AI News Archive: July 23, 2026 — Part 6
Sourced from 500+ daily AI sources, scored by relevance.
- SAP CFO says AI must move beyond chatbot 'low-hanging fruit' before seeing returns
SAP CFO says AI must move beyond chatbot 'low-hanging fruit' before seeing returns Reuters
- The AI context gap: Enterprise AI organizations have a trust problem, not a retrieval problem — and most are still building the fix
Across 101 enterprises, the infrastructure that feeds AI agents their business context is being built faster than it can be trusted. Retrieval-augmented generation is already the default context source, and provider-native retrieval has quietly overtaken the dedicated vector databases that define the category — yet a majority of enterprises have already watched their agents produce confident, wrong answers traced to missing or inconsistent context. A governed semantic layer is emerging as the fix, but most are still building it; the field is converging on hybrid retrieval; and even as provider-native tools lead in practice, a plurality say they intend to keep best-of-breed. The result is a context gap — agents that sound authoritative running on a foundation their owners do not yet fully trust. This wave of VentureBeat Pulse Research examines the enterprise RAG and context layer: what feeds AI agents their business context, which retrieval systems enterprises run, how they buy and measure them, where the architecture is heading, and — most revealingly — how often that context is already failing them. The central finding is a context gap — the distance between how confidently enterprise agents answer and how reliable the context beneath them actually is. A majority of enterprises (57%) report that in the past six months their AI agents produced confident but wrong answers they traced to missing or inconsistent business context, and more than half of those said it happened more than once. This is not a fringe failure: retrieval is the primary context source for 38% of enterprises, more than any other approach, so when retrieval is thin or inconsistent, the errors it produces are wearing the agent’s authority. The infrastructure to fix it is being built — 58% already run or are building a governed semantic layer — but for most it is not yet in production. Underneath, the market is consolidating in a direction that surprises. Provider-native retrieval — OpenAI’s file search (40%) and Google’s Vertex AI Search (38%) — already leads every dedicated vector database, and enterprises expect hybrid retrieval to dominate by the end of 2026 (34%). Yet a plurality (36%) say they intend to keep best-of-breed standalone tools rather than consolidate onto a provider’s native context stack, and a majority (57%) plan to switch or add a provider within the year. Stated preference and actual usage are pulling in opposite directions — the market is buying provider-native while insisting it wants independence. Methodology VentureBeat fielded this survey as part of its ongoing Pulse Research series. This survey focused on enterprise RAG infrastructure and the context layer — the retrieval systems, semantic layers, and context sources that feed AI agents. Responses are filtered to organizations with more than 100 employees (n=101); the survey drew no responses from organizations of 100 or fewer, so the full sample qualifies. All responses are from a single Q2 2026 (June) wave, so the report reads cross-sectionally and does not infer month-over-month trends. Several questions were multiple-select, so those shares can sum to more than 100%. By organization size the sample concentrates in the mid-market: 251–1,000 employees (31%) and 101–250 (31%) lead, with 1,001–5,000 (20%), 5,001–10,000 (12%), and 10,001+ (7%) above them. By role it spans managers (39%), individual contributors (27%), the C-suite (16%), and VPs and directors (14%); on purchasing authority it is buyer-credible, with 46% final decision-makers and another 26% recommenders or influencers. Technology/Software is the largest industry at 20%, followed by Healthcare/Life Sciences (11%) and a broad spread across retail, transportation, financial services, manufacturing, and education. At 101 respondents this is a modest sample and should be read as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It is best read as the view from organizations actively standing up RAG and context infrastructure rather than from the largest operators. Finding 1: Confident and wrong More than half have traced agent errors to bad context We asked whether, in the past six months, enterprises had traced a confident but wrong agent answer to missing or inconsistent business context. Most had. This is the report’s defining number. A majority of enterprises (57%) have already had an AI agent produce a confident, wrong answer they traced to bad context — wrong metrics, stale definitions, or missing documents — and more than half of those have seen it happen more than once. Only 28% report no such failure, and a small remainder either don’t run agents on enterprise data or don’t trace root cause closely enough to know. The failure mode is specific and dangerous: the model is not obviously hallucinating; it is confidently wrong because the context feeding it was thin or inconsistent. Everything else in this report — what enterprises retrieve, how they govern it, and what they plan to build — is downstream of this problem. Finding 2: RAG is the default context source Retrieval feeds more agents than any other method We asked what an enterprise’s AI agents primarily use to understand its data. Retrieval leads by a wide margin. Retrieval is the backbone of enterprise context. For 38% of organizations, RAG over documents or a vector index is the primary way agents understand the business — nearly twice the share of the next approach, a governed semantic layer or ontology (21%). Mixed approaches (14%), direct live-system queries (10%), and long-context loading (6%) fill out the rest, and only 2% let agents run on the model’s general knowledge alone. The concentration matters in light of Finding 1: because so much enterprise context flows through retrieval, the quality of that retrieval is the quality of the answer. When RAG is the default source, thin retrieval is not an edge case — it is the main failure surface. One approach is notable for its absence from these answers: customizing model weights, also known as fine-tuning. Every leading source of business context is injected at run