AI News Archive: July 15, 2026 — Part 4
Sourced from 500+ daily AI sources, scored by relevance.
- Helpful microbes could battle pathogens in our hospitals and schools - with the help of AI to make it work
Helpful microbes could battle pathogens in our hospitals and schools - with the help of AI to make it work EurekAlert!
- VetClaims.ai Surpasses 55,000 Veterans Served as AI-Assisted Claims Preparation Model Highlights Access Gap in VA Benefits System
VetClaims.ai Surpasses 55,000 Veterans Served as AI-Assisted Claims Preparation Model Highlights Access Gap in VA Benefits System USA Today
- India's agentic AI adoption mirrors mobile revolution: DeepMind executive
Indians embracing AI agents is similar to the mobile revolution that propelled the country ahead, said a top executive at Google's artificial intelligence research arm DeepMind, noting that the country is playing a key role in building agentic use cases.
- OpenAI wants its speaker to feel alive. Apple says it’s a stolen idea
OpenAI wants its speaker to feel alive. Apple says it’s a stolen idea Fortune
Score: 63🌐 MovesJul 15, 2026https://fortune.com/2026/07/15/openai-building-human-like-chatgpt-device-apple-jony-ive/ - Gen AI goes mainstream on smartphones, influences purchase decisions: CMR
AI is becoming central to the smartphone experience, with 71 per cent of Indian users reportedly using generative AI features and 59 per cent considering AI when buying a new device
- Samsung wants its upcoming Galaxy Watch to be your AI health companion
Samsung is teasing its next Galaxy Watch as an AI-powered health companion ahead of Galaxy Unpacked on July 22.
Score: 62🌐 MovesJul 15, 2026https://www.digitaltrends.com/wearables/samsung-wants-its-upcoming-galaxy-watch-to-be-your-ai-health-companion/ - AI powers citizen-led disaster relief from afar for Venezuela
TOPSHOT - This aerial view taken on July 2, 2026 in Caraballeda in the state of La Guaira shows collapsed buildings following the June 24 twin earthquakes.
- AI DevOps startup MyDecisive launches with $12M and open-source SmartHub
Artificial intelligence DevOps startup MyDecisive formally launched today and announced $12 million in new funding to bring to market an open-source foundation for managing observability data and a commercial suite it says can slash what enterprises spend running production systems. Ari Zilka founded MyDecisive in San Francisco in 2023, backed by a long track record […] The post AI DevOps startup MyDecisive launches with $12M and open-source SmartHub appeared first on SiliconANGLE .
Score: 62💰 MoneyJul 15, 2026https://siliconangle.com/2026/07/15/ai-devops-startup-mydecisive-launches-12m-open-source-smarthub/ - Amazon AGI director says AI agent reliability, not capability, is blocking enterprise deployment at VB Transform 2026
The enterprise AI industry has a math problem. Cisco data shows 85% of enterprises are piloting AI agents, but only 5% have shipped them to production. At VB Transform 2026 on Tuesday, Bryan Silverthorn , Director of AGI Autonomy at Amazon, explained why that gap persists — and why the answer isn't better benchmarks. Silverthorn, who joined Amazon through its acquisition of Adept AI and now leads multimodal agent training inside the company's AGI lab, argued that reliability must be broken into four distinct dimensions: consistency, robustness, predictability, and safety — a framework he credits to research from Princeton. "It unpacks different factors that I see tangled together in almost every eval I've ever seen," he said. Why AI agents pass internal evals but fail real customers in production The framework matters because agents routinely ace internal evaluations and then collapse in the wild. Silverthorn described a customer that deployed an agent for software QA involving serial number extraction from screens. It worked flawlessly for two months — then began intermittently reading wrong numbers. The culprit: the underlying vision encoder behaved differently depending on where the serial number appeared on screen, and a software change imperceptible to humans triggered the failure. The lesson, Silverthorn said, is about measurement, not just models. "The models have to be better. Obviously, we're working hard on making the models better," he said. But the deeper takeaway, he added, is that teams need to identify their dimensions of variability and match measurement rigor to the stakes of the application. VentureBeat's own proprietary research, presented before the session, reinforces the point: half of surveyed companies shipped agents that passed internal evals but failed real customers, and enterprises overwhelmingly track uptime while ignoring accuracy — checking the pulse without checking the diagnosis. A related finding underscored how few guardrails exist: most enterprises default to the model makers' own evaluations and little else, leaving their testing strategy, as I described it on stage, a coin flip between trusting the vendor and trusting nothing. Inside Amazon's 'intern' framework for managing autonomous AI agents Silverthorn's most memorable prescription was cultural, not technical. Inside Amazon's AGI lab, researchers literally call their agents "interns" — as in, "I'll have my intern talk to your intern." The joke carries a serious operational philosophy. Agents, like interns, are powerful but occasionally clueless, capable of amazing work and spectacular derailment. Managing them, he argued, requires management skills rather than software skills: asking what could go wrong, adding backups and undo capabilities, and consciously deciding what risk you can accept. "You can ask the intern, 'Hey, what might you do wrong here? How might you mitigate your negative outcomes?'" he said. Amazon's lab has embraced that trade-off, accepting agents occasionally running the wrong experiment in exchange for research velocity — including one agent running experiments around the clock on its own high-level research plan. What enterprise leaders should do before deploying agents at scale Silverthorn was candid about the limits of today's technology. Self-improving AI remains "a loaded term," he said — Amazon uses AI to improve its models constantly, but fully autonomous self-improvement is distant. Computer use remains a core focus of his lab, with a commercial trucking customer already using browser automation to stitch together warranty claims across fragmented systems**, though he stressed that no future agent will rely on computer use alone — it will work alongside MCP, APIs, and other tools to complete end-to-end workflows**. And LLM-as-judge techniques, while promising, are just one of several strategies for aligning agent capability with acceptable risk. For enterprises stuck in pilot purgatory, the path forward starts with a mindset shift: stop asking whether your agent can do something impressive once, and start asking whether it can do it correctly a thousand times in a row. In other words, the enterprises that escape the 85% ceiling won't be the ones with the smartest agents. They'll be the ones with the best managers.
