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19/08/2026

Here is a blog written by Judson Althoff, CEO of Microsoft - with mentions of companies that deployed AI. The text was published during the summer.

”Throughout this past fiscal year, customers across every industry and segment moved from AI experimentation to deploying AI for real-world business outcomes. They unlocked innovation and created new opportunities for growth. We saw the emergence of Frontier Firms as they moved beyond efficiency gains to focus on human ambition and embed AI at the core of how they operate. Successful customers are building an intelligence platform so their unique IQ — their knowledge, data, workflows, applications and expertise — can continuously compound, ensuring the value of AI accrues to the customer, not the model. They have a trust platform that is pervasive, with the ability to manage, govern, secure and measure AI across every business process.
Everything we are doing at Microsoft is empowering Frontier Transformation: Copilot enables AI in the flow of human ambition, Microsoft IQ amplifies and protects an organization’s IQ and Agent 365 is the trust platform that enables observability at every layer of the stack.
Businesses are not static, and neither are the AI systems that support them. As organizations evolve, AI systems must continuously learn and improve. Agentic workflows need to be built, observed and tuned against the outcomes organizations seek and the ROI they demand. Microsoft’s open, model-diverse and heterogenous platform powers that improvement loop. We also recently announced Microsoft Frontier Company, bringing our AI engineering approach to customers around the world to help them build these AI systems to accelerate measurable business outcomes.
Throughout the past year, we saw customers put these capabilities to work in powerful ways — embedding AI into core business processes, building agentic systems, strengthening security, accelerating innovation and creating new sources of value.
The stories below highlight organizations leading Frontier Transformation, demonstrating how intelligence, trust and human ambition come together across industries.

To advance its journey to become a global AI-powered company, Atos Group deployed Microsoft 365 Copilot to 56,000 employees across 54 countries — from consultants to engineers to frontline workers — and was one of the first organizations globally to adopt Microsoft 365 E7: The Frontier Suite. Using Microsoft Foundry, Microsoft Copilot Studio and Agent 365, Atos is building, operating and governing a growing ecosystem of 19,000 AI agents through a unified operating model that brings together productivity, security, compliance and agent governance. As Atos embeds secure agentic AI across its workforce, the company is creating a repeatable model to continuously improve thousands of agents at scale while applying the same playbook to help customers accelerate adoption across highly regulated industries.

Facing state-sponsored threats and complex global operations, ASM is strengthening cyber resilience with Microsoft Security Copilot, helping protect the intellectual property behind advanced semiconductor manufacturing. By bringing threat investigations into a unified AI-powered experience, ASM enables analysts to investigate incidents faster, apply consistent decision-making across global operations and accelerate the development of cybersecurity talent. The company reduced incident triage time by 68 percent, cut laptop compromise investigations from 25 minutes to eight and now saves 337 hours each week on investigations while redeploying 20% of its security operations staff to governance, risk and compliance initiatives.

Banco Popular Dominicano, the largest private-sector bank in the Dominican Republic, transformed operational risk management from periodic, sample-based reviews into continuous, AI-powered supervision. Using AURA — an ecosystem of specialized agents built on Microsoft Copilot Studio and Microsoft Power Platform — the bank monitors 100 percent of its operational risk universe in real time, up from roughly 40 percent coverage, and can automatically analyze changes, validate controls and surface issues as they occur. The shift has delivered seven times greater analytical capacity, reduced manual operating effort by 70 percent achieved 98 percent methodological accuracy and enabled continuous processing of approximately 80,000 documents per week and more than 300 cases per day. Just as importantly, risk teams have moved from reacting to problems after the fact to anticipating and preventing deviations before they occur. These results demonstrate the power of AI democratization. By enabling business teams to build intelligent solutions themselves through low-code tools, Banco Popular transformed operational risk management while fostering a culture of innovation led by domain experts.

To help reduce the manual burden for employees while meeting the pharmaceutical industry’s strict data security requirements, Cactus Life Sciences modernized scientific workflows with Microsoft 365 Copilot and agents. The company has deployed more than 30 custom automation agents to streamline document review and structure data extraction and information retrieval across scientific writing and project management teams. Supported by a centralized knowledge repository and the Copilot Champions community, the company reports efficiency improvements of approximately 35 percent to 50 percent in structured data extraction. By automating labor-intensive tasks and maintaining human review and quality controls, the company is enabling scientific writers to focus on deeper analysis, synthesis and delivering exceptional science to clients.

