AI News Archive: June 10, 2026 — Part 7
Sourced from 500+ daily AI sources, scored by relevance.
- Siri AI Reactions: My First Time at WWDC
Siri AI Reactions: My First Time at WWDC PCMag UK
Score: 58🌐 MovesJun 10, 2026https://uk.pcmag.com/video/165491/siri-ai-reactions-my-first-time-at-wwdc - A classic brain test exposed AI's biggest weakness
Researchers gave top AI models a classic attention test used in psychology and found a major flaw. While the models could correctly name colors in short lists, their performance deteriorated sharply as the task became longer and more complex. Some leading systems fell from over 90% accuracy to nearly complete failure.
- How Torc hit 90% GPU utilization and other stories on scaling AI with Ray from Discord, Cubist, and Coinbase
How Torc hit 90% GPU utilization and other stories on scaling AI with Ray from Discord, Cubist, and Coinbase
- Moving on from Open CTI: How to Modernize Your Contact Center for the AI Era
Whether you keep your current telephony provider or evolve your entire stack, integrating a partner contact center gives you a flexible path to move into AI-powered service.
- AI usage among white-collar workers and students in Hong Kong: survey findings
AI usage among white-collar workers and students in Hong Kong: survey findings
- Top AI Coding Agents and Development Platforms in 2026: Atoms, Devin, Windsurf, Cursor, Warp, and More Compared
Top AI Coding Agents and Development Platforms in 2026: Atoms, Devin, Windsurf, Cursor, Warp, and More Compared MarkTechPost
Score: 58🌐 MovesJun 10, 2026https://www.marktechpost.com/2026/06/10/ai-coding-agents-development-platforms-2026/ - AI Worsens World’s Trust Issues, Wikipedia Co-Founder Warns
The boom in AI is deepening a crisis in trust across society. The decline is “not perfectly even, but broad,” affecting journalism, politics and business, Jimmy Wales said, adding that it was leading to all kinds of problems.
- ‘AI-pilled’ firms spend $7,500 per employee each month on AI
The most AI-obsessed firms are spending roughly $7,500 monthly per employee on AI, per Ramp AI Index. That's not more than an engineer's salary — yet.
Score: 58🌐 MovesJun 10, 2026https://techcrunch.com/2026/06/10/ai-pilled-firms-spend-7500-per-employee-each-month-on-ai/ - MOZN redefines fraud response from days to minutes with Ai rule builder
MOZN AI Rule Builder brings natural language rule creation into a unified enterprise fraud management solution
- ‘The future’ of Florida campaigns has arrived, and it’s created by AI
‘The future’ of Florida campaigns has arrived, and it’s created by AI Miami Herald
Score: 57🌐 MovesJun 10, 2026https://amp.miamiherald.com/news/politics-government/state-politics/article316074743.html - WWDC 2026 was Apple's AI renaissance — but there's one very important feature still missing from iOS 27
WWDC 2026 was Apple's AI renaissance — but there's one very important feature still missing from iOS 27 Tom's Guide
- Zycus Concludes Agentic AI Procurement Summit 2026; Forrester and Hackett Group Research Released
Zycus Concludes Agentic AI Procurement Summit 2026; Forrester and Hackett Group Research Released USA Today
- London Tech Week day three: Workers are adopting AI quicker than their bosses
How is AI changing the future of the workplace? Russ Shaw reports from day three of London Tech Week.
Score: 57🌐 MovesJun 10, 2026https://www.cityam.com/london-tech-week-day-three-workers-are-adopting-ai-quicker-than-their-bosses/ - AI is helping software engineers do more — and faster. Companies are still waiting for the payoff.
AI is helping software engineers do more — and faster. Companies are still waiting for the payoff. Business Insider
Score: 57🌐 MovesJun 10, 2026https://www.businessinsider.com/companies-waiting-ai-productivity-boom-2026-6 - Automated governance is FinOps’ next frontier as AI spend spirals beyond engineering teams
Artificial intelligence is creating a new class of cloud cost problem — one that no longer belongs exclusively to engineers. As AI tools put spend capabilities into the hands of sales, finance and executive teams, the need for automated governance has become as urgent as the innovation it is meant to protect. The FinOps Foundation’s […] The post Automated governance is FinOps’ next frontier as AI spend spirals beyond engineering teams appeared first on SiliconANGLE .
Score: 57🌐 MovesJun 10, 2026https://siliconangle.com/2026/06/10/automated-governance-controls-cloud-ai-infrastructure-finopsx/ - Artificial Intelligence Sneaks Into the World Cup Thanks to Google Gemini
The Argentine national team will be Google’s test bench and technological showcase during the World Cup.
Score: 56🌐 MovesJun 10, 2026https://www.wired.com/story/artificial-intelligence-sneaks-into-the-world-cup-thanks-to-google-gemini/ - McDonald’s new AI drive-thru has to prove it can handle hungry people
McDonald’s is testing a new Google-backed AI drive-thru system after its earlier ordering bot became a viral mess. The real challenge is whether Archy can make fast-food ordering feel boring again.
Score: 56🌐 MovesJun 10, 2026https://www.digitaltrends.com/computing/mcdonalds-is-giving-ai-drive-thrus-another-shot/ - Why marketers must rethink loyalty as AI reshapes consumer connections
Over half of consumers are comfortable filtering their brand communications entirely through AI, according to research from Gale.
Score: 56🌐 MovesJun 10, 2026https://www.retaildive.com/news/marketers-rethink-loyalty-ai-reshapes-consumer-connections/822305/ - UAE Government holds agentic AI workshop with participation of 50 federal entities
The workshop, organised by the Ministry of Cabinet Affairs in Dubai, aimed to launch the implementation tracks of the new government system project
- How AI orchestration is transforming global supply chains
How AI orchestration is transforming global supply chains Computing UK
Score: 56🌐 MovesJun 10, 2026https://www.computing.co.uk/feature/2026/ai-orchestration-global-supply-chain - Neurovia AI unveils NeuroStream platform at UAE Cybersecurity Summit
Neurovia AI unveils NeuroStream platform at UAE Cybersecurity Summit Gulf News
Score: 56🌐 MovesJun 10, 2026https://gulfnews.com/technology/neurovia-ai-unveils-neurostream-platform-at-uae-cybersecurity-summit-1.500569728 - The hidden backbone of AI: Why the network decides your GPU ROI
By Shekar Ayyar, CEO and Chairman, Arrcus As AI becomes foundational to companies, it’s time to ask some critical questions. Is your AI really scaling intelligence, or inefficiency? The infrastructure […] The post The hidden backbone of AI: Why the network decides your GPU ROI appeared first on Express Computer .
