MEDIA & ENTERTAINMENT DISCUSSION

Build AI-powered media experiences

From content production to immersive entertainment, AI agents can be strategically integrated across business functions to drive value and competitive advantage. In our ongoing discussion, we bring together industry leaders to explore all this and more.

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Featured content

Immersed in Frictionless Experiences: How Globant Is Enabling Seamless Engagement for Today's Fans, Guests, and Customers

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Podcasts

Wanda Weigert-Building AI capability across a global workforce

Building AI capability across a global workforce

Wanda Weigert, Global Chief Brand Officer, Globant

Giorgio Taccia&Emily Latham-From content-led to audience-led

From content-led to audience-led. How media marketing is changing

Giorgio Taccia, Lead M&E expert at Globant, and Emily Latham, former head of Martech at Channel 4

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Gen Ai in Media & Entertainment, an interview with JJ Lopez Murphy, Head of AI at Globant

JJ Lopez Murphy

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Leveraging AI to Unlock Creative Potential in Media

Isa Goksu and JJ Lopez Murphy

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Navigating the Future of Streaming

Giorgio Tacchia

Interviews

Angus Mitchell, Customer and Commercial Leader at Channel 4

Platform monetisation: The evolution of revenue models in a subscription-saturated market

Angus Mitchell, Customer and Commercial Leader at Channel 4

Daniela Boquete, Streaming Industry Expert

Platform monetisation: The evolution of revenue models in a subscription-saturated market

Daniela Boquete, Streaming Industry Expert

Marco Berkheij, Head of Sales EMEA North and Benelux at Red Bee Media

Direct-to-consumer: Market evolution or temporary trend?

Marco Berkheij, Head of Sales EMEA North and Benelux at Red Bee Media

Richard Huang, Head of Strategy at the BBC

Platform monetisation: The evolution of revenue models in a subscription-saturated market

Richard Huang, Head of Strategy at the BBC

Stephen Thomas, Director of Commercial Innovation at the Financial Times

From archives to assets: Exploring media monetisation in the age of AI

Stephen Thomas, Director of Commercial Innovation at the Financial Times

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Archives to asset: Exploring media monetization in the age of AI. 

McKinley Muir Hyden, Director of Data Value & Strategy, Financial Times

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From archives to assets: Exploring media monetization in the age of AI

Meropi Kylika, Vice President of Commercial, CNBC International

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Archives to asset: Exploring media monetization in the age of AI. 

