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In the rapidly evolving world of artificial intelligence, privacy concerns have become a hot-button issue. As AI systems become more integrated into our daily lives, they also gain access to more of our personal data. This has left many wondering: can we have powerful AI without sacrificing our privacy? Apple’s answer, with its new Apple Intelligence system, is a resounding “yes.” Let’s dive into how Apple is setting a new gold standard for AI privacy and how it stacks up against the competition.

The Apple Approach: Privacy By Design

Apple has long been a champion of user privacy, often positioning it as a fundamental human right. With Apple Intelligence, they’re doubling down on this commitment, baking privacy into the very foundation of their AI system. But what does this actually mean in practice?

On-Device Processing: Your Data Stays With You

At the heart of Apple’s privacy-first approach is on-device processing. Unlike many other AI systems that send your data to the cloud for analysis, Apple Intelligence does most of its heavy lifting right on your iPhone, iPad, or Mac. It’s like having a genius friend who comes to your house to help you out rather than asking you to send all your stuff to their place.

This approach means that your personal information – your photos, messages, emails, and more – never leaves your device. It’s not sitting on a server somewhere, potentially vulnerable to breaches or misuse. Your secrets stay your secrets, locked up tight in the device in your pocket.

Private Cloud Compute: When The Cloud Can’t Be Avoided

Of course, some tasks are too complex for even the most powerful mobile devices. For these situations, Apple has introduced ‘Private Cloud Compute.’ This system allows Apple Intelligence to tap into more powerful server-based models without compromising your privacy.

Here’s the clever bit: when your device needs to use Private Cloud Compute, it only sends the specific data needed for that task. This data is processed on special Apple Silicon servers that are as secure as your iPhone. Your information is never stored, and it’s only used to fulfill your request. It’s like sending a confidential letter that self-destructs after it’s been read.

Transparency And Verification: Trust, But Verify

Apple is also setting a new standard for transparency in AI. They’re making the code that runs on their servers available for inspection by independent experts. This means that privacy watchdogs can verify that Apple is doing what they say they’re doing.

Why Apple’s Approach Matters

Apple’s privacy-first approach to AI is more than just a marketing gimmick – it’s a fundamentally different way of thinking about the relationship between technology and personal data. Here’s why it matters:

1. Trust: By keeping your data on your device and being transparent about its processes, Apple is building trust with its users. In an era of data breaches and privacy scandals, this trust is invaluable.
2. Control: Apple’s approach gives users more control over their data. You’re not asked to blindly trust a company with your personal information – you remain in charge.
3. Innovation without Invasion: Apple is showing that it’s possible to create powerful, helpful AI systems without invasive data collection practices. This could set a new benchmark for the industry.
4. Regulatory Compliance: As privacy laws become stricter around the world, Apple’s approach puts them ahead of the curve. They’re not just meeting current standards – they’re exceeding them.
5. Ethical AI: By prioritizing privacy, Apple is contributing to the development of more ethical AI systems. They’re showing that AI can be helpful and intelligent without compromising personal privacy.

The Road Ahead

As AI continues to evolve and integrate more deeply into our lives, the question of privacy will only become more crucial. Apple’s approach with Apple Intelligence sets a new standard, challenging the notion that we must sacrifice privacy for functionality.

Of course, this approach isn’t without its challenges. On-device processing requires powerful hardware, which could potentially lead to higher costs for consumers. And there may be some tasks that are simply too complex for this privacy-first model.

However, by taking this stand, Apple is pushing the entire industry forward. They’re proving that privacy and powerful AI aren’t mutually exclusive, and in doing so, they’re forcing other companies to re-evaluate their own practices.

As consumers, we should be paying attention. The choices we make about which AI systems to use will shape the future of this technology. By supporting privacy-focused approaches like Apple Intelligence, we’re voting with our wallets for a future where our personal data remains personal.

With AI making it increasingly easy for anyone to create truth-bending content, it’s widely said that we are now entering a “post-truth” era. This means it’s becoming increasingly difficult to tell whether what we see online is real or has been AI-generated by someone who wants to deceive us.

Without a doubt, fake video – sometimes known as deepfake or synthetic video – has the potential to be the most deceptive form of fake content. While video evidence used to be the gold standard of truth, even in legal matters, those days are now long gone.

Today, it’s easy to produce hyper-realistic fake videos that make it look like anyone has said or done anything using readily available tools and affordable hardware.

But that doesn’t mean we have to be defenseless. Here, I’ll outline the steps we can take to help us tell facts from fiction if we want to protect individuals, businesses, and society from the growing threat of fake videos.

The Dangers Of Deepfake Videos

Deepfake videos are an emergent threat of the AI era – meaning they pose risks that individuals, businesses and society have never had to face before. The ability to create synthetic video content that can fool people into thinking it’s real has the potential to influence public opinion and even destabilize democratic processes and institutions.

For example – in the run-up to this year’s U.S. presidential election, former Department of Homeland Security chief of staff Miles Taylor observed that hostile states intending to spread disruption no longer need to influence the vote itself. All they need to do is sow doubt that the process has been carried out fairly.

This isn’t just hypothetical. It was recently revealed that deepfake technology allowed a hostile actor to impersonate a top Ukrainian security official during a video call with a U.S. senator. Although the attempted deception was detected before damage was done, the dangerous nature of this near-miss is clear.

Ukraine was the target of another deepfake attack prior to this in 2022 when synthetic video footage of leader Volodymyr Zelensky appeared to show him surrendering and urging Ukrainians to lay down their weapons shortly after the war started.

