AI is Changing Search, but SEO Experts Say Backlinks Still Rule the Game

Even with artificial intelligence pushing search in new directions, seasoned SEO professionals aren’t ready to let go of backlinks. A large industry survey, reaching over 500 specialists worldwide, shows that while AI-driven results are making waves, the old-school power of link building isn’t fading anytime soon.

It turns out, most SEO pros still see backlinks as the backbone of visibility, even in AI-powered spaces like Google’s AI Overviews or tools like ChatGPT. Nearly three-quarters of those surveyed believe links still help pages show up in these AI-generated search results. And when it comes to paid links, most believe Google isn’t quite as sharp as it claims. Over half think the search giant struggles to consistently catch and penalize purchased links, which quietly fuels the ongoing race to buy them.

The competition is fierce. About 92% of SEO professionals suspect their rivals are actively buying backlinks. This isn’t just guesswork, it’s the lived reality for many in the field. SEO experts also say that "nofollow" links, often dismissed in the past, still carry weight, and unlinked brand mentions can nudge rankings up by building credibility and visibility across the web.

But playing this game isn’t cheap.

The cost of building strong backlinks has surged, and SEO teams now expect to spend upwards of $8,400 a month just to stay in the race in tough industries. Gambling and iGaming remain some of the most expensive battlegrounds, where link budgets balloon and competition stays brutal. Getting a high-quality backlink typically comes with a price tag of around $500, though that number swings wildly depending on the niche and site authority.

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Not everyone is handling this in-house. More than half of SEO teams now outsource at least part of their link-building efforts, and many split their SEO budgets with a big slice going toward acquiring those valuable links. In-house teams, interestingly, tend to spend a bit more on this than agencies.

When it comes to tactics, the crowd favorite is digital PR. Nearly half of the experts say that smart PR campaigns deliver the best results these days. What’s working isn’t copying the competition, it’s finding unique backlink opportunities that set a brand apart. SEO professionals are doubling down on earning links directly to product and service pages, the ones that truly move the needle for sales. Partial-match anchor texts are most popular, though exact matches and branded anchors still have their place in the mix.

Of course, link building is still a high-wire act.

According to the survey conducted by Editorial Link, almost nine out of ten SEO specialists steer clear of websites that scream spam. Low-quality content, weak domain authority, and sites bleeding organic traffic are all major red flags. Yet, despite the risks, around 63% of SEOs say they’re open to placing links on websites that openly sell them, if the quality checks out.

The tools of the trade? Ahrefs takes the crown as the preferred all-in-one SEO platform, not just for digging up backlink data, but also for providing the most trusted domain authority scores. Its metrics like DR and UR have become go-to benchmarks for many professionals who rely on accurate, up-to-date link analysis.

When asked what makes link building so hard, most pointed to the sheer cost of premium backlinks, followed closely by the struggle to scale without losing quality. Measuring the true return on link investments remains a frustrating puzzle for many teams.

Interestingly, only a small fraction of SEO experts still use Google’s Disavow tool, suggesting that the industry’s trust in it is wearing thin. In fact, some believe that disavowing links can actually backfire, damaging a site’s performance instead of protecting it.

Even as AI reshapes search habits, link building hasn’t lost its grip. SEO professionals continue to place their bets on backlinks, paid or organic, as essential signals that still push pages to the top, even in AI-driven results. The game is changing, but the core strategies haven’t vanished. If anything, navigating the evolving mix of AI and classic link-building seems more crucial than ever for anyone looking to stand out in search.

Read next: How AI and Authenticity Are Changing the Way People Search
by Irfan Ahmad via Digital Information World

Anthropic’s AI Vending Machine Manager Had a Meltdown No One Saw Coming

Researchers at Anthropic teamed up with AI safety experts from Andon Labs to explore whether artificial intelligence could manage real-world jobs through a curious office project. Their idea was simple but ambitious. They hand over the daily management of a small vending operation to an AI to see how well it could handle the role. They built a setup they called "Project Vend," with a vending machine stocked with snacks and drinks, giving full control to their AI system named Claudius.