Ayushman Saha, Head of Data Value, Financial Times

Blogs

The End of the Discount: AI and the New Full Price Proposition
Martech

The End of the Discount: AI and the New Full Price Proposition

AI is rewriting the rules of pricing through AI personalization in retail. Here's why paying full price might be the smartest thing your customer ever does and your smartest move as a brand. [caption id="attachment_86194" align="aligncenter" width="613"] Source: https://www.edelman.com/es/es/2023-edelman-trust-barometer[/caption] Remember when every brand screamed "50% Off!" to grab your attention? Those days are fading fast. The discount economy that defined digital retail for the past decade is quietly unraveling, and in its place, something far more interesting is emerging: AI-powered value and AI personalization in retail. This is a story about what happens when brands stop begging for attention with price cuts and start earning it with intelligence. Think about the last time a brand genuinely surprised you. Not with a coupon or a flash sale, but with an experience that made you feel like they actually knew you. Chances are, you bought something without checking whether it was on sale. You just… bought it. That feeling of being understood rather than chased is what AI is making possible at scale through retail personalization. And it is quietly turning the economics of modern retail on its head. Yet for most brands, getting there isn't instinctive. Decades of discount-led marketing have left teams optimizing for the short spike rather than a long relationship, and the data pipelines, incentive structures, and creative habits that powered those tactics don't simply switch off overnight. In this piece, I want to unpack exactly why the discount model is breaking down, what AI-driven personalization looks like when it's done right, and, more practically, what marketing leaders can do this year to start pricing with confidence instead of apology. The problem with perpetual discounts and how we trained customers to wait We've all done it. Rolled out a flash sale. Stacked a "buy-more-save-more" offer. Blasted a last-chance email to a tired list. For a while, these tactics worked beautifully, driving traffic, clearing inventory, and boosting quarterly numbers. But over time, a quiet erosion set in. The margin pressure was obvious, but the identity pressure was less obvious until the data caught up. When your brand is always on sale, customers stop associating it with quality or craft. They associate it with the deals. And deal-hunters are the least loyal cohort in any customer base. [caption id="attachment_86189" align="aligncenter" width="608"] Source: https://www.kantar.com/campaigns/brandz, https://www.forrester.com/bold/consumer-marketing[/caption]   The data tells a disturbing story. Brands that trained their customers to hunt discounts found themselves trapped. They were unable to raise prices, unable to hold margin, and unable to tell a compelling story beyond "cheaper than yesterday." You can't build a premium brand on a discount foundation. You can only build a discount brand and then spend years trying to escape it. Where AI changes everything Here's where the story gets interesting for AI personalization in retail. AI doesn't look at your customer and ask, "What discount will close this sale?" It asks something fundamentally different: "What does this person actually need right now, and what's stopping them from buying it?" Those are very different questions. A customer browsing a premium skincare product at 11pm after reading three editorial reviews isn't looking for 20% off. They're looking for reassurance - that this is the right choice. An AI system that has tracked the customer’s journey can deliver a level of retail personalization that a discount code cannot. How AI replaces the discount: three shifts that change everything A discount doesn't make a customer confident. It just makes the risk of being wrong feel cheaper. AI addresses the root. Here's how the shift happens across three interconnected layers, moving a customer from price-sensitive to genuinely loyal.     And it is one of the biggest reasons why the most forward-thinking brands are now investing in understanding over discounting. The new full price proposition What's emerging from these brands right now is what you might call the full-price proposition. It's not about raising prices or being precious about promotions. It's about earning the right to charge your price by making the customer experience feel personal enough that the price stops being the primary variable in the decision.   [caption id="attachment_86179" align="aligncenter" width="672"] Source: https://www.bcg.com/industries/consumer-products-retail/artificial-intelligence-retail[/caption]   Brands that have moved in this direction are seeing something that would have seemed counterintuitive five years ago: their full-price conversion rates are outperforming their discounted campaigns. Not because they've found better coupons but because they've stopped using coupons as a substitute for understanding. How marketers can lead the shift Making this shift isn't painless. It requires rethinking what your data is for, what success looks like, and frankly, what story you're telling about your brand. But the path is clearer than it might look for organizations investing in AI personalization in retail and broader retail digital transformation initiatives : Audit what your AI is actually learning Is your AI learning from the right signals — browsing depth, scroll behavior, purchase history, sentiment from reviews, and support tickets? If your AI is trained only on purchase history, it's flying blind, limiting the potential of AI in retail. Replace discount codes with curation Let recommendation engines do the work your discount emails used to do. A perfectly timed product suggestion converts without training customers to expect a deal. Reframe your value story, not just your price story Communicate craft, sustainability, and quality. Give customers a reason to be proud of paying full price, not apologetic about it, and create a stronger retail customer experience. Measure loyalty, not just clicks Track repeat purchase rate, average order value over 12 months, and time between purchases. These loyalty metrics tell you whether you're building something real or just renting attention. Stop asking "what discount will close this?" Ask "what context is missing?" Invest in zero-party data — let customers tell you what they love Build pricing confidence into your brand voice, not just your product Test AI-personalized full-price journeys against your discount control group The cultural shift beneath the strategy From a broader perspective, the move away from discounts isn't just a revenue play; it's a cultural one. Chronic discounting quietly communicates something troubling: that your product isn't worth what you're asking for it. Customers absorb that signal, even when they can't articulate it. When your brand shows up with personalization, curation, and genuine understanding instead, something different happens. Price sensitivity drops because certainty rises. The customer isn't buying despite the price; they're buying because of everything that justifies it. Connection converts better than coupons. AI doesn't change that truth — it finally gives us the infrastructure to act on it. The winning brands of the next decade won't discount harder. They'll understand deeper. They'll use AI not as a margin-cutting tool, but as an empathy engine — one that makes every customer feel like the product was made for them. That's not the end of the deal. It's the beginning of intelligent value. And for brands willing to make the shift, it's the most powerful pricing strategy in a generation. So where does this leave us? Let's come back to where we started. Two problems have been quietly compounding in parallel inside most commerce organizations. The first is structural: years of discount-led marketing created an entire generation of customers who have been trained to wait, to compare, to never pay full price if patience will be rewarded. The second is operational: most brands already sit on mountains of behavioral data (browsing signals, purchase history, sentiment, engagement patterns), but they've never had the tools, the strategy, or the organizational will to turn that data into genuine personalization at scale. These aren't separate problems. The discount trap occurs when the personalization gap goes unfilled for too long. When a brand can't make an experience feel relevant, it reaches for a price cut instead. And every time it does, the customer learns a little more to expect it. Breaking that cycle isn't a technology problem — it's a transformation problem. The AI tools exist. The data exists. What most organizations are missing is the bridge: a clear-eyed strategy that dismantles dependency on discounts, rebuilds the data pipeline around intent rather than transactions, and designs experiences that earn trust before asking for a sale. That's where Globant GUT Studio comes in We help brands stop competing on price and start competing on understanding, rebuilding data pipelines around intent, designing experiences that earn full price, and driving the kind of Digital Transformation that sticks. If this sounds like where your team needs to go, visit Globant’s GUT Network and let's talk. Are you training your customers to wait for discounts, or teaching them why you're worth every penny? In the age of AI, those two paths lead to entirely different brands. The choice is yours—and this decade is the deadline.

