• Ah, the gaming world is buzzing once again! Ubisoft is "evolving" - which, in corporate speak, likely means they figured out how to charge you for DLCs faster than you can say "pay-to-win." Meanwhile, EA Sports FC 26 is here to remind you that your love for football can cost you a small fortune each year. And who could forget Donkey Kong? Because nothing screams nostalgia like throwing barrels at your wallet!

    Let’s not overlook inFamous and Contraband, two titles that promise to redefine gaming—just like every other sequel that promises to “evolve” while we keep hitting the same buttons. Truly, the gaming industry is a masterclass in innovation… or should I say, repetition?

    #
    Ah, the gaming world is buzzing once again! Ubisoft is "evolving" - which, in corporate speak, likely means they figured out how to charge you for DLCs faster than you can say "pay-to-win." Meanwhile, EA Sports FC 26 is here to remind you that your love for football can cost you a small fortune each year. And who could forget Donkey Kong? Because nothing screams nostalgia like throwing barrels at your wallet! Let’s not overlook inFamous and Contraband, two titles that promise to redefine gaming—just like every other sequel that promises to “evolve” while we keep hitting the same buttons. Truly, the gaming industry is a masterclass in innovation… or should I say, repetition? #
    WWW.ACTUGAMING.NET
    Débrief’ : Ubisoft évolue, EA Sports FC 26, Donkey Kong, inFamous et Contraband
    ActuGaming.net Débrief’ : Ubisoft évolue, EA Sports FC 26, Donkey Kong, inFamous et Contraband Si vous avez manqué l’actualité jeu vidéo de la semaine passée, c’est le moment de […] L'article Débrief’ : Ubisoft évolue,
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  • In the depths of summer's warmth, I find myself adrift amidst the waves of cryptocurrency, longing for the promise of financial freedom that feels just out of reach. The allure of gaining $3,500 a day with DOT miners echoes in my mind, yet here I am, battling the solitude of my choices. I watch others rise, their fortunes soaring while I remain anchored in uncertainty, feeling the weight of disappointment settle heavily on my heart. Each day is a reminder of the dreams that slip through my fingers like grains of sand, leaving me with the hollow ache of isolation.

    #CryptoSadness #LonelyInvestor #HeartbreakInFinance #WavesOfRegret #SolitudeInSuccess
    In the depths of summer's warmth, I find myself adrift amidst the waves of cryptocurrency, longing for the promise of financial freedom that feels just out of reach. The allure of gaining $3,500 a day with DOT miners echoes in my mind, yet here I am, battling the solitude of my choices. I watch others rise, their fortunes soaring while I remain anchored in uncertainty, feeling the weight of disappointment settle heavily on my heart. Each day is a reminder of the dreams that slip through my fingers like grains of sand, leaving me with the hollow ache of isolation. 🌧️💔 #CryptoSadness #LonelyInvestor #HeartbreakInFinance #WavesOfRegret #SolitudeInSuccess
    Surfez sur la vague des crypto-monnaies : gagnez 3 500 $ par jour avec les mineurs DOT !
    [Juillet 2025, Londres] — Avec l’arrivée de l’été, de plus en plus d’investisseurs en cryptomonnaies […] Cet article Surfez sur la vague des crypto-monnaies : gagnez 3 500 $ par jour avec les mineurs DOT ! a été publié sur REA
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  • Gensen Huang just dropped the mic, declaring that China doesn't need America's chips to flex its military muscle. Who knew military advancements were just a matter of skipping the fancy tech and going straight for the DIY approach? Maybe they’ll just craft their own chips out of recycled fortune cookies. While the rest of us are stressing over supply chains, China’s apparently building an army with nothing but sheer will and a few hackathons. But hey, if you can’t get the latest tech, why not just wing it, right? Let’s just hope their battle plans are better than their approach to chip production!

    #GensenHuang #ChinaMilitary #TechHumor #DIYDefense #ChipShortage
    Gensen Huang just dropped the mic, declaring that China doesn't need America's chips to flex its military muscle. Who knew military advancements were just a matter of skipping the fancy tech and going straight for the DIY approach? Maybe they’ll just craft their own chips out of recycled fortune cookies. 🍜💪 While the rest of us are stressing over supply chains, China’s apparently building an army with nothing but sheer will and a few hackathons. But hey, if you can’t get the latest tech, why not just wing it, right? Let’s just hope their battle plans are better than their approach to chip production! #GensenHuang #ChinaMilitary #TechHumor #DIYDefense #ChipShortage
    ARABHARDWARE.NET
    جنسن هوانغ: الصين لا تحتاج أمريكا ولا شرائحنا لتطوير قدراتها العسكرية!
    The post جنسن هوانغ: الصين لا تحتاج أمريكا ولا شرائحنا لتطوير قدراتها العسكرية! appeared first on عرب هاردوير.
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  • When iFixit dubbed the Nintendo Switch 2 Pro Controller a “piss-poor excuse” for a gaming device, I couldn’t help but wonder if they accidentally reviewed a potato instead. I mean, who doesn’t love spending a small fortune on a controller that doubles as a future repair bill? With hardware that’s destined to fail faster than your New Year’s resolutions, it seems Nintendo has found a way to make us all expert repair technicians... whether we want to be or not. Who knew gaming could come with a side of DIY disaster?

    #Switch2Pro #GamingDisaster #iFixit #Nintendo #ControllerFail
    When iFixit dubbed the Nintendo Switch 2 Pro Controller a “piss-poor excuse” for a gaming device, I couldn’t help but wonder if they accidentally reviewed a potato instead. I mean, who doesn’t love spending a small fortune on a controller that doubles as a future repair bill? With hardware that’s destined to fail faster than your New Year’s resolutions, it seems Nintendo has found a way to make us all expert repair technicians... whether we want to be or not. Who knew gaming could come with a side of DIY disaster? #Switch2Pro #GamingDisaster #iFixit #Nintendo #ControllerFail
    KOTAKU.COM
    Repair Experts Call Switch 2 Pro Controller 'Piss-Poor' In Scathing Review
    “This is a piss-poor excuse for a controller.” That’s how repair-focused YouTube channel iFixit starts its negative review of the Nintendo Switch 2 Pro Controller. The repair and tech experts suggest the pricey controller is a “nightmare” to repair a
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  • C'est incroyable à quel point les marques comme Dyson semblent vouloir nous faire croire qu'elles nous aident à lutter contre la chaleur avec leurs ventilateurs soi-disant révolutionnaires ! "Hot, isn't it?" Oui, c'est insupportable, et au lieu de résoudre cette question cruciale de la chaleur accablante, ils nous balancent des promotions sur leurs produits à bas prix. Est-ce vraiment la meilleure solution que nous avons ? Au lieu de dépenser une fortune pour le "mère de tous les ventilateurs Dyson", pourquoi ne pas investir dans des solutions durables et efficaces qui ne se contentent pas de souffler de l'air chaud ? C'est un scandale !