- Meta backs down on letting its AI generator, Muse, steal Instagram images after privacy backlash
Meta killed Muse’s creepy Instagram photo-grab after privacy fury.
Score: 62🌐 MovesJul 15, 2026https://www.startupdaily.net/topic/social-media/privacy-fears-over-metas-new-ai-image-generation-tool/ - Oracle unveils AI-native builder for agentic apps in Fusion platform
Oracle AI-native builder in AI Agent Studio for Fusion lets customers build and run Fusion Agentic Applications in Fusion Cloud.
Score: 62🌐 MovesJul 15, 2026https://www.techmonitor.ai/news/oracle-unveils-ai-native-builder-for-agentic-apps-in-fusion-platform - TCS launches Nvidia-powered autonomous engineering lab to accelerate AI-led mobility
Tata Consultancy Services has launched a new AI lab in Bengaluru. This facility will accelerate the development and deployment of AI-led solutions. The lab will help enterprises move from pilots to production-scale deployments. It will enable rapid prototyping and validation of industrial AI use cases. This collaboration aims to create intelligent and autonomous enterprises.
- Drones and ensemble AI reveal hidden patterns of urban water pollution
Drones and ensemble AI reveal hidden patterns of urban water pollution EurekAlert!
- That's not Kathy Hochul. AI campaign ads are going too far. | Opinion
That's not Kathy Hochul. AI campaign ads are going too far. | Opinion USA Today
- JPMorgan Chase bets on Seattle to build its AI control layer
JPMorgan Chase is building out a new AI software infrastructure team, with a major presence in Seattle, focused on running AI across its data centers and outside providers in a way that controls costs, protects its intellectual property, and avoids tying its fortunes to any one vendor. Read More
Score: 61🌐 MovesJul 15, 2026https://www.geekwire.com/2026/jpmorgan-chase-bets-on-seattle-to-build-its-ai-control-layer/ - Tesla driver in fatal Texas crash pressed accelerator 100%, NTSB confirms
The safety board confirmed Tesla's account of the crash, which the company shared days after it happened last month.
Score: 60🌐 MovesJul 15, 2026https://techcrunch.com/2026/07/15/tesla-driver-in-fatal-texas-crash-pressed-accelerator-100-ntsb-confirms/ - Creatio 10x Launches With No-Code AI Agents and Enterprise Governance
Creatio 10x adds personal AI agents, centralized governance, and automation tools for sales, marketing, and customer service. The post Creatio 10x Launches With No-Code AI Agents and Enterprise Governance appeared first on TechRepublic .
Score: 60🌐 MovesJul 15, 2026https://www.techrepublic.com/article/news-creatio-10x-ai-agents-crm-tools/ - Your new online friend may work for Beijing
Your new online friend may work for Beijing Nikkei Asia
Score: 60🌐 MovesJul 15, 2026https://asia.nikkei.com/opinion/your-new-online-friend-may-work-for-beijing - NTT Docomo spinout Robots raises seed funding
The company will use the funds to develop its robot integrated control platform and physical AI, with security sites as its first target market.
Score: 60💰 MoneyJul 15, 2026https://www.techinasia.com/google-deepmind-agile-robots-partner-on-industrial-robotics - AI In Chip Design: Lots Of Promise, Plenty Of Unanswered Questions
Where AI will be successful, who will benefit, and how that will affect the design process. The post AI In Chip Design: Lots Of Promise, Plenty Of Unanswered Questions appeared first on Semiconductor Engineering .
Score: 60🌐 MovesJul 15, 2026https://semiengineering.com/ai-in-chip-design-lots-of-promise-plenty-of-unanswered-questions/ - Singapore 2026 Growth Forecasts Raised to 4.8% as AI Demand Holds Up
Singapore 2026 Growth Forecasts Raised to 4.8% as AI Demand Holds Up apac.entrepreneur.com
Score: 60🌐 MovesJul 15, 2026https://apac.entrepreneur.com/business-news/singapore-2026-growth-forecasts-raised-to-4-8-as-ai-demand-holds-up - ChatGPT update is deleting people’s files without asking them, users say
Makers OpenAI warned that new model can be ‘careless’ when taking ‘destructive’ actions
Score: 60🌐 MovesJul 15, 2026https://www.independent.co.uk/tech/security/chatgpt-update-openai-gpt-sol-b3015585.html - Sophos launches Fusion, an AI-native ‘defense system’ to unify security tools
Cybersecurity firm Sophos Ltd. today launched Sophos Fusion, a single platform that ties together its security operations, endpoint, network, identity, email and cloud protection. Sophos calls it the industry’s first and most complete artificial intelligence-native cybersecurity defense system. It’s a rebuild of Sophos Central, the platform 625,000 organizations use. Sophos moved it onto one open architecture and […] The post Sophos launches Fusion, an AI-native ‘defense system’ to unify security tools appeared first on SiliconANGLE .
Score: 60🌐 MovesJul 15, 2026https://siliconangle.com/2026/07/15/sophos-launches-fusion-ai-native-defense-system-unify-security-tools/ - AI vs drugs: How technology helps UAE fight narcotics networks on dark web
AI vs drugs: How technology helps UAE fight narcotics networks on dark web
Score: 60🌐 MovesJul 15, 2026https://www.khaleejtimes.com/uae/ai-vs-drugs-how-technology-helps-uae-fight-narcotics-networks-on-dark-web - Cohere triples UK footprint with new London office to support R&D growth
Cohere expands its UK presence with a new London office to boost research and development.
Score: 60🌐 MovesJul 15, 2026https://cohere.com/blog/cohere-triples-uk-footprint-with-new-london-office-to-support-r-and-d-growth - Gemini’s SEA growth puts local-language AI at the centre of the assistant race
Google said active users of its Gemini app in Southeast Asia have more than doubled over the past year, with adoption in the region outpacing any previous Google app launch, according to the company’s first “Gemini Southeast Asia Report 2026″. The report covers six of Gemini’s largest regional user bases: Indonesia, Malaysia, the Philippines, Singapore, […] The post Gemini’s SEA growth puts local-language AI at the centre of the assistant race appeared first on e27 .