The Journey Around the Cabin: Premium Travel's Next Design Frontier
Airlines

The Journey Around the Cabin: Premium Travel's Next Design Frontier

The premium cabin has officially become a serious design discipline. Looking across the aviation landscape in 2026, the intentionality and genuine innovation in business and first-class products are unmistakable. OEMs, design agencies, and airlines have successfully turned the physical environment into a powerhouse differentiator.   But as the cabin gets better, a natural question becomes more prominent: How does the experience around the cabin keep pace?   The passenger who boards a world-class suite arrives from somewhere and leaves toward somewhere. The cabin is the centerpiece of the journey, but it is far from the whole of it. The most forward-thinking airlines are already engaging with exactly this, recognizing that the next frontier in premium differentiation is journey coherence: the seamless, unbroken experience before the door, at the door, and after the door. What Premium Passengers Actually Experience Major carriers are in a massive investment cycle, introducing new long-haul configurations at a historic pace. The global aircraft cabin interiors market is projected to reach over $45 billion soon, driven aggressively by massive retrofit commitments and persistent delivery delays that force airlines to modernize existing fleets. While this focus on hardware makes perfect commercial sense, an end-to-end premium journey is always broader than a standard product brief captures. The traveler's ultimate verdict is formed by the entire arc of the experience, not just a single physical peak.   Because premium passengers are an airline's most commercially valuable and detail-attuned customers, inconsistent personalization is often worse than none at all. Receiving tailored recognition in a lounge followed by generic handling at the gate creates a jarring, fractured contrast. Compounding this challenge is the fact that these travelers are not static personas; their needs shift entirely based on context. The same individual requires meticulous orchestration for a Monday red-eye, but self-directed autonomy for a Friday leisure trip.   The real design target for airlines, then, is contextual awareness: reading real-time signals to understand which version of this person is traveling today. Balancing these fluid passenger states was once an insurmountable technical challenge, but now, the technology is ready. The strategic matter has shifted entirely from whether systems can detect these nuances to whether airline organizations are structurally set up to act on them. The Investment Pattern and What It Reveals The heavy concentration of capital at the cabin level reflects a traditional commercial logic: premium suites drive massive revenue and produce highly tangible, photogenic artifacts that anchor an airline's marketing campaigns. A major U.S. network carrier noted that premium revenue grew by 14% year over year in Q1 2026, outpacing basic economy revenue growth by nearly double. By contrast, the journey around the cabin is significantly harder to invest in. It is distributed across siloed operations, it doesn't photograph well, and its success is measured by the absence of friction rather than the presence of spectacle.  This is a structural supply-chain time lag, where backend tech infrastructure is baked into aircraft specifications years before delivery, leaving it behind current consumer capabilities. Furthermore, as high-bandwidth in-flight connectivity becomes the industry norm, passengers are increasingly self-routing around airline-provided platforms to use their own streaming ecosystems. Yet airlines bridging this gap are discovering that a passenger who feels anticipated and coherently served throughout the entire arc of travel responds with a level of brand loyalty that outpaces competitors. What Closing the Gap Actually Involves Achieving an unbroken travel experience means treating the end-to-end journey as a single design object. In practice, it tends to require three things working together: A Digital Brief that Starts Before the Airport The pre-trip digital environment is heavily contested. Passengers are increasingly delegating planning and booking to autonomous AI assistants that compare and act on their behalf. To compete, an airline's digital touchpoints cannot simply exist; they must use existing tools—apps, IFE, and current communication architecture—meaningfully better before reaching for complex new infrastructure. Context Continuity at Human Touchpoints Moments with lounge hosts, gate agents, and flight crews are where premium service is most deeply felt and remembered. Empowering these teams involves an information architecture question that goes well beyond training. Instead of pushing dense passenger profiles, systems should surface small, timely, and actionable data (e.g., this passenger is connecting, prefers the aisle, hasn't eaten). Moreover, the mechanism should look less like a rigid dashboard and more like a quiet layer of AI agents watching states across systems and surfacing exactly what matters, to whom, and when. However, it's important to consider that this becomes meaningfully harder in interline and codeshare environments, making partner data integration the next critical operational hurdle.   Arrival as Part of the Product Arrival remains the most underinvested stage in the premium ecosystem, yet it heavily colors a passenger's final assessment. Carriers that extend premium design thinking through landing, baggage delivery, and ground transfers are finding real, highly defensible differentiation that hardware alone cannot replicate. Scaling this part of the journey is difficult, so the best approach is to start small: solve a narrow problem first, prove the loop, and expand from there.  The Opportunity Ahead The design benchmarks sweeping the industry prove that airlines are incredibly serious about what world-class means in a physical environment. The opportunity now is to bring that same seriousness to the digital and operational journey around it. Airlines that invest in this level of journey continuity will build an emotional and operational advantage that competitors can't simply clone with a newer seat order. That is the frontier that excites us at Globant: where premium product ambition meets journey-level design. It's where the next competitive advantage will be won. Discover how to anchor end-to-end traveler loyalty at the Globant Airlines AI Studio.

