While artificial intelligence (AI) technologies are showing a lot of promise, many technologies, including generative AI (GenAI), fell short of expectations this calendar year, exposing the gap between innovation and commercial use.
The struggle in getting a return on investment (ROI) from GenAI projects, the metaverse's lack of mainstream adoption, volatility around bitcoins, the slow uptake of consumer 5G, and limping smart city projects underscored the difficulty of moving from the pilot stage to implementing full-scale projects.
We shed light on the hurdles these technologies face and explore their chances of bouncing back.
GenAI: No Returns
Generative AI (GenAI) has shown immense potential since OpenAI's ChatGPT gained over 100 million users within two months of its launch in December 2022.
Unlike traditional machine learning (ML), which predicts data patterns, GenAI's foundational models and large language models (LLMs) learn the structure of various data types—text, images, proteins, DNA, etc.—to create new content through prompts in natural languages like English or Hindi.
Companies are fine-tuning tools like ChatGPT, Bing Chat, Gemini, and DALL-E 2 to cut costs in customer service, content creation, and more. However, most businesses remain cautious, testing these tools rather than deploying them at scale.
Challenges include hallucinations (generating false information and presenting it confidently as accurate), biases, intellectual property violations, high energy consumption, and uncertain returns on investment.
Goldman Sachs' April report, GenAI: Too Much Spend, Too Little Benefit?, questioned the $1 trillion investment in AI infrastructure without clear benefits.
Similarly, Gartner's July report predicts that 30% of GenAI projects will be discontinued by 2025 due to poor data quality and escalating costs, which range from $5 million to $20 million.
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