Score: 60🌐 MovesJul 15, 2026https://e27.co/geminis-sea-growth-puts-local-language-ai-at-the-centre-of-the-assistant-race-20260714/ - Oppo, Alipay expand AI tie-up to nearly 200 services
Oppo handles on-device intent recognition, while Alipay handles service execution and payment risk controls.
Score: 60🌐 MovesJul 15, 2026https://www.techinasia.com/walmart-retailers-oppose-visa-mastercard-antitrust-settlement - Affordable alternatives to Nvidia servers crop up for Japan AI players
Affordable alternatives to Nvidia servers crop up for Japan AI players Nikkei Asia
- Korea’s ICT exports hit all-time high in H1 on AI boom
Korea’s ICT exports hit all-time high in H1 on AI boom 매일경제
- 73% of tech job listings require AI skills now: How to show off yours
Employers are looking for AI skills - here's how to let them demonstrate you know what you're doing.
Score: 60🌐 MovesJul 15, 2026https://www.zdnet.com/article/73-of-tech-job-listings-require-ai-skills-show-off-yours/ - Top AI tools such as OpenClaw and Github Copilot can be hijacked to create new massive botnets
Researchers find nine of the most popular AI platforms are susceptible to a new attack that exploits hallucinations to set up a botnet.
- New AI risks: 'Cognitive spoofing' and fake expertise
New AI risks: 'Cognitive spoofing' and fake expertise Healthcare IT News
Score: 60🌐 MovesJul 15, 2026https://www.healthcareitnews.com/news/new-ai-risks-cognitive-spoofing-and-fake-expertise - How AI is reshaping strategic foresight and global power
AI is transforming global power and strategic foresight. Discover why the currency of compute requires a hybrid model of human-led intelligence.
- Cohere VP says enterprise AI sovereignty requires control of the full agent stack at VB Transform 2026
Hundreds of enterprise leaders and technical experts packed the main ballroom of the luxurious Hotel Nia in Menlo Park this week for VB Transform 2026 , the year's preeminent conference on using generative AI agents to drive business outcomes. Rachad Alao, vice president of product engineering at the rising Canadian enterprise AI startup Cohere, joined VentureBeat CEO and editor-in-chief Matt Marshall for a fireside chat about building agentic systems without surrendering sensitive data, infrastructure control, or the ability to change vendors. Alao, who previously led responsible AI and trust and safety engineering teams at Google and Meta, argued that AI sovereignty means more than downloading an open model or running an application behind a corporate firewall. Asked how Cohere defines sovereignty, Alao pointed to organizations operating mission-critical systems, including banks, hospitals and governments. “It is important to have very tight control on where the data resides, have tight control on the AI,” he said, adding that AI operations should take place in jurisdictions an organization understands or directly controls. That extends from GPUs and private-cloud infrastructure through governance systems that route requests among models, as well as the connectors, search tools and agent frameworks acting on enterprise data. “You want to have control on the entire stack,” Alao said. Agent workloads could outrun falling token prices Marshall challenged one of the central economic arguments for smaller, locally deployed models: Inference prices continue to fall rapidly, potentially weakening the case for optimizing every token. Alao countered that total consumption is climbing even faster as enterprises move from relatively simple chatbots to agents that reason through problems, call tools, search internal systems and take multiple steps before returning an answer. “Your token utilization is going exponentially up, because you’re dealing with more and more complex agentic use cases,” he said. Those workflows require “a lot of processing, thinking, tools interaction” to complete their objectives, he added. Alao also drew a contrast between providers that bill customers according to token consumption and Cohere’s approach. “If your whole way of charging customers is for token utilization, you want to maximize token utilization,” he said. “We do not sell our models and our platform that way.” Instead, Alao said Cohere tries to help enterprises solve their hardest problems privately and securely while reducing unnecessary model usage. His prescription was straightforward: “Use the right model for the task at hand.” Rather than sending every request to the largest available frontier model, enterprises should route work according to the intelligence required and the sensitivity or regulatory burden attached to the task. Alao cited an unnamed Canadian bank that uses Cohere’s on-premises models for highly regulated workloads, while sending less sensitive tasks requiring greater intelligence through Cohere’s North platform to larger frontier models. “So model routing can become super useful,” he said. Smaller models for most enterprise work Asked by an audience member how Cohere’s open-source North Mini Code , released last month, could compete against proprietary coding models, Alao acknowledged that larger frontier models may perform somewhat better on the hardest tasks. But that advantage may not justify using them indiscriminately. “For 80% of the use cases that they needed, this was a lot more effective, a lot cheaper,” Alao said of developers adopting the model. Cohere’s North Mini Code runs on a single Nvidia H100 GPU and targets agentic software engineering, including terminal work, code review and tool use. The company has also released Command A+ , a 218-billion-parameter mixture-of-experts model with only 25 billion parameters active during each generation step. Its compressed four-bit version reduces the hardware required for private deployment, while its Apache 2.0 license gives enterprises broad freedom to operate and modify it. Search becomes part of the agent Asked about Cohere’s longstanding work on embeddings and enterprise search, Alao said the field is moving beyond retrieving text and inserting it into a model’s context window. “Today, the state of the art is around multimodal search,” he said. “It’s beyond just the text modality.” Search across documents, images and other forms of information is becoming “an integral component of your agentic workflow,” Alao added, with the model deciding when and how to use retrieval like any other tool. Asked what would persuade enterprises to move beyond bundled AI services from existing cloud providers, Alao returned to data control and portability. “If you’re interested in sovereignty, you want to have more control on your data,” he said. Cohere’s governance layer, he added, lets customers route traffic to appropriate models, “breaking that vendor lock-in concern that a lot of our customers have.”