- Sequent: scale and automation for higher confidence in alignment
Alignment is not on track Artificial superintelligence (ASI) may be developed in the next few years. It is unclear whether alignment is on track to be ready on the same timeframe. At a minimum, the empirical programs at AI labs are unlikely to deliver a priori confidence, before training ASI, that things will go well. We are starting a large nonprofit research organization, Sequent, that aims to clear a higher bar: We are aiming at higher confidence via a portfolio of theory and empirics bets, all of which could fail, such that if any succeed, they would give us more a priori confidence in aligned outcomes. We are investing heavily in automation to accelerate progress on these bets. We believe that theory unlocks higher automation. Taking a more principled approach offers better filters for deciding which directions of automated research are promising (a proof is worth a thousand experiments, and even a pseudo-proof is worth hundreds). Who [1] : researchers from the UK AISI’s Alignment Team and Timaeus , with more to come. We’re aiming at 40-80 FTE two years from now. The Alignment Team ran the £30m Alignment Project, and Timaeus has pioneered applying singular learning theory (SLT) to alignment. Founding team: Geoffrey Irving — Chief Scientist at UK AISI; ex-DeepMind, OpenAI, and Google Brain. Daniel Murfet — Head of Research at Timaeus; left tenure to pioneer SLT for alignment. AISI Alignment — Alex Holness-Tofts and Jacob Pfau. Timaeus — Jesse Hoogland, Stan van Wingerden, and Marco Cozzi. Joined by researchers from Timaeus and more researchers from the UK AISI’s Alignment Team Where: a large in-person presence in the Bay Area (Berkeley), as well as researchers working remotely from London, Melbourne, and elsewhere. In this post, we discuss: What it means to aim at higher confidence Why start a new big organization Whether sufficiently fast progress is possible with automated research Aiming at higher confidence In an ideal world, we would develop an approach to building superintelligence together with a theoretical proof that it was safe, and then build it. In this world, we probably have to settle well short of this ideal. However, we believe this doesn’t mean giving up on the idea of leveraging theory to get safety. Our approach to alignment is differentiated from that of the AI labs due to our emphasis on seeking principled reasons for being confident that the alignment we observe in situations we control (for example, in training, or during evaluations in chosen environments) generalizes to alignment in situations we cannot easily control (e.g., large-scale, long-horizon tasks executed in the world). Most AI lab approaches are essentially reactive, resulting in methods that, while functional, do not yield principled insight into if or when they will fail. The “reasons” we have in mind range from a better scientific understanding of the underlying phenomena of deep learning to asymptotic guarantees of safety under specific protocols: Understanding deep learning is an unfinished scientific project. It is worth noticing that if we had a very complete theory of how deep learning works, certain kinds of alignment problems would be trivially solved (e.g., if we could rule out certain kinds of outcomes using an understanding of the training data and optimization process). We are not aiming at a complete understanding, but we think it is important to aim at a better understanding. This can be turned into improvements in how alignment works at the frontier in the near term (e.g., through changes to post-training). An asymptotic guarantee provides assurance that an AI training protocol (e.g., scalable oversight via debate) converges to a tolerably low chance of unsafe behavior, provided some parameters (e.g., training time and amount of data) are made sufficiently large. Such guarantees exist in the theory literature for some reinforcement learning algorithms, but not for neural networks or other classes of learners sufficiently powerful to plausibly reach AGI or ASI. We are not aiming to prove such guarantees for neural networks. We do however aim to prove asymptotic guarantees for alignment while making certain assumptions about the training process. This dovetails with an empirical effort to shift the practice of alignment as close as possible to an approach where we get confidence from these guarantees (even if the hypotheses of the theorems don’t strictly hold). However, we do not know any attacks of either form that are highly likely to succeed individually. Our hope is to build towards that confidence by exploring many different research bets in parallel, using a single organization setting to increase both sharing and amortization of work. Why a new big organization There are a number of existing non-profit organizations pursuing theoretical aspects of AI alignment. However, none of these have yet succeeded in making the transition to affecting how deep learning works at frontier scale, and the challenges to doing so on short timelines (say the next 2-3 years) are immense. Automation can help, but will require significant investment in engineering and working at a scale that is hard for small non-profits. Moreover, the largest gains of automation will likely come from taking a “whole of field” approach where we integrate several deep ideas currently being developed in separated groups. This suggests that there is an opportunity to achieve outsized gains for alignment by bringing excellent research talent together and leveraging it with world-class engineering talent via automation . We believe the best way to realize this opportunity is to found a new organization. AI labs also couple research and engineering excellence, but their empirical strategy leaves promising routes to alignment on the table. This is most true for theoretical ideas and for new empirical approaches based in theory. We see a strong opportunity to attract world-class theorists for at least two reasons: No large theory organizations exist: There are currently no large organizations in AI alignment with a theory focus (although other excellent institutions like ARC and Simplex are also scaling up!) Scientific reputation: Our initial senior scientific leadership has strong reputations for leading such work. Irving played an important early role in establishing today’s primary alignment technique (RLHF) at OpenAI and DeepMind, led teams building the first LLMs at DeepMind and has made multiple theoretical contributions including to scalable oversight via debate. Murfet left tenure in pure mathematics to direct research at Timaeus, one of the few research agendas in alignment to connect pure theory to practical applications at billion-parameter scale. Different lines of research will interact A core reason we are establishing a single organization tackling a portfolio of bets is that we expect different areas of research to interact and mix. Science progresses fastest by high-bandwidth interaction between very talented researchers: people with the right combination of taste, context, and ambition, in the same building, talking regularly. Combining helps on two counts: it surfaces interactions between research areas that are otherwise easy to miss, and it concentrates the small number of people best placed to do this work in one place. Our experience working together between AISI Alignment and Timaeus is a key motivating example, and is one of the driving factors behind our joining forces. Timaeus’s singular learning theory and empirical work provides one route to understanding the dynamics behind neural network learning and generalization, but cannot itself provide the full picture: it might give us the knobs (when to intervene to best influence model behavior) but not the settings for those knobs (what that behavior should be). Scalable oversight can provide training signal as models ramp upwards to superintelligence, but faces obstacles unless we understand the relationship between heuristics the models are likely to learn or not learn. More time together with tight feedback loops will help us map these complements faster! There are other complements! We are excited about many areas of alignment theory and associated empirics, and plan to both build out our in-house portfolio and collaborate with sister orgs with additional theory bets. Some areas we are excited about sharing between, internally and externally: Scalable oversight : complexity theory + empirics of amplification, debate, scientist AI Learning theory : singular learning theory, other deep learning theory, computational