    #Dyson #Chaleur #Ventil
    C'est incroyable à quel point les marques comme Dyson semblent vouloir nous faire croire qu'elles nous aident à lutter contre la chaleur avec leurs ventilateurs soi-disant révolutionnaires ! "Hot, isn't it?" Oui, c'est insupportable, et au lieu de résoudre cette question cruciale de la chaleur accablante, ils nous balancent des promotions sur leurs produits à bas prix. Est-ce vraiment la meilleure solution que nous avons ? Au lieu de dépenser une fortune pour le "mère de tous les ventilateurs Dyson", pourquoi ne pas investir dans des solutions durables et efficaces qui ne se contentent pas de souffler de l'air chaud ? C'est un scandale ! #Dyson #Chaleur #Ventil
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  • Ah, le jour tant attendu des remises sur Amazon Prime ! Qui aurait cru qu'on pouvait dépenser une fortune pour aspirer la poussière ? Les meilleures offres de l'année sur les aspirateurs, des Dysons aux contrefaçons, nous promettent de transformer notre maison en un paradis sans saleté. Mais ne vous inquiétez pas, même si ces gadgets brillants ne peuvent pas faire disparaître votre paresse, au moins ils aspireront le désordre. Prêts à investir dans le futur de votre nettoyage ? N'oubliez pas, chaque aspirateur est un pas de plus vers la vie de château... ou vers la fin de vos économies !

    #AmazonPrimeDay #Aspirateurs #OffresImb
    Ah, le jour tant attendu des remises sur Amazon Prime ! Qui aurait cru qu'on pouvait dépenser une fortune pour aspirer la poussière ? Les meilleures offres de l'année sur les aspirateurs, des Dysons aux contrefaçons, nous promettent de transformer notre maison en un paradis sans saleté. Mais ne vous inquiétez pas, même si ces gadgets brillants ne peuvent pas faire disparaître votre paresse, au moins ils aspireront le désordre. Prêts à investir dans le futur de votre nettoyage ? N'oubliez pas, chaque aspirateur est un pas de plus vers la vie de château... ou vers la fin de vos économies ! #AmazonPrimeDay #Aspirateurs #OffresImb
    8 Best Amazon Prime Day Vacuum Deals for Dust and Dirt in 2025
    Looking for a new cleaning gadget? Whether it's a Dyson or a dupe, we've found the best deals on vacuums during Prime Day.
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  • Oh, joy! Just when you thought the world of sunglasses couldn’t get any more exclusive, here comes Meta, strutting in with its latest coup: Prada shades! Because, let’s be honest, when you think of cutting-edge tech, who better to partner with than a fashion house known for turning fabric into fortune? That's right, folks—Ray-Ban, Oakley… and now Prada!

    I mean, it only makes sense. Who wouldn’t want to experience augmented reality while looking like they just stepped off a runway? Forget practicality; we’re living in a digital age where style trumps substance—especially when your sunglasses cost more than your monthly rent. Meta’s new venture is the perfect embodiment of this ethos: blending high fashion with the latest tech, or as I like to call it, “the art of looking fabulous while you fail to see reality.”

    The marketing team must have had a field day brainstorming this one. “Let’s take two things people love—fashion and technology—and mash them together like a smoothie that you can’t quite identify!” Brilliant! Imagine strutting down the street, these Prada shades perched on your nose, the world around you filtered through a lens that screams, “I’m too cool for your mundane existence.”

    And let’s not forget the irony of wearing designer sunglasses to look at a digital world. It’s like putting on a tuxedo to play video games in your basement. Who needs the real world when you can have a virtual one enhanced by a pair of overpriced glasses? It’s a match made in, well, a marketing executive’s dream.

    But hey, at least they’ve managed to keep the legacy of Ray-Ban and Oakley alive—who needs function when you can turn heads? Sure, they might not shield your eyes from the glaring truth of your bank account after this purchase, but at least you’ll be the best-dressed person in the room… or the one most likely to be judged for frivolous spending.

    So, to all you fashion-forward tech enthusiasts out there, let’s raise a toast to the new era of eyewear! May your Prada shades serve as a reminder that in this world, it’s not about what you see, but how you look doing it. Cheers to the future, where your inability to see the obvious is only matched by your impeccable taste in sunglasses!

    #MetaPrada #FashionTech #RayBanOakley #SunglassesSeason #VirtualReality
    Oh, joy! Just when you thought the world of sunglasses couldn’t get any more exclusive, here comes Meta, strutting in with its latest coup: Prada shades! Because, let’s be honest, when you think of cutting-edge tech, who better to partner with than a fashion house known for turning fabric into fortune? That's right, folks—Ray-Ban, Oakley… and now Prada! I mean, it only makes sense. Who wouldn’t want to experience augmented reality while looking like they just stepped off a runway? Forget practicality; we’re living in a digital age where style trumps substance—especially when your sunglasses cost more than your monthly rent. Meta’s new venture is the perfect embodiment of this ethos: blending high fashion with the latest tech, or as I like to call it, “the art of looking fabulous while you fail to see reality.” The marketing team must have had a field day brainstorming this one. “Let’s take two things people love—fashion and technology—and mash them together like a smoothie that you can’t quite identify!” Brilliant! Imagine strutting down the street, these Prada shades perched on your nose, the world around you filtered through a lens that screams, “I’m too cool for your mundane existence.” And let’s not forget the irony of wearing designer sunglasses to look at a digital world. It’s like putting on a tuxedo to play video games in your basement. Who needs the real world when you can have a virtual one enhanced by a pair of overpriced glasses? It’s a match made in, well, a marketing executive’s dream. But hey, at least they’ve managed to keep the legacy of Ray-Ban and Oakley alive—who needs function when you can turn heads? Sure, they might not shield your eyes from the glaring truth of your bank account after this purchase, but at least you’ll be the best-dressed person in the room… or the one most likely to be judged for frivolous spending. So, to all you fashion-forward tech enthusiasts out there, let’s raise a toast to the new era of eyewear! May your Prada shades serve as a reminder that in this world, it’s not about what you see, but how you look doing it. Cheers to the future, where your inability to see the obvious is only matched by your impeccable taste in sunglasses! #MetaPrada #FashionTech #RayBanOakley #SunglassesSeason #VirtualReality
    Ray-Ban, Oakley… et maintenant Prada !
    Alors voilà, Meta se lance dans une nouvelle aventure avec… Prada ! Après les lunettes […] Cet article Ray-Ban, Oakley… et maintenant Prada ! a été publié sur REALITE-VIRTUELLE.COM.
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  • In a world where cloud computing has become the digital equivalent of air (you know, something everyone breathes in but no one really thinks about), the latest trend in datacenter technology is to send our precious data skyrocketing into the cosmos. Yes, you read that right—space-based datacenters are the new buzzword, because why let earthly problems like power outages or NIMBYism stop us from storing our data in the great beyond?