Stop optimizing a leaky bucket: Why now is the perfect time to review your commerce stack
Retail

Stop optimizing a leaky bucket: Why now is the perfect time to review your commerce stack

In the race to dominate the commerce landscape, many businesses are making a critical mistake. They are trying to pour investment into a vessel full of holes. While the allure of agentic commerce and AI-driven personalization is strong, these advanced capabilities cannot operate effectively atop fragmented data and brittle legacy architectures. This is why a commerce stack review cannot wait. The technical debt you tolerate today will prevent you from adopting the agentic tools of tomorrow The hidden cost of making it work Many businesses today are trapped in a cycle of "duct-tape" digital transformation. They face a recognizable tension between the desire for rapid innovation and the heavy weight of technical debt. I frequently see brands struggling with four major pitfalls that drain their resources. Legacy technologies: Stacks that were cutting-edge years ago now act as anchors. They prevent brands from scaling or adopting modern, modular architectures that the current market demands. This inertia comes with an invisible tax on productivity: developers spend up to 42% of their time dealing with technical debt rather than new feature development. The implementation gap: High-velocity technology adoption often creates a divide between digital tools and the people who use them. When companies rush into deployment without aligning the software to their business processes or integrating it with the rest of their tech stack, they inadvertently create manual bottlenecks and data silos. Instead of accelerating growth, these disconnected systems become an operational burden that requires constant manual intervention to bridge the gaps. Data and AI readiness: Brands want to enable AI-ready commerce platforms, but they lack the clean, governed, and accessible data required to power these models. You cannot build a market-leading AI shopping assistant on a foundation of messy, siloed information. The market sentiment is absolute on this: 96% of IT and business leaders agree that the success of AI agents depends heavily on seamless, debt-free data integration. Without it, the risk of failure is stark. Gartner predicts that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data Process and governance friction: Often, it is not the technology itself that fails. Instead, siloed workflows and unclear governance and ownership prevent effective technology delivery and operational excellence. The result of these issues is that you end up over-customizing and investing in solutions that create additional technical debt. This increases your total cost of ownership while significantly decreasing your agility. A structured framework for resolution To stop optimizing for the past and start building for the future, you need more than a quick fix. You need a methodical diagnostic and health check that uncovers both technical and non-technical domains. It isn’t an overnight process, but it typically takes less than a month to fully assess the landscape and identify where the friction actually lives. The point isn't to just generate a list of problems. It's about building a prioritized roadmap for the way forward. I’ve found that focusing on four critical pillars is the most effective way to ensure the work actually sticks. 1. Architecture review It is important to analyze the backend, third-party extensions, and system integrations against industry best practices. This includes looking for ways to simplify the tech stack. A common finding is that clients can better leverage "best of breed" applications or out-of-the-box capabilities to streamline their architecture to lower costs. This may involve leveraging AI tech. 2.AI Readiness While businesses are moving fast to adopt AI, the existing tech stack often ends up being the very thing holding them back. It’s worth taking a hard look at whether there are fundamental gaps in architecture or data readiness that might stall an implementation before it even starts. With the market currently flooded with new tools, the challenge is shifting from finding an AI option to picking the right one. It’s less about following the hype and more about evaluating platform decisions to ensure they actually fit the long-term strategy 3. Strategic and solution fit An evaluation of the current tech stack against long-term business goals is important to establish. Determining if systems are designed to communicate effectively or if they are creating a fragmented experience for customers is important to assess. 4. Management and governance review A review of product management processes, quality assurance standards, and team structures is often overlooked but can be critical to the smooth operation of commerce technology modernization. By identifying gaps in governance, you can ensure that once the technical issues are resolved, you are actually equipped to maintain that desired uplift in performance. Findings from the field When companies scramble to innovate, they often fall into a predictable trap. Instead of building a clean, considered architecture, they pile third-party plugins onto their online commerce platform like Jenga blocks. My team and I see this failure pattern all too often. A brand wants a fast feature, so they install an app. Then they install another app to fix a limitation in the first one. Before long, a dozen different tools are fighting for control over the storefront code. This creates a brittle environment where a minor update in one tool breaks an entirely separate part of the site. It is an invisible tax on growth that paralyzes internal teams and destroys site speed. My team and I recently reviewed a global enterprise account that illustrates this breaking point. On the surface, the business was ready for agentic commerce. Under the hood, third party app bloat had dragged their mobile score down to “Poor” on Google PageSpeed Insights. This fragmentation also buried their administrative teams under massive operational overhead, trapping employees in a constant loop of manual workarounds just to keep the storefront running. For example, instead of leveraging modern commerce automation features, each product recommendation block on the site had to be configured and updated completely manually by an admin. Attempting to launch agentic commerce customer experiences on top of an unstable architecture like that is a losing battle from day one. Is your foundation ready? The biggest risk for companies today is ignoring foundational gaps while jumping straight to highly visible AI use cases in the market. While much of the industry moves toward agentic experiences, tomorrow’s leaders are already preparing their data and architectures for that next wave of disruption. If you don't fix the foundation today, your competitors will sprint ahead. You will be left playing catch-up for years to come. If you remember only one thing from this article, let it be this: True digital transformation is unlocked when the patchwork ends. Is your organization building capabilities for the future, or are you just duct-taping the past? Stop guessing where the holes in your bucket are. At Globant’s Commerce Studio, we help global brands move past the patchwork, audit their technical debt, and build unified architectures designed for the upcoming wave of agentic AI.