These examples show the truly global scope of the disruptions that deepfake video could potentially cause. So, how do we go about protecting ourselves from falling victim?

Methods For Detecting Deepfake Video.

We can split the possible methods of identifying and mitigating the threat of deepfakes into four general categories. These are:

Detecting visual cues – this means spotting indicators that are visible to the naked human eye. This could be tell-tale irregularities and unnatural movements – particularly involving facial expressions – that just seem “off.” Inconsistent lighting and a fading or blurring of the boundaries between the faked elements of the video (such as mouth movements when lip-synching has been used) are all potential indicators.

Technological tools – this covers a growing number of software applications specifically designed to detect deepfake videos, such as Intel’s FakeCatcher and McAfee Deepfake Detector. These work by applying machine learning algorithms that can detect patterns or visual indicators that would be missed by the naked eye but show up clearly in a digital analysis of the source data.

Critical thinking – This involves checking sources and asking questions. Is the source of the video trustworthy? Is the content of the video likely to be true? Can you cross-reference it with other sources that provide coverage of the same event in order to establish the truth? And are there logical inconsistencies that seem at odds with what is realistically possible?

Professional Forensic Investigation—while beyond the reach of amateurs, larger organizations and law enforcement agencies can access specialized tools, often powered by the same neural networks used to create deepfakes. Forensic analysis involves trained investigators examining videos frame-by-frame for pixel-scale irregularities or using reverse image search to trace the original source of any footage used to create fakes. Professional investigators can also use biometric analysis to detect discrepancies between facial features that indicate manipulation.

Future Implications

So, what lies in store for us in a world where seeing is no longer believing?

With deepfakes now an inescapable fact of everyday life, it becomes the responsibility of individuals, businesses and government to make sure they have protective measures in place.

It’s clear to me that one implication is that precautionary measures, training, and the development of critical thinking skills among workforces should now be a part of any organizational cybersecurity strategy.

Employees should be taught to be on their guard and identify tell-tale signs of synthetic video, just as detecting and evading phishing attacks is now a standard practice.

We can also expect to see a growing reliance on authentication and verification systems. For example, it could become common for deepfake detection to be built into video conferencing tools to detect routine attempts to leach data from apparently confidential conversations.

Ultimately, our response must involve technological development, vigilance and education if we want to minimize the extent to which deepfake video becomes a destabilizing influence on our lives.

If I told you that AI was going to make humans redundant and cause widespread unemployment, you’d probably think that’s a bad thing, right?

But what if the reality is that it’s going to make work unnecessary – and provide all the basic necessities for us to live comfortable lives without us having to work?

That’s the thinking behind the idea that AI will be the catalyst for the introduction of universal basic income (UBI).

The logic goes something like this: If AI can write software, draft legal documents, drive cars and diagnose illness today, what will it be capable of in 20, 30 or 50 years time?

Advocates argue that not only will AI make it necessary to provide some form of UBI, but it will also be the technological leap that makes it possible. But does this idea hold water, or is it just far-fetched (and highly optimistic) thinking?

So What Is Universal Basic Income?

UBI refers to unconditional payments made to citizens designed to cover or contribute towards the basic cost of living. The idea goes back to the first Industrial Revolution, when there were worries that industrialization would lead to large-scale human unemployment, resulting in civil unrest.

Although that isn’t exactly how things turned out, many countries did subsequently implement various forms of social welfare programs to provide assistance to those on low or no income. This was to try to reduce extreme poverty and the associated problems that come with it.

UBI is different from welfare, however, as it’s available to everyone regardless of their wealth, employment status or income. Proponents say that ensuring a basic standard of living for everyone would alleviate many other problems, including ill health, crime and homelessness. Critics, however, say it could lower the incentive to work and result in a decline in enterprise and productivity.

Experiments with UBI in the US go back as far as the 1960s, and more recently, there have been limited-scale pilots in countries including Finland and Canada.

Today, however, the dawn of the AI age has once again surfaced fears of widespread technology-driven redundancy and unemployment. However, it’s also been posited that the arrival of intelligent machines will make it possible to build the infrastructure necessary for a “post-work” society and administer the vastly complex financial framework needed to make it work fairly and efficiently.

Is AI The Answer?

Ok, well, first of all, we have to acknowledge that this is a rather utopian outlook. To believe that it could work, we’d have to take it for granted that many of the challenges currently associated with AI – like hallucination, model collapse and the sustainability problem – will be solved, which is far from a given. But let’s pretend for a moment.

In this hypothetical near future, where AI is reshaping economies and automating many tasks that could previously only be done by humans, productivity is optimized, waste is eliminated, and workflows are streamlined. In short, this means more output is created for less effort, leading to a surplus of value.

This value, so the theory goes, can then be reinvested in social programs like UBI. Imagine, for example, a world where all trains drive themselves. Currently, a lot of the money invested in their businesses by train companies goes towards paying drivers. Additionally, they have to build train cabins that are comfortable for humans to sit in for lengthy periods, implement health and safety measures to keep humans safe, provide them with facilities for their rest and downtime, and so on.

By eliminating this expense, the train company becomes more profitable and, in a future where UBI is a reality, contributes towards providing the now redundant driver (and everyone else) with a basic standard of living.

However, the potential of AI goes beyond making UBI possible (and indeed necessary) because it could also play an important role in its administration. Large-scale social programs usually require a vast administrative framework and comprehensive oversight to ensure they operate fairly, don’t infringe on human rights or privacy, and aren’t compromised by fraud or corruption. Traditionally, this involves implementing expensive and often inefficient layers of human bureaucracy.