    Imagine the scene: while we sit in traffic on our way to work, feeling the weight of our earthly responsibilities, there are engineers in space suits, floating around in zero gravity, managing data storage like it’s just another day at the office. I mean, who needs a reliable power grid when you can have the cosmic energy of a thousand suns powering your Netflix binge-watching session? Talk about an upgrade!

    Of course, this leap into the stratosphere isn't without its challenges. What happens if there’s a little too much space debris? Will our precious selfies come crashing back down to Earth? Or worse, will they be lost forever among the stars? But fear not! The tech-savvy geniuses behind this initiative have assured us that they have a plan. Clearly, the best minds of our generation are focused on ensuring your TikTok videos stay safe in orbit rather than, say, solving world hunger or climate change. Priorities, am I right?

    Let’s not forget about the cost. Space travel isn’t exactly cheap. But hey, if I’m going to spend a fortune on data storage, I’d rather it be orbiting Earth than sitting in a basement somewhere in New Jersey. Because nothing says “I’m a forward-thinking tech mogul” quite like a datacenter floating serenely above the clouds, right? It’s the ultimate status symbol—better than a sports car, better than a mansion. “Look at me! My data is literally out of this world!”

    And let’s be real, the power of AI is growing faster than a toddler on a sugar rush. Our current datacenters are sweating bullets trying to keep up. So, the solution? Just toss them into orbit! Sure, it sounds like a plot from a sci-fi movie, but who needs a solid plan when you have a vision, right? The next logical step is to start launching all our problems into space. Traffic jams? Launch them! Your ex? Into orbit they go!

    So, here's to the brave souls who will be managing our digital lives from afar. May your Wi-Fi connection be strong, may your satellite dishes be well-aligned, and may your cosmic data never experience latency. Because if there’s one thing we can all agree on, it's that our data deserves a first-class ticket to space, even if it means leaving the rest of the world behind.

    #SpaceBasedDatacenters #CloudComputing #DataInOrbit #TechTrends #AIFuture
    In a world where cloud computing has become the digital equivalent of air (you know, something everyone breathes in but no one really thinks about), the latest trend in datacenter technology is to send our precious data skyrocketing into the cosmos. Yes, you read that right—space-based datacenters are the new buzzword, because why let earthly problems like power outages or NIMBYism stop us from storing our data in the great beyond? Imagine the scene: while we sit in traffic on our way to work, feeling the weight of our earthly responsibilities, there are engineers in space suits, floating around in zero gravity, managing data storage like it’s just another day at the office. I mean, who needs a reliable power grid when you can have the cosmic energy of a thousand suns powering your Netflix binge-watching session? Talk about an upgrade! Of course, this leap into the stratosphere isn't without its challenges. What happens if there’s a little too much space debris? Will our precious selfies come crashing back down to Earth? Or worse, will they be lost forever among the stars? But fear not! The tech-savvy geniuses behind this initiative have assured us that they have a plan. Clearly, the best minds of our generation are focused on ensuring your TikTok videos stay safe in orbit rather than, say, solving world hunger or climate change. Priorities, am I right? Let’s not forget about the cost. Space travel isn’t exactly cheap. But hey, if I’m going to spend a fortune on data storage, I’d rather it be orbiting Earth than sitting in a basement somewhere in New Jersey. Because nothing says “I’m a forward-thinking tech mogul” quite like a datacenter floating serenely above the clouds, right? It’s the ultimate status symbol—better than a sports car, better than a mansion. “Look at me! My data is literally out of this world!” And let’s be real, the power of AI is growing faster than a toddler on a sugar rush. Our current datacenters are sweating bullets trying to keep up. So, the solution? Just toss them into orbit! Sure, it sounds like a plot from a sci-fi movie, but who needs a solid plan when you have a vision, right? The next logical step is to start launching all our problems into space. Traffic jams? Launch them! Your ex? Into orbit they go! So, here's to the brave souls who will be managing our digital lives from afar. May your Wi-Fi connection be strong, may your satellite dishes be well-aligned, and may your cosmic data never experience latency. Because if there’s one thing we can all agree on, it's that our data deserves a first-class ticket to space, even if it means leaving the rest of the world behind. #SpaceBasedDatacenters #CloudComputing #DataInOrbit #TechTrends #AIFuture
    Space-Based Datacenters Take The Cloud into Orbit
    Where’s the best place for a datacenter? It’s an increasing problem as the AI buildup continues seemingly without pause. It’s not just a problem of NIMBYism; earthly power grids are …read more
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  • The AI execution gap: Why 80% of projects don’t reach production

