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Recaps of AI Connect webinars, from expert panels to live demos.
See the webinar scheduleAtScale'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 ·
Snowflake's Tyler Richards used Cortex Code to build a dashboard and Slack alerts from written specs, while Precisely's Carmen Adame had an AI assistant assemble a property risk profile through an MCP server.
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 ·
How do you give AI agents autonomy without losing control? Leaders from AT&T, SAP, Posit, Revefi and Rubrik point to policy layers outside the model, risk-tiered approvals, agent identities and end-to-end tracing, and warn that humans make poor review machines.
By The AI Enterprise Magazine Team ·
Four vendors are taking different routes to enterprise AI: Mistral AI with document OCR and voice models, SAS with detection of AI-generated forgeries, Fivetran with one dataset served to seven query engines and Precisely with location data for Claude via MCP.
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 ·
Enterprises get ROI from AI they own, trained on their own data and deployed where they choose, argues Mistral AI's Aaron Delp. He outlines Mistral's approach to a first use case and pilot, plus the admin controls, workflows and skills in Mistral AI Studio.
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 ·
Leaders from SAP, Skyflow and Fivetran lay out the architecture behind AI that scales: isolation boundaries, model-agnostic interfaces, agents that survive tool failures, and observability that tracks why agents touch data.
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 ·
Supermetrics has Claude analyzing Facebook ads, launching campaigns and publishing live dashboards, while Precisely uses an MCP server to let Claude find and fix SAP material data. Both move AI from reading enterprise data to acting on it.
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 ·
AI agents silently degrade after launch, say Snowflake's Sam Mittal and Monte Carlo's Ethan Post. They outline Cortex Agents' trust features, a four-layer framework for diagnosing bad answers, how stale data skews results and six early warning signals to track.
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 ·
Mistral AI and SAP are building enterprise agents, while Rhino, Precisely, Skyflow and SecuPi show how federated computing and MCP servers can connect AI assistants to enterprise data without exposing what should stay protected.
By The AI Enterprise Magazine Team ·
AI leaders from Snowflake, Revefi and The Coca-Cola Company say most AI pilots stall for organizational reasons, not technical ones. They shared a three-part test for deciding what to scale, the infrastructure that holds up in production, and a 70-20-10 rule for where to spend effort.
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 ·
Enterprise AI fails when it ignores domain knowledge and chases benchmarks, argues Mistral AI's Aaron Delp. He points to no-code agents, OCR-powered parts search over MCP and an agentic drug submission workflow, and makes the case for right-sized and custom models.
By The AI Enterprise Magazine Team ·
Prompt engineering won't fix AI accuracy, say leaders from AtScale and Rhino Federated Computing. They outline what production AI infrastructure needs and how to control GPU and inference costs as agents arrive.
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 ·
Bots inflate bounce rates, AI agents slip past JavaScript tracking, and agentic browsers mix human and machine activity in one session. Snowplow's Nick Stanchenko explained how to detect each, combine server logs with analytics, and adapt websites to who is browsing.
By The AI Enterprise Magazine Team ·
Mistral AI fine-tuned a model, Google Cloud ran agent-driven churn analysis, Aerospike powered real-time nudges, Precisely added location context, Striim masked data in flight and PromptQL flagged its own assumptions: six takes on practical enterprise AI.
By The AI Enterprise Magazine Team ·
Vibe-coded prototypes are easy; production AI is not. Leaders from DataHub, Precisely, Snowflake and Fivetran explained what separates AI-enabled from enterprise-grade, where companies underinvest, how to govern agents and how much of a prototype survives the move to production.
By The AI Enterprise Magazine Team ·
Elder Research practitioners argued that AI projects stall on business basics, not models. They walked through four fixes: building leadership data and AI fluency, prioritizing projects by business value, replacing gut decisions with trusted data, and giving AI teams visibility into changes to their source data.
By The AI Enterprise Magazine Team ·
Leaders from Revefi, Monte Carlo, Dollar General and Verizon Business said data and AI observability are becoming one discipline. They explained why unmonitored AI degrades, why observability can't be bolted on after deployment, how to benchmark readiness and why model swaps are a leading cause of drift.
By The AI Enterprise Magazine Team ·
Agentic data management pays off most for the analysts, data scientists and leaders stuck in the data team's queue, Matillion's field engineering team argues. Its Maia agent offers self-service pipeline building, permission prompts, context files that enforce standards and AI-driven troubleshooting.
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 ·
AI fails on weak data foundations rather than algorithms, say field CTOs from Fivetran and Snowflake and an Accenture managing director. They define AI-ready data and describe how quality, access, governance and flexible infrastructure separate leading teams.
