At a time when the AI revolution is sweeing through most aspects of business, one area that has remained surprisingly untouched is business agreements and contracts. That’s about to change, as DocuSign, the company that brought electronic signatures into the mainstream, is now leveraging AI to revolutionize how businesses create, manage, and extract value from their agreements.

The Hidden Problem With Modern Agreements

While we’ve digitized countless business processes, agreements have largely remained stuck in the past. Yes, we edit them in Word and email them around, but as DocuSign’s CEO Allan Thygesen explains, “Everything about agreements remains as brittle, delayed, and unpredictable as it’s ever been.”

The problem goes deeper than just inefficient processing. As Thygesen points out, “Once you negotiate the agreement, the strange thing is you spend all this time on that, and then you put it in a deep, dark place, and there’s no visibility into what’s actually in the agreement.” This lack of visibility means companies often miss crucial deadlines, renewal opportunities, and chances to improve their agreements.

How AI Is Transforming Agreement Management

DocuSign’s approach to solving this problem coincided with perfect timing. “I joined DocuSign just as there was a step change in what we could do with AI, right around the time when GPT 3.5 launched,” shares Thygesen. This technological breakthrough has enabled DocuSign to transform unstructured agreement data into actionable intelligence.

The company’s AI-powered platform can now extract essential data from agreements, make it searchable, and compare it against actual outcomes from various business systems. But it doesn’t stop there. The AI can also assist in creating agreements, customizing templates, and even performing initial legal reviews of incoming contracts.

Real-World Applications That Are Changing Business

The impact of this technology is already being felt across various business functions. Thygesen highlights three key areas where their AI-powered platform is making a significant difference:

In sales, the platform enables businesses to track renewal dates, notice periods, and opportunities for renegotiation. This prevents missed opportunities and empowers sales teams with crucial information that was previously buried in agreements.

For procurement teams, who are typically resource-constrained, the AI helps manage vendor relationships more effectively. “Procurement teams are typically fairly small,” Thygesen notes. “Having tools that can make them more productive is very important.”

In HR and recruitment, where there are many employment contracts that need management and updating, the platform can streamline high-volume processes while ensuring compliance. It enables quick customization of agreements and packages while maintaining regulatory compliance.

The Future Of AI-Powered Agreements

Looking ahead, DocuSign envisions a future where AI could potentially handle entire agreement processes autonomously, particularly for simpler documents. “I think it’ll be technically possible to do that with higher accuracy for simple agreements in fairly short order,” Thygesen predicts. He suggests that standardized documents like NDAs could be among the first to see full automation.

However, Thygesen maintains a balanced perspective about AI’s role: “For a variety of reasons, including risk compliance, regulatory and others, I think it’ll be a while before anything but the most trivial agreements get released. I think there’ll always be a human in the end, at a minimum.”

The Bigger Picture

DocuSign’s ultimate vision is ambitious yet practical. “If we’re successful, we will develop the first system of record for agreements,” says Thygesen. This would replace the current scattered approach where agreements are lost in email threads or buried in various digital drives.

The transformation is already underway. With 1.6 million monthly paying business entities, DocuSign is well-positioned to lead this revolution. The evidence of progress is striking: Thygesen reveals that their “costs to process an agreement have dropped by two orders of magnitude in the last 15 months” thanks to continued technological advancement.

A New Chapter In Business Efficiency

As businesses continue to seek ways to improve efficiency and reduce costs, DocuSign’s AI-powered approach to agreement management represents a significant leap forward. By turning static documents into dynamic, intelligent assets, they’re not just solving a technological problem – they’re addressing a fundamental business challenge that affects organizations of all sizes.

The future of business agreements is being rewritten. But as with all significant technological advances, the key to success will lie in finding the right balance between automation and human oversight, between efficiency and control, and between innovation and reliability.

Imagine one of the most beloved talk show hosts from your country – someone who interviewed generations of celebrities and became a cultural icon through their unique style and warmth. Now imagine that the host has been recreated using artificial intelligence, their voice and mannerisms perfectly replicated by computers. This is exactly what’s happening in the UK with legendary interviewer Michael Parkinson, whose AI-powered digital twin is now conducting celebrity interviews despite his passing in 2023. This groundbreaking development raises profound questions about the future of human connection in broadcasting.