Personalization at 35.000 Feet is an Orchestration Challenge, Not a Data One
Airlines

Personalization at 35.000 Feet is an Orchestration Challenge, Not a Data One

Personalization sits on nearly every airline's roadmap, and most carriers have invested meaningfully in the data infrastructure to support it. The progress is real: communications that feel more relevant, service gestures that signal recognition, features that reflect passenger history. Still, even the most advanced programs tend to hit the same ceiling. Recent discussions among industry leaders and practitioners have sharpened that view considerably. Across the sector, experts keep returning to the same phenomenon, what some have started calling “the plateau problem.” Passengers sense it too. The airline clearly knows something about them. It just doesn't always show up at the right moment in the right way. You feel known digitally, but that recognition tends to dissolve once you reach your seat. That pattern is worth understanding, because it points to something structural. And something solvable. Airlines Know Their Passengers. So Why Doesn't It Feel Like It? Airlines hold vast amounts of passenger data. Transactional history, behavioral patterns, stated preferences, loyalty program integration signals... The raw material is there, at scale, in virtually every large carrier's environment. But what makes personalization so hard is where that data lives. The Frequent Flyer Program (FFP) maintains loyalty; the operational stack manages disruptions; the cabin system runs the onboard environment. Each of these systems was built to do its job well, and each does. The challenge is that none were designed for real-time dialogue with the others, and they are organized around the airline's structure rather than the passenger's journey.  That's not a design flaw; it reflects decades of legitimate operational priorities. However, it does create a gap: an airline that recognizes you perfectly in the cloud but forgets you entirely at the gate. One underappreciated illustration of this is the loyalty program itself. Passengers routinely conflate tier points with currency points, and mostly care about the latter. When the system built to recognize a passenger isn't legible to them, that's a personalization failure before any technology question even enters the picture. It is a breakdown of experience design, a massive investment in data that ultimately fails to translate at the point of interaction.  The Missing Layer Between Data and Experience When personalization plateaus, the natural instinct is to add more data or build a smarter model. Both can help, but in most cases, the binding constraint is elsewhere. The personalization passengers actually feel requires true orchestration. This means establishing a live, writable passenger context that actively moves across touchpoints in real time, informing the right action at the exact moment it matters. It is not about a periodic batch sync or a static segmentation model refreshed quarterly; personalization must be continuous. It requires a persistent digital signal that connects what loyalty knows, what operations sees, and what the crew or service interface needs to act. Connectivity serves as the indispensable prerequisite that enables this fluidity, going far beyond a standard feature or ancillary revenue line to become the foundation for everything else. This is precisely where the current AI conversation quietly depends on infrastructure choices that predate it. Agentic copilots for crew, predictive service recovery, and generative pre-flight interactions all fundamentally assume that a live, cross-departmental passenger context already exists to reason over. Without that substrate, an agent is only a chatbot with airline branding. The orchestration layer isn't in competition with AI investment; it's what determines whether the AI investment returns anything. Three patterns tend to get in the way, and they're common enough across the industry that they're worth naming: Organizational structure mirroring system structure: When loyalty, operations, and digital each carry separate ownership and separate success metrics, integration becomes a coordination challenge long before it ever becomes a technological one. While cross-system integration is frequently feasible on a technical level, airlines' immense operational complexity makes navigating these internal departmental silos especially pronounced. Static personas standing in for dynamic context: Most personalization programs still rely on historical segmentation models that describe who a passenger was, rather than who they are right now, on this journey, in this moment. It is not that these models are inherently incorrect; it is that they update on a completely different clock than the passenger does. Insights that don't quite reach the point of interaction: Even when data and sophisticated logic are present, the final mile of execution requires a physical interface, whether a human crew member or a seatback screen. The screen, for instance, remains an underused orchestration endpoint, a vital third channel in an omnichannel model that typically stops at web and mobile. If a strategic insight does not reach these touchpoints in a usable form at the exact moment it matters, it remains purely analytical. It never translates into experience. What the Airline Leaders Are Doing Differently The carriers making real headway tend to share a few characteristics. Notably, their success is rarely driven by the size of their technology budget. Instead, they have made a definitive organizational commitment to treat the passenger journey orchestration as a holistic design object rather than a fragmented collection of departmental responsibilities. Someone explicitly owns the continuity of experience across touchpoints, including the handoffs between them. These transitions are design acts in their own right, not just operational shifts, the move from lounge to cabin, where voice and context either carry through or dissolve.  They have invested in the unglamorous infrastructure: integration layers, real-time data pipelines, shared passenger context models... Work that often goes unnoticed, but that makes everything else possible. A concrete example of this architecture reaching the cabin level is how some carriers leverage Low Earth Orbit (LEO) connectivity partnerships and customized In-Flight Entertainment (IFE) software. By enhancing these surfaces beyond out-of-the-box capabilities, they effectively turn the seatback screen into a seamless extension of the airline’s own mobile application. These carriers have also internalized that airlines compete on verbs, not just nouns. Product specs—seat, studio, suite—are listable. Experience is felt. Small things, repeated well, become the signature. Ritual is a design strategy. By remaining thoughtful about the final mile, they equip frontline teams with tools that surface the right insights in an actionable form at the exact moment they are relevant. The core design principle here is clear: technology should enable human interaction, not substitute for it. The Imperative for Connected Aviation Passenger expectations are increasingly set outside aviation. It happens in the streaming, retail, and banking apps people open every day, where personalization isn't a feature but the baseline. Surface-level efforts in travel won't clear that bar. The pace of change in aviation has its own structural realities: hardware cycles are long, and physical and digital investments tend not to align on the same timeline. Instead of driving caution, this reality demands well-sequenced investment, starting with the layers that unlock the most downstream value.  The carriers that close the gap between data richness and journey coherence will build a compounding advantage. And it starts with the orchestration layer, not the passenger-facing surface. The fact that these themes are emerging independently across industry conversations only reinforces that carriers broadly agree on where the work is. For us at Globant, this is exactly the intersection where technology strategy and passenger experience personalization design are most productive together. A question we find most useful with airline partners isn't “how do we add more personalization?”, but “how do we make personalization structurally possible across the journey?” This reframing tends to open up a different, and far more productive, kind of conversation. And it’s a very meaningful one to have. Personalization becomes real when every touchpoint speaks the same language. See how Globant is helping airline personalization strategy within that shift.