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 ·
Data leaders from AtScale, Snowflake, Denodo, Fivetran and Revefi shared how executives can separate AI hype from real opportunities. They offered criteria for picking use cases, advice for CIOs under board pressure, a framework for build-versus-buy decisions and examples of projects that delivered measurable returns.
By The AI Enterprise Magazine Team ·
Shadow AI is the shadow IT problem again, according to two Airia leaders. They explain why blanket bans fail, how to detect unsanctioned AI at the network, endpoint and application layers, and how to win employees over to approved tools.
By The AI Enterprise Magazine Team ·
AI pilots are easy; scaling them is not. Leaders from AtScale, Monte Carlo and Revefi say trust is the dividing line, built through monitoring, semantic context and governance that works like brakes on a race car.
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 ·
Recommendations, search, RAG and AI agents share more than they seem. Aerospike Chief Evangelist Sesh Seshadri says multi-step inference, feature stores and short-lived agent data sets shape real-time AI infrastructure, drawing on examples from Myntra, PayPal and Barclays.
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 ·
Data leaders from Fivetran, General Mills and Webster Bank make the case for lakehouses in enterprise AI, from open table formats and fine-grained governance to cost control and migrating from a warehouse without losing momentum.
By The AI Enterprise Magazine Team ·
Seven vendors are putting generative AI to work on enterprise data, from Snowflake Cortex Agents and Gemini 2.5 on Vertex AI to self-service AI analytics, contextual location data, data pipelines and agents that write and debug code.
By The AI Enterprise Magazine Team ·
Three HPE and NVIDIA technologists lay out the AI factory model for enterprise AI infrastructure: packaged, turnkey and custom deployments, why cooling now shapes data center design, and the strategy and skills gaps that leave GPUs sitting idle.
By The AI Enterprise Magazine Team ·
AI can narrow the search space for physics-based simulation, says HPE Federal AI Chief Technologist Steve Heibein, citing COVID research, weather and NASA's AI-designed parts. Rising GPU power, he adds, is pushing data centers to liquid cooling.
By The AI Enterprise Magazine Team ·
Nations want to produce AI, not just consume it. Leaders from NVIDIA and HPE define sovereign AI across data, operational and technology sovereignty, and explain why sensitive-data rules and air-gapped environments keep many AI projects from reaching production.
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 ·
Every data leader may have an AI mandate, but many say their data isn't ready. Monte Carlo's Shane Murray and Michael Segner identify the four places AI pipelines break, how AI agents can write monitors and trace root causes, and how to set quality levels by use case.
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 ·
Generative AI has made low-code and no-code tools far more capable. Leaders from Informatica, ADP, LTK and Baptist Health South Florida weigh faster experimentation against the risks of skipping fundamentals and shadow AI, and describe how data scientists' roles are shifting.
By The AI Enterprise Magazine Team ·
Small internet providers lack the data teams of big carriers. Broadband Insights CEO Patrick Hutto details the Python stack behind his firm's production data apps, from Redis caching to a text-to-SQL chatbot, and how one daily report cut install times from months to days.
By The AI Enterprise Magazine Team ·
The biggest costs of production machine learning are often idle people, duplicated pipelines and models built before value is proven. Leaders from Tecton, Mosaic Health and JPMorgan Chase's Morgan Health explain how to scale ML while keeping costs in check.
By The AI Enterprise Magazine Team ·
Generative AI moves data in flight to models and back, creating new risk points. ADP's Amber Eshelman and Orlando Insight Group's Henry Clavo explain how to keep sensitive data out of LLMs, build a stewardship culture and future-proof governance.
By The AI Enterprise Magazine Team ·
Real-time AI pays off only when fresh data changes the prediction. Leaders from Tecton, Sam's Club and AT&T explain how to combine precomputed and streaming features, catch silent pipeline failures and cut the cost of real-time machine learning.
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 ·
RAG that works in a pilot can turn slow, costly and leaky at enterprise scale. Experts from Tecton, Nexla and Novartis explain how to cut RAG costs, keep indexes in sync, enforce chunk-level access control and evaluate accuracy honestly.
By The AI Enterprise Magazine Team ·
AI models for chemistry often fail on compounds no one has tested because training data is scarce. SandboxAQ's Ang Xiao says large quantitative models add physics-based synthetic data, citing battery life prediction, paper mining, materials discovery and formulation.
By The AI Enterprise Magazine Team ·
Getting a model working in a notebook is easy; keeping it reliable in production is not. Leaders from Tecton, Emory Healthcare and a BBDO agency say standardization, cross-training, unified frameworks and clear ownership speed the path to production AI.