AI, however, could automate many of these functions. For example, it could verify eligibility, route payments to the correct recipients, and detect fraud, cutting the need for labor-intensive administration and oversight.

Hype Or Reality?

So, while in theory, and given an optimal set of circumstances, it seems that AI could make UBI both necessary and possible – what about in reality?

Well, for a start, there’s uncertainty over exactly how widespread AI-driven unemployment and redundancy will be. Many – including the WEF – predict that while it will lead to the replacement of humans in many roles, new roles for us will also emerge.

Then, there will be political challenges to overcome. As previously mentioned, some are against the “something for nothing” philosophy behind UBI out of fear it will undermine economic productivity and disincentivize work. It’s uncertain whether there would be the will to push the kind of changes that would be necessary through political systems, even if technology does make it possible.

And then there’s the question of whether AI will ever actually be good enough to live up to the claims made about it. Will problems with hallucination and model breakdown be overcome, and will society at large trust it enough to hand over significant control of our industrial and economic activity?

Ultimately, while the dream that humanity will be freed from the shackles of labor and freed to pursue a life of leisure and creativity is attractive, the leap to this utopian state is far from guaranteed.

But that isn’t to say it’s impossible, and if we manage to solve the technological, societal and political factors, AI could be the key to building a world where poverty and destitution are a thing of the past.

The manufacturing sector is no stranger to innovation. In fact, it’s always been at the forefront of digital transformation, taking the arrival of robotics, the internet and new developments in material science in its stride.

However, in 2025, it still finds itself braced for disruption, as manufacturers around the world grapple with the implications of artificial intelligence. There’s also the growing importance of improving sustainability as the climate crisis deepens and building resilience in the face of political and societal uncertainty.

In order to meet these challenges, the companies responsible for creating products we use every day are enthusiastically investing in breakthrough technologies as well as adapting to cultural changes, such as the need to rethink skills and training.

So let’s take a look at how this will unfold over the next year by overviewing the key trends and opportunities:

Generative AI: Reimagining Manufacturing And Design

In 2025, we will see manufacturers rolling out many use cases for generative AI designed to speed up and drive efficiency in manufacturing processes. By leveraging the power of generative design, it will be possible to create stronger, lighter components that make more efficient use of available materials. We’ve already seen aerospace companies leveraging genAI to create new aircraft parts, and automotive manufacturers are using it to optimize vehicle designs. I believe we will see many more innovative use cases in the coming year as manufacturers increasingly integrate genAI into their operations.

Smarter Robots

Robots are not new in manufacturing; in fact, robots have been working in factories for more than 50 years. What is new, however, is the new generation of intelligent robots that are able to work safely and effectively alongside humans, apply themselves to different tasks, and learn to become more efficient at their jobs and navigating their environments. As robots move away from the assembly line and into the workforce, humans will develop new skills around leading and interacting with automated co-workers – sometimes referred to as “cobots”.

Leadership In The AI Era

Positioning a business as a leader rather than simply a follower or even a laggard in the AI era will become a growing priority for many manufacturing companies. Put simply, it’s no longer good enough to simply adopt new technologies like robotics, predictive maintenance and automation. As the barriers to entry continue to fall, it has to be done in a way that’s more innovative, effective and efficient than the competition. Developing the capacity not just to follow trends but to identify opportunities to blaze a trail will increasingly become a priority in 2025.

Sustainability In Manufacturing

There are numerous reasons why sustainability is quickly becoming a business priority for manufacturers in 2025. They include consumer demand, stricter regulations and the simple fact that we’re increasingly seeing the impact of climate disruption in the world around us. Due to this, we can expect to see a strategic switch towards cleaner and greener operations, such as the use of renewable energy, recyclable materials, reductions in emissions, excessive packaging, and water use.

Building The Manufacturing Workforce Of Tomorrow

There’s a widely-acknowledged skills crisis among industries hoping to reap the opportunities offered by AI, robotics, advanced data analytics and automation. Bridging this skills gap will require manufacturers to rethink the way they hire and train staff, and for many, this will become a critical business priority in 2025. Addressing this challenge may involve investing in upskilling and reskilling, developing apprenticeship programs or forging new relationships with educators and academia.

Dark Factories

This year, smartphone manufacturer Xiaomi switched on its fully autonomous dark factory close to Beijing, capable of producing 10 million handsets a year without human intervention. This model will become increasingly common as manufacturers chase improved efficiency, sustainability and reduced waste. While “lights out” factories have been around for a while, Xiaomi’s factory is the first that is able to learn how to operate more efficiently and optimize its own processes thanks to its AI-powered “brain”.

Adopting And Adapting To The “AI First” Culture

There can undoubtedly be cultural barriers to AI adoption. Some people are worried it will replace them or make them redundant, while others believe that decision-making shouldn’t be left to machines. While these are all valid concerns, identifying areas where AI can clearly solve problems or create efficiencies while mitigating its potential for causing harm will be a priority for the manufacturing industry in 2025. This will include planning and delivering initiatives fostering an understanding of AI across a workforce and ensuring its benefits are felt by all.

Smart Supply Chains

The logistical challenges around sourcing components and managing complex inventories and production infrastructure are perfectly suited to automated, intelligent solutions. AI-powered tools leveraging real-time data analytics will enable more accurate demand forecasting and automated decision-making, helping manufacturers to build supply chains that are more resilient and adaptive to changing market conditions. In 2025, AI will enable manufacturers to anticipate disruption more effectively and identify opportunities to improve efficiency, ultimately leading to improved customer experience and business performance.

