MIT’s 2025 GenAI Divide report found that 95% of enterprise AI pilots fail to deliver measurable P&L impact. The culprits aren’t the models — they’re organizational: poor data quality, misallocated budgets, and AI tools that never learn. Here’s what separates the 5% that make it to production.
OpenAI’s GPT-5.2 achieved a perfect 100% on AIME 2025 math, while Google’s Gemini 3.1 Pro scored 77.1% on ARC-AGI-2 — more than double GPT-5.2’s 52.9% on that test. These results measure different capabilities, and choosing the right frontier model for your workload requires understanding exactly what each benchmark is and isn’t telling you.
Systematic reviews that once took 18 months now take weeks. In 2026, Elicit, ResearchRabbit, and Scite.ai have moved from curiosity to core research infrastructure — but using them well requires understanding where they break. Here is an honest account of what each tool does, what the academic evidence says about their accuracy, and where human judgment remains irreplaceable.
Three Stanford studies quantify what generative AI actually does to workforce productivity—and the answer is more nuanced than either optimists or skeptics suggest. The gains are real (up to 87% task acceleration for software developers), but they skew toward less experienced workers, and entry-level employment in automation-heavy fields is already declining.
On 2 August 2026, the EU AI Act becomes generally applicable — activating high-risk AI obligations, transparency requirements for chatbots and deep fakes, and fines up to 7% of global turnover. Here is what the deadline means in practice and where most organizations are falling short.
The pilot era for enterprise AI is ending. Data from Deloitte, Gartner, and G2 in 2025-2026 shows a clear majority of organizations have AI agents running in production — and the gap between those scaling successfully and those still stuck in pilots is widening fast. Here’s what the numbers actually say, where agents are working, and why governance is the make-or-break factor.
MIT’s 2025 GenAI Divide report found that 95% of enterprise AI pilots fail to deliver measurable P&L impact. The culprits aren’t the models — they’re organizational: poor data quality, misallocated budgets, and AI tools that never learn. Here’s what separates the 5% that make it to production.