The next wave of artificial intelligence isn’t just knocking at enterprise doors – it’s exposing fundamental flaws in how organizations approach technology transformation. As IT teams race to stay competitive in 2025, they’re making mistakes that could significantly impact their digital initiatives.

Mistake 1: Mishandling AI Governance

Many organizations are mishandling AI deployment by operating without proper guardrails, while employees increasingly turn to unauthorized “shadow AI” applications to boost their productivity. In 2025, we’ll see the consequences of this oversight manifest in data breaches, biased outputs, and compliance violations. Organizations are discovering sensitive data being fed into public AI models through unofficial channels, creating massive security vulnerabilities. Forward-thinking IT leaders are already implementing comprehensive AI governance frameworks that cover everything from model selection to output verification while providing approved alternatives to popular consumer AI tools. This isn’t just about risk management – it’s about building sustainable AI practices that can scale with your organization’s growing needs while keeping shadow AI use in check through education and accessible, secure alternatives.

Mistake 2: Ignoring Regulatory Requirements

IT teams are significantly underprepared for incoming AI regulations. While the U.S. currently lacks comprehensive federal AI legislation, states like Colorado are implementing strict requirements around automated decision-making systems, and the EU’s sweeping AI Act will impact any organization doing business in Europe. By 2025, organizations will need to demonstrate their AI systems aren’t discriminatory, provide transparency reports for high-risk applications, and comply with complex international requirements. Even existing regulations are being reinterpreted through an AI lens – from biometric privacy laws to consumer protection statutes. IT teams building AI systems today without considering these emerging compliance requirements are creating unnecessary technical debt. Smart organizations are future-proofing their AI implementations by designing for transparency, establishing clear governance frameworks, and building systems that can adapt to evolving regulatory demands across multiple jurisdictions.

Mistake 3: Creating Integration Complexity

In rushing to modernize, organizations are creating unnecessary technical debt with brittle architectures that span old and new systems. While everyone wants to talk about their latest AI implementation or cloud migration, organizations are drowning in hundreds of point-to-point connections between specialized tools and aging legacy platforms. Smart organizations are taking a hybrid approach, methodically modernizing their core systems while implementing robust integration frameworks that can scale. They’re replacing brittle connections with flexible architectures that can adapt as systems evolve. This isn’t as exciting as launching the latest chatbot, but building sustainable, maintainable technology ecosystems is fundamental to long-term success.

Mistake 4: Neglecting Data Quality

Organizations are building AI initiatives without addressing fundamental data quality issues. Their data lakes are more like murky swamps – plagued by inconsistent standards, conflicting formats, and quality issues that render them nearly unusable for advanced AI applications. The problem goes beyond mere technical challenges. Business units are hoarding information in isolated silos, data governance policies are outdated or ignored, and metadata management is often an afterthought. The result? AI initiatives that produce unreliable outputs, models that perpetuate hidden biases, and massive costs in data cleanup and rework. Forward-thinking organizations are treating data quality as a board-level priority, investing in robust data governance frameworks, and building centralized data platforms that enforce consistent standards. They understand that in 2025, the difference between AI success and failure often comes down to the quality of the data foundation it’s built upon.

Mistake 5: Compromising Security

IT teams are compromising security in their push for rapid innovation. The pressure to deliver new capabilities at speed is leading to incomplete security reviews and inadequate protections. This is particularly concerning as cyber threats evolve into hybrid attacks that combine AI capabilities with traditional hacking methods. Automated systems are probing for vulnerabilities 24/7, while AI-powered social engineering attacks are becoming increasingly sophisticated and harder to detect. Adding to this perfect storm is the looming threat of quantum computing that is forcing organizations to confront the possibility that their current encryption methods may soon be obsolete. Forward-thinking organizations are adopting zero-trust architectures and implementing DevSecOps practices that bake security into every stage of development. They’re also investing in quantum-safe encryption and AI-powered security tools that can detect and respond to threats in real time. In 2025, a single security breach can undo years of digital transformation efforts.

Mistake 6: Maintaining Outdated Skills Development

Organizations are maintaining outdated approaches to skill development and technical training. The technical skills that were cutting-edge six months ago are now baseline requirements, while entirely new competencies emerge almost weekly. This skills gap is particularly apparent in AI and quantum computing, where the underlying technology evolves faster than training programs can adapt. Progressive organizations are taking a radically different approach, implementing continuous learning platforms that combine foundational principles with real-time skill adaptation. They’re fostering partnerships with AI vendors, cloud providers, and educational institutions to create dynamic learning environments. The focus has shifted from traditional certifications to practical experience and adaptability – because, in 2025, the most valuable skill is the ability to learn and unlearn at the pace of innovation.