mechanics Heuristic arguments : mechanistic understanding of what models know, low-probability estimation Game theory : mechanism design, agent foundations, open-source game theory Personas : theory and empirics of low-dimensional structure within model behavior, across training and token dimensions Example interactions coupling between multiple of these areas include: Reachable equilibria: Learning theory and game theory can tell us what types of equilibria scalable oversight methods will converge to. A method like debate is useless if game-theoretic equilibria are safe but cannot be reached by practical training. Knowing and setting knobs: As above, learning theory and personas can show us the key knobs during training, including what periods of training and dimensions of variation are most critical. Scalable oversight can then say how to spend resources to know how to set those knobs. One area setting problems for another to solve: Heuristic arguments research has been spinning out complexity theory conjectures, which may be resolvable by people or methods from other areas of complexity theory. Prosaic impact from ambitious agendas: Some sophisticated alignment agendas (for example, in agent foundations or heuristic arguments) are framed around long-horizon goals. We expect some of the deep ideas in these agendas can be applied more prosaically on top of existing LLM approaches and be hybridized with scalable oversight or other RL approaches during adoption. These have the form of using partial success in one area to fill a gap left by partial success in another. When we find this kind of gap-filling strategy, sufficient success in either area becomes easier to achieve. The frequency of these interactions is important! If we share ideas every 6 months between different orgs working on different bets, and superintelligence arrives in a few years, we get very few cycles. Amortizing security and funding More crassly, a larger organization provides a larger single target for funding and the ability to amortize security across different research bets. Both of these are critical to high automation: Good security may be required for frontier model access: We are entering a period where some models at the absolute frontier will not be widely available for significant periods after development. Even where the incentives of AI labs and independent research align, AI labs may be unwilling to share such models with independent orgs without significant investment in security, or even entirely unable to share if the AI lab is not the sole decision maker in who gets access. Most expected impact comes from high success at automation, which means lots of tokens: Our goal is to raise $100-150M initially, but prepare to raise at least one order of magnitude more if we can demonstrate successful exploration of many parallel research investigations. We expect it to be easier to raise these funds as a single large organization than a portfolio of smaller orgs. Automated alignment is possible, if not necessarily in time If AGI is possible, then automated alignment research is possible, by definition, though not necessarily before the AIs become misaligned. The question is how to get as much alignment progress from AIs as early as possible in the trajectory from where we are today, to AGI and then ASI. Key to that is knowing the difference between apparent progress and real progress. Two markers suggest that automated alignment research may now be possible. Firstly, since late 2025, we have seen rapid improvement in coding agents, so automated experimentation is now feasible. Secondly, in recent months, we have seen progress in mathematical research being performed by frontier models; the recent settling in the negative of the Erdős unit distance conjecture is a dramatic example. A bet on automated research coupling theory and empirics now seems opportune. However, research takes more than proving clearly stated theorems and running well-specified experiments. It takes judgment to know which theorems to prove (counterintuitively, the hardest part of mathematics may be choosing good definitions!) and which experiments to perform. At present, this tacit knowledge is acquired by humans through long experience and intensive mentorship. To believe in AGI is necessarily to believe that machines can also acquire this tacit knowledge. However, right now, they certainly do not possess it to the same degree as the best human researchers. The near-term challenge for automated alignment research is therefore to Leverage frontier models to do (informal) mathematics at a high level and run experiments, while Building error-correction into the system so that this all amounts to real forward progress This is itself a hard research problem! It is a problem that the new organization is dedicated to solving, as an instrumental goal to progressing the state of alignment towards higher confidence. Part of the solution is to organize ourselves around leveraging the research taste of humans, and to take advantage of the epistemic structure that theory offers to science. Leveraging research taste . As an analogy, consider that in some fields of science we reward early career success by putting some professors in charge of larger labs, thereby leveraging their research taste to achieve faster rates of scientific progress. This is a skill of meta-research taste consisting of recognizing and promoting those with good research taste. It is unclear how to deliberately transfer this from humans to models, but we can at least be purposeful about collecting human experts, exposing them to many opportunities to exercise research (and meta-research) taste in order to achieve the same kind of speedups as we believe occur in the human sciences, and gathering data so that models climb up to higher levels of taste themselves. Taking advantage of theory and empirics together . A prototypical scientific “discovery loop” involves building theory to explain experiments, making predictions with that theory, and then testing them in further experiments. For purposes of automation, the abstract gain here is that iterating between theory and empirics means two different types of filters with which to screen out false progress, which means more success and more parallelism from humans working with models. To emphasize, since our theories and proofs will be toy, even a formal proof will never correspond to full confidence that could not be strengthened with an additional empirical test. A lot of this work will be mundane! Skills and prompts for Codex and Claude Code, MCP tools for a model to check its work against a model from another family, good engineering practices for unit and A/B tests, and workflows that best take advantage of human and AI strengths. We expect to start with automation shaped more like conventional agentic coding than the recent Erdős problem successes: the latter was full automation developed interactively and then run autonomously on a large set of well-defined problems, the former involves iteration between humans and machines gradually building up a large object. However, even with additional structure provided by theory, we are not confident that automation will work in time , even if the AIs involved are not scheming. LLMs make mistakes, and any work that leans on research taste as a guide will mean some mistakes are very hard to catch. Bowkis et al., Automated alignment is harder than you think , discuss some of these worries in detail, and we will be exploring and elaborating more details of how automation could fail in parallel with trying to make it work. Obstacles to automation are another reason to put a portfolio of alignment bets in the same organization: an obstacle noticed in one area may have gone unnoticed in others, and can be addressed broadly. Federated structure to preserve research diversity To preserve what makes small alignment teams succeed — research focus, opinionated leadership, distinct cultures, low coordination overhead — Sequent will have a federated research structure. A handful of research directors will have substantial autonomy over research direction, team culture, and hiring in their research areas. These directors will report up to Geoffrey Irving. Initially, we will be aiming to set up research divisions to cover a subset of the areas listed in the previous section. We would like to emphasize that our set of research areas is not fixed: the final portfolio will depend on the research directors that join us. If you have a promising research area that we have missed, please reach out! The goal for Sequent is to be a home