The Human Touch In A Digital Age

Having recently interviewed BCG’s AI host, GENE, on my own podcast, I’ve experienced firsthand how these AIs can create very engaging conversations. GENE serves as an effective co-host, complementing human presenters while maintaining transparency about its artificial nature through its intentionally robotic-sounding voice. As BCG’s Paul Michelman explains, “We think it’s very important to be fully clear when we’re using technology. And two, to really avoid anthropomorphizing.” This approach demonstrates how AI can enhance broadcasting without attempting to replace the irreplaceable human element.

When Technology Goes Too Far

However, I feel that the launch of Virtually Parkinson crosses a concerning threshold. While is is technically impressive, recreating a beloved broadcaster’s voice and interviewing style after he passed away feels like a violation of the authentic human connections that made Parkinson’s interviews so special. His famous conversations with Muhammad Ali, Billy Connolly, and countless others resonated because of their genuine human chemistry – something that cannot be truly replicated by algorithms, no matter how sophisticated.

Mike Parkinson, Sir Michael’s son, describes the AI recreation as “exactly how he delivered his questions – even the pacing is the same. It stills people when they hear it”. This very perfection, while technically remarkable, raises concerns about authenticity in broadcasting.

The Economics Of Digital Hosts

The financial appeal of AI hosts is undeniable. They can work tirelessly and require no salary. But this efficiency comes at a cost to the broadcasting ecosystem. Jason Saldanha, chief operating officer at PRX, warns that “flooding the market with content to get the lowest level of engagement” is not a “long-term strategy.” He emphasizes that the real power of podcasts lies in “the host-audience relationship,” and the most successful shows have a “one-to-one relationship with their audiences.”

Industry Perspectives And Ethical Considerations

BCG’s experiences with GENE offer valuable insights into responsible AI deployment. Vlad Lukic, Managing Director and Senior Partner at BCG notes that AI “gets into the crux of our business… and it’s going to be fundamental to the toolkit and skills that we need to have”. However, Deep Fusion Films’ Ben Field, creators of the Virtually Parkinson AI, emphasizes the importance of ethical considerations, stating they are “committed to working only with the agreement of a subject’s estate and with the involvement of the relatives.”

Finding The Right Balance

It is importnat to make a clear distinction between using AI as a tool to enhance human-led content and attempting to replace human presenters entirely. When GENE co-hosts podcasts, it adds value while remaining transparently artificial. This approach preserves the authenticity of broadcasting while embracing technological innovation. The key lies in maintaining this balance – using AI to augment rather than replace the human element that gives broadcasting its soul.

The Future Of Human Connection

As these technologies advance, we must ask ourselves what we value most in broadcasting. Is it perfect delivery and unlimited content production, or is it the authentic human connections that have defined great broadcasting for generations? While AI hosts represent an impressive technological achievement, they should enhance rather than replace the human voices that make broadcasting truly meaningful.

The true power of broadcasting doesn’t lie in technical perfection but in those wonderfully imperfect moments of genuine human interaction – the unexpected laughs, the emotional revelations, the spontaneous connections that no algorithm can predict or replicate. When I look to the future of broadcasting, I see AI playing a vital supporting role, but never replacing the raw authenticity of human conversation. Perhaps the greatest irony is that in our quest to create perfect digital hosts, we risk losing the very imperfections that make broadcasting profoundly human. The challenge ahead isn’t just technological – it’s about preserving the soul of communication in an increasingly digital world.

We are already living in the AI age, and there are many ways we can put AI tools and platforms to work to help us in day-to-day life.

It’s quickly becoming clear that while AI won’t replace all humans, humans who can use AI will replace those who can’t. One area where there are many ways to give ourselves an advantage is job seeking.

From researching the options available to preparing cover letters and CVs, generative AI – including many free tools like ChatGPT – can help hone our competitive edge and stand out from the crowd.