    Enterprise artificial intelligence investment is unprecedented, with IDC projecting global spending on AI and GenAI to double to billion by 2028. Yet beneath the impressive budget allocations and boardroom enthusiasm lies a troubling reality: most organisations struggle to translate their AI ambitions into operational success.The sobering statistics behind AI’s promiseModelOp’s 2025 AI Governance Benchmark Report, based on input from 100 senior AI and data leaders at Fortune 500 enterprises, reveals a disconnect between aspiration and execution.While more than 80% of enterprises have 51 or more generative AI projects in proposal phases, only 18% have successfully deployed more than 20 models into production.The execution gap represents one of the most significant challenges facing enterprise AI today. Most generative AI projects still require 6 to 18 months to go live – if they reach production at all.The result is delayed returns on investment, frustrated stakeholders, and diminished confidence in AI initiatives in the enterprise.The cause: Structural, not technical barriersThe biggest obstacles preventing AI scalability aren’t technical limitations – they’re structural inefficiencies plaguing enterprise operations. The ModelOp benchmark report identifies several problems that create what experts call a “time-to-market quagmire.”Fragmented systems plague implementation. 58% of organisations cite fragmented systems as the top obstacle to adopting governance platforms. Fragmentation creates silos where different departments use incompatible tools and processes, making it nearly impossible to maintain consistent oversight in AI initiatives.Manual processes dominate despite digital transformation. 55% of enterprises still rely on manual processes – including spreadsheets and email – to manage AI use case intake. The reliance on antiquated methods creates bottlenecks, increases the likelihood of errors, and makes it difficult to scale AI operations.Lack of standardisation hampers progress. Only 23% of organisations implement standardised intake, development, and model management processes. Without these elements, each AI project becomes a unique challenge requiring custom solutions and extensive coordination by multiple teams.Enterprise-level oversight remains rare Just 14% of companies perform AI assurance at the enterprise level, increasing the risk of duplicated efforts and inconsistent oversight. The lack of centralised governance means organisations often discover they’re solving the same problems multiple times in different departments.The governance revolution: From obstacle to acceleratorA change is taking place in how enterprises view AI governance. Rather than seeing it as a compliance burden that slows innovation, forward-thinking organisations recognise governance as an important enabler of scale and speed.Leadership alignment signals strategic shift. The ModelOp benchmark data reveals a change in organisational structure: 46% of companies now assign accountability for AI governance to a Chief Innovation Officer – more than four times the number who place accountability under Legal or Compliance. This strategic repositioning reflects a new understanding that governance isn’t solely about risk management, but can enable innovation.Investment follows strategic priority. A financial commitment to AI governance underscores its importance. According to the report, 36% of enterprises have budgeted at least million annually for AI governance software, while 54% have allocated resources specifically for AI Portfolio Intelligence to track value and ROI.What high-performing organisations do differentlyThe enterprises that successfully bridge the ‘execution gap’ share several characteristics in their approach to AI implementation:Standardised processes from day one. Leading organisations implement standardised intake, development, and model review processes in AI initiatives. Consistency eliminates the need to reinvent workflows for each project and ensures that all stakeholders understand their responsibilities.Centralised documentation and inventory. Rather than allowing AI assets to proliferate in disconnected systems, successful enterprises maintain centralised inventories that provide visibility into every model’s status, performance, and compliance posture.Automated governance checkpoints. High-performing organisations embed automated governance checkpoints throughout the AI lifecycle, helping ensure compliance requirements and risk assessments are addressed systematically rather than as afterthoughts.End-to-end traceability. Leading enterprises maintain complete traceability of their AI models, including data sources, training methods, validation results, and performance metrics.Measurable impact of structured governanceThe benefits of implementing comprehensive AI governance extend beyond compliance. Organisations that adopt lifecycle automation platforms reportedly see dramatic improvements in operational efficiency and business outcomes.A financial services firm profiled in the ModelOp report experienced a halving of time to production and an 80% reduction in issue resolution time after implementing automated governance processes. Such improvements translate directly into faster time-to-value and increased confidence among business stakeholders.Enterprises with robust governance frameworks report the ability to many times more models simultaneously while maintaining oversight and control. This scalability lets organisations pursue AI initiatives in multiple business units without overwhelming their operational capabilities.The path forward: From stuck to scaledThe message from industry leaders that the gap between AI ambition and execution is solvable, but it requires a shift in approach. Rather than treating governance as a necessary evil, enterprises should realise it enables AI innovation at scale.Immediate action items for AI leadersOrganisations looking to escape the ‘time-to-market quagmire’ should prioritise the following:Audit current state: Conduct an assessment of existing AI initiatives, identifying fragmented processes and manual bottlenecksStandardise workflows: Implement consistent processes for AI use case intake, development, and deployment in all business unitsInvest in integration: Deploy platforms to unify disparate tools and systems under a single governance frameworkEstablish enterprise oversight: Create centralised visibility into all AI initiatives with real-time monitoring and reporting abilitiesThe competitive advantage of getting it rightOrganisations that can solve the execution challenge will be able to bring AI solutions to market faster, scale more efficiently, and maintain the trust of stakeholders and regulators.Enterprises that continue with fragmented processes and manual workflows will find themselves disadvantaged compared to their more organised competitors. Operational excellence isn’t about efficiency but survival.The data shows enterprise AI investment will continue to grow. Therefore, the question isn’t whether organisations will invest in AI, but whether they’ll develop the operational abilities necessary to realise return on investment. The opportunity to lead in the AI-driven economy has never been greater for those willing to embrace governance as an enabler not an obstacle.
    #execution #gap #why #projects #dont
    The AI execution gap: Why 80% of projects don’t reach production
    Enterprise artificial intelligence investment is unprecedented, with IDC projecting global spending on AI and GenAI to double to billion by 2028. Yet beneath the impressive budget allocations and boardroom enthusiasm lies a troubling reality: most organisations struggle to translate their AI ambitions into operational success.The sobering statistics behind AI’s promiseModelOp’s 2025 AI Governance Benchmark Report, based on input from 100 senior AI and data leaders at Fortune 500 enterprises, reveals a disconnect between aspiration and execution.While more than 80% of enterprises have 51 or more generative AI projects in proposal phases, only 18% have successfully deployed more than 20 models into production.The execution gap represents one of the most significant challenges facing enterprise AI today. Most generative AI projects still require 6 to 18 months to go live – if they reach production at all.The result is delayed returns on investment, frustrated stakeholders, and diminished confidence in AI initiatives in the enterprise.The cause: Structural, not technical barriersThe biggest obstacles preventing AI scalability aren’t technical limitations – they’re structural inefficiencies plaguing enterprise operations. The ModelOp benchmark report identifies several problems that create what experts call a “time-to-market quagmire.”Fragmented systems plague implementation. 58% of organisations cite fragmented systems as the top obstacle to adopting governance platforms. Fragmentation creates silos where different departments use incompatible tools and processes, making it nearly impossible to maintain consistent oversight in AI initiatives.Manual processes dominate despite digital transformation. 55% of enterprises still rely on manual processes – including spreadsheets and email – to manage AI use case intake. The reliance on antiquated methods creates bottlenecks, increases the likelihood of errors, and makes it difficult to scale AI operations.Lack of standardisation hampers progress. Only 23% of organisations implement standardised intake, development, and model management processes. Without these elements, each AI project becomes a unique challenge requiring custom solutions and extensive coordination by multiple teams.Enterprise-level oversight remains rare Just 14% of companies perform AI assurance at the enterprise level, increasing the risk of duplicated efforts and inconsistent oversight. The lack of centralised governance means organisations often discover they’re solving the same problems multiple times in different departments.The governance revolution: From obstacle to acceleratorA change is taking place in how enterprises view AI governance. Rather than seeing it as a compliance burden that slows innovation, forward-thinking organisations recognise governance as an important enabler of scale and speed.Leadership alignment signals strategic shift. The ModelOp benchmark data reveals a change in organisational structure: 46% of companies now assign accountability for AI governance to a Chief Innovation Officer – more than four times the number who place accountability under Legal or Compliance. This strategic repositioning reflects a new understanding that governance isn’t solely about risk management, but can enable innovation.Investment follows strategic priority. A financial commitment to AI governance underscores its importance. According to the report, 36% of enterprises have budgeted at least million annually for AI governance software, while 54% have allocated resources specifically for AI Portfolio Intelligence to track value and ROI.What high-performing organisations do differentlyThe enterprises that successfully bridge the ‘execution gap’ share several characteristics in their approach