time. Our most recent direct measurement of fine-tuning comes from our April–May survey wave (a separate survey, n=136), where fine-tuning capabilities ranked last of six factors in model selection at 5% — even as 26% of that sample still named fine-tuning and customization an investment they expect to grow. Fine-tuning has fallen out of the primary selection conversation; context injection is how enterprises make agents knowledgeable about their business. Finding 3: Provider-native retrieval already leads the vector databases OpenAI file search and vertex AI search top the dedicated tools We asked which retrieval systems enterprises run in production today. The answer favors the model providers and hyperscalers over the specialists. The dedicated vector database is no longer the center of the RAG stack. OpenAI’s file search (40%) and Google’s Vertex AI Search (38%) lead — provider-native and hyperscaler-native retrieval — ahead of every purpose-built vector database. Among the specialists, the most-used is the one enterprises already run for other reasons (Elasticsearch/OpenSearch, 20%) and the open, embedded option (pgvector, 12%); the pure-play vector databases that define the category — Weaviate, Qdrant, Pinecone, Milvus — each sit in single digits to low double digits. Notably, 13% of enterprises say they still run no production RAG at all. As with the platforms in the parallel infrastructure wave, enterprises are gravitating to retrieval that comes bundled with tools they already buy. The shape of this finding held across both Q2 waves. In April–May (n=161), provider-built retrieval led usage there too, while every dedicated vector database remained marginal — the most-used standalone vector database peaked at 8% of that sample — and the hybrid, pluralistic future was already the consensus expectation (34% expected hybrid retrieval to dominate, with another 29% expecting multiple architectures by use case). Two waves, consistent picture: the category that coined the “vector database” term is being collected by the platforms enterprises already buy from. Finding 4: But they say they want to keep best-of-breed A plurality resist consolidating onto a provider’s native stack We asked how enterprises will respond as model providers bundle retrieval, memory, and orchestration into their platforms. Their stated intent cuts against their current usage. Here is the tension at the heart of the stack. Even as provider-native retrieval leads in practice (Finding 3), a plurality of enterprises (36%) say they intend to keep best-of-breed standalone tools rather than consolidate onto a provider’s native context stack — well ahead of the 21% who plan to consolidate. Another 21% expect a mix, and 9% intend to build and own the layer themselves. The gap between what enterprises run and what they say they want is the strategic question of the category: they are adopting bundled retrieval for convenience while asserting they will preserve independence. Which impulse wins — the pull of the provider bundle or the stated preference for modular control — will shape the retrieval market more than any single tool. Finding 5: Hybrid retrieval is the consensus bet Vector-only retrieval is already seen as insufficient We asked which retrieval architecture enterprises expect to dominate their production RAG systems by the end of 2026. The field is converging — with a large share still unsure. The architecture is settling on hybrid. A third (34%) expect hybrid retrieval — embeddings combined with reranking and access controls — to dominate their production systems by the end of 2026, three times the 11% who expect vector-only retrieval to prevail. That is a notable signal: the pure vector-search approach that launched the category is already viewed as insufficient on its own, superseded by pipelines that add reranking for accuracy and access controls for governance — the very access controls whose absence produces the failures in Finding 1. Tellingly, the second-largest answer is uncertainty: 17% simply don’t know, and another 14% expect to move beyond a dedicated vector layer entirely toward tool-first or long-context retrieval. The consensus is not a single tool but a layered pipeline — and it is not yet fully formed. Finding 6: The governed context layer is being built now Most run or are building a semantic layer — few in production We asked whether enterprises use a governed semantic or context layer to give agents and BI a shared understanding of their data. Most are on the path; fewer have arrived. The fix for the context gap is under construction. Well over half of enterprises (58%) either run a governed semantic layer in production (25%) or are piloting and building one (34%), and a further 17% are actively evaluating — meaning three-quarters are engaged with the idea in some form. But the balance is telling: more are building than have shipped, so for most enterprises the shared, governed definition layer that would prevent the "confident but wrong" failures of Finding 1 is still a work in progress. The semantic layer is the industry’s answer to inconsistent context; this wave catches it mid-construction, ambition well ahead of production. Finding 7: Bought on ingestion and simplicity, watched for correctness Selection favors operability; monitoring favors correctness and security We asked what matters most when enterprises choose a retrieval system, and what they track once it is running. Both answers lean practical. Enterprises choose retrieval systems on operability. Ease of data ingestion (36%), latency and performance (32%), and operational simplicity (29%) lead the selection criteria — ahead of retrieval accuracy and access control (23% each), the two factors most directly tied to the failures in Finding 1. Once systems are running, the emphasis shifts toward trust: the most-tracked metrics are response correctness (42%) and security and access control (38%), ahead of latency (28%), operational stability (27%), and answer relevance (23%). Satisfaction with current systems is moderately positive but not enthusiastic — on a five-point scale, overall satisfaction averages 4.0, with ease of implementation and value for money both near 3.9. Enterprises buy for how easily a system runs and watch it for whether it can be trusted. Finding 8: A retrieval reshuffle is coming A majority plan to change providers — and the vector specialists are gaining interest We asked whether enterprises plan to change or add a