As we move through 2025, the manufacturing sector stands at a pivotal moment of transformation. While challenges around AI adoption, sustainability, and workforce development remain significant, the convergence of smart technologi

Bitcoin, blockchain and cryptocurrency were all hot topic trends a few years back. But technology waits for no one, and with all the hype around AI, you’d be forgiven for thinking it’s been forgotten. Not so.

In fact, those who have been keeping up with the news will have noticed that there’s been a resurgence of interest in the decentralized digital currency and the revolutionary distributed ledger technology that it’s built on.

So why is this? What impact will it have on the value of bitcoins – one of the best-performing investments in living memory? And what is the current state of play of the technology that many have predicted will be the “future of money”?

Let’s take a look at what’s going on in the world of bitcoin, blockchain and cryptocurrency as we head into 2025!

So Remind Me – What Is Bitcoin Again?

Bitcoin is the first and best-known cryptocurrency, a type of digital currency. Cryptocurrencies (or “crypto”) differ from earlier digital currencies in two key ways. First, they are decentralized, meaning the database that records balances and transactions (called a blockchain) is shared across hundreds of thousands of computers. These computers must reach “consensus,” so no single person or organization controls the network. Second, transactions are secured with encryption, allowing only those with the right keys to access and spend funds in their private wallets.

Some believe Bitcoin or other cryptocurrencies could become the foundation of future financial systems. This is because they can handle transactions without middlemen or central banks, avoiding issues like inflation caused by currency value manipulation. However, critics argue crypto doesn’t solve these problems and introduces others, including high environmental costs and challenges in regulation, which attract money launderers, criminals, and scammers.

However, Bitcoin is probably most famous for its explosive growth in value. In 2010, 10,000 Bitcoin were used to buy two pizzas. Today, one Bitcoin is worth nearly $100,000—an increase of close to five billion percent. In comparison, gold rose by just over 100% in the same period, while the value of the US dollar dropped by about 45% due to inflation.

The Trump Train

Whether you view him as a controversial or transformative figure, Trump’s influence on financial markets as both the 45th and 47th president is undeniable. Trump’s ringing endorsement of Bitcoin – a markedly different attitude to that of former incumbents -is being credited with accelerating the current resurgence of interest in cryptocurrency.

Since announcing his belief that the US should stockpile the digital currency at a convention in the summer of 2024, the price of Bitcoin has rocketed, and mainstream interest in its use as an investment vehicle is off the scale.

Bitcoin fans say that Trump’s interest will drive other countries to integrate cryptocurrencies into their own economic strategies. This will hasten its adoption into the global financial system, further driving up its value and leading to more innovation and disruption.

So What Are Altcoins?

Altcoin is a name used to describe cryptocurrencies other than Bitcoin, so it refers to alternative coins. Currently, the market cap of all cryptocurrencies stands at around $3.5 trillion – slightly higher than the GDP of the UK ($3.4 trillion).

The most well-known altcoin and number-two cryptocurrency is Ethereum, which is blockchain-based like Bitcoin but includes additional functionality. This includes the ability for computer code to be executed on the blockchain, enabling smart contracts. This would allow a blockchain to be programmed to automatically make a payment when pre-determined conditions are met, such as a piece of work being completed.

Another category of altcoin is meme coins. These are cryptocurrencies based on internet memes, the most famous one being Doge Coin, based on a popular image of a dog, frequently shared on social media and internet message boards. Sounds like a joke, right? Except the market cap of meme coins stands at $120 billion as of writing, and Elon Musk is apparently planning on naming a new branch of the US government after Doge.

The Future Of Money?

So, what does the future hold for Bitcoin and cryptocurrency – once seemingly close to forgotten as the AI craze took hold, but now firmly back on the agenda?

The resurgence in interest – not to mention monetary value – suggests that the technology is resilient and unlikely to simply fade into obscurity, as was predicted during its slump.

But will it go on to become the backbone of a new, fairer and more efficient financial infrastructure, as fans believe? Or will it always be a speculative bubble facilitating gambling, get-rich-quick schemes and scams?

Well, a lot may depend on how successful the incoming US president’s planned shake-up of the economy will be. This is a question that economic analysts are currently divided on.

With increasing adoption and high levels of FOMO due to its rocketing price, its status as a store of value and hedge against inflation – which had led to it sometimes being considered as “digital gold” counts in its favor. The ongoing evolution of more innovative features and functionality, such as Ethereum’s smart contracts, will likely add to this.

On the other hand, there are clearly still challenges around regulation, such as the high level of volatility that leads to regular crashes in value and high levels of energy use.

All of this may count for little in the end, however. Bitcoin has already forced us to rethink the way we treat currency and value, demonstrating that it may be possible to build a more efficient and democratic financial system based on technology and mathematics rather than central banks.

And as with other transformative technologies – AI and the internet being two examples – once Pandora’s box is opened, it’s very hard to stop it from changing the world.

The business world isn’t just changing – it’s undergoing multiple simultaneous revolutions. As leaders navigate this complexity in 2025, they’re making decisions that could fatally undermine their organizations’ future success.

Mistake 1: Misunderstanding AI’s Role

Many leaders are treating AI as either a magic solution or just another IT project, missing its true transformative potential. They’re investing heavily in AI solutions without understanding the fundamental changes needed in business processes, decision-making frameworks, and organizational structure. Some are rushing to implement AI without clear use cases, while others are dangerously underestimating its disruptive impact on their industry. Forward-thinking leaders are taking a more nuanced approach, seeing AI as a transformative tool that requires careful integration with human expertise. They’re creating frameworks that combine AI’s analytical power with human judgment, ensuring AI augments rather than replaces human decision-making while carefully managing the expectations of their boards and stakeholders.