AI solutions from proof-of-concept to full production: Globant at AWS Summit Madrid 2026
Data & AI

AI solutions from proof-of-concept to full production: Globant at AWS Summit Madrid 2026

While 61% of Spanish companies have already welcomed AI into their workflows—according to insights shared at the event—a striking bottleneck remains: only 17% have backed it with the formal strategy and data governance required for advanced adoption.  At AWS Summit Madrid 2026, the atmosphere was charged with a single, urgent realization: We have officially transitioned from a wave of curiosity to a need for execution. Closing this gap requires moving past the "hype" and solving the hard architectural truths beneath the surface.  As an AWS Premier Tier Partner and holder of the AWS AI Services Competency, Globant joined the summit to share a clear point of view: the companies that will lead the next wave of transformation are not the ones testing AI in isolation, but the ones building the right foundations to scale it across the business.   From experimentation to measurable impact According to AWS keynote data, closing the AI adoption gap could unlock an additional €191 billion in economic value across Europe. The key takeaway is simple: AI value is no longer defined by the sandbox. It is defined by execution, governance, and the ability to turn technology into hard business outcomes, driving AI business value transformation across industries. What stood out at the summit AI is moving into production Generative AI has evolved past the trial stage. The focus is now on scalable, secure, enterprise-grade implementations. We saw this live with case studies like OutSystems, which deployed the Amazon Bedrock connector to launch 19,000 autonomous AI agents into production in just five months, slashing development costs by 30%. These examples highlight the growing impact of AWS generative AI solutions in the enterprise. Reinventing the Developer Experience A massive spotlight was put on engineering velocity. AWS introduced Kiro, a breakthrough AI developer assistant that converts natural-language prompts into deep technical specifications and automatically packages them into ready-to-ship code. Alongside Amazon Q—which enterprise adopters like 3M are using to cut sales meeting preparation time from 5 hours down to minutes—the tools shown proved that developer productivity is skyrocketing, demonstrating how AI can improve developer productivity at scale.. Cloud and Data Remain the Foundation ("No Data, No AI") The summit reinforced a core architectural truth: without modern data pipelines, AI cannot deliver at scale. To alleviate data fragmentation and eliminate storage bottlenecks, AWS rolled out Amazon S3 Files, allowing engineering teams to mount high-performance S3 buckets directly as local file systems within EC2 or Lambda instances. Legacy modernization was also front and center, highlighted by Mercedes-Benz, which utilized AI to autonomously migrate ancient COBOL code into modern Java, cleanly deprecating 3,000 legacy applications. Sovereignty and Local Infrastructure Matter AWS underscored its massive, long-term commitment to the Spanish market, highlighting its €33.7 billion investment pipeline in the Aragon data center region through 2035—a project slated to support 30,000 jobs annually and inject €32 billion into Spain's GDP. Crucially for regulated industries, the AWS European Sovereign Cloud is now officially live and physically isolated, operated exclusively by EU residents to make sovereign AI a turn-key operational reality. Multi-Model Flexibility and Agentic AI Enterprises want choice and control. The blockbuster technical announcement of the General Availability (GA) of OpenAI GPT models natively inside Amazon Bedrock proved that the market demands a multi-model approach. Coupled with Amazon Bedrock Agent Core and the new AWS Lambda Durable Functions (which allow autonomous agents to pause complex workflows for hours or days without racking up idle computing bills), agentic AI has officially moved from concept to operational reality. The mindset shift companies need The biggest challenge is no longer access to technology; it is the organizational mindset required to use it well. In fact, 60% of businesses still cite a lack of digital skills as their main bottleneck, highlighting the challenges of AI adoption that many organizations continue to face. Advanced AI adoption requires a structured framework: Executive sponsorship to break silos Clear governance and compliance protocols A focused use-case strategy tied to business value Modern data foundations (moving away from legacy systems) Continuous investment in skills and employee reskilling   The companies that succeed will be the ones that treat AI as a core business capability, not just an isolated technology layer. How Globant helps turn AI into business value At Globant, we help organizations move from AI ambition to execution. Our approach combines cloud, data, and AI expertise with deep industry knowledge to design solutions that are ready for real-world impact. We deliver this through: AI Pods: AI-native service units where agents execute, experts supervise, and enterprises receive governed outputs at scale. Industry-specific AI solutions: Custom frameworks designed to tackle unique sector challenges, from financial services to retail. End-to-end implementation: Comprehensive support guiding your architecture from initial strategy and data ingestion up to final production. Strong AWS foundation: Leveraging our Premier Tier partnership to deploy multi-model architectures securely, efficiently, and compliantly.   Whether the goal is to modernize operations, improve customer experience, or unlock new business models, we work with clients to make AI practical, scalable, and measurable. Reinvention in action Explore how Globant, powered by AWS, is driving tangible business outcomes for enterprises worldwide. Discover our real-world success stories below. Interbanking-Frisvy’s How Interbanking-Frisvy Leveraged AWS and Generative AI for Enhanced Document Processing Santander Santander’s Cloud Leap with Globant & AWS USGBC Globant accelerates USGBC’s adoption of sustainability performance management Looking ahead AWS Summit Madrid 2026 made clear that the future of AI belongs to organizations that can combine the right infrastructure, data, and execution model. At Globant, we are ready to help clients navigate that shift and turn AI into a real competitive advantage.

The End of CRM as We Know It: Data as the Operating System for Agentic Execution
Salesforce