So, here’s my simple guide to some of the best prompts for helping you with your job search and maximizing your chances of success.

(Please note that these prompts were created and tested using the paid-for ChatGPT Pro service, but you should also be able to use them on the free version as well as other LLM-based genAI chatbots like Gemini or Claude.)

Act As A Career Advisor

Thinking of looking for a new job? Get some help with working out what kind of roles you might be suitable for and where to look for them:

“Please help me find job vacancies that match my career goals. My current job is [insert job title] and I have [insert number] years’ experience. Act as a career advisor and help work out what my next step should be and where I should look for roles. Ask me any questions you need to know the answer to in order to advise me, one at a time. When you have enough information, suggest roles either within my current industry or outside of it, that I might be suitable for, according to my skills, experience and aspirations. Then advise me on job boards and websites, as well as other channels such as recruiters or networking opportunities, where I will find suitable vacancies, and keywords to use while searching.”

Researching A Job Role

A thorough understanding of the skillset and level of experience required is critical for landing a job. Here’s a prompt to help you research this:

“I want to apply for positions as a [insert job title]. Please provide me with up-to-date information on the skills and qualifications usually required for this role, examples of prior experience or work experience that would make me an exceptional candidate during a recruitment process for this role, information on the soft skills, aptitudes and personality traits that employers are likely to recognize as valuable in this role, and suggestions for recognized certifications or training that would improve my chances of success.”

Write A Cover Letter For A Job Application

Craft on-point cover letters with this handy prompt:

“Please help write a professional and convincing cover letter to go with my application for at [insert company]. First, ask for the job ad or job description; if I have one, then ask me questions, one at a time, to understand why I’m applying for the position, what makes me a good candidate for the role, and why I’m interested in working at the company. Then craft a cover letter of around 200 words that is attention-grabbing, customized to the specific role and company, highlights what makes me an ideal candidate for the position, and details what I can bring to the company. Ensure that the tone of the letter is enthusiastic, positive and professional.”

Writing A Perfect Personal Statement For A CV Or Resume

A personal statement helps recruiters get a quick overview of you as an applicant and should be tailored to the specific requirements of the position you’re applying for:

“Please help me generate a personal statement for a CV to support my application for the role of [insert job title] at [insert company]. First, ask me to provide the relevant job ad or job description of the role, if I have one. Then ask me any questions you need to, to understand why I am the perfect candidate for the job, and how my skills, qualifications, experiences and background are suited to the role. Ask the questions one at a time and when you have enough information, craft a statement of up to 150 words that is polished, attention-grabbing, professional and maximizes my chances of being selected to progress in the recruitment process.”

Preparing For An Interview

Use this prompt to carry out a mock job interview for any position:

“I want you to act as a recruiter for [company name], which is [describe the company and what it does]. Ask me questions that I am likely to be asked in an interview for the position of [insert job title]. Ask the questions one at a time, and ask me follow-up questions if you don’t get all the information you need from me. Ensure questions are appropriate for the level of seniority and the experience requirements of the role. When you have asked me all the questions you need in order to assess my suitability for the role, give me feedback on how well I did, and advice on how I could have improved my interview performance.”

The job market continues to evolve rapidly, and leveraging AI tools effectively can give you a significant competitive advantage. Whether you’re actively job hunting or simply exploring your options, these prompts serve as your AI-powered career coach, helping you navigate every step of the process. Remember, the key is to use these prompts as starting points and customize them to your specific situation. By combining AI’s capabilities with your unique experience and insights, you’ll be well-equipped to take your next career step with confidence.

While generative AI dominates headlines and boardroom discussions, a quiet revolution is taking place beneath the surface. Organizations are fundamentally reimagining how they approach innovation, experimentation, and data sharing – and it’s these changes, not the technology itself, that will determine who succeeds in the AI era.

“There’s a massive transformation happening in how organizations are approaching experimentation and innovation,” explains Tom Godden, Enterprise Strategist and CXO Advisor at Amazon Web Services (AWS). “I’ve seen the beginnings of a distinct move away from what I’ll call the perfection paralysis mindset.”

