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Semantic layers, context engineering and retrieval that make AI answers trustworthy.
AtScale's Mark Palmer argues that enterprises get more AI value from better context than from bigger models. He points to benchmark results on semantic layers, the four most common sources of AI errors and the case for testing AI accuracy every day.
By The AI Enterprise Magazine Team ·
By The AI Enterprise Magazine Team ·
By The AI Enterprise Magazine Team ·
By The AI Enterprise Magazine Team ·
General-purpose AI tools often win analytics pilots and then break down at scale, says WisdomAI's Geetesh Iyer. The missing piece, he argues, is managed enterprise context, and the people best suited to own it, AI context engineers, are already on data teams.
By The AI Enterprise Magazine Team ·
At Dreamforce, Salesforce opened its data, workflows and permissions to outside AI assistants, starting with Claude. The interface is becoming a choice; the controls and context underneath are where enterprise leaders need to focus.
By The AI Enterprise Magazine Team ·
Leaders from Tokio Marine, Morgan Stanley and The Fedcap Group describe what happens after the first agent ships: a validation tax on human reviewers, workflows that fail more often than models, and proprietary context as the place where advantage builds.
By The AI Enterprise Magazine Team ·
Data lakes and lakehouses were built for human users, not agents that run around the clock and scale to thousands, says Teradata's Ben Sanders. He explains why pilots break in production, how missing context drives up cost and latency, and the six components of enterprise context.
By The AI Enterprise Magazine Team ·
AI is only as reliable as the business context behind enterprise data, say leaders from Actian, Fiserv and Slalom. Their prescription: scalable stewardship, risk-based governance, the metadata AI needs, auditability and clear limits on where automation should stop.
By The AI Enterprise Magazine Team ·
Analysis · Data & AI Infrastructure
Databricks raised $5 billion at a $190 billion valuation and reported a run-rate above $7 billion. The numbers show enterprises moving AI budgets into the data layer, along with valuation and lock-in risks leaders should weigh.
By The AI Enterprise Magazine Team ·
PromptQL's Adam Malone says AI analytics projects fail less from weak models than from missing business context. He laid out six levels of AI adoption, four failed text-to-SQL and semantic layer projects, and why data professionals are becoming context providers.
By The AI Enterprise Magazine Team ·
Data built for human analysts isn't ready for autonomous agents, say experts from Snowflake and Precisely. They call for rich metadata, shared data quality rules, lineage and auditability as the foundation for agentic AI.
By The AI Enterprise Magazine Team ·
Actian's AI Analyst lets a compliance lead use plain-language questions to find low-risk bank customers breaching SAR thresholds. Actian's Phil Wall says governed metrics, glossaries and semantic SQL keep the AI analyst from making up answers.
By The AI Enterprise Magazine Team ·
Lumi AI surfaces inventory insights, Precisely enriches addresses through MCP, SAS Viya turns decision flows into agent tools and Domino Data Lab keeps regulated research auditable: four takes on putting agentic AI to work in the enterprise.
By The AI Enterprise Magazine Team ·
Leaders from Collate, Graphwise, Precisely and AtScale disagree on whether metadata is enough for the AI context layer, agree that RAG and memory work together, and predict a fight over who controls enterprise semantics.
By The AI Enterprise Magazine Team ·
Teradata built a LangChain agent that checks loyalty discount eligibility using a vector store and a governed SQL tool. Coder shares what its experiments with agent harnesses, sub-agents and context management did and didn't improve.
By The AI Enterprise Magazine Team ·
Data warehouses built for weekly reports leave AI agents with stale context, say Striim and Google Cloud experts. Change data capture, event stream processing and MCP can feed agents live operational data on Google Cloud instead.
By The AI Enterprise Magazine Team ·
Fragmented metadata and siloed semantic layers are holding back agentic AI, argues independent analyst Mike Ferguson, and governance must become unified and always on. Collate is applying agents that catalog, classify and document data on a shared knowledge graph.
By The AI Enterprise Magazine Team ·
Informatica, PromptQL, Teradata, Precisely and Lumi AI are building agents that find, clean and enrich data, answer business questions and keep humans in the loop. Here is how each approach works.
By The AI Enterprise Magazine Team ·
Leaders from Revefi, Rhino Federated Computing, Precisely, AtScale and Google Cloud define what makes AI truly agentic and argue human review won't scale. Their alternative: human-on-the-loop oversight, two-tier governance, agents that govern agents and the rise of agent ops.
By The AI Enterprise Magazine Team ·
Striim's Jake Bengtson and Google Cloud's Naveen Punjabi say AI stalls in production when data is stale, pulled straight from operational systems or stripped of business meaning. They outlined four signs your data can support AI agents at scale.
By The AI Enterprise Magazine Team ·
Snowflake, PromptQL, Google Cloud, AtScale, Dataiku, OneTrust, SAS and Monte Carlo are building agents that answer business questions, semantic layers that keep answers accurate, risk-based AI governance, GenAI for predictive maintenance and observability for production agents.