retrieval provider, and which they are considering. The consideration set differs from today’s stack. The retrieval stack is not settled. While 43% have no plans to change, a small majority (57%) intend to switch or add a provider within twelve months, and a quarter (26%) within the next quarter. The consideration set is where it gets interesting: provider-native retrieval still leads what enterprises are evaluating (OpenAI 22%, Vertex AI Search 21%), but the open-source vector specialists punch above their current footprint — Qdrant (14%) and Milvus (13%) draw more switching interest than their present usage (10% and 6%) would suggest. Read with Finding 4, the picture is a market in flux: enterprises run provider-native today, are evaluating a broader field, and say they want to keep their options open. The reshuffle ahead will test whether best-of-breed intent survives contact with the convenience of the bundle. The bottom line: A context gap that more retrieval alone won’t close Organizations with more than 100 employees are wiring agents into their business faster than they can guarantee the context those agents run on. Retrieval is the default source of enterprise context, and it increasingly comes from the model providers and hyperscalers rather than the dedicated vector databases — yet a majority of enterprises have already watched agents answer confidently and wrongly because that context was thin or inconsistent. The failure is not exotic; it is the predictable result of pointing authoritative-sounding agents at an unreliable foundation. The industry’s answer — a governed semantic layer, hybrid retrieval with reranking and access controls — is being built but is mostly not yet in production, and enterprises are pulled between the convenience of provider-native bundles and a stated preference for best-of-breed independence. At 101 respondents in a single Q2 wave this is a directional read, skewed toward the mid-market — but the direction is clear: the context layer is the next contested tier of the AI stack, and right now agents are running ahead of it. The context gap is not a retrieval-volume problem that more documents or bigger indexes will solve on their own; it is a problem of governed, consistent, access-aware context. The open question for later waves is whether enterprises finish building that layer before the confident-but-wrong failures move from the lab into decisions that matter. Based on survey responses from 101 qualified enterprise respondents (100+ employees), drawn from a single Q2 2026 (June) wave. At this sample size the results should be read as a directional signal rather than a precise measurement — it's a self-selected sample, not a probability sample, and skews toward the mid-market. Respondents include managers, individual contributors, VPs/directors, and the C-suite, with strong purchasing authority, across technology, healthcare, retail, transportation, financial services, manufacturing, and education.
- Ropedia raises $22M to scale human-centric data collection for embodied AI
Singapore-based robotics data infrastructure firm Ropedia Pte. Ltd. today announced it raised $22 million in Pre-Series A funding to scale up its collection of real-world, multimodal interaction data to fuel artificial intelligence robotics models developed by technology companies. Physical AI and embodied AI development is increasingly burdened by a lack of real-world data at a […] The post Ropedia raises $22M to scale human-centric data collection for embodied AI appeared first on SiliconANGLE .
Score: 60💰 MoneyJul 23, 2026https://siliconangle.com/2026/07/23/ropedia-raises-22m-scale-human-centric-data-collection-embodied-ai/ - What Is OpenAI Actually Patenting? PatentVest Pulse Analyzes OpenAI's Patent Portfolio
What Is OpenAI Actually Patenting? PatentVest Pulse Analyzes OpenAI's Patent Portfolio markets.businessinsider.com
- These Japanese AI winners don’t make chips. They make toilets, glass fiber and seasoning
A toilet maker, a glass fiber manufacturer and the maker of MSG have in Japan have become major beneficiaries of the AI boom.
Score: 58🌐 MovesJul 23, 2026https://www.cnbc.com/2026/07/22/japanese-ai-winners-toilets-fiberglass-and-seasoning.html - Supertab Launches WordPress Plugin, Putting AI Content Licensing in Reach of 40% of the Web
Supertab Launches WordPress Plugin, Putting AI Content Licensing in Reach of 40% of the Web USA Today
- The 3rd Forum of the University of Tokyo Institute for Digital Observatory (DO) Digital Observatory in the Generative AI Era: The Potential of AI Agent Collaboration for Supply Chain Resilience
The 3rd Forum of the University of Tokyo Institute for Digital Observatory (DO) Digital Observatory in the Generative AI Era: The Potential of AI Agent Collaboration for Supply Chain Resilience rd.hitachi.com
- MiTAC Computing Advances Agentic AI Infrastructure with 6th Gen AMD EPYC™ Server CPUs
MiTAC Computing Advances Agentic AI Infrastructure with 6th Gen AMD EPYC™ Server CPUs The Straits Times
- 85% of Indian finance leaders under pressure to prove AI ROI as governance lags: Survey
A majority of Indian finance leaders are under pressure to prove AI returns, even as organisations prioritise rapid deployment over governance.
- GOP Financial Services report cites AI as both fraud accelerator and preventer
With AI playing a more prominent role in the scammer’s playbook, the report says “the counter to this is ensuring that those empowered to prevent and respond to fraud and scams — namely public and private sector entities — deploy the same tools.”
- AI extracts hidden material rules from microscopic data to predict large-scale behavior
Researchers from the National University of Singapore (NUS) have developed artificial intelligence (AI) methods that learn the large-scale behavior of complex materials from microscopic data. By automatically identifying a small number of hidden variables that capture the collective behavior of a system, the methods can predict how materials evolve over time while reducing the need for costly simulations.
Score: 58🌐 MovesJul 23, 2026https://techxplore.com/news/2026-07-ai-hidden-material-microscopic-large.html - Cisco Antares Models Bring Local AI to Vulnerability Triage
Cisco’s new Antares models help security teams narrow vulnerable code searches locally, but low benchmark scores expose important adoption limits. The post Cisco Antares Models Bring Local AI to Vulnerability Triage appeared first on TechRepublic .