The Price Of Inaction

These mistakes are already impacting digital transformation efforts across industries. The organizations that will thrive in 2025 are those that recognize these issues for what they are: predictable, preventable problems that require immediate attention. The time to course-correct is now, before these compounding issues create problems too expensive and complex to fix. The choice is clear: address these challenges head-on today, or watch your digital transformation efforts falter tomorrow under the weight of avoidable mistakes.

Since the launch of ChatGPT just two years ago, the volume of synthetic – or fake – content online has increased exponentially.

Firstly, not all “fake” content is inherently bad. Generative AI text, image and audio tools have streamlined many repetitive tasks, from drafting routine letters and notices to storyboarding and prototyping in more creative projects.

But AI-generated content becomes problematic when it’s intended to mislead, misinform or spread fake news. Some have even gone as far as to say it threatens to destabilize democratic processes and create a “truth crisis.”

So what can be done? Well, luckily, a number of methods of differentiating between AI-generated and authentic content have been developed. These include sociological approaches, emphasizing the importance of education and critical thinking. They also include technological solutions, often leveraging the same generative and machine learning models used to create “fake” content, repurposed to detect it instead.

Here, I’ll focus on the latter. I’ll start by covering how they work, then take a look at some of the most popular tools and applications in this category.

The last few years have seen a big increase in both. However, the term “fake news” covers any deliberately constructed stories, lies, or disinformation designed to deceive. Whereas “deepfake,” “fake content,” or “synthetic content” specifically refers to content that’s not just designed to deceive but also generated by AI.

This deception could simply be for the sake of entertainment – as in the case of viral internet fakes like “deepfake Tom Cruise”, or “Pope In A Puffer Jacket.”

On the other hand, and increasingly, it could also be intended to cause real harm, such as influencing elections, damaging trust in public figures, or spreading geopolitical propaganda.

This year, ahead of upcoming elections in many countries, the WEF recognized AI misinformation as the biggest cybersecurity risk facing society. This all suggests that developing methods and tactics for identifying and combatting the rise of deepfake content is important for all of us.

What Are AI Content Detectors And How Do They Work?

In the simplest terms, most AI content detectors work by analyzing content and attempting to spot patterns that suggest it may have been generated by AI.

Often, they rely on AI itself to do this, leveraging neural networks that are trained to recognize typical traits.

For text, this could be particular phrases or ways of structuring information that is typical of large language models (LLMs), such as those powering ChatGPT or Google Gemini.

With images, this could mean looking out for telltale mistakes. For example, it’s frequently observed that AI image generators will often have difficulty with adding the correct number of fingers to their drawings of hands, correctly rendering text, and dealing with lighting and shadows.

It’s important to remember, however, that even the best tools are not foolproof. For example, it’s easy to mix AI and human-generated content to create hybrid content, but that’s likely to confuse AI content detectors.

Because of this, most of the tools covered here don’t categorically determine whether content is either AI or genuine. Instead, they are more likely to assign a probability or estimate how much of the text is likely to be AI-generated.

(To demonstrate this, I fed the text of this article, which is entirely human-written, into all of the text-based AI detectors mentioned here. You can see the results below)

The Best AI Content Detectors

AI Or Not

This paid-for site detects the use of generative AI in both images and audio.

Copyleaks

AI text analysis that’s widely used by businesses and academia.

Is this article written by AI? No

Deepfake Detector

Identifies fake video and audio with a claimed 92% accuracy.

Deepware

Deploy professional-quality deepfake detection resources on-premises for businesses.

GPTZero

One of the first widely available AI text detectors.

Is this article written by AI? 4%

Grammarly

The real-time grammar-checking plugin also offers an AI content detector.

Is this article written by AI? 50%

Hive Moderation

Designed to provide real-time moderation of video, audio and text content, also detects AI content.

Is this article written by AI? 0%

Is It AI?

Machine learning-powered AI image detector with free and paid-for options.

Originality

This lets you verify that the content you are planning to publish is authentic and trustworthy by checking it for AI, as well as plagiarism and factualness.

Is this article written by AI? 3%

Plagiarismcheck

Powerful AI text detection suite, with specialized tools for educational use cases.

