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Moving from pilots to production, proving value and building an AI-forward operating model.
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 ·
News · Data & AI Infrastructure
By The AI Enterprise Magazine Team ·
By The AI Enterprise Magazine Team ·
News · Data & AI Infrastructure
By The AI Enterprise Magazine Team ·
Anthropic's public IPO filing reveals rapid enterprise demand growth alongside heavy financing needs, as rivals strain to raise money. Here is what CIOs and procurement teams should take from it.
By The AI Enterprise Magazine Team ·
Analysis · Data & AI Infrastructure
Nvidia says its containerized DSX POD can cut AI factory deployment from six months to 14 days. The claim is real but narrow, and the week's news shows the bigger bottlenecks sit in power, labor and the chip supply chain.
By The AI Enterprise Magazine Team ·
News · Data & AI Infrastructure
The two companies at the base of the AI chip supply chain reported strong third quarters and warned that capacity will stay tight for years, a signal that enterprise AI compute will remain scarce and costly.
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 ·
News · Data & AI Infrastructure
A 20-year contract funds $4.3 billion in upgrades at 11 Constellation reactors, part of a 3.6 GW package that shows power, not just chips, now shapes how much AI capacity enterprises can buy.
By The AI Enterprise Magazine Team ·
Claude Haiku 5.5, OpenAI's Decisions API and Google's Nano Banana 2.1 all lowered the cost of AI this week. But tokenizer changes, agent volume and uncertain long-term pricing mean enterprises should measure cost per task, not per token.
By The AI Enterprise Magazine Team ·
Stalled AI initiatives usually suffer from unclear goals and weak alignment, not weak technology, says Analytic Vizion founder Nelson Davis. He lays out an impact-first planning sequence and five questions leaders can use to get projects moving again.
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 ·
Anthropic cut Opus pricing by 20% and OpenAI halved the cost of its mid-tier models in launches hours apart, shifting the enterprise question from which model is smartest to which one does each job for less.
By The AI Enterprise Magazine Team ·
Harvey's gross margin reportedly swung from 50% to negative 50% as agent usage soared, until it moved work to a model built on open weights. Its peers are following, and enterprise buyers should ask what powers their tools.
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 ·
News · Data & AI Infrastructure
A bipartisan 417-3 vote asks state regulators to make large data centers cover the grid upgrades they require, a signal that the power costs of AI capacity are moving toward the companies that build it, and their customers.
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 ·
News · Data & AI Infrastructure
Oracle says AI cloud demand still outruns supply after infrastructure revenue more than doubled and its backlog reached $664 billion. Negative free cash flow and new equity sales kept investors cautious.
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 ·
News · Data & AI Infrastructure
Broadcom's AI semiconductor revenue rose 221% to $16.7 billion as Google, Anthropic, OpenAI and Meta ramp custom chips. For enterprises, the shift changes who controls AI unit costs and where capacity bottlenecks sit.
By The AI Enterprise Magazine Team ·
Opinion · People, Culture & Leadership
New Revelio Labs data shows early-career workers in AI-exposed jobs falling well behind, even as AI-adopting firms keep growing. Our view: quietly cutting junior hiring hollows out the pipeline that enterprise AI will need.
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 ·
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 ·
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 ·
News · Data & AI Infrastructure
Nvidia beat forecasts with $96.2 billion in revenue and guided to $108 billion next quarter. For enterprise buyers, the results point to tight GPU supply, rising component costs and a Rubin transition to plan around.
By The AI Enterprise Magazine Team ·
Google Cloud's new finance and legal editions of Gemini Enterprise put it alongside Anthropic and OpenAI in the vertical agent business. The real competition, and the real work for buyers, now sits in data connectors, permissions and evaluation.
By The AI Enterprise Magazine Team ·
Opinion · People, Culture & Leadership
Opinion: This week's Pew survey shows worry about AI and jobs climbing even as more businesses pay for AI tools. Leaders who frame AI as headcount reduction are eroding the trust their adoption depends on.
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 ·
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 ·
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 ·
Enterprise AI scales when it is tied to a real problem, validated, observed and cost-controlled, not when the model is impressive. Leaders explain how.
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 ·
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 ·
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 ·
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 ·
Drawing on about 120 AI projects, Nanonets CEO Sarthak Jain says the few that succeed rely on long, hands-on iteration, human controls, clean data and bottom-up adoption, not budgets or executive mandates.
By The AI Enterprise Magazine Team ·
Data and AI leaders from The Home Depot, Equifax and Mars Wrigley tie every AI use case to a business OKR, trade off speed, accuracy and cost, and argue that AI belongs in the middle of the process, not first.
By The AI Enterprise Magazine Team ·
AI will make leadership more human, not less, argues Analytic Vizion CEO and founder Nelson Davis. He outlines three keystone habits for data and analytics leaders and a simple matrix for deciding when AI should automate, augment or advise on a decision.
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 ·
Infrastructure cost decides whether real-time AI pays off, argues Aerospike founder and CTO Srini Srinivasan. He explains the company's hybrid memory architecture, how faster lookups improve fraud and recommendation decisions, and why agents will multiply database load.
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 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 ·
Machine learning, generative AI and agentic AI each solve a different problem in a legacy quoting workflow, says KMS Technology CTO Guy Merritt. He outlines a prepare, diagnose and implement path for enterprises and shares KMS's own results from AI-assisted software development.
