The early excitement around generative artificial intelligence (AI) is giving way to a more clear-eyed assessment of its real-world impact, according to Mark Nasila, chief data and analytics officer for FirstRand AI Strategy. The AI hype hangover reflects a shift from experimentation to a value-first mindset focused on measurable business results. Findings from the International Monetary Fund (IMF) and Massachusetts Institute of Technology (MIT) suggest that the vast majority of organisations have yet to see meaningful, measurable returns on their generative AI investments. Scientists and industry experts see the current moment as a genuine inflection point, with productivity gains proving uneven and timelines slipping; milestones like fully autonomous coding expected in a year or two are now pushed into the 2030s. The emergence of efficient, lower-cost challengers such as DeepSeek has disrupted assumptions that scale alone guarantees dominance, contributing to market corrections for incumbents like Nvidia. The burden of verification is rising as models grow more complex, with experienced professionals shifting into reviewer roles, creating bottlenecks that erode productivity gains. A table outlines the transition from the speculative hype of 2024–2025 into the operational reality of 2026, highlighting a move toward specialized multi-agent systems focused on mission-critical ROI.