Mistake 2: Mishandling Workforce Transformation

Organizations are struggling to navigate the human side of technological change. Leaders are implementing AI and automation without adequately preparing their workforce, creating resistance and anxiety instead of enthusiasm and engagement. The skills gap is widening as training programs fail to keep pace with technological change, while many companies still cling to outdated organizational structures that stifle innovation. Successful organizations are taking a people-first approach to transformation, investing in comprehensive reskilling programs, creating clear career paths for the AI era, and actively involving employees in the transformation process. They understand that the key to successful automation isn’t just about technology – it’s about building a workforce that can thrive alongside it.

Mistake 3: Neglecting Data Leadership

Despite years of discussion about data-driven decision-making, many leaders are still failing to treat data as a strategic asset. They’re allowing their organizations to operate with fragmented data strategies, unclear data ownership, and inadequate data governance. This short-sightedness is particularly dangerous as AI becomes central to operations and decision-making. Leading organizations are elevating data strategy to the board level, investing in data quality and accessibility, and creating clear frameworks for data ethics and privacy. They recognize that in 2025, data strategy isn’t just an IT concern – it’s fundamental to business strategy.

Mistake 4: Underestimating Sustainability Imperatives

Too many leaders are treating sustainability as a PR exercise rather than a fundamental business imperative. They’re making token gestures toward environmental responsibility while failing to prepare for incoming climate regulations, changing consumer preferences, and supply chain disruptions. Forward-thinking leaders are embedding sustainability into their core strategy, investing in genuine carbon reduction, and preparing for a radically different operating environment. They understand that by 2025, sustainability won’t just be about compliance or reputation – it will be a key determinant of business viability.

Mistake 5: Maintaining Rigid Cultural Structures

Leaders are clinging to traditional hierarchies and working models in a world that demands flexibility and rapid adaptation. They’re resisting the evolution of hybrid work, maintaining unnecessary bureaucracy, and failing to adapt to changing generational expectations. Progressive organizations are creating more fluid structures that can adapt to rapid change, embracing distributed decision-making, and building cultures that attract and retain next-generation talent. They recognize that in 2025, organizational agility will be impossible without cultural transformation.

Mistake 6: Misreading Customer Evolution

Many leaders are making dangerous assumptions about how their customers will behave in 2025. They’re over-automating customer interactions, ignoring growing privacy concerns, and misunderstanding the balance between personalization and intrusion. Some are pushing ahead with digital-only strategies while underestimating the continued importance of human touch points. Smart organizations are taking a more balanced approach, using AI to enhance rather than replace human interactions, respecting privacy boundaries, and maintaining multiple channels for customer engagement. They understand that in 2025, customer experience will be about finding the right blend of digital efficiency and human connection.

Mistake 7: Ignoring Geopolitical Risk Management

Many leaders are dangerously unprepared for the geopolitical complexities of 2025. They’re treating international tensions as temporary disruptions rather than permanent features of the business landscape. Some are maintaining vulnerable single-region dependencies for critical operations, while others lack contingency plans for sudden regulatory divergence between major markets. Forward-thinking organizations are developing sophisticated geopolitical risk frameworks, diversifying their strategic partnerships across regions, and building adaptable business models that can weather political instability. They understand that in 2025, geopolitical awareness isn’t just for multinational corporations – it’s essential for any business in an interconnected world.

The Cost of Leadership Inertia

These mistakes aren’t just operational missteps – they’re strategic failures that will determine which organizations thrive in the next era of business. The leaders who succeed will be those who recognize these challenges as opportunities for transformation rather than threats to be minimized. The time for incremental change is over. In 2025, the gap between organizations that get this right and those that don’t will become unbridgeable. The question for every leader is clear: will you drive change, or will change drive you?

Credit: Bernard Marr

The marriage of artificial intelligence and winemaking might seem like an unlikely pairing. Still, at Chateau Montelena – the historic Napa Valley winery that helped put American wines on the world stage – cutting-edge technology is transforming how premium wines are produced.

“AI in the wine industry is still in its infancy,” says Matt Crafton, winemaker at Chateau Montelena. Yet the potential applications are already proving revolutionary, offering new insights into every aspect of wine production, from the vineyard to the cellar.

Smart Monitoring In The Vineyard

At Chateau Montelena, AI’s impact begins with innovative vine monitoring systems. Using technology adapted from facial recognition software, vineyard managers can assess vine health by simply walking down rows with their smartphones. “They take all those images, upload them to their server, and based on data they have they can recognize how the leaf angles change based on sun exposure, they can correlate that to vine water stress,” Crafton explains. This real-time data helps optimize irrigation and care for each vine individually.

The winery also employs aerial imaging and pattern recognition. Using high-resolution photographs taken by planes or drones, AI algorithms can detect subtle changes in individual vines that might indicate problems like clogged irrigation lines before they become visible to the human eye.

“We’re getting these little nudges saying like, ‘Hey, go out to row 45 and head about eight vines in and double check that emitter is working,'” Crafton explains. “It’s being able to recognize these subtle changes that would require tremendous amounts of man hours to determine.”

Reimagining Ancient Practices With Modern Technology

Perhaps the most striking example of AI’s impact comes from Chateau Montelena’s recent replanting project. The winery used AI and solar positioning data to determine the optimal orientation for their vineyard rows – a decision that could impact wine quality for decades to come.

Traditional vineyard layouts often follow a simple rule: rows are planted perpendicular to the nearest road, but as Crafton explains, “there’s no other large logic behind that. The people who set out the roads are the California Department of Transportation – they don’t really care about farming.”

