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The tech industry has endured another turbulent year, buffeted by the continued rise of artificial intelligence and the economic threats posed by President Donald Trumps tariffs. Even the most prominent companies encountered challenges they never imagined theyd have to face. As 2025 comes to a close, here are Apple’s biggest wins and greatest failures of the year. Apples biggest wins of 2025 iPhone 17 series Without a doubt, Apples biggest win of 2025 is the iPhone 17 series, which includes the iPhone 17, iPhone 17 Pro, and iPhone 17 Pro Max. Myriad reports suggest that iPhone 17 series sales have exceeded both Apples and investors expectations. Apple redesigned the iPhone 17 and iPhone 17 Pro this year, giving the former a much-improved display, a vastly better camera system, and longer battery life. The Pro versions got an all-new unibody design, the best cameras in an iPhone ever, and up to 39 hours of battery life. While these improvements are mainly iterative, they ticked the boxes that consumers care most about in their phones: camera and battery life. And those consumers have rewarded Apple for it. iPhone 17 sales have surged in the U.S. and, more importantly, in China, the worlds largest smartphone market after the USA. Apples iPhone sales were a key factor in the recovery of the company’s stock, after it got hammered earlier this year due to its possible exposure to Trumps tariffs. Liquid Glass After the iPhone, Apples next most important product is iOS, the operating system that powers its handsets. This year, Apple made the rare move of completely revamping the look of that operating system with the introduction of the Liquid Glass design language in iOS 26. It was the first time the company had radically changed iOS’s look since 2013. While iOS 26s Liquid Glass faced early criticism, as most visual overhauls do, Apple has continued to tweak the look and feel of the new design language. As a result, much of the online furor over the changes seems to have died down. More importantly, iOS 26’s new design gives Apples smartphone software a distinct look that immediately distinguishes it from Android. In the end, the softwares ability to mimic the way light bands and warps through glass has brought a level of fun and playfulness to Apples flagship product not seen since the days of Steve Jobs. Apples simplified branding The final big win for Apple in 2025 is not a product or feature, but a branding strategy. As Apples product lineup has grown in recent years, its product names have become confusing, particularly when it comes to software and services. But this year, Apple decided to simplify things. Previously, Apple’s operating systems were branded with different version numbers (iOS 18, macOS 15, watchOS 11, etc). Now they’re named after the upcoming year: iOS 26, macOS 26, watchOS 26, iPadOS 26, tvOS 26, visionOS 26. This streamlined naming structure makes it easy for users to determine whether their device is running the latest software. And Apple didnt stop there. The company also mercifully decided to drop the overused plus sign from its streaming services name, too. Apples greatest failures of 2025 iPhone Air While the iPhone 17 series may have been full of iterative updates this year (which consumers seem to have loved), Apple swung for the fences with another 2025 iPhone: the all-new iPhone Air. At just 5.6 millimeters, it is Apple’s thinnest iPhone ever. Yet multiple reports say that there has been hardly any demand for the companys newest smartphone. The main problems with the iPhone Air seem to be its subpar camera system and relatively short battery life. As the success of the iPhone 17 series teaches us, those are the two things customers care about most. Demand for the new device is so weak that Apple has reportedly cut production by more than 80%. Still, Apple may have already gotten what it really wanted: proof of concept that it could make an iPhone so thin that it could join two together to create the first dual-screen iPhone foldable. Apple Intelligence 2025 may have been a year of continued artificial intelligence progression across the tech industry, but Apples AI system, Apple Intelligence, hardly added any new AI featuresnot worthwhile ones, anyway. The company added some useful Live Translation features, but other than that, it mainly just enhanced Apple Intelligence with gimmicks that other AI systems have long been capable of, such as on-screen image recognition and new AI slop filters. Those hoping to see a revamped Siri that could compete with the likes of OpenAIs ChatGPT and Googles Gemini will have to wait until 2026or later. Yet I question how much consumers care about Apple lagging in the AI space, given that they can already run nearly any third-party AI app on their iPhone. Still, the lack of innovative AI advancements is a bad look for the company, which is otherwise the de facto innovation leader in the industry. Apple Vision Pro M5 One area in which Applehas undoubtedly innovated in recent years is augmented reality, thanks to its groundbreaking Apple Vision Pro headset. In 2025, Apple announced the successor to the original Vision Pro, updated with the M5 Apple Silicon chip, which enables higher resolution and other display enhancements. Yet Apple didnt address the myriad other issues with the technologically impressive device, notably its heavy weight and eye-watering $3,499 price point. Because of this, the headset remains a niche product that is unappealing or financially out of reach to the average user.