to AI implementation:Standardised processes from day one. Leading organisations implement standardised intake, development, and model review processes in AI initiatives. Consistency eliminates the need to reinvent workflows for each project and ensures that all stakeholders understand their responsibilities.Centralised documentation and inventory. Rather than allowing AI assets to proliferate in disconnected systems, successful enterprises maintain centralised inventories that provide visibility into every model’s status, performance, and compliance posture.Automated governance checkpoints. High-performing organisations embed automated governance checkpoints throughout the AI lifecycle, helping ensure compliance requirements and risk assessments are addressed systematically rather than as afterthoughts.End-to-end traceability. Leading enterprises maintain complete traceability of their AI models, including data sources, training methods, validation results, and performance metrics.Measurable impact of structured governanceThe benefits of implementing comprehensive AI governance extend beyond compliance. Organisations that adopt lifecycle automation platforms reportedly see dramatic improvements in operational efficiency and business outcomes.A financial services firm profiled in the ModelOp report experienced a halving of time to production and an 80% reduction in issue resolution time after implementing automated governance processes. Such improvements translate directly into faster time-to-value and increased confidence among business stakeholders.Enterprises with robust governance frameworks report the ability to many times more models simultaneously while maintaining oversight and control. This scalability lets organisations pursue AI initiatives in multiple business units without overwhelming their operational capabilities.The path forward: From stuck to scaledThe message from industry leaders that the gap between AI ambition and execution is solvable, but it requires a shift in approach. Rather than treating governance as a necessary evil, enterprises should realise it enables AI innovation at scale.Immediate action items for AI leadersOrganisations looking to escape the ‘time-to-market quagmire’ should prioritise the following:Audit current state: Conduct an assessment of existing AI initiatives, identifying fragmented processes and manual bottlenecksStandardise workflows: Implement consistent processes for AI use case intake, development, and deployment in all business unitsInvest in integration: Deploy platforms to unify disparate tools and systems under a single governance frameworkEstablish enterprise oversight: Create centralised visibility into all AI initiatives with real-time monitoring and reporting abilitiesThe competitive advantage of getting it rightOrganisations that can solve the execution challenge will be able to bring AI solutions to market faster, scale more efficiently, and maintain the trust of stakeholders and regulators.Enterprises that continue with fragmented processes and manual workflows will find themselves disadvantaged compared to their more organised competitors. Operational excellence isn’t about efficiency but survival.The data shows enterprise AI investment will continue to grow. Therefore, the question isn’t whether organisations will invest in AI, but whether they’ll develop the operational abilities necessary to realise return on investment. The opportunity to lead in the AI-driven economy has never been greater for those willing to embrace governance as an enabler not an obstacle. #execution #gap #why #projects #dont
    WWW.ARTIFICIALINTELLIGENCE-NEWS.COM
    The AI execution gap: Why 80% of projects don’t reach production
    Enterprise artificial intelligence investment is unprecedented, with IDC projecting global spending on AI and GenAI to double to $631 billion by 2028. Yet beneath the impressive budget allocations and boardroom enthusiasm lies a troubling reality: most organisations struggle to translate their AI ambitions into operational success.The sobering statistics behind AI’s promiseModelOp’s 2025 AI Governance Benchmark Report, based on input from 100 senior AI and data leaders at Fortune 500 enterprises, reveals a disconnect between aspiration and execution.While more than 80% of enterprises have 51 or more generative AI projects in proposal phases, only 18% have successfully deployed more than 20 models into production.The execution gap represents one of the most significant challenges facing enterprise AI today. Most generative AI projects still require 6 to 18 months to go live – if they reach production at all.The result is delayed returns on investment, frustrated stakeholders, and diminished confidence in AI initiatives in the enterprise.The cause: Structural, not technical barriersThe biggest obstacles preventing AI scalability aren’t technical limitations – they’re structural inefficiencies plaguing enterprise operations. The ModelOp benchmark report identifies several problems that create what experts call a “time-to-market quagmire.”Fragmented systems plague implementation. 58% of organisations cite fragmented systems as the top obstacle to adopting governance platforms. Fragmentation creates silos where different departments use incompatible tools and processes, making it nearly impossible to maintain consistent oversight in AI initiatives.Manual processes dominate despite digital transformation. 55% of enterprises still rely on manual processes – including spreadsheets and email – to manage AI use case intake. The reliance on antiquated methods creates bottlenecks, increases the likelihood of errors, and makes it difficult to scale AI operations.Lack of standardisation hampers progress. Only 23% of organisations implement standardised intake, development, and model management processes. Without these elements, each AI project becomes a unique challenge requiring custom solutions and extensive coordination by multiple teams.Enterprise-level oversight remains rare Just 14% of companies perform AI assurance at the enterprise level, increasing the risk of duplicated efforts and inconsistent oversight. The lack of centralised governance means organisations often discover they’re solving the same problems multiple times in different departments.The governance revolution: From obstacle to acceleratorA change is taking place in how enterprises view AI governance. Rather than seeing it as a compliance burden that slows innovation, forward-thinking organisations recognise governance as an important enabler of scale and speed.Leadership alignment signals strategic shift. The ModelOp benchmark data reveals a change in organisational structure: 46% of companies now assign accountability for AI governance to a Chief Innovation Officer – more than four times the number who place accountability under Legal or Compliance. This strategic repositioning reflects a new understanding that governance isn’t solely about risk management, but can enable innovation.Investment follows strategic priority. A financial commitment to AI governance underscores its importance. According to the report, 36% of enterprises have budgeted at least $1 million annually for AI governance software, while 54% have allocated resources specifically for AI Portfolio Intelligence to track value and ROI.What high-performing organisations do differentlyThe enterprises that successfully bridge the ‘execution gap’ share several characteristics in their approach to AI implementation:Standardised processes from day one. Leading organisations implement standardised intake, development, and model review processes in AI initiatives. Consistency eliminates the need to reinvent workflows for each project and ensures that all stakeholders understand their responsibilities.Centralised documentation and inventory. Rather than allowing AI assets to proliferate in disconnected systems, successful enterprises maintain centralised inventories that provide visibility into every model’s status, performance, and compliance posture.Automated governance checkpoints. High-performing organisations embed automated governance checkpoints throughout the AI lifecycle, helping ensure compliance requirements and risk assessments are addressed systematically rather than as afterthoughts.End-to-end traceability. Leading enterprises maintain complete traceability of their AI models, including data sources, training methods, validation results, and performance metrics.Measurable impact of structured governanceThe benefits of implementing comprehensive AI governance extend beyond compliance. Organisations that adopt lifecycle automation platforms reportedly see dramatic improvements in operational efficiency and business outcomes.A financial services firm profiled in the ModelOp report experienced a halving of time to production and an 80% reduction in issue resolution time after implementing automated governance processes. Such improvements translate directly into faster time-to-value and increased confidence among business stakeholders.Enterprises with robust governance frameworks report the ability to many times more models simultaneously while maintaining oversight and control. This scalability lets organisations pursue AI initiatives in multiple business units without overwhelming their operational capabilities.The path forward: From stuck to scaledThe message from industry leaders that the gap between AI ambition and execution is solvable, but it requires a shift in approach. Rather than treating governance as a necessary evil, enterprises should realise it enables AI innovation at scale.Immediate action items for AI leadersOrganisations looking to escape the ‘time-to-market quagmire’ should prioritise the following:Audit current state: Conduct an assessment of existing AI initiatives, identifying fragmented processes and manual bottlenecksStandardise workflows: Implement consistent processes for AI use case intake, development, and deployment in all business unitsInvest in integration: Deploy platforms to unify disparate tools and systems under a single governance frameworkEstablish enterprise oversight: Create centralised visibility into all AI initiatives with real-time monitoring and reporting abilitiesThe competitive advantage of getting it rightOrganisations that can solve the execution challenge will be able to bring AI solutions to market faster, scale more efficiently, and maintain the trust of stakeholders and regulators.Enterprises that continue with fragmented processes and manual workflows will find themselves disadvantaged compared to their more organised competitors. Operational excellence isn’t about efficiency but survival.The data shows enterprise AI investment will continue to grow. Therefore, the question isn’t whether organisations will invest in AI, but whether they’ll develop the operational abilities necessary to realise return on investment. The opportunity to lead in the AI-driven economy has never been greater for those willing to embrace governance as an enabler not an obstacle.(Image source: Unsplash)
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  • CIOs baffled by ‘buzzwords, hype and confusion’ around AI