- Syncity 3000: bootstrapping scene-scale 3D diffusion - ORA
Syncity 3000: bootstrapping scene-scale 3D diffusion ORA - Oxford University Research Archive
- OpenAI Unaware of ‘Any Evidence’ Showing Apple Lawsuit Has Merit
OpenAI said on Tuesday that it’s “not aware of any evidence” that an Apple Inc. lawsuit alleging trade-secret theft has merit. The latest remarks come four days after Apple filed the suit, which claimed that OpenAI took proprietary technology to …
- The AI boom just found two new winners: Goldman Sachs and JPMorgan Chase
Goldman Sachs and JPMorgan showed that Wall Street is a major beneficiary of the AI boom, with record revenue driven by surging trading and investment banking.
Score: 59🌐 MovesJul 15, 2026https://www.cnbc.com/2026/07/14/goldman-sachs-and-jpmorgan-chase-are-emerging-as-ai-winners.html - Microsoft is reportedly training salespeople to talk down OpenAI and Anthropic
Microsoft is looking to sell its in-house AI models as more efficient and cost-effective than its competitors' models.
- Answers at the Speed of Thought: Healthcare Analytics in the Era of Agentic AI
Answers at the Speed of Thought: Healthcare Analytics in the Era of Agentic AI MedCity News
Score: 58🌐 MovesJul 15, 2026https://medcitynews.com/2026/07/answers-at-the-speed-of-thought-healthcare-analytics-in-the-era-of-agentic-ai/ - Cushman & Wakefield Study: AI to Drive Stronger Growth and Higher Real Estate Demand Across Asia Pacific
Cushman & Wakefield Study: AI to Drive Stronger Growth and Higher Real Estate Demand Across Asia Pacific
- AI is paying off, but governance is lagging behind
Enterprises are facing two simultaneous challenges with AI: The risks associated with it are evolving faster than governance frameworks, while the business benefits are often difficult to measure. This is one of the key findings of The Value of AI , a study commissioned by SAP from Oxford Economics. Now in its second year, the study surveyed 2,600 executives from 13 countries worldwide. High expectations, limited preparation On average, the enterprises surveyed plan to spend around $28 million on AI (up from $26.7 million last year), and expect a 21% ROI (from 16% last year). Expectations for AI agents are particularly high, with ROI expected to reach 17% this year, up from 10% last year. Furthermore, 83% of respondents worldwide said agentic AI has the potential to fundamentally transform their organization. On the other hand, only 3% of respondents said their enterprises were fully prepared for the deployment of AI agents. There are gaps, particularly when it comes to governance: Only 12% of respondents said their skills or processes were able to govern AI effectively, 38% do not have human-in-the-loop processes in place for oversight of AI agents, and only 63% have established permissions and access controls for agents. Other concerns include weaknesses in the organization of AI deployment, poor data quality, insufficient employee training, and the widespread use of shadow AI. Governance is the bigger challenge width="1024" height="576" sizes="auto, (max-width: 1024px) 100vw, 1024px"> Sean Kask, Chief AI Strategy Officer at SAP SAP In an interview, Sean Kask , Chief AI Strategy Officer at SAP, commented on the study’s key findings. Mr. Kask, in the study’s foreword, you write that companies are currently facing two challenges simultaneously: The risks associated with AI are evolving faster than governance, while the business benefits are often difficult to measure. Which of these poses the greater problem for companies? Sean Kask: Measuring the business value of IT investments has never been easy. The same applies to AI. That’s why I currently consider the governance issue to be the greater challenge. While traditional governance principles and best practices for secure software development remain important even in the age of large language models and agent-based AI, entirely new risks are emerging at the same time. For example, as soon as companies roll out AI on a broad scale, they suddenly discover hundreds or even thousands of so-called shadow agents that employees are using without central oversight. Or they find that a significant portion of the workforce is copying content into private ChatGPT accounts. Such risks often only become apparent once AI is already being used productively. According to your study, German companies invest an average of nearly $40 million in AI, more than companies in all other countries surveyed. Why is that? Kask: I was less surprised by the amount of investment than by the fact that, overall, the level of investment and the return on investment achieved have developed very similarly across the various countries. There’s no clear answer as to why Germany invests more. In part, it’s likely simply because costs here are higher than in India, for example. However, we’re also seeing a high level of AI adoption among German companies. SAP has a dashboard that allows us to track how our customers are using AI features. Germany is among the countries with particularly high usage. Added to this are the strong industrial base and the political impetus from Europe, which are driving the use of AI. Accordingly, companies there are making targeted investments in building the necessary expertise. According to the study, 47% of German companies are satisfied with the return on investment from their AI investments. At the same time, 77% say they are still far from realizing AI’s full potential. Isn’t that a contradiction? Kask: No, we see this pattern worldwide. Companies initially invest in a few AI use cases and realize: This works; we’re creating added value. Accordingly, they’re satisfied with their investment. But this is precisely what leads them to identify further use cases. They explore AI agents and want to utilize them as well. However, it is exactly at this point that many encounter new challenges in implementation and scaling. The study therefore primarily highlights a learning curve: The more experience companies gain with AI, the greater their awareness of its previously untapped potential becomes. According to the study, only 33% of companies surveyed have KPIs at the executive board level that are directly linked to the implementation of AI. In your view, which metrics should supervisory boards and CEOs definitely be tracking? Kask: For us, a key indicator is employee enablement. How many employees have already successfully completed training or upskilling programs related to AI? Without the appropriate skills, AI adoption will fall short of its potential. Transparency is equally important. Companies should know which AI agents are actually in use within their landscape. SAP offers the SAP AI Agent Hub for this purpose, which automatically discovers and inventories agents from SAP and third-party environments. Customers have already been able to identify thousands of agents this way, which highlights the need for centralized governance and transparency. In addition, companies should have a complete overview of all AI use cases. A robust business case should be in place for each use case. We often see two extremes: Either the executive board is under pressure to implement AI as quickly as possible and allocates a lump-sum budget for this purpose. Or management initially takes a wait-and-see approach. This leads to independent pilot projects springing up throughout the company, with individual departments procuring their own tools and entering into their own contracts. At SAP, we therefore follow a clearly structured selection process. Each idea first undergoes an assessment of its expected business value. We then examine technical feasibility, data availability, and ethical and governance aspects. From management’s perspective, it is crucial to maintain transparency regarding all ongoing AI projects at all times and to consistently prioritize them based on their business value. Agents, too, need a ‘hire-to-retire’ lifecycle Even with the introduction of dozens or even hundreds of AI agents, governance becomes increasingly complex. What capabilities do enterprise platforms need to manage AI agents securely and in a controlled manner at scale? Kask: We make a conscious effort not to anthropomorphize AI too much. Nevertheless, the analogy is helpful: Agents require