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E-Commerce
In the Star Trek universe, the audience occasionally gets a glimpse inside schools on the planet Vulcan. Young children stand alone in pods surrounded by 360-degree digital screens. Adults wander among the pods but do not talk to the students. Instead, each child interacts only with a sophisticated artificial intelligence, which peppers them with questions about everything from mathematics to philosophy. This is not the reality in todays classrooms on Earth. For many technology leaders building modern AI, however, a vision of AI-driven personalized learning holds considerable appeal. Outspoken venture capitalist Marc Andreessen, for example, imagines that the AI tutor will be by each childs side every step of their development. Years ago, I studied computer science and interned in Silicon Valley. Later, as a public school teacher, I was often the first to bring technology into my classroom. I was dazzled by the promise of a digital future in education. Now, as a social scientist who studies how people learn, I believe K-12 schools need to question predominant visions of AI for education. Individualized learning has its place. But decades of educational research are also clear that learning is a social endeavor at its core. Classrooms that privilege personalized AI chatbots overlook that fact. School districts under pressure Generative AI is coming to K-12 classrooms. Some of the largest school districts in the country, such as Houston and Miami, have signed expensive contracts to bring AI to thousands of students. Amid declining enrollment, perhaps AI offers a way for districts to both cut costs and seem cutting edge. Pressure is also coming from both industry and the federal government. Tech companies have spent billions of dollars building generative AI and see a potential market in public schools. Republican and Democratic administrations have been enthusiastic about AIs potential for education. Decades ago, educators promoted the benefits of One Laptop per Child. Today, it seems we may be on the cusp of one chatbot per child. What does educational research tell us about what this model could mean for childrens learning and well-being? Learning is a social process During much of the 20th century, learning was understood mainly as a matter of individual cognition. In contrast, the latest science on learning paints a more multidimensional picture. Scientists now understand that seemingly individual processessuch as building new knowledgeare actually deeply rooted in social interactions with the world around us. Neuroscience research has shown that even from a young age, peoples social relationships influence which of our genes turn on and off. This matters because gene expression affects how our brains develop and our capacity to learn. In classrooms, this suggests that opportunities for social interactionfor instance, children listening to their classmates ideas and haggling over what is true and whycan support brain health and academic learning. Research in the social sciences has long since proved the value of high-quality classroom discourse. For example, in a well-cited 1991 study involving over 1,000 middle school students across more than 50 English classrooms, researchers Martin Nystrand and Adam Gamoran found that children performed significantly better in classes exhibiting more uptake, more authenticity of questions, more contiguity of reading, and more discussion time. In short, research tells us that rich learning happens when students have opportunities to interact with other people in meaningful ways. AI in classrooms lacks research evidence What does all of this mean for AI in education? Introducing any new technology into a classroom, especially one as alien as generative AI, is a major change. It seems reasonable that high-stakes decisions should be based on solid research evidence. But theres one problem: The studies that school leaders need just arent there yet. No one really knows how generative AI in K-12 classrooms will affect childrens learning and social development. Current research on generative AIs impact on student learning is limited, inconclusive, and tends to focus on older studentsnot K-12 children. Studies of AI use thus far have tended to focus on either learning outcomes or individual cognitive activity. Although standardized test scores and critical thinking skills matter, they represent a small piece of the educational experience. It is also important to understand generative AIs real-life impact on students. For example: How does it feel to learn from a chatbot, day after day? What is the longer-term impact on childrens mental health? How does AI use affect childrens relationships with each other and with their teachers? What kinds of relationships might children form with the chatbots themselves? What will AI mean for educational inequities related to social forces such as race