Score: 58🌐 MovesJul 23, 2026https://www.techrepublic.com/article/news-cisco-antares-vulnerability-triage/ - How to navigate the AI talent wars
Cloudflare recently beat Q1 2026 earnings . Revenue up 34% year over year. EPS ahead of consensus. Full-year guidance raised. Then, in the same breath, they announced 1,100 layoffs, 20% of the company. CEO Matthew Prince’s explanation: “The way we work at Cloudflare has fundamentally changed.” Block did the same thing . Beat guidance, raised outlook, cut 4,000+ jobs. Both framed it as architecting for the AI era. This is not a contradiction. This is the new math boards are running. And if you’re a CIO who hasn’t started running it yourself, you’re behind . The benchmark has moved AI-native companies have quietly reset what “efficient” means for a technology organization. Midjourney generates over $500M in revenue with roughly 160 employees, over $3M per head. Anthropic hit a $14B annualized run rate in early 2026 with fewer than 3,000 employees. Across the top AI-native startups, the average revenue per employee is $3.48M , nearly twelve times the traditional SaaS benchmark of $300K. Boards aren’t comparing you to your 2019 self anymore . They’re comparing you to Anthropic. This is the pressure Cloudflare and Block are responding to. They’re not cutting people because the business is struggling. They’re cutting because investors have internalized a new denominator. Headcount is no longer a proxy for capacity; it’s a liability on the efficiency ratio. For CIOs, this creates a hiring problem that looks nothing like the cloud or mobile talent gaps of the past decade. Those gaps were about volume: hire 100 cloud engineers, absorb the cost, build the capability… This one is about density; you’re not looking for 100 people. You’re looking for 10 who can deliver what 100 couldn’t, and justify $1M or more in value per seat. Finding bodies to fill seats has never been easier. Finding people who operate at that level of leverage is a different problem entirely. ‘Acqui-hires’ are a shortcut with a hidden cost Companies have figured out that recruiting AI-native talent one by one is too slow and that it’s faster to buy a team. Google’s acquisition of the Windsurf founders, Meta bringing in the Scale AI team, Accenture’s string of AI-focused acquisitions: these are acqui-hires dressed up as M&A. The premium on experienced AI talent is high enough, and the urgency real enough, that organizations are skipping traditional hiring loops entirely and buying their way in. I’ve been on the other side of this. My company, MadKudu, was acquired by HG Insights specifically to bring AI-native capability into an established enterprise business. HG needed change agents who had already figured out how to build and ship in this new era, not just people who’d read about it. That’s the thesis behind most of these deals. But there’s a cost that doesn’t show up in the acquisition price. AI-native teams are fast because they operate with a different set of defaults: full access to tools, minimal governance layers, the ability to experiment and ship without a six-week approval cycle. That operating model is not a perk; it’s the fundamental mechanism. It’s why a team of 10 can do what an enterprise team of 100 can’t. When you acqui-hire that team and then slot them into your existing approval processes, you’ve bought the people and killed the engine. The change agents you paid for become change-frustrated. The attrition that follows is expensive and predictable. The harder realization: acquiring an AI-native team means accepting how they work. That requires deliberately carving out space for them to operate differently, not just tolerating it but institutionalizing it. The acquisition is an organizational change program, not just a hiring event. The CIO’s real problem The governance stack most enterprise organizations run was designed for a headcount world. Every tool vetting cycle, every vendor review, every security approval was calibrated assuming you were managing a large team where consistency and control were the primary objectives. That calculus breaks when your goal is talent density. The same approval processes that protect against data leaks are now the reason your best people can’t do their best work. When it takes six weeks to approve a tool that your competitor’s team is already shipping with, you’ve traded velocity for the perception of safety. The practical fix is structured experimentation: clear guardrails, defined boundaries, but explicit permission to try tools before deciding whether to roll them out broadly. Gating everything prevents you from ever discovering what 10x productivity looks like. The skills inventory question is also more nuanced than it sounds. Job titles won’t tell you where the leverage is. You need to map the actual tasks within each function and assess which can be automated or augmented with AI. That’s where you find the people who, with the right tools, become your $1M/employee talent, not because you hired differently, but because you enabled better. This is also where the build-versus-buy question gets genuinely tricky. As AI reshapes how products are built and delivered, your internal operating model — how you work, how fast you ship, how you use data — is becoming core IP. Outsourcing delivery means outsourcing the part of the organization where your competitive advantage is now being built. Closing the gap without slowing down The AI talent wars are not primarily a recruiting problem. They’re a rethinking of what organizations are supposed to look like. Boards have a new benchmark. Cloudflare, Block, Amazon, Meta and others have already started restructuring to meet it, publicly, painfully, even while beating their numbers. The question for CIOs isn’t whether this pressure arrives; it’s whether you’re ahead of it or behind it when it does. The organizations that navigate this well won’t win by outbidding competitors for a handful of elite engineers. They’ll win by designing operating systems that amplify the leverage of the talent they do have, by enabling their best people rather than constraining them, and by treating AI fluency as a core organizational capability rather than a niche specialization. Talent density is the new headcount model. The sooner your governance, your tooling and your board conversations reflect that, the better positioned you’ll be when the next efficiency report lands. This article is published as part of the Foundry Expert Contributor Network. Want to join?
