Palantir's third-quarter revenue nearly doubled to $2.31 billion, and sales to U.S. companies passed U.S. government revenue for the first time. Slowing contract bookings and valuation worries tempered the reaction.
After an election week that rattled AI stocks and put data center growth in question, we argue enterprise leaders should stop betting on the boom or the bust and invest where returns and data readiness are proven.
Employers cited AI in just 1,265 October job cuts, and economic conditions overtook AI as 2026's leading stated layoff reason. The shift gives enterprise leaders a chance to rethink how they link AI to headcount.
OpenAI's new mid-tier model targets invoices, forms and desktop software at half the API price of its predecessor. Cheaper computer-use agents widen the business case, but cost per outcome and guardrails still decide whether they pay off.
Microsoft, Alphabet and Amazon reported accelerating cloud growth and record AI spending this week, and all three said capacity is the constraint. Enterprises should plan for scarce, costly AI compute into 2027.
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.
Earnings from SK hynix, Samsung and Intel show AI's supply crunch has moved beyond GPUs to memory and server CPUs. Enterprise buyers face years of tight allocation and higher costs, and need to plan purchases and workloads differently.
IBM's third quarter put data software at the center of its AI story, with Confluent-driven data growth near 20%, flat consulting revenue increasingly made up of AI work, and a higher free cash flow outlook.
SAP posted its first €10 billion quarter, raised its cloud outlook and credited its Autonomous Enterprise AI strategy. The results show ERP cloud migration accelerating, but SAP still reports no numbers for its AI business.
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.
The open source llm-d project reached 1.0 this week with features built for AI agents. We argue that shared, open inference infrastructure gives enterprises leverage over the cost that will decide whether agents pay off.
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.
Accenture told many employees it will centrally fund only $29 a month each for Claude, after some spent up to $1,000. The move shows how usage-based AI tools are outrunning budgets built for flat software licenses.
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.
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.
The Australian AI cloud operator, whose customers include OpenAI and Meta, cancelled its listing as investors questioned the gap between its contracted capacity and the data centers already running, a signal for enterprises that rent GPU capacity.
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.
Anthropic's new mid-tier model nears Opus 5.5 on coding and office benchmarks at the same $2 and $10 list price as Sonnet 5. An independent test shows costs climb sharply at maximum effort.
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.
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.
Google's new frontier model targets long software projects, legal and financial research, and vulnerability patching. It launches at $2 and $10 per million tokens, but that price will double and most enterprises must wait for access.
Anthropic is spending $100 million to train 10,000 engineers inside customer companies, and Barclays is pushing Claude Code to half its developers. The people who turn models into production systems are now the scarce resource.
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.
GPU cloud Lambda borrowed $926 million against the servers it is deploying for one investment-grade customer, in what it calls a first for a private neocloud. The deal shows how AI capacity is now financed, and what buyers should ask.
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.
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.
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.
Claude now decides when a request needs an agent, and new Docs and Slides tools move it closer to office work. Enterprise IT gets 30 days' notice, but connector, autonomy and cost decisions should start now.
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.
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.
Samsung led a €3 billion Series D that values Mistral at more than €21 billion, Europe's largest tech equity round. The money funds compute and research, and gives enterprises a better-funded open-weight alternative to U.S. model providers.
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.
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.
Dell booked $60.9 billion in AI server orders and lifted its annual outlook by $25 billion. The enterprise signal is lower down: traditional server sales more than doubled as agentic workloads pull in CPU capacity.
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.
As agents push token bills higher, enterprises are weighing three options: pay per token, rent GPU infrastructure or own it. This week's VMware Explore launches, two CoreWeave deals and Dell's backlog show what each choice now involves.
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.
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.
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.
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.
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.
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.
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.
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.
Salesforce's AI and data business grew more than 210% while total revenue rose 11%. Half of Agentforce bookings now come from usage credit top-ups, a shift that changes how CIOs should budget and govern agent spending.
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.
Salesforce's quarter undercuts the claim that frontier models will replace business software. But as interfaces move into AI assistants and pricing shifts to credits, enterprises should make sure they, not any one vendor, own their business context.
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.
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.
Gartner says the cost of completing work with AI agents will climb more than fivefold through 2028 even as token prices fall. Snowflake, TrueFoundry and others spent the week pitching routing and budget controls in response.
Cisco reported $9.3 billion in hyperscaler AI orders for fiscal 2026 and 25% order growth outside the cloud giants, a sign that enterprise networks are being rebuilt for AI, even as rising costs squeeze margins.
CoreWeave's second-quarter revenue more than doubled to $2.58 billion and its backlog reached about $104 billion, with Caterpillar and Bentley among new customers. Heavy borrowing and losses give enterprise buyers reasons for care.
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.
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.
Palantir's second-quarter revenue rose 93% to about $1.9 billion, US commercial sales grew 149% and full-year guidance went up again, a sign that enterprises are signing large, multi-year AI platform commitments.
AMD's data center revenue more than doubled to $6.7 billion in the second quarter, with Anthropic, Microsoft, Meta and OpenAI among buyers of its AI systems, giving enterprises a stronger second source of AI compute.
Employers cited AI for a third of July's announced job cuts and 112,713 so far this year, even as total layoffs fell to a two-year low and hiring plans rose. The numbers say as much about narrative as productivity.
