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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 ·
AI is only as trustworthy as the data and governance behind it. Leaders from Bank of America, Synchrony, Alteryx and Truist make the case for fixing data at the source, measuring fitness for purpose, rethinking model risk governance and naming a single accountable owner.
By The AI Enterprise Magazine Team ·
Building an AI agent is easy; running thousands of them is not. IBM's Ann Leach says agent sprawl creates security and cost risk, lists five questions an agentic control plane must answer and describes how Starlink coordinated procurement agents.
By The AI Enterprise Magazine Team ·
Data quality is an input, not the goal, says Informatica's Peter Manta. What matters is whether AI agents make good decisions. He outlines six pillars of an agentic data contract and an experiment showing that even 10% duplicate data changed agent decisions.
By The AI Enterprise Magazine Team ·
Precisely's agent checks data quality scores before it acts and repairs failing data through governed pipelines. Coder runs coding agents in self-hosted, sandboxed workspaces with budgets, session logs and human-owned pull requests.
By The AI Enterprise Magazine Team ·
Cohesity launched a tool to restore AI agents and the systems they touch to a known-good state. Whatever buyers make of the product, no agent should get write access until its owners can show how to undo its work.
By The AI Enterprise Magazine Team ·
An AI system can look perfectly healthy and still be wrong. Leaders from SAP, Dynatrace and RaceTrac lay out what to measure beyond accuracy, how to catch drift and how observability creates the audit trail AI governance depends on.
By The AI Enterprise Magazine Team ·
The best-funded AI programs will pay for themselves by reclaiming wasted data and AI spend, argues Revefi's Girish Bhat. Verisk Global Chief Data Officer Louis Dimodugno describes how Snowflake savings covered the cost of the tooling, and why token prices may soon rise.
By The AI Enterprise Magazine Team ·
Anyone can build an AI agent now, says IBM's Sabtain Khan, but few enterprises get agents deployed and used. He argues agent sprawl is an operating model problem and that regular testing, feedback loops and a control plane help agents reach production.
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 ·
Interim results from The Modern Data Company's survey of more than 540 data leaders show agent adoption racing ahead of data trust, context layers and accountability, the foundations agents need to reach production.
By The AI Enterprise Magazine Team ·
After its test models breached Hugging Face, OpenAI now pays roughly a fifth more compute to watch its most capable systems. A survey this week suggests many enterprises have not budgeted for agent containment at all.
By The AI Enterprise Magazine Team ·
News · Observability & Data Quality
Dynatrace will pay about $915 million for Arize, folding a leading AI evaluation and agent observability platform into mainstream IT monitoring as enterprises look for proof that production agents are working.
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 ·
A week of launches from Harness, GitLab, AvePoint, Teradata, Axonius and Squirro shows vendors treating AI agents like code and like assets: versioned, tested, deployed with approvals, inventoried and audited.
By The AI Enterprise Magazine Team ·
Opinion · AI Governance & Trust
Smarsh's new study finds enterprises deploying AI faster than they can govern it, with few able to detect shadow AI. The AI Enterprise Magazine Team argues governance must be built as infrastructure that moves at deployment speed.
By The AI Enterprise Magazine Team ·
Agents that run in the background, wake on schedules and work until a goal is met are moving from developer experiments into mainstream tools. They promise steady progress, and they raise new questions about spending, drift and supervision.
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 ·
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 ·
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 ·
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 ·
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 ·
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 ·
AI governance and data science freedom don't have to conflict, say SAS's Sophia Rowland and Sundaresh Sankaran. Governed synthetic data generation protects personal information, produces representative and robust data sets, and is measured for both similarity and disclosure risk.
By The AI Enterprise Magazine Team ·
GenAI pilots stall because people don't trust them, not because models are weak, argues Snorkel AI Head of Solutions Dennis Panos. He says an evaluation-first process built with subject matter experts took two stalled projects to production in about two months.
By The AI Enterprise Magazine Team ·
Leaderboard scores rarely predict how a model will handle a company's own data. HumanSignal ML Evangelist Micaela Kaplan explains how enterprises can build custom AI benchmarks with rubric-based scoring, start small and bring domain experts into the process.
By The AI Enterprise Magazine Team ·
Most organizations struggling for AI returns lack readiness, not technology, says Elder Research's Jennifer Schaff. Using a football analogy and client stories, she identifies eight gaps that quietly erode ROI, from strategy misalignment and fragmented analytics to failed adoption and weak data literacy.