Challenging this convention, Chateau Montelena turned to AI to analyze complex solar and weather data patterns. “Using AI and weather climate data, we determined that we should orient our rows approximately 25 degrees East of true North,” says Crafton. This precise angle ensures that during the hottest part of the day, the sun shines on top of the canopy rather than directly on the grapes, protecting the delicate compounds that give the wine its flavor and character.

The impact of this AI-guided decision has been significant. “We’ve seen a 10 to 15 degrees Fahrenheit temperature delta between berries that are in direct sunlight at the hottest time of the day versus berries that are in shade,” Crafton notes. This temperature difference is crucial because “all of the really beautiful aromatics, the phenolics, the tannins that you taste, all of those wonderful flavors are very, very sensitive to heat.” Too much heat can degrade these compounds, resulting in wines that lack character and complexity.

“We have two vineyard blocks that we planted in 2018 that are now in full production using this new system… the fruit is absolutely dynamite.” This innovative approach demonstrates how AI can help reimagine practices that have remained largely unchanged for generations, leading to measurable improvements in wine quality.

From Cork To Bottle: AI In Production

Even the corks sealing Chateau Montelena’s wines benefit from AI innovation. The winery uses specialized corks from a French company that employs AI modeling to predict how each closure will develop and mature over time. “They’ve modeled this using AI… they’re confident enough, and we are too, seeing the data that each individual closure, in addition to being 100 percent clean, comes with a 30-year integrity guarantee,” Crafton shares. This technology helps ensure that premium wines can age gracefully for decades.

The Human Touch Remains Essential

While AI is proving invaluable for data analysis and pattern recognition, Crafton emphasizes that the technology serves to enhance rather than replace human expertise. “AI doesn’t actually create,” he notes. “There are really only two things in the universe that create: one is evolution, and the other one is human beings.”

This philosophy guides how Chateau Montelena integrates technology into its winemaking process. Rather than using AI to standardize the wines, it leverages it to better understand and express the unique characteristics of each vintage and vineyard block.

The Future Of Fine Wine

Looking ahead, Crafton sees AI playing an increasingly important role in helping winemakers sort through the enormous amount of data generated during wine production.

“Unfortunately, I think there is this idea that more data are better all the time, but the reality is that it just gets overwhelming very quickly,” he explains. The hope is that AI will help identify truly actionable insights, freeing up winemakers to focus on the creative aspects of their craft.

A Toast To Innovation

The integration of AI into fine winemaking represents a fascinating balance between tradition and innovation. While the fundamental art of winemaking remains unchanged, AI is helping prestigious wineries like Chateau Montelena optimize their processes, reduce resource usage, and ultimately produce even better wines.

For wine lovers, this technological evolution means that their favorite bottles are being crafted with unprecedented precision and care while maintaining the creativity and human touch that makes each vintage unique. It’s a reminder that even in an industry steeped in tradition, there’s always room for innovation – especially when it enhances rather than diminishes the artistry at the heart of fine winemaking.

In 2025, education is transforming, driven by game-changing technologies like artificial intelligence and the needs of a changing world of work.

The traditional model of front-loading education in youth no longer fits a society where change happens at lightning speed. Meanwhile, skills like emotional intelligence and communication,  sometimes overlooked in traditional education models, are becoming increasingly important as machines take over many routine technical tasks.

Let’s explore the key trends shaping education in 2025 and the opportunities they offer individuals preparing themselves for success in a rapidly changing world.

Human-Centric Skills Back On The Curriculum

As machines become more efficient at analyzing trends, crunching numbers and generating reports, the value of the skills that they still can’t replicate will grow. This means that educators should increasingly focus on nurturing these soft, “human” skills, like critical thinking, big-picture strategy, communication, emotional intelligence, leadership and teamwork. Expect to see greater integration of these into mainstream education as we train to become more effective at high-value tasks involving person-to-person interactions and navigation of complex and chaotic real-world situations. These are tasks that human leaders and professionals will use to distinguish themselves in the AI age.

Generative AI In The Classroom

Generative AI (think chatbots like ChatGPT) is everywhere now, and in 2025 the classroom will be no exception. With 57 percent of workers saying they believe it can make them more efficient, learning how to make it work effectively will increasingly become a priority for educators. As well as learning how to use it, uses will be found for it across the education workload. For example, teachers will use it to assist with grading, providing personalized feedback on work, and creating bespoke content and lesson plans. There will also be a focus on responsible use of AI as well as learning about when and how it can be used; students should also be taught about using it safely and when it is better to rely on real intelligence rather than artificial intelligence.

Personalized Learning

All learners are different – we take in information at different speeds; while some of us absorb knowledge better from videos, some benefit more from group discussions or activity-based learning. Personalized learning promises to deliver education in a way that’s tailored to the specific strengths of individual students. This means tailored lesson plans, assessments and learning materials. In 2025 we will see experiments and pilot projects involving using AI to accomplish this begin to move into the mainstream, as well as the emergence of AI tutoring aids that are able to track the progress of students in real time and adjust the delivery of learning on-the-fly to create dynamic and engaging learning environments.

Lifelong Learning

In 2025, the idea that education ends with graduation is hopelessly old-fashioned. Lifelong learning has become the mantra to live by – for professionals, there’s a need to consistently upskill and reskill in order to keep pace with rapid changes in technology and the world of work. In fact, I’ve heard it said that today, it’s increasingly common to find that tech skills learned in degree courses are frequently outdated by the time a graduate enters their first professional role. Online learning, modular courses and concepts such as micro and nano-learning will be part of the solution, offering bite-sized and stackable learning pathways that can be fit around busy careers.