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 ·
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 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 ·
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 ·
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 ·
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 ·
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 ·
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 ·
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 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, 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 ·
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 ·
Most companies still equate generative AI with chatbots. Leaders from Dollar General, Scripps, TelevisaUnivision, Vertex and PRGX describe content tagging, dynamic dashboards, code translation and survey analysis, and are candid about security risks, unreliable outputs and measuring ROI.
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 ·
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, WhereScape's Patrick O'Halloran compared small, mid-sized and large companies building data foundations in the cloud. He recommended narrow scope, rapid prototyping and treating the work as a business process, with automation to cut testing and rework.
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 ·
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 ·
AI ROI is won or lost at every stage of a project. Leaders from Bank of America, Carelon Health, Lockton and PRGX break down business cases, the three E's of measuring value, data and training bottlenecks, and how unmonitored cloud and GPU spending erodes returns.
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 ·
At COLLIDE 2024 in Atlanta, Pure Storage's Chad Hendren said AI pilots often break when demand grows, because storage, metadata and power limits start to bite. He shared his company's claims on parallelized file tools, flash density and customer results.
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, 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 ·
At the 2024 AI Connect K-12 Data Science Conference, MIT AI Fellow Deborah Kariuki reviewed where data science education stands in U.S. schools. She covered state standards, a federal data literacy bill and the push to let data science count in place of algebra.
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 ·
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 ·
At COLLIDE 2023 in Atlanta, a panel of data consumers from Home Depot, Mobilewalla and Major League Baseball explained what makes data science useful to them. They described aligning on business goals, translating metrics into plain language, and building relationships between analysts and decision-makers.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023, six data leaders from Warner Bros. Discovery, Inspire Brands, AT&T, Morgan Stanley, Kaiser Permanente and American Express discussed paths to leadership. They stressed soft skills, continuous learning, alignment with business goals and the ability to translate technical work.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023, Graft CEO Adam Oliner said a good AI strategy must use all of a company's data, support experimentation, lift the whole organization and empower everyone. His conclusion, from a vendor's perspective, was to adopt a modern AI platform rather than build one.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023, Morgan Stanley Wealth Management's Atul Dalmia described AI systems for personalized client content, adviser next-best-action and lead matching, with reported lifts in contact, engagement and conversion. His lesson: solve the business problem first and augment what already exists.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023 in Atlanta, AI Connect co-founder Brian Mink explained how economic decision makers differ from technical practitioners. He outlined why data executives are risk averse and five tactics for reaching them.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023 in Atlanta, H2O.ai founder and CEO Sri Ambati demonstrated open-source LLMs, RAG over private documents and AutoML. He also argued that prompts and community are the new moats and predicted data scientists will lead Fortune 500 companies by 2030.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023, Sol Madadha of Regions said businesses can serve customers better by combining the many signals they emit. He pointed to clean data, clear use cases, return on investment and governance as the prerequisites for enterprise adoption.
By The AI Enterprise Magazine Team ·
At COLLIDE 2023 in Atlanta, Intuit Factory founder Roop Singh argued that the winners in enterprise technology will combine AI, blockchain and data. He predicted priced data marketplaces and cheaper models that let small firms compete.
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 ·
In a session sponsored by Lenovo at COLLIDE 2023 in Atlanta, Mike Leach argued that AI teams should match hardware to the workload and treat workstations as an on-ramp to the data center. He also said IT, data science and finance need to plan AI spending together.
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 COLLIDE 2023, AnswerRocket's COO and CTO discussed how to get past generative AI hype in business analytics. They argued for higher-order business questions, using models to plan analysis rather than write SQL, and starting with one recurring, labor-intensive task.
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 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 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, Medidata product manager Srishti Gupta argued that data science teams need product managers with data science experience. She covered how data science products differ, how to prioritize by complexity and value, and when to stop a project.
By The AI Enterprise Magazine Team ·
In a session at the Data Science Innovators Conference 2023, Comcast's Doug Wynne described a time series machine learning system that runs 4,230 daily micro models to forecast subscriber additions. He shared six lessons on picking problems, beating baselines and working with stakeholders.
By The AI Enterprise Magazine Team ·
In a talk at the Data Science Innovators Conference 2023, Meta data science manager Jen Wang described how machine learning supports marketing decisions. She contrasted per-campaign models with a centralized, modular platform that reduces audience overlap and improves budget allocation.
By The AI Enterprise Magazine Team ·
In a session at the Data Science Innovators Conference 2023, Pfizer's Nechama Katan outlined a four-step process, Define, Advocate, Team and Act, for getting data science projects funded and adopted. Her core advice: solve a business problem in business language and make the leader the hero.
By The AI Enterprise Magazine Team ·
In a session at the Data Science Innovators Conference 2023, Taimur Rashid of Redis argued that leaders should accelerate digital innovation while building systems that outlast them. He covered vector databases, customer examples from Capital One and Dick's Sporting Goods, and security, sustainability and inclusion.
By The AI Enterprise Magazine Team ·
In a talk at the Data Science Innovators Conference 2023, Kaiser Permanente's Faizan Javed explained why chief data and analytics officers average about 2.5 years in the role. He outlined common obstacles and a playbook built on data defense, data offense and measurable business value.
By The AI Enterprise Magazine Team ·
In a session at the Data Science Innovators Conference 2023, Selma Dogic of Carter's described how to build a business-led data and analytics strategy. She covered winning champions, tying analytics to corporate goals, certified self-service data, and embedded and augmented analytics.
By The AI Enterprise Magazine Team ·