By The AI Enterprise Magazine Team ·
Poor data quality undermines AI in ways that are easy to miss, from silent data failures to unrepresentative data, say experts from Revefi, Snowplow, Informatica and Fivetran. They urge a shift to data and context quality and explain how to win executive buy-in for governance.
By The AI Enterprise Magazine Team ·
Supermetrics puts governed marketing data in Claude through MCP, Striim streams real-time operational data with PII masking, and Lumi AI builds an AI data analyst on a curated knowledge base: three approaches to giving agents data they can trust.
By The AI Enterprise Magazine Team ·
Snowflake, Denodo, Zenlytic, PromptQL, Fivetran, Google Cloud and Precisely are attacking the same problem from different angles: conversational agents, logical data layers, verified metrics, confidence signaling, fresh pipelines, the Agent2Agent protocol and location data through MCP.
By The AI Enterprise Magazine Team ·
Searching video and images means keeping context that flat vectors lose. Vespa.ai's Bonnie Chase and Zohar Nissare-Houssen explain how tensors, co-located compute and multi-phase ranking let a hybrid query find a single scene inside a cartoon.
By The AI Enterprise Magazine Team ·
Data democratization efforts often stall for reasons that have little to do with tools. Leaders from AtScale, T-Mobile and Mars say success depends on balanced governance, deep data literacy and leadership, and that AI is now exposing every gap.
By The AI Enterprise Magazine Team ·
Leaders from Zenlytic, Nexla, Alation and Dataiku define AI agents as chatbots with a feedback loop. They argue that context and memory are separate problems, that explainability builds trust and that task-specific agents scale better than one generalist.
By The AI Enterprise Magazine Team ·
Enterprise GenAI faces a higher bar than consumer AI tools. Leaders from AtScale, Nexla, Informatica, Vespa and Skyflow weigh in on data minimization, semantic layers, single-number evaluation sets, architecture for scale and whether MCP will matter.
By The AI Enterprise Magazine Team ·
Snowflake's Cortex Agents work across structured and unstructured data, TiDB builds a knowledge graph for GraphRAG inside one database, and Zenlytic answers an open-ended marketing question with governed metrics: three approaches to AI agents on enterprise data.
By The AI Enterprise Magazine Team ·
Text extraction throws away the charts and tables in visually rich documents. Vespa CEO Jon Bratseth and RavenPack's Aakarsh Ramchandani explain how ColPali retrieves PDF pages by what they look like, where it still falls short and how to scale it.
By The AI Enterprise Magazine Team ·
Enterprises are using retrieval-augmented generation for more than chatbots. Leaders from UPS, TSYS and Shipt describe a digital twin operations copilot, internal use cases that build trust, the risks of customer-facing RAG and how to prioritize generative AI under top-down pressure.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar, executives from Informatica, Graft, Metaphor and Snowflake discussed how generative AI can widen access to data. They said semantic context, data quality, permissions and focused first use cases decide whether it succeeds.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023 in Atlanta, Zilliz developer advocate Yujian Tang explained why vector databases matter for AI applications. He covered retrieval augmented generation, semantic similarity, and why SQL databases and search libraries fall short at scale.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023 in Atlanta, Georgia State University's Peter Molnar compared symbolic AI with neural networks and large language models. He said the approaches have complementary strengths, and that LLMs could help automate knowledge graphs and rule-based expert systems.
By The AI Enterprise Magazine Team ·
In a keynote at COLLIDE 2023 in Atlanta, Stardog VP of Product Navin Sharma argued that enterprise knowledge graphs give generative AI the business context it lacks. He said the pairing can improve accuracy, cut hallucinations and make answers explainable.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023 in Atlanta, RelationalAI's Nikolaos Vasiloglou explained why cloud data platforms with hundreds of millions of columns need a semantic layer. He described how ontologies, human review and knowledge graphs help language models answer questions.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023 in Atlanta, Neo4j's Phani Dathar said knowledge graphs can address LLM inaccuracy and missing company knowledge. He described a connect, enrich and consume approach that pairs vector search and generated graph queries with an LLM.
By The AI Enterprise Magazine Team ·
In a session at the Data Science Innovators Conference 2023, Stardog knowledge graph consultant Joe Fagan built a semantic layer live on the royal family tree. He showed how ontology rules and inference add facts that never appear in the source data.
By The AI Enterprise Magazine Team ·
In a Data Science Innovators Conference 2023 keynote, Stardog's Navin Sharma said metrics layers capture only part of what a semantic layer can do. He argued a knowledge graph-powered layer that federates data, infers relationships and keeps business meaning can unify hybrid multicloud data.
By The AI Enterprise Magazine Team ·
In a session at the Data Science Innovators Conference 2023, Navin Sharma of Stardog showed how a knowledge graph can infer relationships through domain rules, using a royal family dataset and demos in manufacturing, life sciences and finance. He also cited vendor ROI figures and answered questions on ontologies and Microsoft Purview.
By The AI Enterprise Magazine Team ·
In a hands-on workshop at the Data Science Innovators Conference 2023, Stardog co-founder Mike Grove built a customer 360 knowledge graph live. He covered data modeling, virtual mapping of source data, and an inference engine that derives new relationships.
By The AI Enterprise Magazine Team ·