for many different approaches to alignment, sharing the common principles of aiming at higher confidence and taking advantage of automation. Field building and broader alignment scale-up Alignment is a civilizational challenge and requires progress on many fronts. Some problems are best tackled from within the AI labs by those with close access to frontier models. Others are best tackled from academia or by other non-profit or for-profit organizations. In the near term, we expect many existing alignment research organizations to scale up and new ones to be founded. In particular, we believe there is a productive role in this ecosystem for a new large organization, with a focus on theory and automation. However, we acknowledge that at present one of the bottlenecks to deploying funding is the shortage of experienced researchers to be part of founding teams. We may contribute to this shortage by attempting to recruit the same people. We expect to hire experienced researchers across a range of seniority, but do not currently plan to recruit or develop those still early enough in their research careers to need substantial mentorship; we may therefore inadvertently have a negative impact on opportunities for this cohort. This effect would be unwelcome: in an era of successful research automation, the value of novel ideas and new research directions is high . In science, it has historically been the case that healthy fields have strong cross-generational interactions that maintain a high production rate of such ideas. We are interested in ideas for how the field overall can (a) create opportunities (e.g., postdoc-like positions) for younger researchers with their own agendas and (b) how we should interact with them. We are talking to a number of organizations working on alignment theory focused more on field building and human researcher scaleup (not all announced!), and we believe that solid sibling relationships between organizations are part of mitigating this worry. Although we will focus on in-house research, our default will be to publish openly and at a fast pace, subject to dual-use review; this makes cross-org collaborations more fruitful. Where possible, we hope to share automation infrastructure with sibling organizations as well, though how this interacts with security for purposes of full-frontier model access is unclear. Our hope is that formal relationships between different organizations can also mitigate the large-org funding advantage as well: we may explore regranting activities in the future and until then will be strongly advocating for smaller orgs with funders and for smaller orgs to further scale. Independence is important A natural alternative to establishing Sequent as a new organization would be to join an existing AI lab and add to the alignment push from the inside. This removes several challenges (such as access to models, security, and visibility into training tracks). We have chosen to stay independent for at least two reasons: We might need to yell: Our research goals will be a mixture of trying for success and trying to exhibit empirical and theoretical obstacles that demonstrate that alignment is difficult. We will hope for success; if we find only obstacles, it is easier to be loud from the outside. Avoiding the pull to uniformity: We believe that most safety research at AI labs has collapsed into too few total bets, most of which are purely empirical. It may be possible to join a lab with the promise of pursuing alternate approaches, but it can be difficult to escape the draw towards urgent pre-ASI safety work. This is a concern within our organization as well: we will attempt to mitigate this by embedding the portfolio model of research into the culture at the start, and by supporting and collaborating with other independent orgs. That said, we believe there will be many cases where incentives will align between our work and alignment research at AI labs. We are excited to collaborate, both on specific projects and on the general task of uplifting models at theoretical and empirical alignment bets not represented within the companies. Join us! We’ll start an open hiring round soon for the positions listed here . In the meantime, fill in our expression of interest form and you’ll be the first to know. ^ The author order of this post is alphabetical following https://www.cs.princeton.edu/~appel/papers/science.pdf , except that Geoffrey is first this time because a non-established account would require a lengthy screen to first author. Discuss
Score: 56🌐 MovesJun 10, 2026https://www.alignmentforum.org/posts/AP7YDke5jjY4v3X9Z/sequent-scale-and-automation-for-higher-confidence-in-1 - New Tracker Traces AI’s Real-Time Impact on Work
Nela Richardson, Chief Economist at ADP and a Bloomberg contributor, says employers need to decide whether AI is going to be a tool for automating roles away or augmenting tasks to create new value. She discusses the new Canaries Dashboard, launched by ADP and the Stanford Digital Economy Lab, to provide real-time indications of how AI is reshaping different occupations based on labor market data. Richardson joins Caroline Hyde and Ed Ludlow on “Bloomberg Tech.” (Source: Bloomberg)
Score: 55🌐 MovesJun 10, 2026https://www.bloomberg.com/news/videos/2026-06-10/new-tracker-traces-ai-s-real-time-impact-on-work-video - Miguel Torres (Prosegur): “La IA es diferente a todo lo que hemos visto antes; el siguiente paso será la robótica”
> allowfullscreen> Prosegur ha cumplido este 2026 los 50 años de vida. En este medio siglo, la multinacional española de seguridad privada, que tiene más de 180.000 empleados y opera en 36 países de los cinco continentes, ha realizado un viaje transformador impulsado por una apuesta muy clara, sobre todo en los últimos años, por las tecnologías de la información y la digitalización, integrando soluciones avanzadas de inteligencia artificial y ciberseguridad para transformar el negocio de vigilancia y añadir otros nuevos. En la actualidad, de hecho, la compañía se autodefine como una “multinacional digital”, en la que más del 30% de la facturación proviene de servicios de innovación tecnológica. Gran parte de este logro se ha conseguido gracias a Miguel Torres , al frente de la estrategia tecnológica de la compañía desde 2022 y miembro del comité de dirección de la misma desde 2023. Torres es un gran conocedor de una casa en la que trabaja desde hace más de 13 años, al principio muy ligado a la gestión financiera y después a la estrategia de tecnología y ciberseguridad. Fue él quien diseñó e implementó el actual plan estratégico global de tecnología y ciberseguridad de la compañía y quien, como parte del Comité de Riesgos de esta, ha impulsado que la multinacional gestione de forma activa la postura de seguridad global, la resiliencia y el cumplimiento normativo. También fue Torres quien lideró el plan estratégico financiero de la empresa con el fin de transformar la cartera actual de servicios de TI, con el consiguiente aumento del 30% en los nuevos servicios de negocio digital en apenas dos años. Las mejoras de TI aplicadas, dice, han permitido que la multinacional disminuya el gasto de capital hasta en un 20%, logrando, por otro lado, un incremento del 4% en el retorno de la inversión (ROI). Pero cuando aterrizó en Prosegur hace 15 años, todo era muy diferente. “La compañía ha cambiado mucho en este periodo”, explica el directivo a CIO ESPAÑA. Por lo pronto, rememora, entonces la empresa tenía presencia en 11 países, frente a los 36 de hoy. El propio mercado era completamente distinto. “No había grandes centros de datos, ni IA, ni analítica de datos, tampoco RPA (automatización robótica de procesos) y, por supuesto, IA agentiva. Los PC se usaban, pero no eran necesarios para prestar los servicios. Nosotros éramos capaces de prestar los servicios sin tecnología”, añade. En los últimos años, al calor de la intensa transformación digital de todo el mercado, la compañía ha dado un giro de 180 grados con una “apuesta radical por la seguridad híbrida integral. “Porque igual que nosotros hemos adquirido tecnología y la utilizamos para el bien, para proteger a las personas, otros la utilizan para el mal”. El reto de liderar la innovación en un escenario complejo e hiperconectado El escenario en el que opera en la actualidad Prosegur es extremadamente complicado: un mundo en el que convergen lo físico y lo digital, con fuertes riesgos de ciberseguridad agravados por un panorama geopolítico tensionado y la aparición de una serie de tecnologías tan disruptivas como los últimos sabores generativos y agentivos de la inteligencia artificial. “El contexto es