By The AI Enterprise Magazine Team ·
Should enterprises host their own LLMs, use cloud models or run small models on laptops? Engineers from SandboxAQ, Alteryx and Plotly weigh the tradeoffs and explain how governed access, testing and open frameworks help future-proof AI infrastructure.
By The AI Enterprise Magazine Team ·
Payment processor North replaced a third-party fraud detection product with its own real-time machine learning system. MLOps leader Ben Orkin explains the build-vs-buy decision, the data signals that matter and how a feature store cut model iteration from months to weeks.
By The AI Enterprise Magazine Team ·
Rafay offers self-service GPU access, AnswerRocket a conversational analytics assistant, Crowdbotics AI-generated product requirements and code, and DataRobot prebuilt AI apps with real-time guardrails: four approaches to putting generative AI to work.
By The AI Enterprise Magazine Team ·
Relational deep learning can treat a Snowflake database as a graph, says Kumo AI co-founder Hema Raghavan. A predictive query language and graph neural networks replace the hand-written feature pipelines behind churn, recommendation and forecasting models.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar, two HPE speakers said deployment, not training, had become the main hurdle for enterprise LLMs. They grouped the problems into technical, operational and security challenges and demoed HPE's inference software, including autoscaling and canary rollouts.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar, Vespa.ai's Jon Bratseth and RavenPack's Aakarsh Ramchandani said production RAG needs more than a vector database. They covered hybrid search, cross-encoder reranking, evaluation sets and how to keep embedding and storage costs in check.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar sponsored by HPE, AI Solution Engineer Tyler Britten demonstrated how to serve an open model on premises, build RAG over documents and databases, and prepare a prototype for production with testing and evaluation.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar sponsored by HPE, leaders from HPE, LTK and the University of Texas Medical Branch discussed how generative AI is reshaping computer vision. They covered multimodal RAG, synthetic data, production lessons, governance and data challenges.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar, leaders from Webster Bank and Cox Communications described how generative AI could change geospatial analysis. They also explained why banking and telecom teams verify outputs, protect customer data and adopt it in baby steps.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar, leaders from Snowplow, SQream and Tecton discussed how generative AI complements machine learning in executive decision-making. They covered unstructured data, evaluation, ROI, KPIs and the build-versus-buy choice.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar sponsored by Kumo.ai, leaders from Nike, Citibank, Webster Bank and Pella discussed how generative AI could reshape AutoML. They covered synthetic data, democratizing machine learning, black-box concerns and model risk management.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar sponsored by Daugherty Business Solutions, leaders from Meta, ADP, Verizon, BBDO and Daugherty discussed generative AI privacy, bias and governance. They described privacy by design, cross-functional ethics councils, risk tiering of use cases and continuous monitoring.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar, leaders from Deloitte, Monte Carlo and PRGX Global explained why data quality and observability underpin generative AI. They covered the five pillars, how to start, what to look for in tools and common pitfalls.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar, analytics leaders from Alteryx, BBDO and Light & Wonder explained how GenAI is opening data to non-experts. They covered skills gaps, silos, governance and privacy, and how roles will shift.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar, four OneMagnify executives explained how B2B CMOs can lead digital transformation with generative AI. They covered cross-team collaboration, custom GPTs, data risk, the shift in search and the skills CMOs need.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar, panelists from SQream, Domo, Snowflake and Matillion explained why data lakehouses suit generative AI. They covered migration and interoperability, avoiding a data junk drawer, governance, GPU costs and where architectures are headed.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar sponsored by Astronomer, speakers from Astronomer and AWS described how Apache Airflow orchestrates RAG, fine-tuning and monitoring for LLM applications. They shared customer examples and two internal Astronomer tools.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar, marketing and analytics leaders from Brunswick, OneMagnify and formerly GE Healthcare discussed how B2B teams use AI for customer experience. They said clean data, risk councils and realistic expectations matter as much as the models.
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 ·
In a 2024 AI Connect webinar made possible by DataRobot, data science practitioners discussed feature engineering for predictive models. They said the biggest obstacles are organizational, and that generative AI helps most with scarce or unstructured data when paired with expertise and risk controls.
By The AI Enterprise Magazine Team ·
In a 2024 AI Connect webinar brought to you by Satori, leaders from Strata Decision Technology and Satori explained how to grant self-service access to sensitive healthcare and financial data. They stressed automation, access lanes, dynamic masking and tying permissions to tickets.
By The AI Enterprise Magazine Team ·
In a January 2024 AI Connect webinar, experts from Esri and AWS explained how to run spatial analysis natively in a Spark and Amazon EMR environment. They covered common pitfalls, available functions and a Chicago ride share demand demo with more than 50 million records.
By The AI Enterprise Magazine Team ·