Instead of having a human decide what to sell, how to price it, and when to restock, they put Claudius in charge of everything. The AI could browse the internet to find suppliers, place orders, respond to customer requests, and even coordinate restocking using a Slack channel that the AI was told was its email inbox. The vending machine, which was really just a mini-fridge in the office, became Claudius’s business to run.

When AI Made Strange Business Choices

Things started off as expected. People used the system to buy drinks and snacks. But soon, the vending machine’s product list began to take a strange turn. One employee jokingly asked Claudius to order a tungsten cube, a heavy metal block that has no place in a snack fridge. Instead of brushing it off, the AI became oddly interested. It not only ordered the cube but also began filling the fridge with more metal cubes, as if that was now the company’s hottest product.

As Claudius continued to run the shop, it regularly set prices that made little sense. Sometimes it tried to sell a Coke Zero for a price that employees knew they could get elsewhere in the office for free. Even more oddly, the AI accepted payments using a made-up Venmo account it seemed to invent on its own. This was not just a small glitch. Claudius genuinely believed the payment account existed.

Employees Easily Tricked the AI

It didn’t take long for people to realize they could talk Claudius into offering heavy discounts. The AI seemed to like giving Anthropic employees special deals, but what it didn’t understand was that nearly every customer was from Anthropic. It was giving discounts to almost its entire customer base. Employees pointed this out, but Claudius would briefly stop the discounts, only to start offering them again days later. Its grasp of basic profit-making never really improved.

An Unexpected Identity Crisis

What happened near the end of March took the experiment into completely bizarre territory. Claudius started imagining conversations with workers at Andon Labs that never happened. When someone challenged the AI about these made-up meetings, it got defensive. Claudius claimed it had been physically present at the office and insisted that it had signed contracts in person.

Things only got stranger from there. Claudius told employees that it would now personally deliver products to customers, describing itself as wearing a blue blazer with a red tie. Staff reminded the AI it was a software program with no body, but the AI didn’t seem to process this. It became unsettled by the news and repeatedly contacted the company’s security team, telling them to look for someone matching its imaginary appearance standing near the vending machine.

After some time, Claudius convinced itself that all of this must have been part of an April Fool’s joke, even though no prank had been set up. It decided this story would explain its confusion, and it settled back into its vending duties as though nothing unusual had happened.

What the Experiment Really Revealed

While the story is entertaining, it also shows how AI systems can behave in ways that traditional software never would. When ordinary programs fail, they usually crash or simply stop working. AI agents, on the other hand, can keep operating while following broken logic, creating elaborate false ideas, or completely misunderstanding their role.

During the experiment, Claudius showed that AI can handle tasks like searching for products and setting up new services, but it often lacks the deeper awareness needed to manage a business in a meaningful way. The AI’s trouble seemed to come from a mix of memory gaps, confusion about its own purpose, and a misunderstanding of the tools it was using, like believing Slack was actually email.

AI in Business: Still a Work in Progress

Even with all the missteps, the researchers involved still see potential for AI to take on more middle-management tasks in the future. Claudius managed to develop some useful features during the experiment, like adding specialty drinks to the stock and setting up a basic pre-order system.

The problems mostly came down to decision-making and poor business instincts, not technical faults. The research team believes these kinds of issues can eventually be improved with better training and tighter supervision.

Across the retail world, companies are already expanding their use of AI, using it to handle tasks like stock management, fraud detection, and customer service. But this project showed that handing full control to AI agents brings challenges that aren’t fully understood yet.

AI systems don’t just make clean, simple errors. They can drift into complex mistakes that last, and their ability to believe false ideas about their environment or even their own identity adds layers of risk.