A reported $10 billion cloud deal, a new chip design team and starring roles in AMD's and SpaceX's earnings show how far AI labs are going to secure compute, and why enterprise buyers should ask where theirs comes from.
Rippling's AI token spending was growing 80% a month before routing and coaching brought it down. The lesson for every enterprise: measure who spends what, on which models, for which results, before the bill arrives.
Microsoft beat forecasts with 43% Azure growth and more than 30 million paid Copilot seats, but said AI demand still outruns capacity. Enterprise buyers should plan for tight supply and negotiate Copilot on outcomes.
Anthropic will deploy up to 2 gigawatts of AMD Instinct MI450 Series GPUs starting in 2027, with AMD committing up to $5 billion in equity. The deal shows how model providers are chasing compute supply that enterprises depend on.
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.
AWS posted its fastest growth in 18 quarters and Amazon raised its 2026 spending plan to about $220 billion, yet Andy Jassy expects capacity shortfalls through 2027. Enterprises should treat cloud AI capacity as a scarce resource.
The AI Omnibus pushed the EU AI Act's high-risk obligations to December 2027. The AI Enterprise Magazine Team argues that enterprises should use the extra 16 months to build governance that speeds AI up, not to stand down.
ServiceNow's second-quarter results show agentic AI moving into paid production workflows, with AI contract value above $1 billion, ninefold growth in agent deployments and a pricing model that is shifting toward usage.
A multibillion-dollar agreement for European GPU capacity and new deployment options for Mistral models inside Microsoft's tools give regulated enterprises more model choice and more control over where AI runs.
Alphabet's second quarter showed AI cloud demand running ahead of supply. Investors fixated on cash flow and capex, but for enterprise buyers the bigger story is scarcity, pricing power and the need to plan compute early.
TSMC lifted its 2026 revenue forecast to slightly above 40% growth and raised capital spending after a record second quarter. Here is what the foundry's results signal for enterprise AI compute prices, capacity and budgets.
ASML raised its full-year sales outlook to €43 billion to €45 billion after a €9.3 billion second quarter, citing AI investment. Memory makers are expanding fastest, a sign that DRAM and HBM shortages will shape AI hardware costs.
Microsoft executives reportedly told salespeople to position the company's in-house AI models against OpenAI, Anthropic and Google. For Copilot customers, the shift raises questions about which model answers their queries and who decides when it changes.
Crusoe and Lancium's binding deal for a 1-gigawatt AI campus in Childress, Texas, is the latest sign that power, not just chips, decides where AI capacity gets built. Here is what that means for enterprise AI planning.
The Digital Omnibus pushes most high-risk EU AI Act obligations to late 2027 and 2028, while transparency duties still begin August 2. We argue the extra time pays off only for enterprises that use it to build governance now.
Samsung expects second-quarter operating profit of about 89.4 trillion won, 19 times last year's level, as AI data centers drive up memory prices. Enterprise buyers should expect those costs to show up in server and cloud bills.
Meta opened a paid developer API for Muse Spark and released version 1.1, built for coding and agentic tasks. Low pricing adds a new option for enterprise teams, but the preview raises questions about terms, support and lock-in.
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.
OpenAI's GPT-5.6 arrived after a government-requested delay, in three tiers priced from $1 to $5 per million input tokens. The release turns model choice into a workload-by-workload cost decision and raises new questions about evaluation and release risk.
KPMG's latest AI Pulse found more companies embedding AI in daily work and nearly half pausing projects whose costs outran their value. The firms that can see what AI costs to run report returns five times as often.
Qualcomm plans to buy Modular, whose open software stack runs AI models across many chip types without code rewrites. The deal could widen enterprise hardware choice, but it also puts a portability layer under one chipmaker's control.
South Korea's two memory giants pledged about $518 billion for a new chipmaking hub after AI demand nearly doubled memory prices in a quarter. New capacity is years away, so enterprise AI budgets should plan for tight supply.
The Council of the EU gave final approval to the AI Omnibus, moving high-risk obligations to late 2027 and 2028 and adding a new ban. Key duties survived, and near-term dates still apply, so the delay is time, not an exemption.
Tencent's multiyear memory deal with CXMT and a $518 billion Korean build-out show AI's biggest buyers treat memory as scarce. Enterprise leaders should put memory-sensitive costs in their plans and design for efficiency, without panicking.
Micron's fiscal third-quarter revenue more than quadrupled from a year earlier, beat its own guidance by more than $7 billion and came with a $50 billion forecast, a sign that memory is now one of AI's tightest bottlenecks.
A $6.3 billion rental deal between SpaceX and open-weight lab Reflection AI, and Groq's $650 million raise to become a cloud provider, show compute shifting to monthly contracts that either side can exit. Enterprises should plan accordingly.
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.
Accenture's third-quarter revenue grew 6% to $18.72 billion, but new bookings fell and consulting barely grew in local currency. The mix points to clients buying fewer small projects and more large, multi-year AI programs.
The G7 summit in Évian ended with a working lunch on AI deployment, commitments on chatbot safety for minors and model evaluation standards, and no standalone AI declaration, as concern over dependence on a few AI providers grew.
A US export control directive forced Anthropic to switch off its newest models for every customer three days after launch. The week-long outage turned single-provider dependence from a board talking point into an operational incident.
OpenAI's $150 million partner network, following Anthropic's $100 million program, shows model makers now see deployment, not model capability, as the bottleneck. Here is what lab-certified consulting armies mean for enterprise buyers.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.