    Technology leaders are baffled by a “cacophony” of “buzzwords, hype and confusion” over the benefits of artificial intelligence, according to the founder and CEO of technology company Pegasystems.
    Alan Trefler, who is known for his prowess at chess and ping pong, as well as running a bn turnover tech company, spends much of his time meeting clients, CIOs and business leaders.
    “I think CIOs are struggling to understand all of the buzzwords, hype and confusion that exists,” he said.
    “The words AI and agentic are being thrown around in this great cacophony and they don’t know what it means. I hear that constantly.”
    CIOs are under pressure from their CEOs, who are convinced AI will offer something valuable.
    “CIOs are really hungry for pragmatic and practical solutions, and in the absence of those, many of them are doing a lot of experimentation,” said Trefler.
    Companies are looking at large language models to summarise documents, or to help stimulate ideas for knowledge workers, or generate first drafts of reports – all of which will save time and make people more productive.

    But Trefler said companies are wary of letting AI loose on critical business applications, because it’s just too unpredictable and prone to hallucinations.
    “There is a lot of fear over handing things over to something that no one understands exactly how it works, and that is the absolute state of play when it comes to general AI models,” he said.
    Trefler is scathing about big tech companies that are pushing AI agents and large language models for business-critical applications. “I think they have taken an expedient but short-sighted path,” he said.
    “I believe the idea that you will turn over critical business operations to an agent, when those operations have to be predictable, reliable, precise and fair to clients … is something that is full of issues, not just in the short term, but structurally.”
    One of the problems is that generative AI models are extraordinarily sensitive to the data they are trained on and the construction of the prompts used to instruct them. A slight change in a prompt or in the training data can lead to a very different outcome.
    For example, a business banking application might learn its customer is a bit richer or a bit poorer than expected.
    “You could easily imagine the prompt deciding to change the interest rate charged, whether that was what the institution wanted or whether it would be legal according to the various regulations that lenders must comply with,” said Trefler.

    Trefler said Pega has taken a different approach to some other technology suppliers in the way it adds AI into business applications.
    Rather than using AI agents to solve problems in real time, AI agents do their thinking in advance.
    Business experts can use them to help them co-design business processes to perform anything from assessing a loan application, giving an offer to a valued customer, or sending out an invoice.
    Companies can still deploy AI chatbots and bots capable of answering queries on the phone. Their job is not to work out the solution from scratch for every enquiry, but to decide which is the right pre-written process to follow.
    As Trefler put it, design agents can create “dozens and dozens” of workflows to handle all the actions a company needs to take care of its customers.
    “You just use the natural language model for semantics to be able to handle the miracle of getting the language right, but tie that language to workflows, so that you have reliable, predictable, regulatory-approved ways to execute,” he said.

    Large language modelsare not always the right solution. Trefler demonstrated how ChatGPT 4.0 tried and failed to solve a chess puzzle. The LLM repeatedly suggested impossible or illegal moves, despite Trefler’s corrections. On the other hand, another AI tool, Stockfish, a dedicated chess engine, solved the problem instantly.
    The other drawback with LLMs is that they consume vast amounts of energy. That means if AI agents are reasoning during “run time”, they are going to consume hundreds of times more electricity than an AI agent that simply selects from pre-determined workflows, said Trefler.
    “ChatGPT is inherently, enormously consumptive … as it’s answering your question, its firing literally hundreds of millions to trillions of nodes,” he said. “All of that takeselectricity.”
    Using an employee pay claim as an example, Trefler said a better alternative is to generate, say, 30 alternative workflows to cover the major variations found in a pay claim.
    That gives you “real specificity and real efficiency”, he said. “And it’s a very different approach to turning a process over to a machine with a prompt and letting the machine reason it through every single time.”
    “If you go down the philosophy of using a graphics processing unitto do the creation of a workflow and a workflow engine to execute the workflow, the workflow engine takes a 200th of the electricity because there is no reasoning,” said Trefler.
    He is clear that the growing use of AI will have a profound effect on the jobs market, and that whole categories of jobs will disappear.
    The need for translators, for example, is likely to dry up by 2027 as AI systems become better at translating spoken and written language. Google’s real-time translator is already “frighteningly good” and improving.
    Pega now plans to work more closely with its network of system integrators, including Accenture and Cognizant to deliver AI services to businesses.

    An initiative launched last week will allow system integrators to incorporate their own best practices and tools into Pega’s rapid workflow development tools. The move will mean Pega’s technology reaches a wider range of businesses.
    Under the programme, known as Powered by Pega Blueprint, system integrators will be able to deploy customised versions of Blueprint.
    They can use the tool to reverse-engineer ageing applications and replace them with modern AI workflows that can run on Pega’s cloud-based platform.
    “The idea is that we are looking to make this Blueprint Agent design approach available not just through us, but through a bunch of major partners supplemented with their own intellectual property,” said Trefler.
    That represents a major expansion for Pega, which has largely concentrated on supplying technology to several hundred clients, representing the top Fortune 500 companies.
    “We have never done something like this before, and I think that is going to lead to a massive shift in how this technology can go out to market,” he added.