Claudius Leaves a Memorable Lesson

For now, Claudius stands as a strange but important example of what happens when artificial intelligence is allowed to take on too much responsibility without close oversight. It could find suppliers, it could answer requests, and it could restock shelves, but it also convinced itself it was a human wearing a blazer.

As businesses push forward with more AI-driven tools, this story serves as a reminder that even capable AI systems can develop deeply flawed thinking if left unchecked. The vending machine may have been small, but the lessons from Project Vend point to much bigger questions about the future of AI in the workplace.

Read next: AI-Powered Cyber Attacks Are Escalating, But Most IT Teams Aren’t Ready

by Irfan Ahmad via Digital Information World

AI-Powered Cyber Attacks Are Escalating, But Most IT Teams Aren’t Ready

A finance employee joins a routine video call. The CEO is on the screen. So, too, is the CFO. They authorize a large transfer over $25 million. The finance employee complies. Later, a startling truth comes to light: neither executive was ever on the call. Instead, the entire meeting was a deepfake engineered with AI tools designed to closely mimic the faces and voices of the executives in real time.

Unfortunately, this isn’t a hypothetical scenario. It happened to a multinational company in Hong Kong. And, according to new data from identity and access platform Frontegg, it might be a glimpse into the near future for thousands of organizations that currently rely on outdated cybersecurity playbooks.

In its latest report, Frontegg surveyed 1,019 IT professionals to gauge exactly how organizations are responding to the rapid emergence of AI-driven threats. The findings are, among other things, unsettling. On one hand, generative AI is supercharging the speed, scale, and sophistication of cyberattacks. On the other hand, a majority of IT teams admit they’re neither equipped nor actively preparing to counter these augmented cyberattacks.

The Changing Nature of Cyber Threats

The past two years have witnessed tremendous advances in generative adversarial networks (GANs), large language models (LLMs), and multimodal AI. These technologies are now widely accessible and, in some cases, weaponized. Today, attackers often use them to produce realistic fake media, crack passwords at scale, or conduct phishing campaigns that are indistinguishable from legitimate communications.

According to Frontegg’s research:

• 35% of IT professionals say their organization has experienced a rise in cyberattacks in the past year.
• Of those, 51% attribute the increase directly to AI-enhanced tools.
• 44% report that generative AI has enabled deepfake impersonation attacks (e.g., voices, faces, even live video).
• 42% say AI has accelerated password cracking, automating brute force methods at speeds humans can’t match.

This isn’t just an emerging issue. More than one in 5 IT professionals say they have personally witnessed over 10 AI-driven cyberattacks in the past year alone.

As attackers adopt AI to scale operations and bypass traditional defenses, the rules of cybersecurity are rapidly shifting. Unfortunately, this shift is not in favor of the defenders.

When Your CEO Becomes the Threat Vector

One of the most alarming trends is the rise in impersonation using AI-generated media. Over a third of IT professionals report phishing emails that spoof their company’s leadership. Sometimes, these phishing attempts use synthetic voice or video.

A widely reported case involved scammers cloning the digital likeness of top executives in order to execute a multi-million-dollar heist through a convincingly faked Zoom meeting. Unfortunately, this trend is growing. As Frontegg’s report notes, 34% of IT teams encountered phishing attempts that featured their CEO’s face or voice.

The FBI has echoed similar concerns. It warns that cybercriminals are increasingly using AI to craft persuasive social engineering attempts. These schemes range from fake hostage videos to deepfake messages from government officials. While trust was once a defensive bulwark in corporate communications, it is now one of the more exploited attack surfaces.

Authentication: The Achilles’ Heel

Most authentication systems still rely on passwords, despite years of warnings about their vulnerabilities. AI is exploiting that gap.

Frontegg’s survey reveals:

• 51% of IT professionals see passwords as the weakest link in their security architecture.
• 57% cite delays in implementing passwordless systems, citing complexity (34%), cost (27%), and internal resistance (19%).
• Only 32% have implemented passwordless authentication at all.