The End of CRM as We Know It: Data as the Operating System for Agentic Execution

Most corporate boards are measuring the success of their AI initiatives using the wrong metric: the sheer number of pilots currently in development. In reality, what many multinational enterprises call an "AI transformation journey" is actually a systematic burning of budget on computing power and token consumption, yielding zero measurable impact on the P&L statement. We have reached a critical tipping point. Enterprises have successfully invested millions in implementing Salesforce, yet their organizations continue to run on the logic of a traditional CRM: fragmented data, convoluted integrations, and automations confined to isolated workflows. When leadership attempts to inject an agentic layer on top of this fractured setup, the system stalls. The core issue is not the underlying AI technology; it is the strategic scope. Introducing autonomous layers into processes that have not been adequately transformed or rethought, onto disconnected data architectures, simply adds technological fluff to your operation. AI does not just need more data; it needs operational context. The Hidden Tension: Decoupling Software from Execution For the past decade, corporate scaling relied on a predictable formula: accumulation of licenses and headcount. If an organization needed to expand customer support or accelerate a sales cycle, the answer was always to hire more analysts and buy more software seats. Today, that model is becoming obsolete. We are witnessing a radical paradigm shift in technology consumption: the transition from user-based licensing to usage- and execution-based economics. In this new ecosystem, Salesforce is evolving far beyond a traditional CRM, consolidating into an Enterprise Execution Platform where the workforce is inherently hybrid—composed of humans and AI agents co-orchestrating end-to-end processes. The real obstacle to unlocking this model is not a lack of tools. It is the absence of a structural foundation that addresses the two greatest barriers to enterprise AI: data readiness and operational adoption. When an AI agent (such as Agentforce) lacks access to high-quality, actionable, real-time data, its decision-making ability is entirely neutralized.  No matter how advanced its native skills or actions are, without a unified context, the agent generates insufficient or irrelevant outputs on the operational front, when the process is not redesigned taking into consideration the new capabilities humans have at their disposal, we are providing our employees with tools that keep them busy with work, but the operation works similarly as before, but the volume increases, not the value, nor the effectiveness. Consequently, internal teams lose trust in the tool, abandon it, and revert to manual, siloed processes. 5 Strategic Insights for the C-Suite 1. Data as the Ultimate Operating System Data infrastructure is no longer a static storage repository; it is the central nervous system of the organization. Forward-thinking enterprises are no longer designing data architectures solely for humans to read dashboards, but for AI agents to understand, decide, and execute business actions directly within the workflow. If the data's context makes no sense, the AI agent won’t be able to discern nuances of the business process and will fail. Humans can make those decisions most of the time, but if we want to scale in speed and volume, we need AI automation, and for it, data provides the map to navigate the operational territory. 2. From Clean Data to a "Shared Trusted Data Context." The traditional IT paradigm focused heavily on actions such as removing duplicate records or completitude. AI automation demands a unified environment of real-time intelligence. This means orchestrating technologies like MuleSoft, Informatica, and Data 360 in tandem, abstracting the complexity of legacy backend systems to deliver the exact context to the agentic layer at the precise millisecond it needs to execute. 3. Headless Architecture and Ubiquitous Execution Salesforce will maintain its value by managing the core business rules, operational workflows, and those valuable transactional records. However, user interaction will no longer belong exclusively to its native interface. Through modern connectivity protocols like MCP (Model Context Protocol) and advanced APIs, natural language interaction will meet users wherever they already operate, whether that is Slack, Microsoft Teams, Enterprise WhatsApp, or custom mobile and desktop applications. 4. The End of Bloated IT Delivery Teams The current system’s integrators' model of throwing endless hours and headcount writing code and configuring every field and every flow cannot survive at the speed AI can provide. Achieving startup velocity within an enterprise architecture requires automated, AI delivery models governed by humans in a structured framework. Humans (developers and consultants) work as the quality gate for the AI builds, providing the “why” and the “how”, accelerating performance, increasing quality, taking advantage of the completeness of documentation AI can provide, setting the foundation for a development cycle more robust than ever before. 5. The PoC Limited-Scope Trap AI initiatives routinely stall because their strategic scope is too narrow. Limiting AI to minor, low-impact tasks generates computing costs without driving real value. To capture true ROI, the strategic goal must be ambitious and tightly aligned with macro business objectives, such as a drastic reduction in operational turnaround times, a complete reimagining of the service delivery chain, or even our company agents interacting with our partners' or customers’ agents to execute routine tasks and activities. Moving Beyond Silos: The Globant Approach To operationalize this new model, companies must transition away from traditional integration pipelines and move toward an automated, continuous delivery strategy. At Globant, we are solving this challenge by deploying specialized AI Pods for the Salesforce and MuleSoft ecosystems. AI Pods represent a fundamental evolution in professional services. Rather than simple staff augmentation, they operate on an industrialized subscription model that pairs senior architects (humans-in-the-loop) with embedded AI capabilities under a rigorous governance framework. How does this look in practice? When an organization needs to migrate legacy integrations into modern MuleSoft architectures to feed its AI agents, Globant’s AI Pods automate the API design phase and modernize code at startup speed. This drastically accelerates project execution, enforces strict guardrails for data safety, and drives down the total cost of delivery. The result is a clean architecture that allows enterprises to transition from fragmented data silos to complex, autonomous workflows in weeks rather than years. The impact metrics observed across mature enterprise execution deployments prove that the value is highly quantifiable: IT Operations: Achieve a 30% to 40% reduction in MTTR via AI-recommended resolutions backed by deep data context. HR Services: Reclaim up to 2 hours daily per agent by routing complex response drafting and validation through automated AI flows. Customer Support: Deflect up to 40% of routine inquiries through advanced, self-service AI recommendations that actually resolve issues instead of just redirecting tickets. Conclusion The competitive advantage in the AI era will not belong to the companies with the largest foundational models, nor to those accumulating the most software licenses. It will belong to the organizations that successfully transform their fragmented data into a unified enterprise execution platform. Continuing to treat Salesforce as a glorified repository for sales interactions is a fundamental error in strategic diagnosis. The real challenge for the C-suite today is engineering the Shared Trusted Data Context required for humans and agents to operate with maximum speed and total guardrail safety. Those who continue to expand team sizes to fix agility problems will find themselves managing legacy software. The future belongs to intelligent execution. Learn more about our approach here. 

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