Reimagining The Center Of Excellence

One particularly intriguing shift is happening in how organizations structure their AI initiatives. The traditional Center of Excellence (COE) model is being turned on its head. “What I’m usually advocating for now is the center of engagement,” says Godden. “It’s not about a small group of experts dictating best practices… it’s about creating an environment where everyone is encouraged to experiment, report results, learn from success and failures.”

This shift is already bearing fruit. Godden shares an example of a manufacturing company that “completely reimagined their approach to technology adoption” by creating innovation pods – cross-functional teams combining business and domain experts with technical specialists, empowered to experiment with AI use cases. These pods represent a fundamental shift in how organizations approach experimentation and innovation, moving away from what Godden calls the “perfection paralysis mindset.”

Breaking Down Data Silos: From ‘Why Share?’ To ‘Why Not?’

Perhaps the most significant transformation is occurring in how organizations think about data sharing. The old mentality of treating all data as crown jewels is becoming obsolete. As Godden points out, “If you had a marketing team that had a lot of valuable customer preference data, but doesn’t share it with product development, you’re not just missing opportunities. You’re hampering your ability to compete.”

One financial services company has found an innovative solution through “data discovery days” – regular events where teams showcase their data assets and potential use cases. This approach has led to unexpected collaborations between departments that previously didn’t even know certain data existed. According to Godden, companies need to pivot from asking “why should I share this data” to asking “why wouldn’t I share this data?”

What Makes Data ‘Good’ For Generative AI?

Despite widespread agreement about data’s importance – with 93% of chief data officers acknowledging its crucial role in AI success – many organizations struggle to adapt their data strategies. Godden outlines five essential characteristics of “good” data for generative AI:

First, data must be trustworthy and verified – what Godden calls the “proverbial good housekeeping seal of approval,” ensuring it comes from verified sources and is safe to use in language models. Second, it needs to be current and well-governed, as illustrated by Godden’s cautionary example from a health insurer where historical data recommended people take up smoking – highlighting why outdated data can be dangerous. Third, it must be accessible across the enterprise, breaking down departmental silos. Fourth, data should be representative and inclusive to avoid perpetuating biases, requiring diverse data sets and diverse groups governing that data. Finally, security is non-negotiable, particularly when dealing with internal data and language models, which is why the AWS strategy focuses on bringing models to the data rather than the other way around.

The Human Side Of AI Adoption

Success with generative AI isn’t just about technology and data – it’s about people. Organizations need to foster what Godden calls a “culture of experimentation,” where innovative thinking is rewarded and calculated risk-taking is encouraged. According to Godden, effective data champions need to be individuals who “match their IQ with their EQ,” combining domain knowledge with technical expertise. They should focus on “governing by enabling rather than governing by restricting,” making it easy for people to do the right thing.

Looking Ahead: The Next Wave Of Innovation

The convergence of IoT and edge computing with AI capabilities is opening new frontiers. As Godden explains, we’re seeing “the beginnings of advances and ML algorithms and silicon to be able to create that next generation of IOT devices to be able to run without requiring an internet connection or a local data center.” Digital twins are becoming increasingly sophisticated, moving beyond simple monitoring into predictive maintenance and experimentation, particularly in manufacturing and healthcare settings.

But perhaps most importantly, sustainability is becoming a crucial consideration in AI implementation. As Godden notes, “We need to be leaning into these technologies in an excited way, but doing it in a way that is sustainable for our environment and our society.” He points out that cloud computing can significantly reduce carbon footprints, with AWS infrastructure being nearly four times more efficient than the median for U.S. data centers.

The Path Forward

The message is clear: while generative AI offers extraordinary potential, success depends not on the technology itself but on the foundational elements of culture, data strategy, and human capability. Organizations that focus on these elements while fostering a spirit of experimentation and collaboration will be best positioned to harness AI’s transformative power.

As Godden succinctly puts it, “Turns out that people are pretty important in any transformation.” Indeed, in the rush to embrace AI, we must remember that technology is just one piece of a much larger puzzle.