Virtual Classrooms And Remote Learning

Imagine educational experiences where students can explore ancient civilizations or distant planets without leaving their homes. In 2025, this is increasingly becoming a reality as learners adopt virtual and augmented reality technology into their study. This trend also covers the growing use of online learning platforms and course providers to enhance educational opportunities and provide increasingly collaborative digital learning environments. As well as opening the doors to more immersive and engaging study, remote learning and virtual classrooms will improve accessibility for those who can’t get to school because of where they live or due to political reasons.

New Partnerships To Foster High-Tech Vocational Training

Increasingly, we will see partnerships emerging between schools, colleges, and businesses as employers and education providers seek new ways of bridging the skills gap. Right now, there simply aren’t enough graduates coming out of the education system to meet industry demands for critical high-tech skills like artificial intelligence and cyber security. This collaboration will help educators formulate courses to fit real-world requirements and ensure students are graduating with the skills that employers need.

Ed-Tech Becomes Big Business

Providing the technology needed to make the trends covered here a reality is a fast-growing business, with the ed-tech industry forecasted to grow from $142 billion to nearly $350 billion by 2030. As enthusiasm in the education sector for online learning platforms, AI assistants and immersive VR learning experiences grows, technology will become increasingly integral to transforming the way that education is delivered in 2025. Continued investment and innovation in the sector will ensure that educators are able to meet the demands of a fast-changing world.

The educational landscape of 2025 represents a pivotal shift from traditional models to a more dynamic, technology-enhanced, and human-centered approach to learning. As AI and automation continue to reshape our world, the fusion of cutting-edge technology with enhanced focus on uniquely human capabilities isn’t just transforming how we learn – it’s redefining what it means to be educated in the digital age. The future of education isn’t just about absorbing information; it’s about creating adaptable, lifelong learners who can thrive in an increasingly complex and rapidly evolving world. For educators, students, and professionals alike, embracing these trends isn’t optional – it’s essential for success in tomorrow’s world.

Credit: Bernard Marr

Consumer technology covers all of the tech we buy to make our lives more convenient, productive or fun. It’s a broad category, covering everything from home appliances to cars, DIY, health and fitness products and connected smart home devices – and understanding these trends is crucial not just for manufacturers, but for any business looking to meet modern customer and employee expectations.

In a tough economic climate, both consumer tech manufacturers and enterprise solution providers know they have to offer real innovation to stay competitive. As the line between consumer and enterprise technology continues to blur, the expectations set by consumer tech increasingly shape workplace technology demands.

In 2025, the driving force behind innovation will be artificial intelligence (AI), with its promise of devices and solutions that are smarter and more useful than the ones we already own. This shift will impact everything from product development to workplace tools and customer service platforms.

Will this be enough? Time will tell – but in the meantime, let’s explore the hottest trends that I believe will shape not only consumer buying decisions but also enterprise technology strategies over the next year.

Personalized User Experiences In Consumer Technology

In 2025, manufacturers understand the importance of creating technology that we feel “gets” us. With AI beginning to be embedded in everything from smart home devices to wearables, fitness equipment and toys, we will increasingly expect personalized experiences that adapt to fit in with our lives. The idea is to go beyond creating tech that offers convenience, to offering experiences that feel tailor-made to us. Think of smart home devices that adapt to our daily routines, or smartphones equipped with AI assistants that effortlessly manage our lives.

Conversational Interfaces And Chatbots

Forget the stilted interactions we’ve grown accustomed to having with assistants like Alexa or Siri. In 2025, advances in large language models (LLMs) and natural language processing (NLP), such as those demonstrated by ChatGPT, mean we can talk to our devices almost as if they are fellow humans. This means we’re soon going to be having conversations with kitchen appliances and chewing the fat with our cars, as voice increasingly becomes our primary method of operating and interacting with consumer tech.

Sustainability In Consumer Technology

With buying decisions increasingly driven by concerns over sustainability and our environmental footprints, manufacturers understand that eco-friendly principles are now a business priority. This means devices that are more energy efficient, designed and built in a way that minimizes water consumption, emissions and waste, with a focus on reusability and recyclability. Computer chips like Apple’s M-series processor, designed to minimize power consumption, smartphones constructed from modular, replaceable parts, and increasing use of recycled materials and environmentally-friendly inks in packaging are all examples of this trend in action.

Healthcare-Focused Consumer Technology

Smart healthcare devices like fitness trackers and remote monitoring tools have been around for a while. But while the market for these products will continue to grow in 2025, we’ll also see them become smarter – capable of predicting and even diagnosing conditions, rather than simply monitoring basic health metrics. From the ECG and blood oxygen sensors on smartwatches, we can expect to see devices offering insights based on genetic or gut microbiome data, which, when paired with telehealth and virtual healthcare services, will begin to offer truly personalized wellness experiences.

Real-Time Language Translation

Communicating during our travels will be easier than ever before in 2025, thanks to the proliferation of devices enabling real-time language communication. These capabilities will be available in earbuds, smart glasses, and watches, as well as dedicated portable translation devices like the Poketalk, leveraging machine learning and LLMs to break down language barriers wherever we are.

Diversity And Inclusion In Consumer Technology

We can expect to see a strong push towards inclusivity in consumer technology during 2025. This hasn’t always been the case – last-gen voice assistants often struggled with understanding non-standard accents and dialects, and there have been instances where health wearables like heart rate and oxygen sensors have been less accurate on users with dark skin. Signs that this may be changing include a move towards more accessibility features on smart devices, a focus on AI features that are more culturally aware, and improved female-centric functionality on healthcare devices, such as menstrual and pregnancy tracking.

