The generative AI boom of 2025 didn’t quite deliver on all its promises. While the technology made significant strides, many anticipated breakthroughs remained “half-baked,” according to recent analysis. As we move into , the focus is shifting from hype to practical implementation, regulatory compliance, and a more realistic assessment of what generative AI can achieve.
The Unfulfilled Promises of 2025
Last year saw considerable excitement around fully autonomous agents capable of handling tasks from start to finish. However, McKinsey reported that nearly two-thirds of companies didn’t progress beyond pilot projects, with only 39% reporting any financial impact on EBIT – and even then, the impact was modest. Wharton Business School echoed this sentiment, noting that 2025 was a year of “responsible acceleration,” prioritizing productivity gains over widespread autonomous deployment. The vision of AI agents independently managing complex workflows remains largely unrealized.
Another key area where expectations fell short was in addressing the issue of “hallucinations” – instances where AI systems generate inaccurate or misleading information. Despite advancements, accuracy and reasoning challenges persisted. OECD.AI warned of operational risks stemming from incorrect responses, emphasizing the need for robust risk reporting frameworks. Stanford’s AI Index 2025 confirmed these ongoing challenges, slowing down the adoption of AI in critical applications requiring high reliability.
Regulatory frameworks also lagged behind the rapid pace of technological development. While the European AI Act came into force in 2024, its implementation was gradual, with full enforcement of stricter requirements for high-risk systems not arriving until . Many companies in 2025 were focused on preparation rather than full compliance.
The anticipated reduction in costs and barriers to entry also didn’t fully materialize. Investment in generative AI reached $644 billion in 2025, according to Gartner, but nearly 80% of that spending went towards hardware – devices and servers – rather than software. The demand for AI infrastructure outstripped supply, hindering broader accessibility.
Finally, widespread adoption and double-digit productivity gains proved elusive. While adoption reached 54.6% in the US by (according to the St. Louis Fed), generative AI accounted for only 5.7% of working hours. Stanford’s AI Index indicated modest savings of less than 10% and revenue increases of less than 5% in most cases, suggesting that transformative benefits are still some time away.
Looking Ahead: Trends for 2026
AI Act Compliance and Implementation
The full implementation of the EU AI Act in will be a defining moment. All requirements for high-risk systems – including risk management, data governance, transparency, human oversight, robustness, and cybersecurity – will come into effect. The rules surrounding transparency of generated content (Article 50) will also be tightened. This will necessitate conformity assessments, labeling, traceability, and ongoing post-launch monitoring for companies deploying generative AI.
From Pilots to Measured Value
Data from 2025 suggests that 2026 will be a crucial year for demonstrating tangible ROI. Companies that focus on redesigning processes – rather than simply “coupling” AI models to existing workflows – and establishing clear growth objectives will be best positioned to succeed. Measuring benchmarks and financial impact will become paramount.
Agentic Architectures with Governance
PwC predicts a shift from demonstration projects to practical applications of AI agents, but with a strong emphasis on governance and security. The focus will be on controlled autonomy, multimodal capabilities, and seamless integration with existing data and APIs. In industries like telecommunications, this could translate to proactive problem resolution, improved ticket management, and optimized deployment strategies.
Infrastructure Investment
The gap between expectations and reality in 2025 is driving increased investment in scalable storage, integrated data architectures, and model monitoring tools. IDC anticipates a surge in demand for high-performance infrastructure and unified data management solutions to support both cloud and edge environments. This includes investment in NPUs (Neural Processing Units) and GPUs to handle the computational demands of generative AI.
Skills and Adoption
While adoption will continue to grow, the benefits will be unevenly distributed. Companies that invest in AI training, establish clear risk policies, and cultivate specialized talent will see the greatest returns. Deloitte and Wharton note that spending will continue to increase, and measuring ROI will become standard practice.
A Year of Strategic Action
2025 served as a valuable lesson: generative AI, like any emerging technology, doesn’t deliver on all promises immediately. The hype surrounding autonomous agents has subsided, and the challenges of hallucinations and regulatory compliance remain. However, the technology has firmly established itself as a strategic tool for improving productivity and efficiency, albeit with a measured approach.
2026 is shaping up to be a year of maturity. The AI Act will force transparency, traceability, and robust governance. Companies will need to move beyond isolated pilots to scaled projects with measurable ROI and well-defined objectives. We can expect to see smarter, more controlled agents, secure-by-design architectures, and a robust infrastructure capable of supporting the next wave of data-driven innovation. The key difference won’t just be the technology itself, but the people who can effectively leverage it – individuals with AI literacy, specialized skills, and a clear understanding of the associated risks.
