The key keyword for the AI industry in 2026 is ‘practicality.’ Artificial intelligence, which has been swept up in hype for the past few years, is now entering a stage where it must prove its actual business value. The bubble is bursting, and an era is opening where only truly useful AI will survive.
TechCrunch analyzed that AI will move from hype to pragmatism in 2026. In reality, companies no longer aim to adopt AI itself. Instead, they are calculating specific ROI and focusing on improving practical work efficiency. Unlike the generative AI craze of 2023-2024, the atmosphere is now one of coldly asking, ‘Does this AI really make money?’ MIT Technology Review also predicted in its 2026 AI outlook that practical technologies such as agent AI and inference models will take the lead. In particular, AI solutions specialized for specific tasks such as coding, customer service, and data analysis are performing better than general-purpose AI. MIT Sloan Management Review pointed out that data quality and governance have emerged as key factors determining the success or failure of AI. The perception that good data is more important than a good model is spreading. Changes are also being detected in the startup investment market. It has become difficult to receive investment with just the label ‘AI-based,’ and it is necessary to prove specific problem-solving abilities.
This trend signifies the maturity of the AI industry. Realistic value is filling the space where exaggerated expectations have disappeared. 2026 is likely to be the first year that AI quietly but surely permeates everyday life. Companies and individuals who make good use of this transition will be the winners of the next stage. Hope this helps.
FAQ
Q: What is the biggest change in the AI market in 2026?
A: The paradigm is shifting from hype-centered to practicality and ROI-centered. Companies have begun to rigorously evaluate the practical effects of AI adoption.
Q: Which AI technologies are attracting attention in terms of practicality?
A: Agent AI, inference models, and AI solutions specialized for specific tasks are representative. Tools that solve specific problems well are being evaluated more highly than general-purpose AI.
Q: What should companies prepare for in the era of AI commercialization?
A: You must first establish a data quality and governance system. No matter how good the AI model is, it is difficult to achieve results if the data is poor.