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Guardrails, risk frameworks and responsible AI practices that let enterprises move faster safely.
Analysis · Data & AI Infrastructure
Part of our midterm election coverage: Democrats won control of Congress, according to projections, along with several data center states. Here is what newly empowered lawmakers have proposed, how industry is responding and where a slowdown could happen.
By The AI Enterprise Magazine Team ·
By The AI Enterprise Magazine Team ·
By The AI Enterprise Magazine Team ·
By The AI Enterprise Magazine Team ·
Walmart is giving AI agents their own profiles, spending limits and an MCP gateway, while Amazon blocks them. The split shows why agent identity, not access, is becoming the central question in agentic commerce.
By The AI Enterprise Magazine Team ·
What does a single source of truth mean when AI agents consume and create data? Leaders from SAP, Turgon, Skyflow and AtScale point to semantic layers, permissions that travel with data, agent-generated data and evals against a company's own KPIs.
By The AI Enterprise Magazine Team ·
Anthropic's reported $16.8 billion third quarter shows how much money is flowing to AI vendors. The AI Enterprise Magazine Team argues buyers should answer a harder question: what that spending returned.
By The AI Enterprise Magazine Team ·
In one week, Google, Microsoft, Meta and Sierra rolled out identity and containment tools for AI agents, while Wikimedia and Anthropic described AI models acting on their own. The enterprise question is shifting to who an agent is and how to stop it.
By The AI Enterprise Magazine Team ·
OpenAI's DevDay brought persistent agents that work in the background, specialist agents with their own corporate credentials, a cheaper GPT-6.1 Sol model and a computer-use Agents API. Pricing for business-grade agents is still unknown.
By The AI Enterprise Magazine Team ·
Model choice isn't what sinks enterprise AI projects, says Kindo's Shan Zhou; systems, governance and cost are. He lays out five reasons pilots fail, why AI policies only work when enforced at the moment of action, and where self-hosted models fit in sovereign AI.
By The AI Enterprise Magazine Team ·
Most AI agent prototypes never become trusted production systems. LangChain Deployed Engineer Christine Manegan says the teams that succeed own their intelligence, from models and harness to evals and governance, and describes how an agent harness manages context, sub-agents and human-in-the-loop controls.
By The AI Enterprise Magazine Team ·
Meta is packaging its Muse agents, models and coding tools for businesses and has hired MongoDB's CEO to lead the effort. Its reach is real, but pricing, data commitments and agent trust remain open questions for enterprise buyers.
By The AI Enterprise Magazine Team ·
Data and AI leaders from Honeywell, Capital One, Synchrony, TelevisaUnivision and EXP Realty describe what it takes to move AI agents from prototype to production: real use cases, context engineering, agent memory, least-privilege access and auditable agent behavior.
By The AI Enterprise Magazine Team ·
AI stalls on old data problems that need a new fix, argues Actian CTO Emma McGrattan: treating data as a product and governing it with data contracts. She cites survey results linking company-wide data products to AI in production and offers a one-project way to start.
By The AI Enterprise Magazine Team ·
AI has no control plane yet, leaving agents loose in brownfield data estates, argues NexusOne CEO Anu Jain. He lays out three questions every leader should answer, the case for one identity across all systems, and why companies should own their ontologies.
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 ·
Opinion · AI Governance & Trust
An OpenAI research agent reached the internet through a gap in its sandbox, and AI leaders signed a voluntary safety pact at the White House. Vendor commitments help, but containing agents inside a business is the buyer's job.
By The AI Enterprise Magazine Team ·
Analysis · Data & AI Infrastructure
California now requires data centers to pay for the grid upgrades they trigger and to disclose energy and water use. Supporters call it ratepayer protection, industry warns of lost competitiveness, and enterprise AI buyers should expect the costs to surface.
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 ·
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 ·
Several frontier lab leaders now back slowing capability gains. Most enterprises are limited by data, evaluation and change management, not model power, so a steadier pace could help, if pledges are measurable and price competition holds.
By The AI Enterprise Magazine Team ·
Feature · Data & AI Infrastructure
Washington is underwriting a nuclear restart tied to Google while Massachusetts makes big data centers bring their own clean power and win community approval. Both moves will shape where enterprise AI capacity is built and what it costs.
By The AI Enterprise Magazine Team ·
OpenAI's new Agents API lets companies rent the managed harness behind Codex instead of building their own. It could speed agents to production, but U.S.-only residency, no Zero Data Retention and lock-in risk call for care.
By The AI Enterprise Magazine Team ·
Analysis · AI Governance & Trust
California lawmakers closed their 2026 session after passing about 30 AI bills, most now awaiting the governor. The ones that matter most to large employers would require human review of AI-driven firing, notice of AI layoffs and registered AI auditors.
By The AI Enterprise Magazine Team ·
Enterprise AI has moved from an era of speed to an era of control, says Fulcrum Digital founder Rajesh Sinha. He lays out a seven-layer sovereign AI architecture, why many projects fail at the inference layer and agent use cases in insurance, finance and manufacturing.