a complete hire-to-retire lifecycle. This begins with the detection and registration of an agent. It is then integrated into the enterprise environment, granted the necessary permissions, and given access to the data sources it needs to perform its tasks. Observability is just as important. Companies must be able to track what an agent is actually doing in the system at all times. In addition, they should track key performance indicators: Is the agent achieving the desired results? How efficiently is it working? How many tokens does it consume? How many processing steps does it require for a task? Ultimately, this involves several key components: a complete inventory of all agents, appropriate governance, risk, and compliance (GRC) mechanisms, transparency regarding agent behavior, and continuous monitoring. This is the only way to ensure that AI agents consistently operate within defined parameters and deliver the desired business value. In your estimation, which business processes will companies actually delegate entirely to AI agents over the next two to three years? Kask: Currently, such agents work particularly well in clearly defined use cases. SAP will release more than 50 (currently 34) specialized AI agents. One example is periodic financial reporting. In this context, journal entries must be made based on numerous rules stored in documents, emails, or previous transactions. The agent analyzes these various sources of information, derives a recommendation from them, and suggests the appropriate journal entry to the user. Based on what we’ve heard from customer projects, employees at medium-sized companies currently spend about twelve hours per month on these tasks. With the help of an AI agent, this effort can be reduced to two to three hours. Another area of application is production planning. If delivery dates change or new orders come in at short notice, the entire production plan must be adjusted. It is precisely these kinds of complex optimization tasks that are ideally suited for AI agents. In principle, there are virtually no limits to the narrowly defined business processes in which agents can be deployed. However, they will not operate completely autonomously at first. Trust in AI begins with a stable foundation Many companies still struggle to trust AI agents. After all, large language models operate probabilistically and can produce false information. This is particularly problematic in financial processes. How do you build trust? Kask: Trust begins with a stable foundation. ERP systems remain the reliable system of record. They operate deterministically, contain the business logic, and hold the relevant company data. AI agents build upon this foundation. They do not replace it. Equally important is the human-in-the-loop principle. Employees must be able to understand what the agent is doing, verify its results, and intervene if necessary. That’s why employee training also plays a crucial role. They must understand how generative AI works and where its limitations lie. Of course, language models can hallucinate. At the same time, we must not forget that humans are not infallible either. The key lies in the collaboration between humans and AI. This allows us to improve both the efficiency and the quality of many business processes. Another important component is transparency. Our global AI ethics policy, for example, stipulates that users must always be able to recognize when AI is involved. In Joule, it’s possible to trace which data sources the agent used and which steps it went through in reaching its decision. This traceability is an essential prerequisite for trust. What distinguishes an SAP agent from a general AI agent that merely accesses an ERP system? Kask: The key difference is that Joule and the SAP agents are directly embedded in the ERP system. There, for example, we’ve built a knowledge graph that describes the semantic relationships between all tables, business objects, and data fields. To put this into perspective: The SAP S/4HANA Knowledge Graph is based on approximately 452,000 ABAP tables, 7.3 million data fields, and thousands of analytical views. The semantic relationships between these artifacts are modeled in the Knowledge Graph and made available for AI applications. For example, if a user wants to view all open purchase orders, the agent does not first have to laboriously search for the relevant information. It immediately knows which tables and objects are relevant and also understands the relationships between a purchase order, a purchase requisition, the responsible approvers, and other business objects. As a result, the agent not only works much more precisely but also requires significantly fewer tokens because it can greatly narrow down the search space. If, instead, one attempts to simply overlay AI onto an existing system or extract data from a relational ERP system, many of these relationships are lost. In a sense, this destroys the semantic context that is crucial for precise answers. That is why we view the ERP system as an enormous strategic advantage. It has been the system of record for decades and contains roughly 50 years of codified business and process knowledge. This knowledge forms the foundation for what we call the autonomous enterprise . The agents build upon this knowledge and continue to develop it. In the future, SAP agents will also communicate bidirectionally with agents from other providers via standards such as Agent-to-Agent (A2A). According to your study, AI currently creates the greatest added value in decision-making, customer interaction, and gaining new insights, rather than in traditional productivity gains. Will this change the way companies justify AI investments in the future? Kask: In our study, productivity was simply rated slightly lower than, for example, gaining new insights. In the long term, however, productivity remains the ultimate goal. Europe, in particular, has been suffering from comparatively weak productivity growth for years. At SAP, we therefore first evaluate every new AI feature based on its specific business value. For all agents and AI features that we include in our AI Feature Catalog, we first conduct a value analysis. We ask: What benefit does the feature offer the user? Does it contribute to higher revenue? Does it increase productivity? Only then is it developed further. At the moment, the greatest added value often still lies in consolidating information from structured and unstructured data sources and making it accessible via natural language. The next step, however, is to translate these insights directly into more efficient business processes. That is precisely where the greatest productivity gains will be realized in the future. If you could give CIOs just one or two pieces of advice for the transition from generative AI to AI agents, what would they be? Kask: In my view, the biggest mistake would be to try to transform the entire company all at once or to attempt to perfectly prepare all the data right from the start. Instead, you should consider what kind of agent can create significant added value, and then implement it. Of course, this agent needs access to consistent and context-rich enterprise data. That’s exactly what we’re working on at SAP with technologies like the knowledge graph, which maps the semantic relationships within enterprise data. In addition, with data products and the SAP Business Data Cloud, we provide tools that make data from various sources usable for AI agents. Thanks to zero-copy and data fabric approaches, information from legacy systems, Snowflake, or ERP systems can be consolidated without first having to extensively replicate the data. For a procurement agent, this makes it possible to provide exactly the relevant data for the specific use case. The key point is this: Companies do not have to wait until they have fully migrated to the cloud or consolidated their entire data landscape. With the technologies available today, data can already be made usable for specific AI agents, managed in a controlled manner, and used to quickly generate initial business value. On the other hand, those who wait for the perfect starting point run the risk of falling behind. This article is adapted from one first published by Computerwoche.