Credit: Bernard Marr

The quantum computing revolution isn’t coming – it’s already here. In my recent conversation with Mitra Azizirad, President and COO of Strategic Missions and Technologies at Microsoft, she made it crystal clear that we’ve entered what she calls “the reliable quantum computing era.” This isn’t just another technological advancement; it’s a seismic shift that could redefine competitive advantage across industries.

Why Quantum Computing Is Different This Time

The game-changer is what experts call “reliable quantum computing,” which uses logical qubits that are far more stable than their predecessors. As Azizirad explained it to me with a delightfully simple analogy, “It’s like putting noise-cancelling headphones on qubits.” These logical qubits can detect errors, correct them, and maintain stability during computations – fundamental capabilities that make quantum computing practical for real-world applications.

The AI Parallel That Business Leaders Can’t Ignore

Drawing a fascinating parallel with recent history, Azizirad pointed out that we’re at a moment similar to the generative AI breakthrough in 2023: “Those business leaders who were prepared got more of an early competitive edge. And I believe that reliable quantum computing will be just as transformative, particularly in solving complex problems that are intractable for classical computers.”

The Hybrid Future Of Computing

One of the most important insights from our conversation was that the future won’t just be defined by quantum solutions in isolation. “The future of computing is hybrid,” Azizirad emphasized. “It’s not just like waiting for some far-off quantum computer. It’s really bringing together quantum, AI and HPC to leverage each where they’ll be the most useful.” This means organizations need to think holistically about how these technologies will work together.

Real-World Applications Already Emerging

One area where quantum innovation promises to have a near near-term impact is chemistry and materials science. According to Azizirad, “96 percent of all manufactured goods rely on chemicals and materials science.” Microsoft has already demonstrated a hybrid workflow for a chemistry application, combining HPC, AI, and quantum computing to solve complex catalyst problems with high accuracy.

The applications are both diverse and profound. In the automotive sector, quantum computing could help design efficient electric vehicle batteries that extend range and reduce charging times. Environmental innovations could include developing materials that serve their purpose but biodegrade quickly afterward – a potential game-changer for addressing our global plastic crisis. In the pharmaceutical industry, quantum computing could accelerate drug discovery by simulating molecular interactions with unprecedented accuracy. AI and HPC are already delivering results for many of these areas. As Azizirad explained, Microsoft has recently made a breakthrough: “Microsoft Research just actually introduced this AI-driven simulation system, which can accurately model protein behavior down to the individual atom at these orders of magnitude faster than ever before.”

These aren’t theoretical possibilities for some distant future – they’re being developed and implemented now. As Azizirad shared, “This past year, our teams ran an end-to-end chemistry workflow that combined that magical HPC, AI and quantum and invented a quantum algorithm for a catalyst problem and ran it on logical qubits… we were able to solve that problem very quickly with a high degree of accuracy.” The application of hybrid approaches can extend to solving problems in, for example, sustainability, clean water, packaging and food – essentially touching every aspect of manufacturing and consumer goods.

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Your Quantum Readiness Roadmap

Perhaps the most sobering statistic Azizirad shared was that only 12% of business leaders surveyed feel their organizations are prepared for the opportunities and risks associated with quantum computing. This knowledge gap between theoretical understanding and practical application is becoming increasingly critical to address.

Azizirad outlined several key steps for business leaders. Start by educating teams on quantum fundamentals and giving them opportunities to experiment with quantum computing. Analyze potential use cases specific to your business and create an application roadmap. Think about how quantum will integrate with your existing AI and computing infrastructure.

Most crucially, address quantum security now. As Azizirad warned, “Becoming quantum-safe is of paramount importance… It’s the future risk associated with our cryptography systems. And the solution is clear. It’s transitioning to post-quantum cryptography.”

Looking Ahead: The Dawn Of A New Era

After 33 years at Microsoft, Azizirad shared something that really struck me: “I have never been as excited as I am today because it’s really about harnessing the full potential of quantum and what that really means. I feel like it’s the kind of work that I’ll be able to talk to my future grandchildren about.”