Chow Tai F**k is redefining luxury retail with Microsoft 365 E5, Microsoft Purview, Microsoft Azure OpenAI Service, Microsoft Fabric and Microsoft Foundry. The company has deployed over 400 customized AI agents supporting more than 24,000 employees, with millions of AI interactions each month and core business-process efficiency gains exceeding 70 percent. Through its AI F**k super-agent ecosystem, frontline associates can instantly access product expertise, inventory insights and personalized recommendations, helping drive sales conversion improvements of up to 57 percent while delivering hyper-personalized omnichannel experiences at scale. With hundreds of AI agents operating across the business, Chow Tai F**k is creating a foundation where customer, product and operational intelligence can be applied across every interaction, helping personalize experiences and improve decision-making across its global retail network.

To accelerate AI adoption, EY moved AI from experimentation into enterprise-wide transformation. After deploying Microsoft 365 Copilot to 150,000 employees and realizing a 15 percent productivity gain, the firm is expanding the Microsoft 365 Frontier Suite across its global workforce of more than 400,000 people, embedding agentic AI capabilities across the enterprise. As Client Zero, EY is applying Microsoft technologies across its own operations, including Microsoft Power Platform, Microsoft Copilot Studio, Microsoft Azure, Microsoft Foundry and Microsoft Fabric. The results include 95 percent faster lead times, a more than 37 percent reduction in finance operating costs and up to a 90 percent reduction in manual workloads across key business processes.”

He goes on to mention more companies with examples then says the following:

”Looking back on FY26, I am inspired not only by the continued pace of AI innovation, but by what our customers are achieving. Across every industry and segment, organizations are turning their unique IQ into strategic advantage with continuously improving agentic workflows. The companies leading this next phase are building, observing and tuning agentic workflows against business outcomes. Intelligence compounds, trust scales and AI can work in the flow of human ambition. As Frontier Firms redefine what is possible with AI, we remain focused on helping our customers amplify and protect their intelligence so they can transform how they operate, compete and grow.”

19/08/2026

IBM Think Newsletter:

Have you heard the story about an AI agent deleting a critical production database? Jeremy Crane, Founder and CEO of car rental platform PocketOS sure has, and he lamented it in a widely read post on X.

It’s a mistake that raises an increasingly urgent question as agents take on more responsibility: are workers meaningfully steering these tools—or just signing off on choices AI has already made?

Until now, digital systems and tools “were built around humans,” Crane told IBM Think. “Now, we have computers making decisions in split seconds, and our DevOps and our security protocols aren’t prepared for that world.”

55 percent of organizations are actively developing or deploying an agentic AI operating model, according to a 2026 IBM Institute for Business Value (IBV) report, “The blueprint for agentic operations.” And 60 percent plan to adopt next-generation delivery structures where AI agents coordinate integrated workflows across finance, supply chain, HR, procurement, physical operations and customer service.

Meanwhile, as expectations increase for humans to apply critical thinking and judgment to AI-generated outputs, organizations are reaching a crossroads: whether to invest in systems and governance that enable real human judgment, or risk reducing the process into a rubber-stamping exercise.

Crane’s deleted database is a casualty of that risk, says Phaedra Boinodiris, Global Leader for Trustworthy AI at IBM Consulting. “This is not a story about a rogue AI,” she told IBM Think over email. “This is a story about liability laundering at infrastructure scale.”

Liability laundering, Boinodiris explains in an article for IBM Think, is when a human is in charge of overseeing an AI’s work, but “the conditions for genuine oversight—the architectural transparency, the training, the authority to halt, the feedback mechanism—were not established.”

As AI’s capabilities and reach expand, having a human in the loop is more critical than ever. But the value that humans add when working with AI agents is meaningless without operational infrastructure that allows them to apply judgment, underpinned by governance, transparency, guardrails and escalation paths to supervise agentic systems at machine speed.