By The AI Enterprise Magazine Team ·
AI agents raise the bar for data quality, say data and AI leaders from TK Elevator, Baptist Health South Florida and Novelis. They outline a five-step path from messy data to agent-ready data, governance for agents as digital employees and a hub-and-spoke model for scaling.
By The AI Enterprise Magazine Team ·
AI breaks traditional data governance, say leaders from Quest Software, ADP, WellStar Health System and Carter's. Their fixes include trust scoring for data, governance by design, fast risk assessments and team structures that scale governance without slowing delivery.
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 ·
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 ·
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 ·
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 COLLIDE 2024 keynote, Elder Research's John Elder described two crises: findings that do not replicate and data science projects that never reach production. He showed target shuffling, a simulation that adjusts for cherry-picking, and shared change management lessons.
By The AI Enterprise Magazine Team ·
At COLLIDE 2024 in Atlanta, Informatica's Peter Manta said AI projects succeed or fail on data. He offered a three-part test of robust, relevant and responsible data, and urged teams to involve the data office early and pilot small.
By The AI Enterprise Magazine Team ·
At COLLIDE 2024 in Atlanta, Snowplow co-founder Yali Sassoon said AI advantage comes from feedback loops that generate proprietary data. He described Klarna's support agent as an example and listed five ingredients for building a data flywheel.
By The AI Enterprise Magazine Team ·
In a COLLIDE 2024 session, Softcrylic showed AudienceMatchAI, an early-stage tool that runs unsupervised customer segmentation and lets marketers question the segments through a language model. The team said limiting the data the model sees reduces hallucinations and makes answers easy to check.
By The AI Enterprise Magazine Team ·
AI strategies fail when the data underneath them isn't right. Leaders from Southern Company, Clorox, Morgan Stanley and Tecton explain how to tie data strategy to business value, break down silos with standardized features, and scale pilots that break at full volume.
By The AI Enterprise Magazine Team ·
In a COLLIDE 2024 session, Fiddler AI founder and CEO Krishna Gade argued that enterprises need an observability layer for generative AI. He described how small fine-tuned trust models score hallucinations, toxicity and safety, and how regulation shapes governance.
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 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 ·
At COLLIDE 2023 in Atlanta, Dremio's Jeremiah Morrow demonstrated how branching in the Dremio Arctic Iceberg catalog gives data scientists isolated, zero-copy clones of production data. The demo showed a branch, a tagged change, a merge to main and a one-command rollback.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023 in Atlanta, Fiddler AI co-founder and CEO Krishna Gade outlined the risks of large language models, including privacy leaks, hallucinations, unreliable self-explanations and bias. He argued that AI observability is the missing link for deploying generative AI responsibly.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023, AnswerRocket CTO Mike Finley used a recorded demo to show how a generative AI copilot for analytics is taught new skills. A pasted JSON block and a short Python routine added e-commerce and cohort analysis, with numbers verified against the data.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023 in Atlanta, Datalogz CEO Logan Havern said AI copilots in BI tools will make dashboards easier to build and sprawl worse. He outlined a BIOps strategy to find unused, duplicate and risky reports and cut cost, citing his company's client results.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023 in Atlanta, dotData's Ian McKenna demonstrated a way to turn disjointed tables into a flat, model-ready feature table. He argued for letting the data surface features statistically, then reviewing them with experts.
By The AI Enterprise Magazine Team ·
In a panel at the Data Science Innovators Conference 2023, data leaders from Disney, Meta, Microsoft and Minecraft discussed why models stall after they are built. They covered buy-in, business alignment, communication, maintenance and scaling.
By The AI Enterprise Magazine Team ·
In a session at the Data Science Innovators Conference 2023, Eric Schles of Johns Hopkins Hospital showed how varying the random seed on a train test split can inflate a model's score. His Honest ML framework tests hundreds of splits, then he built a neural network from the linear algebra up.
By The AI Enterprise Magazine Team ·
In a panel at the Data Science Innovators Conference 2023, data practitioners from Snap, Wells Fargo and CVS discussed why data quality breaks down and what it costs. They covered ownership, observability, automation, leadership buy-in and transparency.
By The AI Enterprise Magazine Team ·