Score: 58🌐 MovesJul 15, 2026https://www.cio.com/article/4197428/sap-study-ai-pays-off-but-governance-is-lagging-behind.html - Most RAG Hallucinations Are Retrieval Failures: How the Retrieval Brick Decides What the Model Can Invent
Enterprise Document Intelligence [Vol.1 #7quinquies] - Hallucination is usually garbage-in. Fix retrieval, and the model has nothing left to make up The post Most RAG Hallucinations Are Retrieval Failures: How the Retrieval Brick Decides What the Model Can Invent appeared first on Towards Data Science .
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- Agentic orchestration: Enterprise AI organizations have a deployment problem, not a platform problem — and most are calling chatbots agents
Across 101 enterprises, agent orchestration is consolidating onto model-provider platforms — Anthropic’s Claude leads by a wide margin — chosen for the gravity of the underlying model and judged on reliable multi-step execution. But the ambition runs well ahead of the reality: most deployed “agents” are still chatbot wrappers, the control plane enterprises expect is deliberately hybrid to avoid lock-in, and real-time fiscal control over token burn remains the exception. This wave of VentureBeat Pulse Research examines enterprise agent orchestration: which platforms enterprises run on, what drives the choice, what they optimize for, how they expect agent control to be structured, and — most revealingly — how orchestrated their deployed “agents” actually are and how tightly they control the cost of running them. The central finding is a gap between orchestration ambition and orchestration reality. Enterprises are consolidating fast onto the major model platforms: Anthropic’s Claude is the primary platform for 40%, more than double any rival, followed by Microsoft (18%) and OpenAI (13%). The choice is driven by “model gravity” — native alignment with a state-of-the-art base model (21%) — and success is judged by reliable, multi-step execution (task completion reliability 32%, multi-step workflow management 28%). Yet asked to assess their portfolios honestly, 71% say a quarter or fewer of their deployed “agents” are true multi-step orchestrated workflows rather than single-prompt chatbot wrappers, and only 10% have crossed the halfway mark. The orchestration layer is being built well ahead of the orchestrated portfolio it is meant to run. That gap shapes the architecture enterprises are putting in place. By the end of 2026 a clear majority (51%) expect a hybrid control plane — provider-native plus external orchestration — and only 6% expect to hand control to a provider-managed service, because vendor lock-in (35%) is the risk they fear most if control lives inside a model provider. Investment follows the build-out: agent workflow tooling leads the spend (34%), with security and permissions enforcement (25%) behind. And fiscal control lags throughout — more than a quarter (27%) have no real-time way to stop a runaway agent before the bill arrives. Methodology VentureBeat fielded this survey as part of its ongoing Pulse Research series, this instrument focused on enterprise agent orchestration. Responses are filtered to organizations with 100 or more employees (n=101), drawn from a single June 2026 wave; because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. By organization size the sample is spread evenly across the enterprise bands: 100–499 employees, 2,500–9,999, and 50,000+ (21% each), with 10,000–49,999 and 500–2,499 (19% each). By role it is senior and buyer-credible: product and program managers (15%), CIO/CTO/CISO (13%), consultants and advisors (13%), and a spread of data, AI, and engineering directors and VPs, with an “Other” function at 18%. On purchasing, 81% are recommenders, influencers, or final decision-makers for AI solutions (66% recommender/influencer, 15% final decision-maker). Technology/Software is the largest industry at 44%, followed by Financial Services (17%) and Healthcare/Life Sciences (8%). At 101 respondents the sample is robust enough to read directionally with reasonable confidence, though it remains self-selected and is not a probability sample. Finding 1: Orchestration runs on model-provider platforms Anthropic’s Claude leads; open frameworks are marginal We asked which agent orchestration platform enterprises primarily use today. The answer concentrates on the major model providers — and on one in particular. A note on reading these shares. As described in the methodology section, the respondents are self-selected, and this question asked them for a single primary platform — so the figures measure which platform leads each enterprise's deployment, within a self-selected audience of AI-active technical decision-makers. A sample built this way can diverge substantially from spend-weighted market measures, and each VB Pulse survey draws its own sample with its own company-size mix, so vendor figures should not be compared across our surveys either. Read these shares as a portrait of where this cohort has placed its primary orchestration bet today, rather than as market share. The model platforms dominate. Anthropic, Microsoft, OpenAI, Google, and Amazon together account for roughly 80% of deployments (81 of 101), while the open frameworks (LangChain/LangGraph) and custom in-house builds that anchor engineering discussion sit in single digits. Anthropic’s lead — 40%, more than double the next platform — mirrors the “model gravity” selection logic in Finding 2: enterprises are choosing the orchestration layer that comes with the model they want to build on. As with the security vendors in the prior agent-security wave, the tools that define the category in technical circles are not yet where enterprise deployment concentrates. A small 3% are not orchestrating at all. Respondents rate the platforms they run at 3.94 out of 5 overall (109 answered), with “value for money” specifically at 3.94 and “ease of implementation” the weakest score, at 3.85 — placing orchestration near the bottom of our five-tracker satisfaction range, ahead of only evaluation tooling. A rating just under 4 out of 5, from users of whom 96% plan to change their orchestration approach within the year, reads as provisional acceptance: the platforms work well enough to run today, and not well enough to stop the search for something better. The ratings sit alongside near-universal intent to change; this is a layer enterprises tolerate more than they love. Finding 2: Model gravity drives platform selection The base model, not the tooling, decides the platform We asked what most influenced the orchestration platform choice. The single largest factor is the pull of the underlying model — though flexibility and ease of development follow close behind. Model gravity leading is the selection-side explanation for Anthropic’s platform lead: enterprises pick the orchestration environment closest to the frontier model they have standardized on. But the next tier complicates the picture — flexibility across models and tools (17%) and ease of development (17%) say enterprises also want to avoid being trapped by that choice, foreshadowing the lock-in fear in Finding 6. Security and permissions (14%) and total cost of ownership (11%) round out a pragmatic buying logic. Performance (latency/memory) sits last at 4%, a reminder that at this stage of adoption the binding constraints are model