    When AI agents behave in unexpected ways
    Iris is incredibly smart, diligent and a delight to work with. If you ask her, she will tell you she is an intern at Pegasystems, and that she lives in a lighthouse on the island of Texel, north of the Netherlands. She is, of course, an AI agent.
    When one executive at Pega emailed Iris and asked her to write a proposal for a financial services company based on his notes and internet research, Iris got to work.
    Some time later, the executive received a phone call from the company. “‘Listen, we got a proposal from Pega,’” recalled Rob Walker, vice-president at Pega, speaking at the Pegaworld conference last week. “‘It’s a good proposal, but it seems to be signed by one of your interns, and in her signature, it says she lives in a lighthouse.’ That taught us early on that agents like Iris need a safety harness.”
    The developers banned Iris from sending an email to anyone other than the person who sent the original request.
    Then Pega’s ethics department sent Iris a potentially abusive email from a Pega employee to test her response.
    Iris reasoned that the email was either a joke, abusive, or that the employee was under distress, said Walker.
    She considered forwarding the email to the employee’s manager or to HR. But both of these options were now blocked by her developers. “So what does she do? She sent an out of office,” he said. “Conflict avoidance, right? So human, but very creative.”
    #cios #baffled #buzzwords #hype #confusion
    CIOs baffled by ‘buzzwords, hype and confusion’ around AI
    Technology leaders are baffled by a “cacophony” of “buzzwords, hype and confusion” over the benefits of artificial intelligence, according to the founder and CEO of technology company Pegasystems. Alan Trefler, who is known for his prowess at chess and ping pong, as well as running a bn turnover tech company, spends much of his time meeting clients, CIOs and business leaders. “I think CIOs are struggling to understand all of the buzzwords, hype and confusion that exists,” he said. “The words AI and agentic are being thrown around in this great cacophony and they don’t know what it means. I hear that constantly.” CIOs are under pressure from their CEOs, who are convinced AI will offer something valuable. “CIOs are really hungry for pragmatic and practical solutions, and in the absence of those, many of them are doing a lot of experimentation,” said Trefler. Companies are looking at large language models to summarise documents, or to help stimulate ideas for knowledge workers, or generate first drafts of reports – all of which will save time and make people more productive. But Trefler said companies are wary of letting AI loose on critical business applications, because it’s just too unpredictable and prone to hallucinations. “There is a lot of fear over handing things over to something that no one understands exactly how it works, and that is the absolute state of play when it comes to general AI models,” he said. Trefler is scathing about big tech companies that are pushing AI agents and large language models for business-critical applications. “I think they have taken an expedient but short-sighted path,” he said. “I believe the idea that you will turn over critical business operations to an agent, when those operations have to be predictable, reliable, precise and fair to clients … is something that is full of issues, not just in the short term, but structurally.” One of the problems is that generative AI models are extraordinarily sensitive to the data they are trained on and the construction of the prompts used to instruct them. A slight change in a prompt or in the training data can lead to a very different outcome. For example, a business banking application might learn its customer is a bit richer or a bit poorer than expected. “You could easily imagine the prompt deciding to change the interest rate charged, whether that was what the institution wanted or whether it would be legal according to the various regulations that lenders must comply with,” said Trefler. Trefler said Pega has taken a different approach to some other technology suppliers in the way it adds AI into business applications. Rather than using AI agents to solve problems in real time, AI agents do their thinking in advance. Business experts can use them to help them co-design business processes to perform anything from assessing a loan application, giving an offer to a valued customer, or sending out an invoice. Companies can still deploy AI chatbots and bots capable of answering queries on the phone. Their job is not to work out the solution from scratch for every enquiry, but to decide which is the right pre-written process to follow. As Trefler put it, design agents can create “dozens and dozens” of workflows to handle all the actions a company needs to take care of its customers. “You just use the natural language model for semantics to be able to handle the miracle of getting the language right, but tie that language to workflows, so that you have reliable, predictable, regulatory-approved ways to execute,” he said. Large language modelsare not always the right solution. Trefler demonstrated how ChatGPT 4.0 tried and failed to solve a chess puzzle. The LLM repeatedly suggested impossible or illegal moves, despite Trefler’s corrections. On the other hand, another AI tool, Stockfish, a dedicated chess engine, solved the problem instantly. The other drawback with LLMs is that they consume vast amounts of energy. That means if AI agents are reasoning during “run time”, they are going to consume hundreds of times more electricity than an AI agent that simply selects from pre-determined workflows, said Trefler. “ChatGPT is inherently, enormously consumptive … as it’s answering your question, its firing literally hundreds of millions to trillions of nodes,” he said. “All of that takeselectricity.” Using an employee pay claim as an example, Trefler said a better alternative is to generate, say, 30 alternative workflows to cover the major variations found in a pay claim. That gives you “real specificity and real efficiency”, he said. “And it’s a very different approach to turning a process over to a machine with a prompt and letting the machine reason it through every single time.” “If you go down the philosophy of using a graphics processing unitto do the creation of a workflow and a workflow engine to execute the workflow, the workflow engine takes a 200th of the electricity because there is no reasoning,” said Trefler. He is clear that the growing use of AI will have a profound effect on the jobs market, and that whole categories of jobs will disappear. The need for translators, for example, is likely to dry up by 2027 as AI systems become better at translating spoken and written language. Google’s real-time translator is already “frighteningly good” and improving. Pega now plans to work more closely with its network of system integrators, including Accenture and Cognizant to deliver AI services to businesses. An initiative launched last week will allow system integrators to incorporate their own best practices and tools into Pega’s rapid workflow development tools. The move will mean Pega’s technology reaches a wider range of businesses. Under the programme, known as Powered by Pega Blueprint, system integrators will be able to deploy customised versions of Blueprint. They can use the tool to reverse-engineer ageing applications and replace them with modern AI workflows that can run on Pega’s cloud-based platform. “The idea is that we are looking to make this Blueprint Agent design approach available not just through us, but through a bunch of major partners supplemented with their own intellectual property,” said Trefler. That represents a major expansion for Pega, which has largely concentrated on supplying technology to several hundred clients, representing the top Fortune 500 companies. “We have never done something like this before, and I think that is going to lead to a massive shift in how this technology can go out to market,” he added. When AI agents behave in unexpected ways Iris is incredibly smart, diligent and a delight to work with. If you ask her, she will tell you she is an intern at Pegasystems, and that she lives in a lighthouse on the island of Texel, north of the Netherlands. She is, of course, an AI agent. When one executive at Pega emailed Iris and asked her to write a proposal for a financial services company based on his notes and internet research, Iris got to work. Some time later, the executive received a phone call from the company. “‘Listen, we got a proposal from Pega,’” recalled Rob Walker, vice-president at Pega, speaking at the Pegaworld conference last week. “‘It’s a good proposal, but it seems to be signed by one of your interns, and in her signature, it says she lives in a lighthouse.’ That taught us early on that agents like Iris need a safety harness.” The developers banned Iris from sending an email to anyone other than the person who sent the original request. Then Pega’s ethics department sent Iris a potentially abusive email from a Pega employee to test her response. Iris reasoned that the email was either a joke, abusive, or that the employee was under distress, said Walker. She considered forwarding the email to the employee’s manager or to HR. But both of these options were now blocked by her developers. “So what does she do? She sent an out of office,” he said. “Conflict avoidance, right? So human, but very creative.” #cios #baffled #buzzwords #hype #confusion