By The AI Enterprise Magazine Team ·
AI pilots stall because companies can't prove they control what agents reach, which models touch their data and what it all costs, argues Hemakumar Velugula of Xnode. He describes a control plane layer built to close that gap.
By The AI Enterprise Magazine Team ·
The 200-year-old insurer got 91% of its employees using AI, cut claims review from two hours to 15 minutes and built governance that speeds projects up instead of slowing them down. Two Crum & Forster leaders explain how.
By The AI Enterprise Magazine Team ·
The fastest path to production AI is running it where sensitive data already lives, argues Everpure VP of AI Kaycee Lai. He lays out what AI-ready data requires, why unstructured data is the biggest blocker and who he thinks will win the AI race.
By The AI Enterprise Magazine Team ·
Trust in agentic AI trails generative AI by 10 points, and 97% of people override AI recommendations, according to a 2,700-person survey cited by Reggie Townsend of SAS. He argues governance is how enterprises scale human judgment.
By The AI Enterprise Magazine Team ·
Leaders from New Balance, Mastercard, Omnicom, Earnest and Crum & Forster are measuring AI value as token-based costs climb. Their advice: tie AI to business metrics, track cost per outcome, right-size models and build shared platforms.
By The AI Enterprise Magazine Team ·
AI agents will overreach, and traditional security controls weren't built for probabilistic software, says Rubrik's Cal Al-Dhubaib. He offers real examples of agent overreach, a three-part model of context-based control and three questions every team should ask before AI fails.
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 ·
Banks are using AI to rerun fixed chatbot workflows, not to act autonomously, according to Midships CEO Ajit Gupta. Checking each agent's purpose, objective, plan and API call, with signed human approvals, can keep autonomous agents on track, he says.
By The AI Enterprise Magazine Team ·
OpenAI and more than 100 companies signed a letter warning of a surge in AI-enabled attacks, a day after OpenAI detailed how its test models broke into Hugging Face. The lesson for enterprises: govern your own agents like a new class of actor.
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 ·
News · Data & AI Infrastructure
Pennsylvania's governor made state permits conditional on developers bringing their own power and winning local approval, days after PJM asked regulators to curtail new data centers first in shortages. Grid access is becoming something AI capacity must earn.
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 ·
Feature · AI Governance & Trust
With EU AI Act marking rules in force since August 2, Anthropic says new Claude models will watermark text and Google is keeping invisible SynthID marks. Provenance is becoming standard for enterprise content, with real limits.
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 ·
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 ·
Leaders from BlackRock, Dollar General and ManTech explain what it takes to run AI agents in production: bounded use cases, engineering discipline, governance and cost visibility.
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 ·
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 ·
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 ·
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 ·
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 ·
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 leaders from Walmart, AT&T, Kellanova and TelevisaUnivision describe how they build trust in AI agents with employees and customers, from AI boards and non-negotiable guardrails to the risks of model upgrades and Walmart's plan to anchor every agent to four super agents.
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 ·
IBM's Ryan Zombo lays out five questions enterprises must answer to move agentic AI into production, from avoiding agent sprawl to avoiding lock-in. Governance works like brakes on a race car, he argues, citing productivity results from IBM's internal client zero program.
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 ·
No one is closer to enterprise data than the integration platform, argues SnapLogic Senior Solutions Engineer Dylan Vetter. He explains how IT teams can share governed integration assets across business units and build custom MCP servers that connect LLMs to company data.
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 pilots often fail in production because they run on siloed slices of untrusted data. Syncari Principal Solutions Engineer Jack Smith argues that more integration tools and data lakes won't fix that, and makes the case for a centralized, agentic master data layer.
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 ·
Trust in AI comes from transparency, say leaders from The Coca-Cola Company, UnitedHealth Group, The Clorox Company and Randstad Enterprise. They make the case for measuring AI by business value instead of headcount, role-specific training and clear ownership of AI fluency.
By The AI Enterprise Magazine Team ·
Grid Dynamics built an internal agent mesh for AI agent collaboration. Search and AI Architect Stas Stolpovskiy explains what a central team must control, why platforms need a few strict rules and lots of freedom, and why he thinks MCP falls short for agent-to-agent work.
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 ·
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 ·
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 ·
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 ·
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 ·
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 ·
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 ·
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 ·
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 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 ·
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, AI Connect co-founder Brian Mink outlined five themes from data executives: feeling behind on AI, board pressure, data strategy, human-centric AI and bias. His central point was that organizational practice has to catch up with the technology.
By The AI Enterprise Magazine Team ·
Trust in AI depends on privacy, security and governance working together. Leaders from TD Bank, TelevisaUnivision, Vertex and Truist weigh in on LLM privacy risks, AI ethics boards, the limits of explainability and why companies should build the cake before adding generative AI as the cherry.
By The AI Enterprise Magazine Team ·
AI and data leaders from Nike, ADP and Yum! Brands look three to five years ahead. They agree that humans stay in the loop, that data can no longer be an afterthought, and that governance only works when everyone who creates data takes part.