and disability? More broadly, I think now is the time to ask: What is the purpose of K-12 education? What do we, as a society, actually want children to learn? Of course, every child should learn how to write essays and do basic arithmetic. But beyond academic outcomes, I believe schools can also teach students how to become thoughtful citizens in their communities. To prepare young people to grapple with complex societal issues, the National Academy of Education has called for classrooms where students learn to engage in civic discourse across subject areas. That kind of learning happens best through messy discussions with people who dont think alike. To be clear, not everything in a classroom needs to involve discussions among classmates. And research does indicate that individualized instruction can also enhance social forms of learning. So I dont want to rule out the possibility that classroom-based generative AI might augment learning or the quality of students social interactions. However, the tech industrys deep investments in individualized forms of AIas well as the disappointing history of technology in classroomsshould give schools pause. Good teaching blends social and individual processes. My concern about personalized AI tutors is how they might crowd out already infrequent opportunities for social interaction, further isolating children in classrooms. Center childrens learning and development Education is a relational enterprise. Technology may play a role, but as students spend more and more class time on laptops and tablets, I dont think screens should displace the human-to-human interactions at the heart of education. I see the beneficial application of any new technology in the classroomAI or otherwiseas a way to build upon the social fabric of human learning. At its best, it facilitates, rather than impedes, childrens development as people. As schools consider how and whether to use generative AI, the years of research on how children learn offer a way to move forward. Niral Shah is an associate professor of learning sciences & human development at the University of Washington. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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E-Commerce
In todays corporate landscape, optics often precede outcomes, especially in technology-led transformations. Announcements of new platforms, AI-powered strategies, or digital-first pledges frequently come long before the underlying infrastructure to support them. That was Teds reality as the chief growth officer at a global bank when his CEO unveiled a high-profile AI-Powered Growth Strategy positioned as a bold leap forward. The announcement made headlines and thrilled investors, but behind the scenes, the organization wasnt prepared. Ted was given a skeletal team of two direct reports, a patchwork of third-party tools, and the mandate to partner with five global banking divisions serving more than 500 employees. He was expected to turn the AI vision into reality with little structural support. This tension is commonand survivable. Leaders who maintain credibility dont scrap such pledges or decry them. Instead, they manage the gap between promise and proof. A well-intentioned CEO may launch an initiative to signal innovation, but when systems or skills lag, ambition can outpace execution. WeJenny, as an executive adviser and learning & development expert, and Kathryn, as an executive coach and keynote speakerhave identified five strategies to help executive teams navigate these moments with integrity and strategic foresight, especially when the initiative is more symbolic than substantive in its early stages. 1. Balance bold aspiration with candid honesty In the early stages of transformation, perception often outpaces progress. Stakeholders want visible proof that change is real. McKinsey found that 70% of digital transformations fail to meet their intended outcomes because senior executives either overpromise or disengage when early wins dont materialize. Those charged with execution must balance bold aspiration with candid honesty, communicating both the vision (Heres where were heading) and gap (Heres what it will take to get there) to maintain trust and momentum. Behind the scenes, Ted allocated 20% of the budget to data cleanup and capability-building, unseen but essential work such as strengthening data quality and governance, building the pipelines and quality controls that support mission-critical AI, and elevating the organizations baseline AI literacy. Within a year, three pilots validated the transformation narrative and quieted early skeptics. Edelmans Trust Barometer shows that stakeholders extend grace when leaders communicate with clarity and consistency, not performative certainty. Credibility, not charisma, sustains momentum through uncertainty. Try this: Balance vision with transparency. Use confident yet realistic language, such as Were learning in real time or This is a multi-year capability build. 