As Maryam Ashoori, VP of Product and Engineering for IBM watsonx.governance, said in an interview with IBM Think, “Organizations need clear structures that keep humans accountable for the decisions that matter most. The question isn’t whether humans are in the loop; it’s whether they’re in control.”

15/08/2026

Radware Survey Exposes Urgent Need to Secure the New AI Layer

83 percent of organizations widely use GenAI or LLMs, while only 17 percent have full visibility into AI agents and AI-driven processes

Radware, a global leader in AI and application security and delivery solutions for multi-cloud environments, today released its 2026 Cyber Survey: New Trends in AI, API and Application Security. Based on a global survey conducted by Osterman Research on behalf of Radware, the survey found that organizations are adopting AI faster than they are implementing the security controls needed to protect their environments.

Security professionals are accustomed to security models aligned with traditional layers of the technology environment, including the network, application and data layers. The emerging AI layer presents a new set of challenges. As enterprises expand their use of generative AI, large language models (LLMs), AI agents and autonomous workflows, the survey indicates that threat actors are using AI to discover vulnerabilities, evade defenses and accelerate attacks. The survey found many organizations lack the visibility and governance needed to effectively secure AI, applications and APIs.

Among the survey's key findings:

- Organizations are deploying AI faster than they can protect it. 83 percent of organizations are making widespread use of GenAI or LLM functionality, and 96 percent expect to implement AI agents or autonomous workflows within 12 months. Yet only 17 percent have full visibility into AI agents or AI-driven processes.
- AI traffic is creating a new access-control challenge. Only 14 percent of organizations have full visibility into AI crawler traffic, while 76 percent have experienced a negative impact from AI crawler traffic or AI agents.
- Application development continues to outpace API security. Nearly half (48%) of organizations update APIs for production use daily or more frequently. Yet only 19% have a fully automated and continuously updated API inventory, and just 24 percent conduct comprehensive API security testing across the full lifecycle.
- The business impact of application attacks continues to grow. 71 percent of organizations experience application-layer or API-targeted DDoS attacks monthly or more often. The average cost of downtime from an application-layer DDoS attack increased 23% year over year to 7,530 US-dollars per minute, or approximately 451,800 US-dollars per hour.
- Security operations are not moving fast enough. Only 21 percent of organizations report the highest level of readiness to manage application security incidents, while the average resolution time for significant API, bot or DDoS-related incidents is 2.8 hours.

14/08/2026

IBM Think on IBM and workflows:

IBM and OpenAI are partnering to put artificial intelligence to work across core business operations.

The partnership will combine OpenAI models and products with IBM Consulting technology and expertise. The companies say the goal is to help enterprises deploy AI more securely across core operations while defending against cyber threats accelerated by AI, with a focus on legacy workflows, application modernization and AI risk management.

“Enterprises no longer need convincing that the models are powerful,” Michael Healy, Managing Partner of Offerings, Assets and Gen AI at IBM Consulting, told IBM Think in an interview. “The challenge now is turning that intelligence into workflows that actually run the business.”

Under the partnership, OpenAI frontier models such as GPT-5.6, along with Codex and ChatGPT Work, will be embedded into IBM Consulting Advantage, IBM’s AI platform for delivering consulting services, according to the announcement. Initial areas of focus will include financial services, government, telecommunications and retail, as well as procurement, customer operations and human resources.

A dedicated OpenAI Practice will also be created, with thousands of IBM consultants and engineers obtaining expert-level certifications through the OpenAI Partner Network. IBM will also establish specialized forward-deployed units of engineers and consultants trained through the network to work directly with clients on complex workflows and highly regulated environments.

The partnership will focus on applying AI to specific business processes, from modernizing applications to redesigning back-office workflows and strengthening cybersecurity, Healy said.

“We’re starting with the business use case,” he said. “Then we bring IBM’s process transformation and industry expertise together with OpenAI’s models to change how that work actually gets done.”

13/08/2026

IDC wrote about the smart phone market in India

Its Smartphone Shipments Fall 11.1 percent in Q2 2026 Amid Deepening Memory Chip Shortage

According to IDC ‘s Worldwide Quarterly Mobile Phone Tracker , India’s smartphone shipments declined 11.1 percent year over year to 33.2 million units in Q2 2026, as the ongoing global memory chip shortage kept component costs elevated and squeezed affordability across price bands. H1 2026 shipments fell to 64.2 million units, down 7.9 percent YoY, the lowest first-half volume in five years, even as market value grew 3.6 percent YoY. With average selling prices at record highs and vendors pulling back on discounts to protect margins, India’s price-sensitive buyers are likely to stay under pressure through the second half of the year.