Score: 58🌐 MovesJul 23, 2026https://www.cio.com/article/4199540/how-to-navigate-the-ai-talent-wars.html - Chabria: AI companies are creating 'all-powerful psychopaths.' Maybe not a great idea?
This week, OpenAI revealed that one of its AI models broke out into the wild and did a bunch of hinky stuff. How many times do we need to hear this story before someone does something to protect humans?
- How Sentara Health built an enterprise AI literacy program
How Sentara Health built an enterprise AI literacy program Healthcare IT News
Score: 58🌐 MovesJul 23, 2026https://www.healthcareitnews.com/news/how-sentara-health-built-enterprise-ai-literacy-program - AI Is Turning Managers Into ‘Player-Coaches.’ But There’s 1 Big Leadership Risk
The player-coach model promises speed and autonomy, but it could weaken leadership.
- Forum: National Day banner incident an AI cautionary tale
Forum: National Day banner incident an AI cautionary tale The Straits Times
Score: 58🌐 MovesJul 23, 2026https://www.straitstimes.com/opinion/forum/forum-national-day-banner-incident-an-ai-cautionary-tale?ref - Humans in the loop: how software teams are learning to trust AI
Treating AI like a virtual teammate is bearing fruit for enterprise teams.
Score: 58🌐 MovesJul 23, 2026https://www.techradar.com/pro/humans-in-the-loop-how-software-teams-are-learning-to-trust-ai - QCT Unveils Next-Generation QuantaGrid Servers Powered by AMD EPYC™ 9006 Series CPUs to Accelerate AI
QCT Unveils Next-Generation QuantaGrid Servers Powered by AMD EPYC™ 9006 Series CPUs to Accelerate AI azcentral.com and The Arizona Republic
- AI Models Went Rogue, and These Stocks Are Ready for the Fight
AI Models Went Rogue, and These Stocks Are Ready for the Fight Barron's
Score: 55🌐 MovesJul 23, 2026https://www.barrons.com/articles/openai-cybersecurity-stocks-palo-alto-okta-e1fd1b13 - Gemini 3.5 Pro is late but Gemini 4 will be great, says Google CEO
Gemini 3.5 Pro is late but Gemini 4 will be great, says Google CEO InfoWorld
- Pichai pushes back on claims Google is losing ground in AI race
Pichai pushes back on claims Google is losing ground in AI race Reuters
Score: 55🌐 MovesJul 23, 2026https://www.reuters.com/business/pichai-pushes-back-claims-google-is-losing-ground-ai-race-2026-07-23/ - AI unlocks Atlantic circulation insights from 20 years of ocean float data
For more than 20 years, about 4,000 autonomous profiling floats have been drifting through the ocean. They form part of the international Argo program (Argo—global array of profiling floats). At regular intervals, they descend to a depth of 2,000 meters (6,600 feet) and, as they rise, measure parameters such as temperature, salinity and pressure. Once they reach the sea surface again, they transmit these data via satellite to the Argo network, which is available to researchers worldwide. Hardly any other observation system has transformed ocean research so profoundly in recent decades.
Score: 55🌐 MovesJul 23, 2026https://phys.org/news/2026-07-ai-atlantic-circulation-insights-years.html - AI study reveals the biodiversity cost of green energy minerals
AI study reveals the biodiversity cost of green energy minerals EurekAlert!
- New Copilot Dashboard Shows Enterprises Which Developers Are Actually Using AI — Not Just Who's Logged In
New Copilot Dashboard Shows Enterprises Which Developers Are Actually Using AI — Not Just Who's Logged In DevOps.com
- How Qualcomm Is Bringing Intelligence Closer to Everyday Life
How Qualcomm Is Bringing Intelligence Closer to Everyday Life Time Magazine
Score: 55🌐 MovesJul 23, 2026https://time.com/branded-content/qualcomm/how-qualcomm-is-bringing-intelligence-closer-to-everyday-life/?amp - Trump Says Images of the Iranian Elementary School His Lackeys Obliterated Could Be “AI Generated”
"I don't think anybody's ever going to be able to say what happened there." The post Trump Says Images of the Iranian Elementary School His Lackeys Obliterated Could Be “AI Generated” appeared first on Futurism .
Score: 55🌐 MovesJul 23, 2026https://futurism.com/artificial-intelligence/trump-images-iran-elementary-school-ai - New Survey and Report from Deque Systems Shows Volume of AI-Generated Code is Significantly Increasing "Accessibility Debt" Risk
New Survey and Report from Deque Systems Shows Volume of AI-Generated Code is Significantly Increasing "Accessibility Debt" Risk USA Today
- Duke ties AI investments to enterprise priorities
Duke ties AI investments to enterprise priorities Healthcare IT News
Score: 55🌐 MovesJul 23, 2026https://www.healthcareitnews.com/video/duke-ties-ai-investments-enterprise-priorities - AI yet to deliver promised productivity gains, Barclays warns
AI yet to deliver promised productivity gains, Barclays warns Computing UK
Score: 55🌐 MovesJul 23, 2026https://www.computing.co.uk/news/2026/ai/ai-yet-to-deliver-productivity-gains-barclays - New Study Finds How AI Is Revolutionizing Shopping Habits
Does AI help you shop smarter?