fit and optionality, not raw speed. Finding 3: The job is reliable multi-step execution Enterprises just orchestration by whether it completes the work We asked what enterprises optimize for — their primary success metric for orchestration. Reliability and multi-step workflow management dominate; developer- and user-facing metrics trail. Task completion reliability (32%) and multi-step workflow management (28%) together account for 59% of responses (60 of 101): orchestration succeeds, in the enterprise view, when it reliably carries a task through multiple steps to completion. Developer productivity (17%) matters but is secondary — the inverse of its prominence in framework discussion — and end-user experience (9%) is a minor concern, consistent with orchestration being an internal execution problem rather than a UX one. This reliability-first standard is exactly what makes the Chatbot Trap finding so pointed: enterprises define success as dependable multi-step execution, yet most of their deployed “agents” do not yet do multi-step work at all. The trap is not evenly distributed. Splitting the sample by organization size, 77% of smaller enterprises say a quarter or fewer of their agents do true multi-step work, against 62% of larger ones. Larger enterprises are meaningfully further into genuine multi-step deployment; the chatbot trap is, directionally, a mid-market condition. Finding 4: Consolidate, productionize, and build in-house Three strategic moves are nearly tied for the year ahead We asked what major change enterprises anticipate in their orchestration strategy over the next 12 months. Three moves cluster at the top, almost evenly split. The top three — building in-house control (25%), standardizing on one framework (24%), and moving agents from sandbox to production (23%) — are statistically indistinguishable and tell a single story: enterprises are moving from experimentation to operational consolidation. They want fewer frameworks, more production exposure, and more ownership of the control layer; only 4% expect no change. The appetite for custom in-house control planes is notable alongside the platform concentration in Finding 1 — enterprises are standardizing on model-provider platforms while simultaneously planning to wrap them in control logic they own, the hybrid posture that Finding 6 makes explicit. Finding 5: Investment flows to workflow tooling Tooling and permissions lead the spend; monitoring trails We asked which orchestration-related investment will grow most next year. Agent workflow tooling leads, with security and permissions enforcement behind. Workflow tooling leading (34%) is the budget-side expression of the reliability-and-multi-step priority in Finding 3: the money is going to the machinery that strings steps together dependably. Security and permissions enforcement (25%) and scaling infrastructure (20%) follow — the investments required to take agents from sandbox into production, the strategic move in Finding 4. Monitoring and debugging draws a smaller 11%, with another 11% reporting flat budgets. The weight on tooling, permissions, and scaling over pure observability signals that enterprises are spending to build and harden orchestration, not merely to watch it run. Finding 6: The control plane will be hybrid — and lock-in is why Enterprises expect to split control between providers and their own layer We asked where enterprises expect the primary control plane for agents to live by the end of 2026, and what worries them most if that control sits inside a model-provider platform. A clear majority expect a hybrid model — and vendor lock-in is the reason. Hybrid control is the dominant expectation by a wide margin (51%), and only 6% expect to hand control to a provider-managed service outright. Read together, the hybrid, custom, and externally-abstracted options — every architecture that keeps control at least partly outside the provider — sum to 88% (89 of 101). The reason surfaces directly when we asked about the risk of provider-resident control: vendor lock-in leads at 35% (35 of 101), ahead of security and permissioning limitations (28%) and inflexibility across models and tools (21%). The pattern echoes the prior wave’s “don’t trust the model to police itself” posture — here, enterprises will build on a provider’s platform but decline to be governed entirely by it. The hybrid control plane is the architectural hedge against the lock-in they most fear. The June figure asserting a preference for a hybrid control plane marks movement from earlier. In the April–May survey (n=145), only 34% expected a hybrid control plane, and a greater number (12%) expected to hand control fully to a provider-managed service. These two snapshots don’t yet measure a confirmed longitudinal trend — but the direction of the conversation is unambiguous: toward keeping control. Lock-in is also a new arrival as a top concern. In the April–May wave, the leading concern was security and permissioning limitations (32%), with lock-in second at 24%; by June the two had traded places. The worry about provider platforms appears to be maturing from whether they can be secured to whether they can be replaced. Finding 7: The chatbot trap — most “agents” aren’t agents yet Enterprises admit most deployments are still chatbot wrappers We asked enterprises to assess their portfolios honestly: what share of their deployed “agents” are true multi-step orchestrated workflows versus simple single-prompt chatbot wrappers. The answer is the defining finding of this wave. This is the gap at the center of the report. Combining the bottom two bands, 71% of enterprises (72 of 101) say a quarter or fewer of their deployed “agents” are genuinely orchestrated — and just 10% (10 of 101) have crossed the halfway mark. The ambition documented in the earlier findings — model-provider platforms, reliability-first success metrics, production rollouts, a deliberate control architecture — runs well ahead of the deployed reality, which remains overwhelmingly single-prompt assistants dressed as agents. This is less a contradiction than a roadmap: the platforms, budgets, and strategies are being put in place precisely because the orchestrated portfolio is still so thin. The open question for later waves is how fast the reality closes on the ambition. Finding 8: Fiscal control is still reactive Only a minority can stop a runaway agent before the bill arrives Finally, we asked how enterprises enforce fiscal control over agent token consumption — the risk that an autonomous loop exhausts a budget before anyone intervenes. Most rely on native caps or after-the-fact monitoring; real-time programmatic control is the exception. More than a quarter of enterprises (27%) admit they have no real-time, programmatic way to stop an agent before a budget-breaking bill arrives — they learn of it from the logs afterward. Another 32% lean entirely on the native caps and throttles built into their primary platform, a control only as good as the provider’s tooling and one that ties back to the lock-in concern of Finding 6. The enterprises building custom gateways (23%) or exploiting cross-model routing to arbitrage cost (19%) are the ones treating token burn as an engineering problem to be controlled deterministically. As with orchestration maturity, fiscal control is an area where the operational reality lags the ambition: agents