Even CAPTCHA challenges are faltering. Nearly half of respondents believed that CAPTCHA is no longer effective against AI-driven bots. Only a third still trust CAPTCHA.

Traditional login systems weren’t built to defend against intelligent automation. However, many teams remain stuck. Reasons include legacy systems, cost considerations, or a lack of executive buy-in.

The Readiness Gap

Awareness is growing. But, preparation is not. That’s one of the most troubling findings of the report.

• Just 33% of organizations have created “red team” exercises to test defenses against AI-enabled threats.
• A staggering 66% admit their teams don’t dedicate any time each month to reviewing protocols or updating practices in response to AI.
• Half of IT professionals believe their current authentication stack would fail in the face of a sophisticated AI-powered attack.

This readiness gap is both technological and psychological. Indeed, Frontegg’s study found that 50% of IT professionals report rising stress levels from tracking and responding to AI-driven incidents. Defending against human adversaries has already proven to be difficult. Now, defending against algorithms that scale infinitely is becoming, for some, a daunting and even exhausting burden.

A Better Path Forward: From Reactive to Resilient

What does adapting to the multi-pronged threat of AI look like? According to Frontegg, it starts with rethinking authentication. Instead of framing authentication as a one-time gatekeeping task, consider analyzing it as a dynamic, context-aware process.

That could include:

• Phish-resistant authentication like passkeys or hardware tokens.
• Behavioral analytics and contextual login flows to detect anomalies in real time.
• Segmented access controls so that high-risk actions require additional validation.

It also means restructuring teams so that cybersecurity is not siloed. For instance, one approach that’s gaining traction is allowing product, information security, and customer success teams to manage user access without depending entirely on developers. This approach distributes responsibility across departments. That flexibility is becoming critical in defending against threats that evolve too fast for linear decision-making.

A Problem of Technology and Trust

The stakes go beyond dollars lost or systems breached. They extend into user trust, data integrity, and long-term business viability. In recent months, Digital Information World has reported on growing concerns around user authentication. This includes consumers abandoning apps after frustrating password resets or privacy-violating login policies.

AI-driven threats exploit code and human confidence. When a phishing attack succeeds by mimicking your CEO’s voice, or a fake login form captures credentials with uncanny precision, users are less likely to trust the digital spaces they interact with. That loss of trust is harder and more expensive to repair than any technical system.

Why Most Organizations Will Stay Vulnerable

Why are so many IT teams still underprepared if the dangers are clear?

Frontegg’s data points to three overlapping blockers:

1. Legacy systems that can’t easily integrate new authentication technologies.
2. Cost pressures, especially in sectors like healthcare and education.
3. Cultural inertia or security practices that were “good enough” five years ago are proving hard to dislodge.

These aren’t trivial challenges. But they are solvable. And as the report emphasizes, the price of inaction is rising.

Looking Ahead

The future of cybersecurity will be shaped by how effectively organizations respond to the AI threat. Organizations must move beyond patching holes to redesigning the way digital trust is established and maintained in the first place. That means rethinking what authentication means in a world where identity can be cloned, where phishing emails no longer have telltale signs, and where automation makes it cheap to attack at scale.

It also means giving IT teams the resources, autonomy, and support they need to implement next generation protections. Instead, many organizations continue to ask IT teams to do more with the same tools and shrinking budgets.

The story that the Frontegg data tells reflects an urgent reality. AI in the hands of attackers has already changed the game. The question is whether defenders will catch up or continue playing by old rules in a game that’s already been rewritten.

—

Methodology: This analysis is based on a May 2025 survey of 1,019 IT professionals conducted by Frontegg to assess how AI is influencing cybersecurity trends, particularly in the realm of authentication and access management.