2. Map Whats Performative vs. Whats Possible Not every element of a high-visibility initiative will yield immediate results. The key is distinguishing symbolic actions that signal intent from those that build lasting capability. Theresa, chief digital officer at a consumer goods firm, launched a public digital transformation week with town halls and press coverage. She brought in her AI agency partners and major retail customers to show alignment and signal momentum, partnership, and focus. The event created attention, but she knew the real work would happen out of sight. She used a short-horizon/long-horizon approach. The short horizon created urgency and rallied stakeholders, while the longer horizon anchored on execution. She reassigned 30% of her team to integrate legacy systems, clean priority datasets, and run joint sprints with her AI partners. That groundwork created a technical foundation strong enough to support advanced modeling. Within nine months, they delivered a demand-forecasting model that reduced inventory outages by 18%, transforming a performative launch into measurable operational value. When mapping an initiative, clarify two horizons: Short horizon (06 months): What signals matter? (e.g., visible executive sponsorship, internal messaging, external storytelling) Mid / long horizon (624+ months): What structural enablers must be built? (e.g., data platforms, technology partnerships, governance, skills) Visibility matters, but only when its paired with substance. Try this: Separate the symbolic from the structural. Create a two-horizon map to test balance: Which actions build momentum? and Which build capability? Then ensure both are visible. 3. Leverage Visibility as Currency When a high-profile initiative captures attention, use that spotlight to build political capital and secure future resources. Leaders who link early symbolic wins to longer-term learning sustain engagement and trust. Julie, a chief marketing officer we advised, leveraged her companys Digital Reinvention campaign to secure additional funding for employee upskilling, positioning it as the bridge between aspiration and execution. Try this: Treat visibility not as validation, but as leverage. Ask, What can this attention buy us: credibility, talent, or momentum? That perspective turns optics from vanity to value. 4. Build Small Wins that Prove Real Value Symbolic gestures lose power without substance. Once the spotlight fades, stakeholders want proof. Anchor your narrative in small, visible wins: projects, pilots, or behaviors that validate early promises. Start with pilots that address real pain points: automate a reporting process, improve data access for a critical team, or integrate AI into a single workflow. For Ted, that meant delivering credible proof pointsan AI-powered lead scoring model that lifted conversion rates by 12%, a unified customer insights dashboard, and a monthly What Were Learning series to build internal momentum. Small, visible progress converts skepticism into trust and gradually shifts perception from Its all optics to Its starting to work. Try this: Start small, but make progress visible. Choose one pilot that solves a visible pain point within 90 days. Publicize lessons learned, not just the result, to show that momentum is real, even if imperfect. 5. Reframe the Narrative: From Optics to Opportunity The best leaders dont deny the optics, they reframe them as stepping stones to a larger transformation. Gary, a nonprofit CEO we coached, introduced his first AI pilot as symbolic but necessary. It wasnt yet transformative, but it sparked a mindset shift: leaders began talking about data ethics, digital fluency, and decision-making transparency. As he put it, The project wasnt about the tool. It was about changing how we think. Reframing is essential. Deloitte and BCG both show that real value emerges when strategy, technology, and human systems align. Symbolic gestures only matter if they lead to lasting capability and behavior change. When leaders treat optics as openings rather than distractions, they turn visibility into belief. Stakeholders who see learning, transparency, and follow-through extend trust, and grant the runway needed for real transformation. Try this: Name the signal and the shift. Say, This initiative signals where were headed. Then ask, What new conversations or capabilities did this open up? In complex transformations, optics are not the enemy. Theyre a catalyst for belief. What matters is how leaders use those moments to align teams, secure investment, and guide the narrative from promise to proof. Integrity isnt about rejecting optics; its about ensuring they serve a larger purpose. The most effective leaders turn visibility into accountability and symbolic beginnings into lasting systems.
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E-Commerce
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