AI-Enhanced Friends And Companions

From smarter-than-ever toys that can converse and play games, to robots designed to keep us company or act as teachers, AI will breathe new life into companion devices. Also known as “social robots,” these are machines designed to engage and interact with us in order to provide entertainment or in-home assistance. Relatively simple toys like Poe, the bedtime-storytelling bear, are early attempts at using AI to turn everyday objects into “friends” we can relate to, and they will become more numerous and sophisticated as time goes on.

Micro-Mobility

In recent years, we’ve seen an explosion in the number of micro-vehicles such as e-bikes, scooters and compact EVs on the market, and in 2025, we will continue to see them getting smarter, more user-friendly and more deeply integrated into urban life. Longer battery life and innovations like smartphone integration will bring real-time route optimization and GPS functionality, and the vehicles themselves will continue to become more convenient and affordable. All of this means micromobility will be an increasingly prominent trend as our approach to commuting, living locally, and day-to-day transport in urban areas evolves.

The consumer technology landscape of 2025 promises to be more personal, sustainable, and inclusive than ever before. While AI clearly emerges as the driving force behind many of these innovations, understanding these trends isn’t just important for consumer tech manufacturers – it’s crucial for every business. As consumer expectations evolve, these technological advancements will inevitably influence workplace technology demands, with employees expecting the same level of personalization, sustainability, and AI-powered convenience they experience in their personal lives. Companies that stay ahead of these consumer tech trends will be better positioned to attract talent, meet changing employee expectations, and maintain competitive advantage in an increasingly tech-driven marketplace. The future of consumer tech isn’t just about smarter devices – it’s about understanding and adapting to fundamental shifts in how people expect to interact with technology, both at home and in the workplace.

Credit: Bernard Marr

As we venture deeper into the digital era, the scope and possibilities of data and artificial intelligence (AI) in human resources (HR) are expanding at an unprecedented rate.

As I discuss in my new book ‘Data-Driven HR: How to Use AI, Analytics and Data to Drive Performance’ (2nd edition), HR is no longer just a supporting actor in companies. People management departments are now stepping into the spotlight and are actively guiding and informing business strategies with rich, data-derived insights.

By deploying AI tools, HR is becoming more adept at providing better experiences for employees — and these technologies are also being used to streamline HR processes and services.

Let’s dig into the three most significant areas where data and AI are bringing considerable value and fundamentally influencing the way businesses operate.

The Three Pillars Of Data-Driven HR: Decision-Making, Employee Services, And Efficiency

Data and AI can create value in organizations in three crucial ways:

● Facilitating a more informed decision-making process

● Improving service delivery to employees (and potential candidates)

● Optimizing HR service efficiency

Let’s dive deeper into each of these.

Empowering Decisions With Data-Driven HR Insights

HR professionals are now using data and AI to equip decision-makers with critical information, create valuable people-centric reports, and enlighten leaders about what’s happening on the ground from moment to moment.

AI can also forecast upcoming people-related issues, acting as an early warning system, which can help organizations future-proof their systems and processes. For example, AI could predict a skill deficit so HR could ramp up recruiting in a particular area.

Juniper Networks, a networking hardware firm, uses LinkedIn data and analytics to track where high-performing employees come from and understand where they migrate when they leave Juniper. This information allows the company to understand career trajectories better and make more informed decisions to attract and retain talent.

Google’s Project Oxygen is another instance of data-driven decision-making in HR. The project aimed to identify the characteristics of effective managers within Google. By analyzing data sets, the project identified the key characteristics of successful managers and then developed specific training programs and feedback mechanisms to improve management quality across the company.

Enhancing Service Delivery For Employees

The primary role of HR revolves around catering to the organization’s workforce. This can include:

● Providing exceptional recruitment services

● Delivering opportunities for training and development

● Creating and implementing comprehensive wellness programs

● Designing safe working conditions

By leveraging data and AI, HR teams can improve all of these services and provide more value to employees throughout their journey in the organization.

Walmart, for example, uses AI to guide employees in choosing the most suitable medical providers for their needs.

IBM uses AI in its HR operations to provide a better experience for its employees. For instance, the AI virtual assistant “Watson” helps IBM employees with different queries, ranging from company policies to technical support, offering personalized answers and reducing response times.

Boosting HR Service Efficiency

AI can be a game-changer for HR teams looking to improve efficiency.

Using data and AI can help HR teams automate procedures with chatbots, create better onboarding processes, leverage metaverse environments for more immersive training, and more.

Johnson & Johnson deployed an AI-based writing tool, Textio, to identify unconscious bias in their job listings. Upon identifying a masculine tilt in the language of many of their job postings, they made some AI-driven adjustments that led to a 9% uptick in female applicants.

Unilever employs AI to streamline the initial stages of its recruitment process. Candidates are asked to play a number of games that test their logic, aptitude, reasoning, and appetite for risk. Then the HR team uses machine learning algorithms to assess candidates’ suitability for the role they have applied for, by matching their profiles against previously successful employees. This approach has not only improved the efficiency of Unilever’s recruitment process but also provided a more engaging candidate experience.

Reimage HR Through Data And AI

Data and AI are more than just buzzwords — they are the drivers of meaningful, beneficial change within HR. As businesses move forward in this digitally connected world, prioritizing the three HR domains we’ve discussed above can create a significant difference in achieving strategic goals and building a work environment where employees thrive.