Is this article written by AI? 0%.

Quillbot

Free-to-use AI text checker with no sign-up requirements.

Is this article written by AI? 0%

Winston

Winston is a comprehensive AI checking tool that can detect fake images as well as text. It also offers a certification program, certifying content as human-created.

Is this article written by AI? 0%

As AI-generated content becomes increasingly sophisticated, the tools and technologies we use to detect it must evolve in parallel. While today’s AI content detectors offer valuable insights, they’re not infallible – as demonstrated by the varying results when testing this human-written article. The key lies in using these tools as part of a broader approach to content verification, combining technological solutions with critical thinking and digital literacy. As we navigate an increasingly complex information landscape, these detection tools will become essential components in our collective effort to maintain digital truth and combat harmful misinformation.

Credit: Bernard Marr

How do you know if the data you have is considered big data? There are generally four characteristics that must be part of a dataset to qualify it as big data—volume, velocity, variety and veracity. Value is a fifth characteristic that is also important for big data to be useful to an organization.

Our world has become datafied. From data that shows activity such as our Google searches and online shopping habits to our communication and conversations through text, smartphones and virtual assistants, and all the pictures and videos we take to the sensor data collected by internet-of-things devices and more, there are 2.5 quintillion bytes of data created each day. The better companies and organizations manage and secure this data, the more successful they are likely to be. How do you know if the data you have has the characteristics that qualify it as “big”? Most people determine data is “big” if it has the four Vs—volume, velocity, variety and veracity. But in order for data to be useful to an organization, it must create value—a critical fifth characteristic of big data that can’t be overlooked.

Volume

The first V of big data is all about the amount of data—the volume. Today, every single minute we create the same amount of data that was created from the beginning of time until the year 2000. We now use the terms terabytes and petabytes to discuss the size of data that needs to be processed. The quantity of data is certainly an important aspect of making it be classified as big data. As a result of the amount of data we deal with daily, new technologies and strategies such as multitiered storage media have been developed to securely collect, analyze and store it properly.

Velocity

Velocity, the second V of big data, is all about the speed new data is generated and moves around. When you send a text, check out your social media feed and react to posts on Facebook, Instagram or Twitter or make a credit card purchase, these acts create data that need to be processed instantaneously. Compound these activities by all the people in the world doing the same and more and you can start to see how velocity is a key attribute of big data.

Variety

Today, data is generally one of three types: unstructured, semi-structured and structured. The algorithms required to process the variety of data generated varies based on the type of data to be processed. In the past, data was nicely structured—think Excel spreadsheets or other relational databases. A key characteristic of big data is that it not only is structured data but also includes text, images, videos, voice files and other unstructured data that doesn’t fit easily into the framework of a spreadsheet. Unstructured data isn’t bound by rules like structured data is. Again, this variety has helped put the “big” in data. We are able to use technology to make sense of unstructured data today in a way that wasn’t possible in the past. This ability has opened up a tremendous amount of data that have previously not been accessible or useful.

Veracity

The veracity of big data denotes the trustworthiness of the data. Is the data accurate and high-quality? When talking about big data that comes from a variety of sources, it’s important to understand the chain of custody, metadata and the context when the data was collected to be able to glean accurate insights. The higher the veracity of the data equates to the data’s importance to analyze and contribute to meaningful results for an organization.

Value

While this article is about the 4 Vs of data, there is actually an important fifth element we must consider when it comes to big data. This is the need to turn our data into value. In fact, organizations that have not created a data strategy to yield insights and to drive data-driven decision-making are going to fall behind competitors. Big data that’s analyzed effectively can provide important understanding of customers and their behaviors and desires, how to optimize business processes and operations and to improve a nearly endless amount of applications. Whether you use data to create a new product or service or to understand a way to cut costs, it is incredibly important that big data creates value. This value is why organizations of every size must have a data strategy in place in order to ensure the data needed to achieve the business objectives they adopted are being collected and analyzed.

Apple’s announcement of Apple Intelligence has sent waves of excitement through the tech world. This new AI-powered system promises to revolutionize how we interact with our devices, making them smarter, more intuitive and more helpful than ever before. But the burning question on everyone’s mind is: “Will my device be able to run Apple Intelligence?” Let’s dive into the details and find out if you’ll be joining the AI party or if it might be time to consider an upgrade.