are moving toward production faster than the cost-control plane around them is being built. It’s worth noting, a split appears according to company size: roughly one in three enterprises under 2,500 employees (34%) exercises only reactive control of agent spend, against 20% of larger enterprises — directional figures, but consistent with the chatbot-trap split. The mid-market is running the least mature agents on the least instrumented budgets. The bottom line: The layer is real; most of the agents aren't yet Organizations with 100 or more employees describe an orchestration strategy that is consolidating quickly and maturing slowly. They are standardizing on model-provider platforms — Anthropic’s Claude leads at 40% — chosen for the gravity of the underlying model, and they judge success by reliable multi-step execution. Investment is flowing to workflow tooling and permissions, the strategy is to consolidate frameworks and push agents into production, and the control plane they expect is deliberately hybrid, because vendor lock-in is the risk they fear most. But the honest self-assessment punctures the ambition. Seventy-one percent say a quarter or fewer of their deployed “agents” are truly orchestrated, only 10% are past the halfway mark, and more than a quarter cannot stop a runaway agent in real time. The orchestration layer — the platforms, the budgets, the control architecture — is being built ahead of the orchestrated portfolio it is meant to run. At 101 respondents in a single June wave this reads as a clear directional signal rather than a precise measurement: enterprises have decided how they want to orchestrate agents well before most of their agents are doing anything an orchestration layer is for. The question for subsequent waves is whether the deployed reality closes the gap on the ambition — or whether the chatbot trap proves stickier than the roadmap assumes. Based on survey responses from 101 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. Because this is one wave rather than a pooled multi-month sample, results read directionally rather than as a confirmed trend. Respondents include product and program managers, CIOs, CTOs and CISOs, consultants and advisors, and directors and VPs of data, AI, and engineering, across Technology/Software, Financial Services, Healthcare, and other sectors.
- Warsh: AI spending may lift prices without fueling lasting inflation
Federal Reserve chairman Kevin Warsh said on Wednesday that the AI investment boom will likely raise prices over the next year, but argued that those increases might not automatically be inflationary. Why it matters: Warsh drew one of his clearest distinctions yet between the AI boom's immediate price effects and persistent inflation — a nuance that could shape policymakers' response as investment remains strong. What they're saying: " Will it increase measured prices over the course of the next 12 months? I suspect it will," Warsh told lawmakers. "Whether that's inflationary or not, that's up to the Federal Reserve — and we're going to have something to say about that." Warsh said the AI boom is already driving capital spending and bidding up the price of chips, while policymakers are still largely guessing when the technology's broader productivity benefits will materialize. "I don't view a one-time change in prices as necessarily being inflationary, because I think there's a supply response in that way," he said. The big picture: Fed officials are debating whether the AI investment boom's near-term burst of demand will add to inflation before its potential productivity gains arrive — on top of the price surges fueled by the Iran war. New York Fed President John Williams has said the technology buildout is increasing demand for certain goods and electricity, with rising costs beginning to affect prices. Fed governor Christopher Waller has similarly pointed to AI investment as a source of strong economic demand. "This is one of the good family fights," Warsh said in response to a question from Sen. Jack Reed (D-R.I.) about his colleagues' views. Zoom in: Warsh made the comments during the second day of his semiannual monetary policy testimony before Congress, a legally mandated appearance by the Fed chairman. The House and Senate hearings were Warsh's first testimony on Capitol Hill since taking the reins of the Fed in May . The intrigue : In an exchange with Sen. Chris Van Hollen (D-Md.), Warsh would not confirm whether he has spoken with President Trump since becoming Fed chairman. "I just don't want to be in the business of sharing discussions that the president and I have," Warsh said. "I can offer you this assurance: the President has not — before I took this office, before I raised my right hand — he has not tried to influence the conduct of monetary policy," Warsh added. What to watch: Warsh said the findings from five outside-led task forces reviewing the Fed's monetary policy framework will arrive in the months ahead. "I'm not a very patient person. People have said that to do all this good work you'll need years. I gave them six months," he said. The task forces are studying the Fed's communications, balance-sheet strategy, economic data, productivity and employment and inflation frameworks, and are co-led by a roster of prominent outsiders , including economists and business executives.
- OpenAI’s First AI Device Could Be a Smart Speaker with Built-In Camera, GPT-Live Capabilities: Report
OpenAI has long been rumoured to foray into the consumer hardware business with an AI-powered device. While the rumour mill previously claimed that the ChatGPT maker could introduce an AI headset as its first-ever hardware device, a recent leak suggests otherwise. According to a report, the company is developing a portable, screen-free smart speaker that could serve a...
Score: 58🌐 MovesJul 15, 2026https://www.gadgets360.com/ai/news/openai-ai-smart-speaker-launch-features-report-11773664#rss-gadgets-ai - OpenAI’s hardware device may partly compete with AirPods Ultra – and lose
Jony Ive and Sam Altman made a big promise when they teased the launch of a completely new concept in AI hardware more than a year ago. The most recent report suggests that OpenAI may technically be delivering on this promise, but could end up at least partly competing with an upcoming AirPods model. If so, I can easily see AirPods Ultra proving the more popular choice …
Score: 58🌐 MovesJul 15, 2026https://9to5mac.com/2026/07/15/openais-hardware-device-may-partly-compete-with-airpods-ultra-and-lose/ - OpenAI is showing Kalshi’s World Cup odds inside ChatGPT, its first prediction market deal
OpenAI has begun displaying prediction market odds from Kalshi inside ChatGPT search results, marking the first time the company has partnered with a regulated betting exchange. The integration, first reported by the New York Times, shows real-time odds for 2026 FIFA World Cup matches with a “Source: Kalshi” label but no outbound links or Kalshi […] This story continues at The Next Web
Score: 57🌐 MovesJul 15, 2026https://thenextweb.com/news/openai-kalshi-chatgpt-world-cup-prediction-markets - Zoho deepens vertical SaaS push with AI-powered education platform
Zoho Classes will be free for central and state government educational institutions as well as individual teachers handling up to 100 students, while private institutions will pay for the software.