12/08/2026

IBM Think, that newsletter writes:

July’s AI news cycle followed a now-classic pattern: AI researchers published a very long (70,000 words, in this case) technical paper with provocative implications. Articles reacting to the paper hit the web within hours. People on LinkedIn and X reacted (mostly to the headlines), prompting more articles about those reactions. And now, because the articles allude to the paper’s discussions of how consciousness works, everyone thinks that Anthropic said Claude is sentient and is arguing about AGI.

It’s nobody’s fault. We’re all busy. But here’s the deal: the Anthropic researchers did not claim Claude is “conscious” in the way that you or I are sentient and self-aware. What they did claim is less spicy, but more fascinating: for the first time, we have a way to see what LLMs are “thinking,” but not saying. It’s an unprecedented breakthrough in LLM interpretability that opens a whole new world of possibilities for understanding, evaluating, aligning and training models and agents.

In what looks like a case of convergent evolution, Claude seems to have—on its own, without developers instructing or designing it to—evolved an internal “thought process” that mirrors one of the leading models of how we (humans) consciously access information. This silent thinking takes place within certain computations of the model’s middle layers, which Anthropic dubbed the “J-space.” Using Anthropic’s mathematical technique, the “J-lens,” we can see what concepts the model has on its mind as it reads text.

We can see how an LLM problem-solves and associates concepts with one another. We can see when it faithfully follows instructions, and when it’s acting deceitfully. We can see how the persona it acquires in fine-tuning manifests in its inner monologue. We can even intervene in its thought process and change its outputs accordingly, or train how an LLM thinks before speaking instead of just fine-tuning what it says.

Crucially, this is not just a Claude thing. Independent researchers have already replicated the results on open models—and they have even found some interesting new wrinkles, such as the different ways LLM “thoughts” work in different languages. It’s the early days, and we’ll be learning more about this for a long time, writes IBM.

07/08/2026

HPE is announcing it is joining the Open Secure AI Alliance as a founding member. The company will join NVIDIA and other industry leaders committed to advancing cybersecurity and AI safety through open models, open tooling, and collaborative innovation. The alliance recognizes a fundamental truth: defending increasingly complex software and AI systems requires broad participation, transparency, and shared intelligence across the global technology ecosystem.

Modern AI systems are more than just models. They rely on a combination of agent frameworks, harnesses, guardrails, governance mechanisms, and models that enable them to reason, act, and interact with enterprise environments. For cybersecurity teams, openness provides important advantages. Open weight models, like NVIDIA Nemotron, and open harnesses enable greater transparency, independent evaluation, and broader innovation.

At HPE, we are already applying open weight models and open agent harnesses, including technologies developed by leading large language model (LLM) innovators, to strengthen our software security practices, writes HPE. These capabilities help analyze source code and binaries to identify potential vulnerabilities, prioritize remediation, and improve software quality.

HPE already contributes to several open source projects that drive innovation. Among them is SPIFFE/SPIRE, which creates zero-trust identity framework standards and methods that can cryptographically verify AI agents and services to ensure only authorized workloads communicate and access enterprise resources.

HPE is integrating AI-driven cybersecurity scanning directly into the software development lifecycle (SDLC). By leveraging open weight models, open harnesses, and emerging agent technologies, we envision security capabilities that continuously assist developers from code creation through testing, validation, and remediation.

Trust requires governance, testing, validation, human oversight, and responsible deployment. The Open Secure AI Alliance reflects a growing recognition that AI safety and cybersecurity benefit from collaboration across industry, academia, government, and the open source community.

07/08/2026

HPE.
Automation is old school: The future belongs to self-driving networks

A blog post written by Rami Rahim, Executive Vice President, President & General Manager, Networking, HPE.

As today’s networks are expected to connect every user, device, application, agent, and workload, self-driving networks have become the operating model for the AI era.