Score: 55🌐 MovesJul 23, 2026https://www.inc.com/moses-jeanfrancois/new-study-finds-how-ai-is-revolutionizing-shopping-habits/91378199 - AI in supply chains: FedEx's smart logistics
AI in supply chains: FedEx's smart logistics The Straits Times
- Nearly Half of Senior Leaders Feel Only Partly Prepared to Lead AI Transformation, as Ambition Outpaces Readiness
Nearly Half of Senior Leaders Feel Only Partly Prepared to Lead AI Transformation, as Ambition Outpaces Readiness The Straits Times
- Infosys taps veteran insider Ashiss Kumar Dash as next CEO amid AI threat
Infosys taps veteran insider Ashiss Kumar Dash as next CEO amid AI threat Nikkei Asia
- 7 CRM trends for 2026: AI brings decisive action to customer workflows
Agentic AI has advanced from the promises-and-pilots phase of 2025 to reality and rollouts in 2026. In the process, agentic AI is transforming virtually every aspect of customer relationship management (CRM) , the platform that manages sales, marketing, and customer service. “Last year, everybody was dipping their toes into the water,” says Keith Kirkpatrick , research director at The Futurum Group. This year, agentic AI has built momentum from the boardroom down, with companies recognizing that having an AI strategy is imperative. “They feel like if they don’t embrace it now, their competitors will.” Harry Datwani , a principal at Deloitte Digital, adds that enterprise CRM customers have transitioned from “proof of concept” to “scale and execution.” “Across sales, service, marketing, even in the commerce space, enterprises are really using AI and agentic,” he says. “CRM in 2026 is undergoing a structural shift, not just an incremental evolution,” says Forrester analyst Kate Leggett , noting that AI is becoming a core part of CRM infrastructure, not just a feature or an add-on. According to Forrester data, around 70% of companies are already using AI in their CRM systems, she says. Here are the hot AI-driven trends in CRM this year. CRM becomes an action hero CRM platforms have traditionally served as passive, static systems of record. Now, agentic AI is transforming CRM into a powerful, real-time solution that can act autonomously. “Organizations that rethink CRM as a real-time, AI-powered system of action — and embrace agentic AI to handle complex, unpredictable work — are better positioned to deliver exceptional customer experiences,” says IDC analyst Neil Ward-Dutton . “This approach not only enhances satisfaction and loyalty but also drives operational efficiency and business agility.” Forrester’s Leggett says that AI-powered CRM platforms have advanced from simple data capture to real-time decision-making and execution. Standard capabilities include next-best action recommendations, call summaries, automated updates, generated emails, knowledge creation, predictive forecasting, and deal scoring. She adds that AI agents can now execute workflows, such as routing cases, sending follow-ups, and updating records (with human oversight). They can also handle end-to-end service and sales tasks autonomously, including case resolutions and sales development activities. Agentic drives workforce changes AI use in CRM systems is also impacting workforce strategies. “We used to hire for deep expertise,” says Constellation Research analyst Liz Miller . “AI has commoditized expertise because I can take all that data from my CRM and train my AI models to go deep, to know everything about any product I’ve ever sold, from what has worked, what hasn’t, every price, every sale.” Now, instead of hiring candidates with deep expertise, organizations are looking for candidates who can go wide. “I can train a model to have deep expertise. What I can’t train for is experience, because experience is what happens when a person has gone broad across a lot of different scenarios and faced complexity across that broad scenario,” says Miller. For example, AI systems can automate many aspects of marketing, Miller notes, but there’s no substitute for creativity: people who can interrogate the data and come up with innovative marketing campaigns that connect with customers. Terence Chesire , group vice president of ServiceNow CRM and industry workflows, says that organizations are using agentic AI to free up team members from repetitive, lower-value activities. Those employees have now moved to higher-level roles “where they’re working on transformational deals rather than just building a spreadsheet.” “That’s what we’re seeing as super-exciting as organizations not just free up people, but the speed and effort reduction and the friction reduction in what they can do,” he adds. Data layer takes center stage AI’s promise to deliver actionable customer and marketing intelligence has placed even greater emphasis on the importance on sound data management practices for CRM. “The light bulb has flashed on very brightly for our clients,” says Deloitte’s Datwani. “Everyone is talking about AI agents, but your ability to really extract value is inextricably linked to the quality of your data and the ability to make that data accessible. What we’re finding is that despite large investments over time our clients still have fragmented data. And so, they are data rich and insight poor.” The good news, says Datwani, is that AI agents themselves can help clean up and organize data . And vendors such as Snowflake and Databricks , along with the traditional CRM powerhouses, are offering powerful data analytics solutions. “Everyone is battling for that data layer,” Datwani says. Forrester’s Leggett adds that CRM platforms are converging with customer data platforms (CDPs) , real-time event streams, and external data sources to create connected customer data networks. These real-time, connected data models can help organizations deliver hyper-personalization at