Enterprise AI

In the age of natural language interfaces to databases (NLIDBs), asking the right question has never been easier—or more perilous. While systems like ChatGPT or SQL-Palm can convert everyday English into valid SQL, they often do so without interrogating the quality of the question itself. And as Peter Drucker warned, “The most dangerous thing is asking the wrong question.” Enter VeriMinder, a system built not to improve SQL syntax or execution accuracy—but to diagnose and refine the analytical intent behind the user’s query. It tackles a deceptively simple yet far-reaching problem: a well-formed SQL query that answers a poorly formed question can yield confident but misleading insights. This is particularly problematic in enterprise settings where non-technical users rely on LLM-based BI assistants. ...

Retrieval-Augmented Generation (RAG) systems are fast becoming the connective tissue between Large Language Models (LLMs) and real-world business data. But while RAG systems excel at fetching relevant passages from documents, they often stumble when the data isn’t narrative but numerical. In enterprise environments, where structured formats like HR tables, policy records, or financial reports dominate, this mismatch has become a bottleneck. The paper “Advancing Retrieval-Augmented Generation for Structured Enterprise and Internal Data” by Chandana Cheerla proposes a much-needed upgrade: a RAG system that treats structured and tabular data as first-class citizens. It doesn’t just flatten tables into linear strings or hope LLMs can reason through semi-garbled inputs. It restructures the entire RAG pipeline to respect and preserve the meaning of tables, rows, and metadata. ...

When most people think of AI today, they picture text generation, image synthesis, or copilots answering emails. But beneath the surface of digital transformation lies an often-overlooked backbone of enterprise work: tables. Spreadsheets, databases, and semi-structured tabular documents are still where critical operations happen — from finance to health records to logistics. A recent survey paper, Toward Real-World Table Agents, pushes us to rethink how AI interacts with tabular data. Instead of treating tables as static inputs, the authors argue that tables are evolving into active data canvases — and LLM-based Table Agents are poised to become their intelligent orchestrators. ...

Enterprise data workflows have long been a patchwork of scripts, schedulers, human-in-the-loop dashboards, and brittle integrations. Enter the “Data Agent”: an AI-native abstraction designed not just to automate, but to reason over, adapt to, and orchestrate complex Data+AI ecosystems. In their paper, “Data Agent: A Holistic Architecture for Orchestrating Data+AI Ecosystems”, Zhaoyan Sun et al. from Tsinghua University propose a new agentic blueprint for data orchestration—one that moves far beyond traditional ETL. ...

Grounded and Confused: Why RAG Systems Still Fail in the Enterprise If you’ve been following the RAG (retrieval-augmented generation) hype train, you might believe we’ve cracked enterprise search. Salesforce’s new benchmark—HERB (Heterogeneous Enterprise RAG Benchmark)—throws cold water on that optimism. It exposes how even the most powerful agentic RAG systems, armed with top-tier LLMs, crumble when facing the chaotic, multi-format, and noisy reality of business data. Deep Search ≠ Deep Reasoning Most current RAG benchmarks focus on shallow linkages—documents tied together via entity overlap or topic clusters. HERB rejects this toy model. It defines Deep Search as not just multi-hop reasoning, but searching across unstructured and structured formats, like Slack threads, meeting transcripts, GitHub PRs, and internal URLs. It’s what real enterprise users do daily, and it’s messy. ...

The Multimodal Mirage In recent years, there’s been growing enthusiasm around combining unstructured text with time series data. The promise? Textual context—say, clinical notes, weather reports, or market news—might inject rich insights into otherwise pattern-driven numerical streams. With powerful vision-language and text-generation models dominating headlines, it’s only natural to wonder: Could Large Language Models (LLMs) revolutionize time series forecasting too? A new paper from AWS researchers provides the first large-scale empirical answer. The verdict? The benefits of multimodality are far from guaranteed. In fact, across 14 datasets spanning domains from agriculture to healthcare, incorporating text often fails to outperform well-tuned unimodal baselines. Multimodal forecasting, it turns out, is more of a conditional advantage than a universal one. ...

Introduction Recently, 01.AI launched its enterprise AI platform, aiming to provide businesses with access to open-source LLMs, retrieval-augmented generation (RAG), model fine-tuning, and AI-powered assistants. This move is part of 01.AI’s broader effort to demonstrate relevance in the ongoing AI arms race, especially as the company has previously secured significant funding under the reputation of Li Kaifu. Given the rapid evolution of AI, 01.AI faces mounting pressure to show tangible business value to its investors—yet, its latest offering falls into the common trap of many AI enterprise solutions: prioritizing model deployment over true business integration. ...