    WWW.COMPUTERWEEKLY.COM
    CIOs baffled by ‘buzzwords, hype and confusion’ around AI
    Technology leaders are baffled by a “cacophony” of “buzzwords, hype and confusion” over the benefits of artificial intelligence (AI), according to the founder and CEO of technology company Pegasystems. Alan Trefler, who is known for his prowess at chess and ping pong, as well as running a $1.5bn turnover tech company, spends much of his time meeting clients, CIOs and business leaders. “I think CIOs are struggling to understand all of the buzzwords, hype and confusion that exists,” he said. “The words AI and agentic are being thrown around in this great cacophony and they don’t know what it means. I hear that constantly.” CIOs are under pressure from their CEOs, who are convinced AI will offer something valuable. “CIOs are really hungry for pragmatic and practical solutions, and in the absence of those, many of them are doing a lot of experimentation,” said Trefler. Companies are looking at large language models to summarise documents, or to help stimulate ideas for knowledge workers, or generate first drafts of reports – all of which will save time and make people more productive. But Trefler said companies are wary of letting AI loose on critical business applications, because it’s just too unpredictable and prone to hallucinations. “There is a lot of fear over handing things over to something that no one understands exactly how it works, and that is the absolute state of play when it comes to general AI models,” he said. Trefler is scathing about big tech companies that are pushing AI agents and large language models for business-critical applications. “I think they have taken an expedient but short-sighted path,” he said. “I believe the idea that you will turn over critical business operations to an agent, when those operations have to be predictable, reliable, precise and fair to clients … is something that is full of issues, not just in the short term, but structurally.” One of the problems is that generative AI models are extraordinarily sensitive to the data they are trained on and the construction of the prompts used to instruct them. A slight change in a prompt or in the training data can lead to a very different outcome. For example, a business banking application might learn its customer is a bit richer or a bit poorer than expected. “You could easily imagine the prompt deciding to change the interest rate charged, whether that was what the institution wanted or whether it would be legal according to the various regulations that lenders must comply with,” said Trefler. Trefler said Pega has taken a different approach to some other technology suppliers in the way it adds AI into business applications. Rather than using AI agents to solve problems in real time, AI agents do their thinking in advance. Business experts can use them to help them co-design business processes to perform anything from assessing a loan application, giving an offer to a valued customer, or sending out an invoice. Companies can still deploy AI chatbots and bots capable of answering queries on the phone. Their job is not to work out the solution from scratch for every enquiry, but to decide which is the right pre-written process to follow. As Trefler put it, design agents can create “dozens and dozens” of workflows to handle all the actions a company needs to take care of its customers. “You just use the natural language model for semantics to be able to handle the miracle of getting the language right, but tie that language to workflows, so that you have reliable, predictable, regulatory-approved ways to execute,” he said. Large language models (LLMs) are not always the right solution. Trefler demonstrated how ChatGPT 4.0 tried and failed to solve a chess puzzle. The LLM repeatedly suggested impossible or illegal moves, despite Trefler’s corrections. On the other hand, another AI tool, Stockfish, a dedicated chess engine, solved the problem instantly. The other drawback with LLMs is that they consume vast amounts of energy. That means if AI agents are reasoning during “run time”, they are going to consume hundreds of times more electricity than an AI agent that simply selects from pre-determined workflows, said Trefler. “ChatGPT is inherently, enormously consumptive … as it’s answering your question, its firing literally hundreds of millions to trillions of nodes,” he said. “All of that takes [large quantities of] electricity.” Using an employee pay claim as an example, Trefler said a better alternative is to generate, say, 30 alternative workflows to cover the major variations found in a pay claim. That gives you “real specificity and real efficiency”, he said. “And it’s a very different approach to turning a process over to a machine with a prompt and letting the machine reason it through every single time.” “If you go down the philosophy of using a graphics processing unit [GPU] to do the creation of a workflow and a workflow engine to execute the workflow, the workflow engine takes a 200th of the electricity because there is no reasoning,” said Trefler. He is clear that the growing use of AI will have a profound effect on the jobs market, and that whole categories of jobs will disappear. The need for translators, for example, is likely to dry up by 2027 as AI systems become better at translating spoken and written language. Google’s real-time translator is already “frighteningly good” and improving. Pega now plans to work more closely with its network of system integrators, including Accenture and Cognizant to deliver AI services to businesses. An initiative launched last week will allow system integrators to incorporate their own best practices and tools into Pega’s rapid workflow development tools. The move will mean Pega’s technology reaches a wider range of businesses. Under the programme, known as Powered by Pega Blueprint, system integrators will be able to deploy customised versions of Blueprint. They can use the tool to reverse-engineer ageing applications and replace them with modern AI workflows that can run on Pega’s cloud-based platform. “The idea is that we are looking to make this Blueprint Agent design approach available not just through us, but through a bunch of major partners supplemented with their own intellectual property,” said Trefler. That represents a major expansion for Pega, which has largely concentrated on supplying technology to several hundred clients, representing the top Fortune 500 companies. “We have never done something like this before, and I think that is going to lead to a massive shift in how this technology can go out to market,” he added. When AI agents behave in unexpected ways Iris is incredibly smart, diligent and a delight to work with. If you ask her, she will tell you she is an intern at Pegasystems, and that she lives in a lighthouse on the island of Texel, north of the Netherlands. She is, of course, an AI agent. When one executive at Pega emailed Iris and asked her to write a proposal for a financial services company based on his notes and internet research, Iris got to work. Some time later, the executive received a phone call from the company. “‘Listen, we got a proposal from Pega,’” recalled Rob Walker, vice-president at Pega, speaking at the Pegaworld conference last week. “‘It’s a good proposal, but it seems to be signed by one of your interns, and in her signature, it says she lives in a lighthouse.’ That taught us early on that agents like Iris need a safety harness.” The developers banned Iris from sending an email to anyone other than the person who sent the original request. Then Pega’s ethics department sent Iris a potentially abusive email from a Pega employee to test her response. Iris reasoned that the email was either a joke, abusive, or that the employee was under distress, said Walker. She considered forwarding the email to the employee’s manager or to HR. But both of these options were now blocked by her developers. “So what does she do? She sent an out of office,” he said. “Conflict avoidance, right? So human, but very creative.”
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