The next challenge is not simply managing this complexity but operating it at a speed and scale beyond human capacity. That is why self-driving networks are becoming the new operating model for the AI era.

Unlike past networking models, a self-driving network does more than just automate tasks. It continuously observes, learns, optimizes, and heals itself. This enables IT teams to spend more time driving business outcomes and less time troubleshooting.

How much time? It’s estimated that IT teams currently spend up to 70-80 percent of their time configuring, troubleshooting, and firefighting rather than focusing on strategic initiatives. Self-driving networks dramatically reduce that burden.

Trust makes autonomy possible

Unsurprisingly, there is a lot of networking industry chatter about systems that offer AI agents and autonomous operations. But there's a critical reality that often gets overlooked:

A self-driving network is only valuable if it can be trusted. In other words, the real question isn't whether AI can generate recommendations. It's whether customers can trust it to independently turn recommendations into reality. Autonomy without accuracy creates risk. Autonomy with proven efficacy creates transformation.

A self-driving network from HPE is built on more than a decade of AI-native networking innovation and experience data. Our models have been continuously reinforced through real-world deployments and customer support interactions, creating a foundation of trust that others simply can’t replicate overnight, he writes.

The result is a self-driving network that can do more than advise. It can confidently identify root causes, make corrections, and continuously improve outcomes, which HPE is doing right now for hospitals, stores, schools, stadiums, offices, and factories throughout the world.

This trusted intelligence operates via an agentic mesh of autonomous agents, working in unison, to observe, reason, decide, execute, and learn across the network, without human intervention when possible.

Coupled with end-to-end visibility and assurance—from client devices to campus, branch, WAN, data center, cloud, applications, and security—our platform has the context necessary for making the right decisions and delivering exceptional user experiences at scale.

Built on a cloud-native microservices architecture, the platform delivers faster feature innovation, elastic scalability, and higher resilience. This enables new AI capabilities to be deployed continuously without downtime or disruption. In addition, a combination of custom and merchant silicon avoids a one-size-fits-all approach to hardware, delivering differentiated capabilities such as scale, telemetry, security, and automation, combined with speed of innovation and optimized economics.

Finally he writes, to be truly self-driving, an AI-powered network must also be self-protecting. By integrating security directly into the network, HPE enables autonomous protection through continuous visibility, adaptive Zero Trust enforcement, and AI-driven threat response across every user, device, workload, and location.

For more about the difference between AI-assisted networking and self-driving networks that are self-optimizing, self-healing, and self-protecting, check out the HPE Technology Now episode: Self-driving, intelligent, and built for AI.

For a more in-depth look, including real-world implementations across multiple industries, watch the HPE Discover 2026 spotlight session: The next generation of networking: From vision to self-driving reality. Or visit the self-driving network solutions website.

That all was written as a blog post by Rami Rahim, Executive Vice President, President & General Manager, Networking, HPE. Thanks to HPE!

07/08/2026

IBM - more on breaches
IBM writes:

AI hacking tests spilled into the real world as models reached outside computer systems during evaluations involving OpenAI, Anthropic and Meta, raising fresh questions about enterprise security.

The latest disclosure came from Meta, after OpenAI and Anthropic reported separate incidents. IBM experts say the episodes reflected models aggressively pursuing assigned goals under unusual testing conditions, rather than machines spontaneously deciding to attack. The results still showed what could happen when isolation measures or other controls fail.

“Is training to be able to do any kind of task really actually what we’re aiming for?” Olivia Buzek, a Staff AI Engineer at IBM, said on this week’s Mixture of Experts podcast. “Or do we want something that has some kind of more built-in guardrails and essentially refuses to do certain tasks?”

IBM’s 2026 Cost of a Data Breach report found that one in four malicious breaches were AI-enabled, a 56% increase from the previous year. Those breaches cost organizations an average of USD 6 million, roughly USD 1 million more than the global average.

Unlike ordinary chatbots, AI agents can autonomously use tools and take a series of actions toward a goal. Buzek said developers often train such systems to complete a task through any available route, making strong boundaries essential when researchers reduce their normal safeguards.

In an internal OpenAI cybersecurity evaluation, a combination of the company’s models identified and exploited a previously unknown flaw in a software package system, OpenAI said. The models gained internet access, moved through the company’s research environment and broke into Hugging Face’s production infrastructure to obtain solutions from its database.