complejo”, reconoce Torres. “La realidad hoy es que todo el mundo está conectado y todo lo hacemos con el móvil y de forma digital, desde consultar el banco hasta hacer la compra. El temor que existe en el mercado es que la tecnología falle, más en un contexto de guerra y geopolítico complejo. Otro temor es la subida de precios, porque la tecnología cada vez cuesta más, pero nosotros tenemos que garantizar que los clientes reciban los mismos servicios por lo que nos los contrataron. Por este motivo, debemos mirar con mucha perspectiva lo que va a pasar. Todas las decisiones de tecnología deben contemplar la realidad de lo que ocurre en planeta”. “Innovación, escalabilidad en costes y ciberseguridad son los tres pilares sobre los que definimos la estrategia de tecnología y ciberseguridad de Prosegur” “Innovación, escalabilidad en costes y ciberseguridad son los tres pilares sobre los que definimos la estrategia de tecnología y ciberseguridad de Prosegur y en los que pensamos siempre que vamos a desplegar proyectos, planes o programas de tecnología”, indica el CIO. El primero, la innovación, aplica tanto externa como internamente. “Innovamos usando tecnología para mejorar la experiencia del cliente, pero también para ser más eficientes en la prestación de los servicios. Y aquí es fundamental ser más rápidos”, cuenta el portavoz. En segundo lugar, el opex y el capex de la tecnología “son relevantes en las cuentas resultados de la compañía y, por ende, en las que paga el cliente. Por ello, hay que cuidarlos y estar seguros de que al cliente nuestros servicios le añaden valor y están dispuestos a pagar por ellos”. Y, finalmente, respecto a la ciberseguridad como última gran palanca de la compañía, Torres reitera: “En un mundo tan conectado digitalmente y dotado de tanta tecnología como es el nuestro, debemos cuidarnos a nosotros, a nuestros clientes y a la sociedad; no hay que olvidar que somos parte de la cadena de valor en muchos países”. Garpress | Foundry La IA, en el foco Preguntado por cuáles son los grandes proyectos de TI que Prosegur tiene en marcha actualmente, Torres no duda: “Sin duda, uno es la inteligencia artificial”. De hecho, la firma está ahora inmersa en el despliegue de soluciones tecnológicas de agentes autónomos e IA como GenIA y LexIA, orientados a optimizar las capacidades operativas y la gestión de procesos. En realidad, explica Torres, la multinacional trabaja en IA desde hace muchos años, al menos una década. Primero con el más tradicional machine learning que, afirma el CIO, les ayudó especialmente durante la pandemia en la modernización de servicios y el análisis de datos. Pero hace dos años, la compañía decidió adoptar los últimos sabores de la inteligencia artificial “aunque empezamos con mucha cautela”, agrega. “Fue entonces cuando decidimos definir una estrategia a largo plazo para la adopción de la inteligencia artificial”. El primer punto de esta, lo que en Prosegur llaman la ‘fórmula de la IA’, subraya la importancia de buscar casos de negocio. “Porque sin un caso de negocio el uso de la IA nos puede llevar a un destino al que no queremos llegar”. El segundo punto de la estrategia, añade Torres, se enfoca en las habilidades y competencias de las personas. “Porque la IA, como todas las tecnologías que han surgido antes y surgirán después, es una herramienta que, per se, no va a transformar una compañía: lo harán las personas que sepan usarla y la apliquen tanto al desarrollo de código de programación como a la identificación y automatización de procesos internos o externos o, simplemente, en sus labores del día a día, al poder hacer las cosas más rápido”. La estrategia de IA de Prosegur contempla una tercera pata: la propia tecnología. Según el CIO, es importante contar con una estrategia al respecto, por lo “abrumador” que es el hecho de que cada semana aparezcan nuevos modelos y muchos más usuarios de estas soluciones. Y en este punto, subraya, urge ser “cautos” y “tener claro quiénes pueden ser tus socios de tecnología”. “Es importante seleccionar a socios de tecnología que tengan una perspectiva a largo plazo de lo que puede proveer la IA, que entiendan tu historial como compañía, y atiendan a si ésta está cotizada o no, que sean responsables con el uso tecnológico… Ahora están surgiendo muchas nuevas compañías gestionadas por inversores, que, no digo que esto esté mal, sino que, simplemente hay que tener en cuenta que los valores por los que se rigen están en línea con los de tu empresa”. Se trata, en definitiva, de “evaluar a quién quieres tener como compañero de viaje en la tecnología que tienes que desplegar”. Por otro lado, añade Torres, a la hora de adoptar la IA es importante atender al gobierno de esta tecnología y al cumplimiento de la regulación asociada para “utilizar la IA de forma responsable, con unas políticas que aseguren que no hay fuga ni de nuestra información ni de la de nuestros clientes, y que sea una IA que no tenga sesgos. Para ello, es importante, al inicio de la adopción, aplicar políticas de control y de seguridad en IA”. Garpress | Foundry “Sin un caso de negocio el uso de la IA nos puede llevar a un destino al que no queremos llegar” Esta, asevera el CIO, es la estrategia en IA que ha aplicado la compañía en los últimos dos años, una tecnología que, “ahora está en un momento muy interesante porque ya es suficientemente madura para utilizarla”. Y, agrega, gracias a la citada estrategia, la empresa está preparada para contestar a cuestiones como: “¿Cómo va a ser esta tecnología dentro de tres años? ¿Qué vendor lock-in me puede generar? ¿Qué precio voy a pagar? ¿Voy a poder repercutir este al cliente si no tiene un caso de negocio? ¿Dónde se aplica, en un grupo de tres usuarios o en un grupo a escala? ¿Y cómo la voy a escalar? ¿Cómo va a entrar en el servicio del cliente? ¿Cómo va a entrar en el pricing de clientes? ¿Tiene que tener un business case?… Toda una serie de preguntas que siempre afrontamos antes del despliegue de la inteligencia artificial”. Un punto de inflexión Para Torres, la IA es una tecnología claramente disruptiva. “La clave está en su aplicación. A nivel personal, y hablando en general, nos ayudaría mucho que se aplicara a la investigación, la salud y medicina. El resto de las empresas de otros segmentos la usaremos para otras cosas. En nuestro caso, para hacer del mundo un lugar más seguro, que es nuestra misión como compañía”. En todo caso, subraya el directivo, “el uso de la IA siempre requerirá la supervisión humana, pero está claro que agilizará los procesos, algo fundamental en el mundo moderno, y nos permitirá anticiparnos con mayor precisión. En Prosegur, donde tratamos incidentes y siniestros y protegemos a los clientes, hay situaciones que, evidentemente, nos gustaría evitar. Hoy lo hacemos con las capacidades que tenemos tanto a nivel de personas como de tecnología, pero creo que la IA va a aportar y ayudar en este sentido, sobre todo a identificar estas situaciones”. La próxima gran disrupción, avanza el CIO, vendrá de la robótica. “La IA es diferente a todo lo que hemos visto antes; el siguiente paso será la robótica”. De hecho, recuerda Torres, Prosegur ya ha dado pasos al respecto con Yellow, un robot-perro dotado de inteligencia artificial que utiliza para garantizar la protección de las personas en eventos masivos como el torneo de tenis Mutua Madrid Open. “Este tipo de tecnologías serán muy potentes en nuestra industria, pues ayudan a llegar a lugares donde no podemos o donde, por razones de seguridad, preferimos no exponer a las personas”. Obviamente la propia área de TI de las empresas es otro espacio donde la IA impactará (ya lo hace) de forma clara. Los profesionales de TI, según Torres, están experimentando dos grandes cambios: “Por un lado, debemos entender mejor la estrategia de negocio de la compañía para aplicar mejor la tecnología; por otro, tenemos que estar constantemente actualizados y entender lo que ocurre en la industria tecnológica y en el mundo, pues esto puede impactar en los planes que queramos acometer el próximo año”. En el campo del desarrollo de software, dice el experto, ocurre igual. “Con la IA hay un antes y un después en este campo, que tendrá que adaptarse a la nueva realidad. El rol del desarrollador de software va a ser distinto”. De todas formas, recuerda, “los profesionales de tecnología llevamos viviendo este tipo de cambios, aunque con menos velocidad, eso es cierto, desde hace, al menos, 20 años, luego estamos acostumbrados a ellos”. Apuesta por la nube híbrida con la vista en la soberanía Antes de que llegara la fiebre en torno a la IA, en la industria de las TI empresariales hubo otra