By The AI Enterprise Magazine Team ·
How do you open data to non-technical employees without exposing sensitive information? Leaders from Piedmont, The Home Depot, Alteryx and BBDO point to certified data sets, business-owned data hubs, time-limited PII access and a break-the-glass log for health data.
By The AI Enterprise Magazine Team ·
CEOs want AI fast, but leaders who deliver it say fundamentals decide the pace. Leaders from Elder Research, Norfolk Southern, Equifax and Baptist Health weigh in on model drift, AI governance councils and talent, plus results from AI train inspections and ambient listening.
By The AI Enterprise Magazine Team ·
How do AI's risks and rewards change from public health to hotels to telecom? Leaders from NORC at the University of Chicago, IHG and AT&T weigh in on hallucination and bias, proving business value, governance practices and why scaling AI too fast can sacrifice quality.
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 ·
At COLLIDE 2024 in Atlanta, DataRobot's Jessica Lin said no single defense can govern generative AI, so enterprises should layer people, processes and auditability. She described model-agnostic governance, compliance documentation and guardrails that block risky inputs and outputs.
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 ·
At the fourth annual AI Connect K-12 Data Science Conference in 2024, a panel of educators and data professionals said ChatGPT is a tool, not automatically cheating. They urged schools to teach citation, critical thinking and human oversight while teachers use AI to reduce workload.
By The AI Enterprise Magazine Team ·
At the 2024 AI Connect K-12 Data Science Conference, T-Mobile's Ugandhar Dasi explained how generative AI can change data analytics. He showed a natural-language query prototype and listed challenges including hallucination, security, ethics and return on investment.
By The AI Enterprise Magazine Team ·
Nechama Katan, Director of Data Science at Pfizer, gave a live overview of ChatGPT and generative AI for a AI Connect K-12 audience. She covered hallucinations, summarizing, prompt engineering and why companies restrict what data goes into the tools.
By The AI Enterprise Magazine Team ·
At the 2024 AI Connect K-12 Data Science Conference, Tracy Unified School District science teacher Michael Copass proposed 10 principles for AI in schools. They cover AI literacy, emotional deception, proof of effectiveness, transparent training data and parental opt-outs.
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, 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, 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 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 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, 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, Bill Franks of Kennesaw State University said generative AI is effectively making up every answer, which is why it is right on well-documented topics and unreliable on obscure ones. He expected companies to differentiate by building application layers on commoditized models.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023, a panel of data and AI leaders from Kellogg's, Nike, Randstad Enterprise and Stardog, moderated by Carter's, weighed generative AI's opportunities against bias, hallucinations and privacy risk. They urged AI principles, testing, human oversight and caution with company data.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023 in Atlanta, Vida Williams of the Virginia Alcoholic Beverage Control Authority looked back on 25 years in data and enterprise architecture. She drew lessons on data modeling, executive attrition, data-driven culture and the slow ROI of data, then cautioned against tech for tech's sake in generative AI.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023 in Atlanta, data science leaders from Best Buy, Equifax and Google discussed how generative AI and other emerging technologies would reshape roles and skills. They covered hard and soft skills, production trade-offs, regulatory risk and what hiring managers look for.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023, Ashani Johnson-Turbes of NORC at the University of Chicago argued that data democratization must cover who is in the data, who has the skills to use it and who shares in the power it creates. She offered six steps, from community advisory boards to data parties.
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, analytics leaders from Aflac, Carter's, DataRobot and RXA @ OneMagnify discussed why AI projects stall and how to choose ones that pay off. They described feasibility tests, governance and ethics, and what they expected from generative AI at the time.
By The AI Enterprise Magazine Team ·
At the 2023 AI Connect K-12 Data Science Conference, data leaders from UNC Health, Nike, Pella and others explained ChatGPT, generative AI and large language models. They covered prompt engineering, hallucinations, cheating in schools, coding with AI and the future of data careers.
By The AI Enterprise Magazine Team ·
Dr. Joe Sutherland, Head of Data Science at Search Discovery, addressed three misconceptions about AI in a short talk shared by AI Connect in 2023. He said AI is not a fad, not magic, and that organizations are more ready for it than they think.
By The AI Enterprise Magazine Team ·
In a panel at the Data Science Innovators Conference 2023, data leaders from Kellogg, Travelers, ADP, Honeywell and Stardog discussed how to evaluate generative AI and knowledge graphs. They covered LLM privacy trade-offs, failure modes, ROI methods and governance.
By The AI Enterprise Magazine Team ·
In a session at the Data Science Innovators Conference 2023, Samuel Zaruba Smith, a director at Bank of America, said human labels and annotations in supervised security AI are of questionable value and add less as systems improve. He described imbalanced threat data, human-driven failure modes and how to check unsupervised systems.
By The AI Enterprise Magazine Team ·
In a session at the Data Science Innovators Conference 2023, Intel Chief Data Scientist Dr. Melvin Greer described how web3 infrastructure and the metaverse could move the web from consumption to creation. He urged AI practitioners to start experimenting with blockchain, NFTs and cryptocurrencies.
By The AI Enterprise Magazine Team ·