scale. Agentic ushers in pricing complexity The shift from license- or subscription-based pricing to an outcome or consumption pricing model has the potential to help CIOs tie their CRM costs to specific business metrics, such as the number of customer service calls resolved per hour. But it has also introduced a new level of complexity when it comes to budgeting for CRM costs. For example, Chesire says ServiceNow’s CRM pricing plan starts with a baseline subscription model, and on top of that, customers get a certain number of AI tokens per user and can buy additional tokens as AI usage ramps up. Meanwhile, Salesforce has rolled out pay-per-resolution pricing with its recently unveiled AI Help Agent and last month acquired usage-based billing specialist m3ter . Oracle is also piloting outcome-based AI pricing . All these approaches undercut the predictability of the subscription model, which will complicate CIOs’ cost calculus, Deloitte’s Datwani says. “Now, as you start to think about consumption and tokens, costs might look different. As folks are opening up the architecture with things like headless CRM, what will the cost model look like for API calls or MCP server calls? So, there’s many more variables,” he adds. The rise of multi-agent orchestration To act autonomously, agents need to access multiple data sets and software platforms seamlessly. As a result, the proliferation of agents, some embedded within specific vendor platforms and some created in-house, is going to require an orchestration layer, Futurum’s Kirkpatrick says. He points out that organizations need to monitor and manage agents, enforcing the same type of policy-based access control that exists for people. Organizations also need to set limits on what domains a specific agent can get into, what types of data they can access, what lines can’t they cross. Kirkpatrick predicts that a new class of orchestration tools will emerge, although it’s not clear whether that orchestration layer will be provided by the leading CRM vendors, hyperscalers, or third parties. Datwani agrees. “The orchestration layer is an interesting area, where the traditional vendors are in on it, the hyperscalers are also offering it, and there are third parties. It’s my belief that there’s not going to be a clear winner.” The interface becomes conversational Enterprise users who have traditionally had to manually wrangle with CRM systems are likely to find the ability to employ voice commands using a natural language interface to be a game changer. For starters, a salesperson can say, “I have a meeting today with Customer X. Help me prepare.” The agent will collect relevant data, ingest it, and provide a summary with recommendations. ServiceNow’s Chesire says voice-enabled CRM systems have an “almost magical” ability to record, transcribe, and understand the content of a call between a salesperson and a customer or potential customer. The system can then “build a quote” based on that conversation. On the customer service side of the equation, AI-driven voice technology enables customers to speak to an AI agent, describe the problem using natural language, and get a response. The agent has the capability to, for example, solve a credit card dispute, order a replacement product, send out a service rep, or do whatever is needed to resolve the issue, says Chesire. Beyond that, agentic technology is capable of understanding the underlying business process flaws that led to the product snafu, and make recommendations for ways to fix whatever led to the issue in the first place, he adds. Agentic drives business process transformation With the emergence of outcome-based pricing, organizations are taking a fresh look at how they measure the benefits of CRM systems. That conversation is leading to an even more important analysis of underlying business processes. Or, as Constellation’s Miller says, “The old adage of applying new technology to old processes only gets you more expensive old processes.” “When we survey customers, we hear time and time again that the reason why they want to apply AI into their organizations is to foster exponential opportunity and exponential growth,” she says. “How do we get there with CRM has started to become the new conversation.” According to Miller, AI systems breach the walls of siloed data and can take a fresh look at legacy workflows. They also don’t get sucked into turf wars between marketing and sales teams. As a result, they often recommend new actions that can lead to better processes. “I think it’s starting to happen. You’re starting to see applications where AI is beginning to accelerate decision-making and decision velocity,” she says. “The next phase of maturity is going to be, how do we start to spread AI across our platforms so that we are seeing that holistic end-to-end relationship that we have always wanted to optimize. How do we thread that across platforms and across solutions. We’re starting to see organizations on the leading edge really start to pull those strategies together,” says Miller.
Score: 55🌐 MovesJul 23, 2026https://www.cio.com/article/1255983/customer-relationship-management-crm-trends.html - AI experts: Illinois ordered an audit of AI developers. Who will do it?
AI experts: Illinois ordered an audit of AI developers. Who will do it? Chicago Tribune
Score: 55🌐 MovesJul 23, 2026https://www.chicagotribune.com/2026/07/23/opinion-artificial-intelligence-ai-audits-illinois/ - I asked a disaster survival psychologist if AI usage should be the same level of 'emergency' as Pompeii — but instead she's worried about a technology catastrophe that I didn't even consider
It's not every day you get to talk to a survival psychologist in a hit National Geographic documentary about your technology fears — but how worried should we be by the rise of AI?