The Hardware Requirements: It’s All About The Chips

As with any major software advancement, the ability to run Apple Intelligence comes down to hardware. In this case, it’s all about the chips powering your device. Here’s the lowdown:

iPhones: The A17 Pro Takes The Lead

If you’re an iPhone user, you’ll need the latest and greatest to get the full Apple Intelligence experience. According to Apple, Apple Intelligence will be available on iPhone 16 as well as iPhone 15 Pro models, which are powered by the A17 Pro chip. This powerhouse of a processor packs the necessary punch to handle the complex on-device AI processing that Apple Intelligence requires.

But what if you don’t have the latest Pro model? Don’t worry, you’re not entirely out of luck. While the full suite of Apple Intelligence features may require the A17 Pro, it’s likely that some features will be available on other recent iPhone models. However, Apple hasn’t provided specifics on this yet, so we’ll have to wait for more details.

iPads: M1 And Beyond

For iPad users, the entry point for Apple Intelligence is the M1 chip. This means if you have an iPad Pro from 2021 or later or an iPad Air from 2022 or later, you’re in business. These devices pack serious computing power, making them capable of handling the demands of Apple Intelligence.

Macs: The M-Series Club

When it comes to Macs, if you’ve got an M-series chip, you’re good to go. This includes MacBooks, iMacs and Mac Studios with M1, M2 or M3 chips. The power and efficiency of these chips make them ideal for running Apple Intelligence.

What About Older Devices?

If your device doesn’t meet these requirements, don’t despair just yet. While you may not get the full Apple Intelligence experience, it’s possible that some features will be available on older devices. Apple has a history of bringing some new features to older hardware, even if the most advanced capabilities are reserved for the latest models.

Moreover, it’s worth noting that Siri, Apple’s existing virtual assistant, will continue to work on older devices. While it may not have all the bells and whistles of Apple Intelligence, it will still be there to help with basic tasks.

The Software Side: iOS 18, iPadOS 18 And macOS Sequoia

Hardware is only half the story. To use Apple Intelligence, you’ll also need to be running the latest operating systems: iOS 18 for iPhones, iPadOS 18 for iPads and macOS Sequoia for Macs. These new OS versions are set to be released this fall.

The good news is that Apple has been known for supporting older devices with new software updates for several years. So even if your device is a few years old, you may still be able to update to the latest OS and get at least some Apple Intelligence features.

A Phased Rollout: Patience Is A Virtue

It’s important to note that Apple is planning a phased rollout for Apple Intelligence. While some features will be available immediately with the release of the new operating systems, others will be rolled out over the course of the following year.

Initially, Apple Intelligence will be available in U.S. English, with support for additional languages and regions coming later. So, if you’re not in the U.S. or prefer a different language, you might need to wait a bit longer to experience all that Apple Intelligence has to offer.

The Cloud Factor: Private Cloud Compute

One interesting aspect of Apple Intelligence is its use of “Private Cloud Compute” for more complex tasks. This system allows devices to tap into more powerful server-based models when needed while still maintaining strong privacy protections.

The good news is that this could potentially extend some Apple Intelligence capabilities to older devices. Even if your device isn’t powerful enough to handle all the processing locally, it might be able to use Private Cloud Compute to access some features.

What If Your Device Isn’t Compatible?

If your current device isn’t compatible with Apple Intelligence, you have a few options:

1. Wait and see: Apple may bring some features to older devices in future updates.
2. Upgrade your device: If you’re due for an upgrade anyway, this could be a good reason to take the plunge.
3. Use alternative AI tools: There are many third-party AI apps available that can provide similar functionality, although they may not be as deeply integrated into your Apple ecosystem.

The Bigger Picture: The Future of Apple Devices

Apple Intelligence represents a significant shift in how our devices operate. It’s clear that AI is becoming an integral part of Apple’s ecosystem, not just an add-on feature. This suggests that future Apple devices will likely be designed with AI capabilities in mind from the ground up.

If you’re in the market for a new Apple device, it might be worth considering one that’s compatible with Apple Intelligence. Not only will you be able to enjoy these new features now, but you’ll also be better positioned for future AI advancements.

The Verdict: A New Era, But Not For Everyone (Yet)

Apple Intelligence is ushering in an exciting new era of personal computing, but it’s clear that not everyone will be able to join in right away. If you have the latest Pro iPhone, a recent iPad,or a Mac with an M-series chip, you’re all set to experience the full power of Apple Intelligence.

For everyone else, it’s a bit of a waiting game. Some features may trickle down to older devices, and the phased rollout means that even compatible devices won’t get all features right away.