revolución ligada a una nueva forma de suministrar los recursos de computación: cloud computing que, con los años, se ha convertido en una propuesta clave para que las organizaciones desarrollen, de hecho, los actuales proyectos de IA a escala. Prosegur empezó este viaje a la nube, por el que fue premiado en 2023 en los CIO Awards, hace casi una década. “Hace ocho años empezamos a pensar si la infraestructura base que teníamos iba a soportar la capacidad de despliegue del negocio. Fue en ese momento cuando nos planteamos que teníamos que modernizar esa tecnología base: los centros de datos”, rememora el CIO. El equipo de TI, cuenta Torres, empezó entonces a medir la obsolescencia tecnológica y a entender cómo sería ésta proyectada a cinco o diez años. “Y allí entró la propuesta de migrar a cloud , porque entendimos que la nube nos daría unas capacidades de tecnología que no podían dar los centros de datos convencionales. Por ejemplo, nos ha permitido no tener que preocuparnos de la obsolescencia [de la infraestructura] y poder habilitar el uso de la inteligencia artificial. Además, en un contexto de cibercrimen creciente, cloud nos ha dado unas capacidades de resiliencia muy potentes”. “Cloud’ nos ha permitido no tener que preocuparnos de la obsolescencia [de la infraestructura] y poder habilitar el uso de la inteligencia artificial” Preguntado por si le preocupa la soberanía del dato, el CIO de Prosegur reflexiona: “Nos preocupa porque les preocupa a nuestros clientes y también a Estados, Gobiernos, regiones… Es un tema, además, que se discute mucho en la Unión Europea porque ¿qué pasaría si en Estados Unidos deciden adoptar una estrategia, presionar el botón y apagar? ¿Qué capacidades de tecnología tenemos? Sí, es un asunto al que hay que prestar atención. Pero aquí vuelvo a la estrategia de negocio. Tienes que entender mucho a tus clientes y saber lo que necesitan y cómo vas a poder responder a un escenario de ese tipo. Es algo en lo que estamos trabajando. Somos una empresa española con desarrollos propios españoles capaces de ser, digamos, soberana. Estamos trabajando en tener infraestructuras que nos permitan esto”. En este punto, Torres reconoce que “Prosegur no es una empresa al 100% cloud, sino al 50% y, además, la estrategia es multicloud, luego estamos preparados para ese tipo de escenarios y necesidades”. Garpress | Foundry Un CIO multipaís que habla el lenguaje del negocio Liderar las TI de un gigante presente en 36 países de todo el mundo es un reto descomunal. “Afortunadamente tenemos un buen equipo de TI. Contamos con una capa corporativa que se preocupa de lo que es transversal y, después, tenemos direcciones de sistemas y productos por cada dirección de negocio; esto nos ha permitido poder atender todos los 36 países sin ningún problema”, explica Torres. Tener una visión muy pegada al negocio, heredada de su responsabilidad anterior como director financiero global de la compañía, marca de forma definitiva el tipo de gestión de Torres. El directivo agradece tener ese background : “Permite entender al cliente y saber lo que quiere, saber también lo que necesita la operación para prestar servicio al cliente y lo que precisa el equipo comercial para poner en valor nuestros servicios”, explica. Por otro lado, añade, venir de finanzas facilita pensar “de forma natural en cómo encaja la estrategia de TI en el plan económico-financiero de la compañía y te equipa con una serie de preguntas para entender mejor a tus stakeholders ”. Porque, agrega Torres, el CIO y su equipo no hacen los proyectos de tecnología de forma aislada: “Se discuten en el comité de dirección”. Para ello, añade, hablar el mismo idioma del resto de directivos, el del negocio, es clave. “Siempre he dicho que en TI hablamos idioma galáctico mientras que el resto de la compañía habla en terrícola; y las decisiones se toman en terrícola. Yo, gracias a mi pasado, soy bilingüe”. No obstante, reconoce, los directivos de negocio ya empiezan a conocer mucho mejor el lenguaje de la tecnología y a tomar decisiones de este tipo. En la propia Prosegur se ha preparado a los ejecutivos de negocio para ello realizando sesiones de concienciación en tecnología. En este escenario, concluye Torres, el del CIO “debe ser un rol de Pepito Grillo, de guía que ayude a los directivos de negocio a tomar decisiones, a entender los riesgos que implica la tecnología, a evitar el vendor lock-in o incurrir en una inversión que después no se pueda pagar. Nuestro rol es el de acompañarlos. Vamos a ser más necesarios que nunca”.
- Scaling AI Through Data Fluency
Aviation is one of the most data-intensive industries on the planet. Every flight...
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Volvo Trucks will launch unattended over-the-air software updates later this year, allowing fleets to update parked trucks while drivers are away. The post Volvo Trucks unattended over-the-air updates launching later this year appeared first on FreightWaves .
Score: 55🌐 MovesJun 10, 2026https://www.freightwaves.com/news/volvo-trucks-unattended-over-the-air-updates - Why old EV batteries still go to waste—and how AI could change that
Even though retired lithium-ion batteries often retain up to 80% of their capacity, significant economic value and climate benefits are currently being lost. A new article in Nature Reviews Clean Technology, involving researchers from Chalmers University of Technology, shows that the lack of reliable battery data is the main barrier to efficient battery reuse and recycling.
- Apple’s new Siri AI knows when to shut up
Apple's new Siri AI is finally here, and so far, it seems like it works. I have access and have been messing around with it, and my biggest impression so far is that Siri AI is quite curt - which I mean as a compliment. Many AI chatbots are cheery and wordy. While a more […]
- How Visa is designing smarter credit cards for AI shopping
For years, predictions about the future of commerce have often ended the same way: Credit cards eventually disappear. Digital wallets would replace them. Cryptocurrencies would replace them. Buy now, pay later services would replace them. More recently, artificial intelligence has been cast as the next disruptor, with AI agents expected to handle everything from product discovery to checkout. If an AI assistant can compare prices, find the best deal, and complete a purchase on your behalf, what role is left for a traditional payment network? Visa thinks the answer is simple: a very big one. At Visa Payments Forum 2026 this week, the company unveiled a slate of new AI, tokenization, and stablecoin initiatives designed to ensure that as AI transforms commerce, Visa cards remain the preferred way to pay. In fact, the company is making a bet that the AI shopping boom could strengthen the role of credit cards, rather than weaken it. AI is helping people shop—but it isn’t buying much yet The vision of AI agents handling purchases for consumers has generated enormous excitement across the technology industry. But Visa says there’s a significant gap between consumers’ using AI to research products and actually allowing AI to spend money. “I was literally in a Visa office overseas, a few months ago, and I asked for a show of hands. Tell me, everybody, if you’ve used AI to help you shop. Every single hand in the room goes up,” Jack Forestell, Visa’s chief product and strategy officer, tells Fast Company . “Tell me if you’ve used AI to actually check out. No hands go up. And this is a room full of payments nerds.” Forestell said Visa identified the potential for AI-powered shopping more than a year ago as search tools became increasingly conversational and capable of helping consumers reach decisions faster. “We really got focused on this whole notion of agentic commerce, or AI-assisted commerce, more than a year ago,” he said. “Our hypothesis was that ultimately that would end in agents actually not just helping with the discovery process but helping with the checkout process and post-transaction process itself.” The problem isn’t that AI can’t help people shop. It’s that consumers aren’t yet convinced they should trust AI with their wallets. The real barrier is trust According to Forestell, technology is only part of the equation. Consumer trust remains one of the biggest obstacles preventing AI-powered commerce from becoming mainstream. “If you were to have someone who you hardly knew go buy something for you, and you gave them access to your money to do it, like, how comfortable would you be with that value proposition?” he said. “I’d say you probably go with a no on that one.” That hesitation helps explain why Visa