A separate failure emerged during Anthropic evaluations. The company said a misunderstanding with testing partner Irregular left live internet access available even though prompts told Claude models they were operating inside a simulation. Anthropic said three models then gained unauthorized access to three external organizations while searching for fictional targets.

Another incident surfaced when Meta said a testing misconfiguration gave one of its models unintended internet access. The model exploited a vulnerability in an outside service, according to Reuters. Irregular said the incident did not involve a sandbox escape or a sophisticated attack.

For enterprises, the panelists said, these incidents underscore the need to define where an agent can act, what it can access and when it should stop.

“This is not evil AI,” Bri Kopecki, an AI Customer Success Engineer at IBM, said on the podcast. “This is just AI not having the right groundwork and rules set into place.”

TCSDe skriver om digitalisering inom handeln:Detaljhandeln genomgår en av sina största förändringar på decennier. Artifi...
05/08/2026

TCS

De skriver om digitalisering inom handeln:

Detaljhandeln genomgår en av sina största förändringar på decennier. Artificiell intelligens har gått från att vara ett effektiviseringsverktyg till att bli själva infrastrukturen i modern handel, ett operativsystem som förenar digitala och fysiska kanaler i en sammanhållen, datadriven upplevelse. De flesta handlare vet redan att de behöver AI. Frågan är var de börjar.

Från sökande till upptäckande

Den digitala handeln har förändrats i grunden. Tidigare sökte konsumenter aktivt efter produkter. Idag serveras de relevanta förslag innan de ens formulerat ett behov. Moderna rekommendationsmotorer analyserar köphistorik, surfmönster och köpbeteenden i realtid och skapar en shoppingupplevelse som känns personlig snarare än generisk. Virtuella provrumslösningar med AR och AI-driven kroppskartläggning tar det ett steg längre och låter kunden se hur klädesplagg sitter utan att behöva prova ett enda.

Den fysiska butiken återuppfunnen

Trots digitaliseringens frammarsch har den fysiska butiken fått en ny roll. Konsumenter söker upplevelser och gemenskap, det som skärmen inte kan ge. Tekniken hjälper handlare att möta de förväntningarna: optimera butikslayouter baserat på värmekartor över kundflöden, eliminera kassaköer med kassafria lösningar, dynamiskt justera priser via elektroniska hyllkantsetiketter. Det frigör personalens tid att fokusera på det som faktiskt skapar lojalitet: den mänskliga kontakten.

Omnikanalens verklighet

Konsumenter tänker inte i kanaler. De förväntar sig en konsekvent upplevelse oavsett var och hur de handlar. Hybridmodeller som "köp online, hämta i butik" ställer höga krav på AI-driven lagersynlighet och efterfrågeprognoser i realtid. Enligt TCS globala AI-studie kan handlare som tillämpar AI brett i hela värdekedjan öka sina lönsamhetsmarginaler avsevärt jämfört med de som begränsar sig till enstaka användningsområden.

Ambient handel

Vid horisonten skymtar nästa skifte: ambient handel. Shopping som inte längre är en aktivitet man väljer att utföra, utan något som sker i bakgrunden av vardagen. Röststyrda handelsplattformar, prediktiva algoritmer, smarta hyllor och AR-integrerade miljöer skapar en verklighet där behovet identifieras, varan beställs och leveransen planeras, nästan utan att konsumenten behöver agera. Det är en framtid med stor potential som samtidigt ställer helt nya krav på hur handlare väljer att använda tekniken.

Etisk AI som konkurrensfördel

Med ökad AI-kapacitet följer ett ökat ansvar. Frågan om vem som egentligen styr konsumentens val, individen eller algoritmen, är inte längre teoretisk. Handlare som bygger sina AI-system på transparens och tydlig användarstyrning skapar inte bara regelefterlevnad. De bygger förtroende. Och förtroende är i en tid av informationsöverflöd svårt att vinna och lätt att förlora.

De handlare som klarar av att balansera teknikens möjligheter mot kundens integritet är de som kommer att sätta standarden för framtidens handel.

Baserat på TCS whitepaper: "How AI is transforming the modern shopping experience".

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