate its transformative potential, and the challenges early adopters face are merely growing pains that will be overcome.

• Statistics: There is no specific number, but the argument suggests that successful projects are often overrepresented in discussions about technology adoption, leading to an overly optimistic view of the technology's overall performance.

Understanding the impact of survivor bias is crucial for accurately assessing generative AI's true potential and challenges.

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📌 What’s next and considerations

Is the current obsession with generative AI a distraction from more pressing societal and technological challenges?

• Deeper dive into case studies: Analyzing specific generative AI projects that succeeded or failed can provide concrete examples to support or challenge the arguments presented.

• Expert interviews: Gathering insights from experts in the field of AI, business strategy, and technology adoption can offer additional perspectives and validate or refute the claims made.

• Comparative analysis: Comparing generative AI to other disruptive technologies (e.g., cloud computing, blockchain) can reveal common patterns and unique challenges.

• Ethical implications: Exploring the ethical considerations surrounding generative AI and how they impact project success or failure can add depth to the analysis.

• Policy and regulatory analysis: Examining the role of government policies and regulations in shaping the generative AI landscape can provide valuable insights.

Considerations

• Target audience: Determining the content's target audience will help determine the appropriate depth, tone, and focus of the analysis.

• Purpose: Clearly articulating the purpose of the piece will guide the selection of information and the overall structure.

• Evidence-based approach: Relying on credible data, research, and expert opinions is essential for building a strong and persuasive argument.

• Counterarguments: Anticipating and addressing potential counterarguments will strengthen the overall analysis.

• Clarity and conciseness: Ensuring that the content is clear, concise, and easy to understand is crucial for engaging the audience.

By carefully considering these factors, we can develop a comprehensive and impactful analysis of the generative AI hype cycle and its implications.

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Continue exploring

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1. Hype Cycle for Emerging Technologies, 2023 by Gartner

2. The ‘how’ of transformation (2016) by McKinsey & Company

3. Benchmarks and assessments by The Standish Group