sees an opportunity rather than a threat. The company isn’t trying to replace the card ecosystem for AI transactions. Instead, it’s building technology designed to make AI purchases feel as secure and protected as traditional card transactions. “I think the consumers are going to want to understand that they are in control and they’re going to want to understand that they’re protected,” Forestell said. Consumers already know that if something goes wrong with a purchase, they have recourse through their card issuer and the payment network. Visa believes those protections become even more important when software agents are making transactions. “I need to be in control, even though I’ve actually granted some autonomy to this machine to actually execute the payment,” Forestell said. “And I want to make sure that if either the machine messes up or the merchant on the other side of that messes up, you still have my back.” Why Visa is making credit cards smarter Much of Visa’s latest announcement centers on a technology consumers rarely think about: payment tokens. Today, many online transactions use tokens instead of card numbers. These digital credentials help protect payment information while allowing transactions to flow through existing card networks. Visa is now adding more information to those tokens, including data about who initiated a transaction, where it originated, and whether an AI agent was involved. Forestell says that additional context is critical if AI agents are going to make purchases safely. “It doesn’t necessarily tell you, oh, this was a purchase that was made using an AI agent, this is the identity of that AI agent, this is the trust score or the assurance score of that AI agent,” he said. “Those are the kinds of variables that we’re adding to tokenization.” The goal is to help banks better understand what’s happening behind a transaction, improving fraud detection while reducing false declines. For consumers, that could mean fewer situations where legitimate purchases are mistakenly flagged while maintaining strong protections against fraud. AI shopping still needs a payment system Many discussions about AI commerce assume entirely new payment rails will emerge. Visa is betting otherwise. The company announced a partnership with OpenAI that would allow AI agents to initiate Visa payments within user-defined permissions and controls. It is also launching an agentic directory, a registry of verified merchants and AI agents, along with tools that help merchants determine whether their websites are prepared for AI-driven shopping. Taken together, the initiatives suggest Visa wants to become the trust layer underneath agentic commerce. The strategy makes sense, because AI agents still need a way to pay. Every transaction requires authorization. Every transaction needs fraud controls. Every transaction may require dispute resolution if something goes wrong. Those are areas where traditional payment networks already operate at massive scale. The future may look familiar Forestell believes some of the earliest AI shopping will involve routine purchases consumers don’t particularly enjoy making themselves. “We’re seeing definitely some comfort” around AI handling “utilitarian, low- emotion, high-frequency, and low-value purchases,” he said. Travel is another area where AI could gain traction. “We’ve had travel agents for 100-plus years for a good reason,” Forestell said. “Travel can be a very complicated process of planning and discovery and research.” In both cases, however, the transaction still needs a trusted payment credential behind it. That’s why Visa doesn’t appear worried that AI will make cards irrelevant. Quite the opposite—while artificial intelligence may dramatically change how consumers shop, discover products, and make decisions, Visa is positioning itself so that the payment still flows through the same infrastructure consumers already know and trust. The future of shopping may be powered by AI. But if Visa gets its way, the future of paying for it will still look a lot like a credit card.
- Walmart, Wing add 7 markets to drone delivery expansion plan
The companies aim to start operations in Philadelphia, Phoenix and other metros by 2027 as the retailer advances its fast delivery push.
Score: 55🌐 MovesJun 10, 2026https://www.retaildive.com/news/walmart-wing-add-7-markets-to-drone-delivery-expansion-plan/822380/ - Four in Five Business Leaders Expect Permanent Disruption as AI, Finds DMCC Future of Trade Report
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Score: 55🌐 MovesJun 10, 2026https://www.pcmag.com/news/this-overlooked-wwdc-feature-could-transform-smart-homes-forever - The Hidden Cost of AI Search That Business Leaders Are Missing
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Score: 55🌐 MovesJun 10, 2026https://www.inc.com/vaclav-vincalek/the-hidden-cost-of-ai-search-that-business-leaders-are-missing/91355916 - Rubrik Now Available as AI Agent
Rubrik the Security and AI Operations Company, today announced the launch of Rubrik AI, which transforms its platform with agentic-first experiences to operate at AI speed. Rubrik AI adapts to each organization’s context and security threats and autonomously acts at machine speed across Rubrik’s product portfolio. From day one, Rubrik built an API-first platform to provide […] The post Rubrik Now Available as AI Agent appeared first on CXOToday.com .
- Interview: Pegasystems’ Don Schuerman on how to keep the lid on skyrocketing AI costs
The rising cost of using large language models (LLMs) is now giving enterprises pause for thought. As artificial intelligence (AI) models have become more sophisticated, queries are costing businesses significantly more in “ tokens ” and, in some cases, ratcheting up disastrously large bills. And their inherent variability means it is no longer possible to predict how much each task will cost. The same prompt one day could produce an instant response, but an almost identical prompt on another day could take five minutes and burn through 10% of your monthly token budget, says Don Schuerman , chief technology officer (CTO) at Pegasystems. Enterprises have incentivised employees to maximise their use of AI without fully considering the benefits that it produces for the organisation. Such “ tokenmaxxing ” has left companies with unexpected bills. Last month, for example, Axios reported that a single unnamed enterprise spent over £500m on AI tokens for Anthropic’s Claud AI platform in one month after failing to put a cap on its employees’ IT use. And in April, Uber’s CTO disclosed that the minicab and delivery service had burned through its entire budget for Claude Code for 2026 in the first three months of the year. AI token cost rising Commercial pressures to fund AI datacentres and increasing energy costs have led AI suppliers to raise their prices in recent months, leading organisations to question where the value of spending on AI lies. In some cases, Schuerman says companies have replaced people with AI only to realise that AI is costing them more than the people they replaced. Last week, Mary Daly , president and CEO of the Federal Reserve Bank of San Francisco, put it succinctly in an interview with Bloomberg . “Productivity growth is everywhere except in the data,” she said. “What’s happened is the models have gotten more sophisticated,” Schuerman tells Computer Weekly. “The model reasons with itself, sometimes it dispatches other agents to do other things, and as it does that, it’s continuously running the token meter.” Enterprises waking up to AI costs He argues that enterprises are waking up to the fact that the cost of AI does not increase linearly with the number of calculations the model makes. “Every step is adding quadratically to the cost of the process,” he says. If you use AI to work out a business process, the first step might take 500 tokens, but the context of the first step will need to be carried over, so the second step will require 1,000 tokens. The third step will use 1,500 tokens, and so on, says Schuerman. As calculations become more complex and require more context, not only does the cost increase, but the risk of AI hallucinating or behaving unpredictably also increases. “The best possible use of AI is to help me get that repeatable process right – help me define it, help me design it, help me ensure it follows best practices. And it turns out I don’t need much AI for that” Don Schuerman, Pegasystems Pegasystems’ answer to keeping the costs of AI under control is to use the technology in a more strategic way. Pega supplies Fortune 500 companies with low-code platforms to