The latest State of AI in the Enterprise report from Box is out! One year ago, 8% of organizations described themselves as advanced or leading edge in AI. Today that figure is 64%. But adoption is not the same as impact. Half of leading edge companies report significant ROI. Among early stage organizations it is just 1 in 9. We surveyed 1,640 IT decision makers across the US, UK, France, and Japan to find out.

Unfortunately, Claude Fable will no longer be available in the Box AI Studio. We hope this issue gets resolved and we can offer it again to customers soon.

Most enterprise AI programs fail not because the models are weak. They fail because organizations invest in AI before they have the data, governance, content, and execution readiness to support it. Our guide breaks down what a real enterprise AI strategy looks like, why most programs stall at the pilot stage, and the seven steps to build one that actually delivers at scale. Read here.👇 blog.box.com/enterprise-ai-s…

.@Box Agent is built on Deep Agents. ✅ Cross-library search ✅ Multi-doc synthesis ✅ Structured reports ✅ All within Box's existing security and permissions model

When Box transitioned Box AI into a multi-step agentic system, we chose @LangChain's LangGraph over building a custom execution engine from scratch. Our full architectural breakdown covers how agents are defined and compiled at runtime, how the Global Agent spawns sub-agents dynamically, how LangGraph's checkpointing powers session resumability, and how Box enforces user-scoped security at the platform level, not the LLM level. Read here.👇 blog.box.com/how-box-built-i…

At Box, we just surveyed 1,640 IT leaders across the US, Japan, and Europe about agentic AI adoption. Many standout findings, but a big one was that the companies that adopted AI the most are planning to grow headcount the most. Obviously lots of ways you can read that data and variables mixed in, but it’s actually quite intuitive that the companies that become most productive want to (and are able to) reinvest back into the business to keep getting the gains going. The narrative of jobs being wiped out assumes that companies will take a fixed approach to what they want to be able for work on. What’s happening in practice is it’s causing companies to want to light up more engineering projects, sell to more customers, automate more processes to give time back, and more. That all leads to more work to be done by people.

Lots of evidence of huge jumps in capability for Fable across coding (and related) tasks. It’s also a major jump in accuracy and success in complex knowledge work tasks. In our Box AI Complex Work Eval, we tested the model against Opus 4.8 and saw huge boosts across almost every industry. For our eval we give the Box AI Agent, using Fable, a set of hard real world knowledge work problems that deal with enterprise documents. Then score how the agent performs the tasks. The main differentiators for Fable vs Opus 4.8 is that it doesn't take shortcuts on complex reasoning, it gets multi-step calculations right, and it's significantly more consistent across runs. We saw the biggest leaps in Media & Entertainment (78% vs 61%), Technology (81% vs 73%), Financial Services (89% vs 83%), and Healthcare (66% vs 60%). Here are some specific examples: * Legal M&A due diligence: On a task reviewing NDA terms against a semiconductor company's contracting policy, Fable correctly identified that a joint-ownership clause violates exclusivity requirements while a liability cap is permitted under a Super Cap exception. Fable scored 100% vs Opus's 78%. * Healthcare: On a clinical radiology error audit across 12 reports, Fable precisely categorized each error by severity grade and correctly concluded no Grade 3 errors existed. Opus prematurely escalated a case to "major error requiring immediate departmental review" when the evidence didn't support it — Fable 63% vs Opus 41%. * Media & Entertainment: On a genre profitability projection task, Fable correctly recognized that a 20% Argentine tax deduction was already embedded in the source spreadsheet figures and didn't double-apply it. Opus applied it again on top — a compounding error across 4 genre calculations that took its score negative on the task vs Fable's 74%. * Retail analytics: On a task analyzing high-growth product articles against an investment benchmark, Fable correctly computed each article's growth rate individually and identified that only 2 of 5 exceeded the threshold. Opus confused "high growth relative to average" with "above the benchmark" — scoring 61% vs Fable's 94%. * Financial Services: On a 5-year debt facility projection, Fable correctly applied interest to opening balances and used the right capex figure. Opus applied interest to the total facility amount and computed tax from the wrong base — two compounding errors. Fable scored 83% vs Opus's 62%. * Technology: On a SaaS feature valuation requiring computation of a Feature Value Index across multiple regions, Fable applied the formula correctly and got exact values for the markets. Opus got the arithmetic wrong on multiple criteria — Fable scored 100% vs Opus's 74%. Overall, huge step change in complex analysis, work that requires analytical reasoning, and deep domain understanding. Fable will be available shortly in the Box AI Studio for customers to build agents with.