The quantum revolution isn’t some distant future we need to prepare for – it’s unfolding right now. The businesses that thrive in this new era will be those that start building their quantum capabilities today. As Azizirad succinctly put it, “My recommendation is to begin preparing today. Without a doubt.”

Artificial intelligence is transforming the legal industry just as it is reshaping almost every other sector of industry.

The convergence of legal services and technology—commonly referred to as lawtech—is automating routine tasks, enabling legal professionals to focus on more complex and rewarding challenges.

This is happening at a critical time. With changing client expectations and increasingly complex regulatory environments, technological innovation is quickly becoming essential for keeping pace with a rapidly evolving legal landscape.

So here I’ll take a look at some of the most impactful technology trends in the field of lawtech, and examine how they will impact the role of lawyers and other legal professionals as we head into 2025.

LLMs Take The Strain

Large language models (LLMs) are the AI engines powering chatbots like ChatGPT and Gemini. Their ability to generate virtually any kind of text – including legal contracts, summaries or answers to legal questions – means they are playing an increasingly prominent role in the work of legal professionals. It will also become more and more common to see them in client-facing applications, such as answering frequently asked legal questions. These models will continue to become more powerful and flexible throughout 2025, reshaping the work of legal departments.

AI Regulation And Compliance

As AI and digital transformation become increasingly central to the operations of most businesses, legal professionals will play an ever-more critical role in helping companies comply with regulations and guidelines around its use. Mitigating the legal risks of AI applications themselves (such as the potential for IP infringement or breach of equality regulations) will also involve assessing liabilities around data governance and information security. Drafting policies, auditing systems for bias and potential breaches of DEI requirements, and developing specialist knowledge of emerging legal technologies will all be key trends.

Lawtech Accelerates The Democratization Of Justice

In 2025, the proliferation of lawtech doesn’t just make lawyers’ lives easier; it improves everyone’s access to justice and legal services. Cloud-based self-service portals enable the automated generation of legal documents, chat-powered legal advice and other affordable solutions. Put together, these have the effect of simplifying and lowering the cost to individuals and small businesses of accessing effective legal recourse.

Legal Process Automation

The ability of AI to automate repetitive elements of work will transform the day-to-day activities of lawyers and legal professionals in 2025. This includes automating document reviews and discovery, contract management and anything involving processing data. As legal departments learn to become efficient in using this technology to accelerate workloads and reduce costs, professionals will shift their focus to more strategic, high-value tasks involving interpersonal communication, relationship-building and complex problem-solving. Law firms that successfully adapt to this paradigm shift will find they are able to deliver more cost-effective services and hone their competitive advantage.

Predictive Justice And Litigation

The ability of AI to predict the future – by analyzing vast datasets of prior case law and judicial decisions to forecast the outcome of trials and litigation – will be a valuable tool for legal professionals in 2025. This will help legal representatives provide better services for their clients by enabling them to more accurately predict results and model the likelihood of success of different strategies and resource allocations. This means that legal advice given to clients becomes more data-driven and less reliant on speculation and chance.

Responsible Legal AI

The need for legal departments, law firms and professionals to audit the use of AI in their operations, to make sure it’s being used responsibly, will grow in 2025. This means using it in ways that are fair (unbiased), secure, transparent, accountable and ethical. On top of this, lawyers will find that a growing part of their workload will involve advising clients or colleagues on their own AI operations, to ensure they are responsible. Embracing responsible AI is critical not only to safeguarding an organization’s reputation but also to positioning it as a leader in an industry undergoing rapid digital transformation.

Wrapping Up The Case

The rapid evolution of lawtech that will take place in 2025 will reshape the work of legal departments and law firms in many profound ways. We will see processes streamlined, access to justice improved, and the role of the lawyer redefined. However, care must undoubtedly be taken to approach this transformation in an ethical and responsible way if we want to make sure that the convergence of law and technology results in a fairer and more just future for everyone.

The world’s population is becoming increasingly urbanized. Over the past century, millions of people have moved from the countryside, creating vast “mega-cities” – a term defined as a city with more than 10 million inhabitants.