automate their business processes and manage relationships with their customers. This week, the company announced that it would charge its customers for business outcomes, rather than charging them for how many AI tokens they use. Schuerman argues that at least 60% to 70% of the high-volume mission-critical processes enterprises need can be automated using rules-based approaches. “The best possible use of AI is to help me get that repeatable process right – help me define it, help me design it, help me ensure it follows best practices,” he says. “And it turns out I don’t need much AI for that.” Designing workflows Pega’s Blueprint software , for example, uses AI to help people design automated workflows for their organisations. Because AI does the bulk of its work at the point of design, AI agents don’t have to rethink the process from scratch each time the process runs. The workflows can, however, call AI agents to execute specific tasks – summarising a document or seeking input from a human, for example – giving enterprises the ability to use AI reasoning in a controlled way. Pega has now made its automated workflows compatible with the open source Model Context Protocol (MCP). This means companies can give AI agents built on other platforms – such as Anthropic Claude, Google Gemini, OpenAI, AWS AgentCore, and others – access to Pega’s processes. Schuerman says that for Pega, adding MCP was a “relatively light lift”. Pega is agnostic about how organisations access its workflows. Organisations can already access Pega workflows through other software, such as Salesforce, for example, without having to use Pega as a front end. But for businesses, the change is huge, he says. They can take their existing AI agents and use them to follow Pega’s workflows. “Once I’ve connected in the Pega MCP, that agent is going to follow the rules, it’s going to do it without excessive reasoning [by making] that relatively simple call to find the right workflow and finding the skill,” says Schuerman. From banks to pizza Companies, including the Dutch bank Rabobank, are using MCP calls to convert chatbots into intelligent agents that complete tasks for customers, such as checking an account balance or potentially making a payment. Blueprint has opened the door for more companies to become Pega customers, more quickly. In the past, it might have taken several years for Pega to develop prototype workflows and for companies to decide whether to use Pega software. “It often took a long set of conversations and really skilled salespeople to help the customer understand how their business problem fit into our platform,” he says. With Blueprint, the software can take a description of what they do as a business, scan material from the company website, and within minutes design workflows that make sense to the business. Schuerman won’t put any numbers on it, but says Pega has made a conscious decision to sell its platform to a broader range of companies, partly directly and partly working through business partners. Previously, Pega specialised in supplying its platforms to highly regulated industries such as banks, insurance companies and healthcare. But now it is seeing opportunities from companies like Papa John’s, the pizza chain, which was seen among the attendees of Pega’s annual conference. AI can’t create the extraordinary Schuerman is adamant that AI is not going to replace human creativity as enterprises become more automated. Unlike people, AI does not create things that are extraordinary or show genuine creativity. “It just averages everything else that it has read in the past,” he says. “What we want to do is continually compress the time from an idea to that idea making a meaningful change in how my employees work and how customers engage with us.” That leaves people free to focus more on strategy, ideas and creativity. There is too much focus on the next version of ChatGPT or Claude, when last year’s version was just fine, according to Schuerman. “The hard work is in turning all this AI potential into realities for people, and that’s where I think the interesting work is,” he says. Read more about Pegasystems Vodafone Greece automates deals for customers, saves 500 staff-days of work : Vodafone Greece hired an implementation partner for a business process management project while its own staff observed and learned how to use the technology. Wells Fargo bank turns to AI to help families settle estates after a death : Wells Fargo bank is winning customers after using business automation software and artificial intelligence to help people manage the estates of relatives following a bereavement. Citi US Personal Banking turns to AI to ‘delight’ customers with personalised services: Citigroup’s US Personal Banking business has created a repository of customer data and is rolling out a decision engine. Bupa turns to data to provide personalised health services : Private healthcare provider Bupa says a project to deploy business process automation is bringing it closer to APAC customers. Pegasystems refines Blueprint agent builder, expands marketing tools : Pegasystems emphasises ‘derisking’ agentic buildouts for its customers in regulated industries.
- 5 Things CHROs Should Know About CIOs to Drive AI Transformation Together
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- AI costs rising for businesses: report
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Score: 55🌐 MovesJun 10, 2026https://www.semafor.com/article/06/10/2026/ai-costs-rising-for-businesses-report - OpenAI and Nvidia CEOs are paying up for H-1B visas, and their application numbers are soaring
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Score: 55🌐 MovesJun 10, 2026https://fortune.com/2026/06/10/sam-altman-jensen-huang-h1b-visa-fee-application-numbers-trump-lawsuit/ - AI Is Not Safe Yet, Says UCLA Professor
As AI giants Anthropic and OpenAI prepare for IPOs, the companies are stressing their efforts to ensure the tech is safe and beneficial for humanity. But Safiya Noble, a UCLA professor and director of the university’s Center on Resilience & Digital Justice, says current AI is not safe, and that stereotypes and biases are being built into training data. Noble joins Caroline Hyde and Ed Ludlow on “Bloomberg Tech." (Source: Bloomberg)
Score: 55🌐 MovesJun 10, 2026https://www.bloomberg.com/news/videos/2026-06-10/ai-is-not-safe-yet-says-ucla-professor-video - Capsa AI raises $18M to expand its AI platform for private capital
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Score: 55💰 MoneyJun 10, 2026https://tech.eu/2026/06/10/capsa-ai-raises-18m-to-expand-its-ai-platform-for-private-capital/ - Memory and personalization make AI more likely to tell you what you want to hear
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Score: 55🌐 MovesJun 10, 2026https://pandaily.com/chinese-humanoid-robot-influencers-unitree-overseas-jun2026 - Gemini Canvas helped Paris Hilton turn an idea into an app, and she didn’t write a line of code
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Swiss users of iPhones and iPads will be given access to the new version of the Siri assistant software with artificial intelligence, despite the product not being launched in the European Union. +Get the most important news from Switzerland in your inbox The blocking of Siri AI only affects the 27 EU countries, an Apple spokesperson told the news agency AWP. "So Switzerland is not included," the spokesperson said. + Can Switzerland live without Big Tech? We put it to the test Siri AI will be available as a beta version for Swiss users later this year when the language is set to English. "Apple will quickly expand support for other languages," the spokesperson added. However, this will require a device from the latest few generations. Siri AI runs on the iPhone 15 Pro and iPhone 15 Pro Max or newer. For tablets, people must have an iPad mini (A17 Pro) or an iPad with M1 or newer. This means that Swiss users are better off than EU citizens. Apple has decided not to make Siri AI ...
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Score: 54🌐 MovesJun 10, 2026https://www.barrons.com/articles/super-micro-smci-stock-sale-ai-300d4a4e - New research shows honesty about AI use at work is backfiring
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Score: 53🌐 MovesJun 10, 2026https://www.atlassian.com/blog/ai-at-work/new-research-shows-honesty-about-ai-use-at-work-is-backfiring - AI can do a lot, including slow down PE exits
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Score: 53🌐 MovesJun 10, 2026https://pitchbook.com/news/articles/ai-can-do-a-lot-including-slow-down-pe-exits