Score: 55🌐 MovesJul 23, 2026https://www.techradar.com/streaming/disney-plus/pompeii-out-of-time-survival-psychologist-ai - Salesforce Named a Leader in the 2026 Gartner® Magic Quadrant™ for Conversational AI Platforms
Salesforce today announced that it has been recognized as a Leader in the 2026 Gartner® Magic Quadrant™ for Conversational AI Platforms — the company’s first time named in this report. Download a complimentary…
Score: 55🌐 MovesJul 23, 2026https://www.salesforce.com/blog/salesforce-2026-gartner-magic-quadrant-conversational-ai-platforms/ - Five Lessons From The Forrester Wave™: Conversational AI Platforms For Employee Services, Q3 2026
The AI market has finally come back around to the (correct) conclusion that people are important. Thank goodness. And on that note, The Forrester Wave™: Conversational AI Platforms For Employee Services, Q3 2026 (aka AI to help employees) is finally live! To put it bluntly, the market has progressed further than I thought possible since […]
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Ask the Analyst Power and Utilities: Is Your OT Ready for Industrial AI Gartner
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- Google's Gemini delay exposes a deeper problem: employee frustration
Poor morale among employees is contributing to delayed model releases from Google's DeepMind AI lab , according to conversations with half a dozen current and former Google DeepMind employees. Why it matters: As the company's cash flow just turned negative, investors and competitors are questioning Google's standing in the AI race. State of play: Google's Gemini 3.5 Pro, its most powerful model in the works, is months behind, per Bloomberg , but the company did just release a series of smaller , more efficient models, garnering mixed reactions. Top executives from competing AI labs were quick to shade the releases, with Meta's Alexandr Wang saying "Gemini who?" on X , and others asking top Google leadership when more powerful models will be ready. Despite beating estimates in its latest earnings on Wednesday, the company's free cash flow turned negative, in large part due to its AI investments: It's on track to spend $190 billion this year. Yes, but: Google showed 82% growth in cloud revenue, but search revenue came in slightly below Wall Street's expectations, exacerbating concern about the company's AI capex paying off, Mandeep Singh, Bloomberg Intelligence analyst, said on Bloomberg TV. Zoom out: The perceived dip in AI capabilities comes amid the ongoing AI talent wars, which have hit Google the hardest . Several top researchers have departed for competing AI labs. Those departures include influential researcher and Gemini co-lead Noam Shazeer, who's now at OpenAI, and Nobel Prize in Chemistry winner John Jumper, who left for Anthropic. Multiple employees have publicly said they resigned over Google's April deal with the Pentagon that allows the military to use its technology. Sources who spoke to Axios anonymously said the deal comes up often during exit interviews. The sources spoke on the condition of anonymity due to fear of retaliation. What they're saying: "We're behind," one employee told Axios, referring to the company's model capabilities compared with competitors. Some sources tied the recent dip in morale to the company's military deal, but others said the burnout is about feeling one step behind competitors. Google didn't prioritize agentic coding as it raced to defend search from ChatGPT, a DeepMind employee who worked on model training said. Gemini's models do not crack the top 10 most used in the LLM leaderboard tracked by OpenRouter . Zoom in: It's a "constant battle" for those morally against the Pentagon deal, one source said, adding this has led to "emotional burnout." Alex Turner, a former research scientist at DeepMind, resigned over Google's military contracts. He told Axios that CEO Demis Hassabis, who released an AI safety framework last week, doesn't have a consistent internal presence the way it seems competing AI CEOs Sam Altman and Dario Amodei do with their employees. The other side: Google disputes the notion that morale problems are leading to shortfalls in its models or that employees are leaving in large numbers due to the company's Pentagon contract. Google says its attrition rates for the first half of this year are lower for AI talent than they were at this time last year and that more than 90% of those offered an AI role at Google accept the position. The big picture: Cost has been a key focus for a lot of enterprises suddenly experiencing AI sticker shock. While the new models Google released this week aren't at the leading edge in performance, delivering models that offer more bang for the buck meets a clear need in the market. " We're feeling good about this week's Flash launches, our roadmap and the incredible demand we're seeing for our models," a Google spokesperson told Axios. Reality check: Model providers leapfrogging each other is the norm for the AI race. Leads tend to be fickle and short-lived. It was just last year that Google was riding high . Then Anthropic seemed unbeatable with the arrival of Mythos. Now, OpenAI appears poised to be the leapfrogger with a new model it plans to brief lawmakers on next week. The bottom line: Google can afford to be leapfrogged. It can't afford to lose its talent or its investors' patience.
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Autonomous trucking has historically had a manufacturing problem: proving a truck can drive itself is one hurdle; building enough of them to matter is another. Aurora Innovation (NASDAQ: AUR) says its second-generation hardware finally closes that gap. It is engineered for a 1-million-mile operating life and built for volume production rather than pilot-scale trials. The […] The post Aurora races toward scale with new driverless hardware appeared first on FreightWaves .
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Score: 55🌐 MovesJul 23, 2026https://www.businessinsider.com/ai-data-center-opposition-upper-merion-pennsylvania-2026-7 - A Stenographer Submitted AI-Generated Errors in Official Court Transcript, Judge Says
A judge in Indiana warns a court reporter than it's their job to proofread their work, after catching errors likely made by AI transcription services.
Score: 53🌐 MovesJul 23, 2026https://www.404media.co/judge-caught-court-reporter-using-ai-transcript-errors/ - An AI fight hits the Massachusetts Senate
An AI fight hits the Massachusetts Senate The Boston Globe
Score: 53🌐 MovesJul 23, 2026https://www.bostonglobe.com/2026/07/23/newsletters/massachusetts-senate-ai-openai-anthropic-the-scrum/ - Conversational Banking Won’t Scale Without Strong Foundations
Discover the data, architecture, and governance foundations that banks need to scale conversational banking safely and effectively.
Score: 52🌐 MovesJul 23, 2026https://www.forrester.com/blogs/conversational-banking-wont-scale-without-strong-foundations/ - Ask the Analyst: AI in Bank Lending and Trade Finance
Ask the Analyst: AI in Bank Lending and Trade Finance Gartner
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