This level of population – examples include Tokyo (population 37 million), Delhi (30 million) and Sao Paulo (20 million) – creates new challenges that society hasn’t previously had to deal with.

In response, technological solutions have emerged designed to improve lives, enable new forms of civic administration, and mitigate the environmental impact caused by so many people living in such close proximity.

It’s a dynamic and fast-moving field of technology where innovation has a real impact on millions of human lives. So, let’s take a look at some of the key trends in smart city and civic governance tech that will top the agenda in 2025.

AI In Urban Planning And Governance

Increasingly, we will see artificial intelligence (AI) used to plan and deliver services to those living in urban areas more efficiently. This covers every aspect of AI, from machine learning algorithms crunching data to enable more efficient allocation of resources to predictive modeling for infrastructure requirements to real-time alerts that give vital information to citizens as they go about their day.

Addressing Water Scarcity

The global urban population facing water shortages is set to double by 2050, and technological solutions to this challenge will be a focus of civic planning in the coming years. This will include both predictive measures for anticipating fluctuating levels of availability and usage, as well as advanced techniques for recycling, distribution and desalination. Smarter water management means adapting the way water is collected, stored and used in the face of rapid population growth and changing climate.

Digital Identity And Citizenship

Digital citizenship will play a growing role in the future of urban life, as governments and administrators roll out plans for identity verification and civic engagement. This will include new digital solutions for delivering services like applying for permits, obtaining welfare payments and paying taxes. Implementation is likely to vary massively according to cultural factors, but wherever they live in the world, citizens will become increasingly aware of the implications of privacy and data security.

Smart Transport Infrastructure

In the smart city of 2025, the daily commute will increasingly be revolutionized thanks to the deployment of integrated systems connecting public transport with micro-mobility solutions, ride-sharing infrastructure and the emergence of autonomous and semi-autonomous transport. More intelligent traffic management infrastructure will predict hotspots in order to reduce both congestion and emissions. Critically, all this infrastructure will be connected and capable of sharing data to gain a new understanding of how we navigate cities and what can be done to make everyone’s journeys smoother, safer and less damaging to the environment.

Health-Centric Urban Planning

The era of smart city technology creates new opportunities for designing urban environments in ways that are conducive to better human physical and mental health. Leveraging this potential will be another key trend in 2025. This will include the use of sensors and data to monitor and detect pollution or unhealthy noise levels, as well as the adoption of predictive solutions for healthier urban living.

City-Scale Digital Twins

The digital twin concept involves creating virtual replicas, modeled using real-world data, in order to create simulations that can be used for planning and managing development. A digital twin can model anything from a simple object or mechanical system to an environmental ecosystem or, as is increasingly the case, a city. City-scale digital twin projects currently underway include Singapore, Helsinki, and Dublin, and in 2025, we are likely to see an explosion of activity in this field of smart city technology.

Climate Resilience – Weathering The Storm

From Rotterdam’s plazas designed to double up as flood plains, to New York’s Internet of Things (IoT) powered FloodNet, preparing for an increasingly unstable and unpredictable climate is a core focus of tech-driven urban planning. Globally, extreme weather events are forecast to become more frequent and severe, and meeting this challenge will involve harnessing technology to improve preparedness and enable more efficient response and recovery.

Renewable Energy Infrastructure

Moving towards sustainable and renewable energy sources, as well as improved energy security in the face of geopolitical uncertainty, will be another key trend in 2025. Smart grids incorporating AI-driven predictive resource allocation will undoubtedly be a part of the solution, but increasing adoption of solar, wind and tidal energy, as well as shifts towards micro-grids and new forms of battery storage, in order to improve reliability and consistency of supply, will also be an essential part of the solution.

The Year Ahead

City life is changing, and in 2025, urban planners and administrators have more technological options than ever before when it comes to managing and implementing that change. Leveraging the technological opportunities covered will be part of the solution to the challenges of growing urban populations, demographic change, and climate emergency.

However, political will is also needed, as well as a societal acceptance of the necessity of this change. Understanding these trends will be key to improving the lives of the millions of us living in today’s modern cities and urban environments.

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.