But don’t let that dampen your enthusiasm. Apple Intelligence represents the direction that personal computing is heading, and it’s only a matter of time before these kinds of AI capabilities become standard across all devices.

So, whether you’re gearing up to dive into Apple Intelligence this fall or planning your future upgrade, one thing is clear: the future of our devices is intelligent. The question isn’t if you’ll be using AI like this, but when. And for many Apple users, that “when” is just around the corner.

Our understanding of the human genome has continued to increase since it was first accurately sequenced in 2003. Building on that success, biotechnology is advancing to the point where it’s viable to alter the DNA encoded within a cell. This process is known as gene editing. Understandably, gene editing has creepy connotations for some, but it could deliver some drastic leaps forward in the fight against disease, as well as delivering other benefits, like ensuring we have enough food to feed the planet.

Here are seven things you need to know about gene editing:

1. Gene editing can be used to “fix” harmful genes

Gene editing can have particular advantages when “bad” genes are detected – genes that could endanger the health of the organism (be it a human, animal, or plant) or its descendants. These harmful characteristics can, in theory, be altered. In humans, this could ensure children do not suffer the same inherited diseases as their parents, or mutations could be fixed before they result in disease. In animals, common problems associated with certain breeds, such as breathing problems or susceptibility to blindness, could be eliminated. And in plants, crops can be developed that are resistant to pests and disease.

2. CRISPR (pronounced “crisper”) is the method to watch

There’s one particular method of gene editing that is showing enormous potential. CRISPR, which was first developed in 2012 at the University of California, Berkeley, was adapted from a naturally occurring gene-editing system in bacteria. Given that the human body contains around 37 trillion cells, the microscopic scale involved in gene editing is truly amazing. The nucleus, where most DNA resides, takes up around 10 percent of the mass of a typical cell, so the level of accuracy needed to cut something that tiny is almost inconceivable. At present, CRISPR is the simplest and most precise way of identifying where a DNA strand needs to be cut and then splitting the strand. 

3. Gene editing could transform healthcare

Much of the work being done with gene editing is in the field of healthcare, with some of the most exciting projects focusing on the “correction” of DNA mutations that can lead to serious illnesses. In one example, gene editing has been shown to fix the mutation responsible for Duchenne Muscular Dystrophy – a devastating condition that affects one in 3,500 young boys and results in early death. Gene editing for Duchenne Muscular Dystrophy has been trialed on beagle dogs, and the hope is it will lead to a viable treatment for humans.

4. Gene editing could solve the problem of food allergies

Gene editing could also potentially eliminate the dangers caused by allergens. That’s because the compounds and substances within foodstuffs such as cereals, dairy products, and nuts that are responsible for allergic reactions can potentially be eliminated through gene editing. In one project, researchers at Wageningen University in the Netherlands are removing antigens in gluten from wheat, making it digestible for those with a gluten intolerance.

5. There are two main types of gene editing

Gene editing work carried out to prevent disease or improve health can be classified into two types. The first is germline therapy, which can cause changes in reproductive cells (eggs and sperm) and therefore cause changes that will be inherited by offspring. The second is somatic, which targets non-reproductive cells and can potentially cure or slow down the spread of disease in the target organism.

6. Many countries do not allow gene editing

Germline editing in humans is currently banned in many countries, including much of Europe, as its long-term results are not understood. This will possibly change in the coming years as a public discussion on the ethics and implications advances, or the need for eradicating disease becomes more urgent. Interestingly, germline editing isn’t banned in China or the US.

7. Gene editing could boost plant health, too

As well as human health, plant health can also be improved with gene editing. We all know that vegetable and cereal crops are susceptible to pests and diseases. By editing plant genomes, their resistance to these threats can be increased, leading to higher yields and less dependence on harmful chemical interventions. For example, researchers at Penn State University are working on creating genetically enhanced cacao trees that will be resistant to the disease and fungus that destroys up to 30 percent of the worldwide cacao crop before their pods can be harvested. This is done by suppressing a gene that decreases the plant’s ability to fight off infections. As well as increasing the global supply of cacao (great news for chocoholics), this could also hugely improve the livelihoods of cacao farmers, who are some of the most deprived agricultural workers. At present, the cultivation of genetically modified crops is banned in some countries, including the UK. However, many countries do permit the cultivation of crops created by gene editing, with varying degrees of regulation. These countries include Canada, China, the US, Australia, and Brazil.