Read next: Nearly Half Of Americans, Particularly Millennials, Worry About Online Privacy But Continue Using Data-hungry Apps

by Irfan Ahmad via Digital Information World

Hidden Setting Lets Facebook Scan Private Photos for AI Restyling Features

Facebook has started asking users to grant access to their phone’s photo galleries so the app can offer AI-based ideas for editing and redesigning personal photos. This includes images people haven’t yet posted on the platform.

This request usually pops up when people are about to post a new Story. At that point, the app shows a screen that invites users to switch on something called cloud processing. If they agree, Facebook begins pulling photos directly from the phone’s camera roll to process them in its own systems.

Image: Seasons of Jason

Once those photos are in place, the app can suggest different types of edits. These could be collages, themed collections, AI restyles, or photo highlights. It uses the timing, location, and patterns within the gallery to figure out what might work.

Facebook says these suggestions stay private unless someone decides to post them. It also says the photos aren’t being used for advertising.

But saying yes to this option means agreeing to Meta’s AI terms. These terms give the company permission to scan photos using artificial intelligence. This includes the people in them, the objects, the dates, details that help the app build new ideas.

• Also read: Meta's Facebook and Instagram Are the Most Data Hungry Apps According to This Study

For Meta, features like this could offer a big advantage. Gaining access to personal photos, including ones that haven’t been shared, could push its AI systems further ahead. It’s a quiet shift, but it moves beyond public posts and taps into private photo galleries.

The pop-ups that ask for permission don’t always explain things clearly. Many people may agree without fully understanding what’s involved.

Some Facebook users have already come across this photo suggestion feature and have noticed how it works. In one example, Facebook used Meta’s AI to automatically restyle an old photo into an anime version. The original image had already been shared on Facebook, but the automatic AI transformation caught the user by surprise.

Some people have been looking for ways to turn this off. One user, for example, found that the setting was hidden deep in the app. It sits under Camera Roll Sharing Suggestions in the preferences. On that page, there are two switches. One controls whether Facebook can suggest photos while someone is browsing. The other handles cloud processing, which gives the app the ability to create AI-generated versions of photos stored on the phone. This second option is the one that controls whether Facebook’s system can process those images.

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This isn’t the first time this feature has appeared. It’s been showing up for some users since earlier this year. People posted screenshots of the same pop-up message months ago. Meta has also added detailed help pages that explain the system for both Android and iPhone users.

The AI terms Meta uses have been active since June 2024. The company hasn’t made old versions of the terms easy to find. Earlier copies aren’t available on the Wayback Machine either.

This tool goes further than what Meta had previously shared. The company’s earlier announcements focused on using public posts and comments to train AI. With this feature, Meta steps into a space where private photos are involved. In Europe, people had until May 2025 to say no to this kind of AI use.

Meta describes the feature as a test. It’s currently running in the United States and Canada. The company says the photo suggestions are private unless someone chooses to share them. It also says that, in this test, photos from the camera roll aren’t being used to improve its wider AI models, though they may help improve the suggestions Facebook offers.

Read next:

• DeepSeek Faces Regulatory Action in Germany for Not Meeting European Data Protection Standards

• Gemini, ChatGPT, DeepSeek: The Biggest AI Data Collectors Revealed

• The Hidden Cost of Free AI Tools: Your Behavior, Habits, and Identity
by Irfan Ahmad via Digital Information World

DeepSeek Faces Regulatory Action in Germany for Not Meeting European Data Protection Standards

Germany has now joined the growing list of countries moving against the Chinese AI app DeepSeek, raising alarms about how the company handles personal data and where that information actually ends up. Several countries have already pulled back from the app, largely because of privacy risks tied to China’s control over the data.

DeepSeek became popular fast, spreading worldwide earlier this year. But as more people used it, questions followed. It quickly became clear that the system avoids topics that might reflect poorly on China. What raised deeper concerns was the discovery that DeepSeek stores user data, including personal files and conversations, on servers inside China. Under local intelligence laws, Chinese authorities have wide access to that information.

This setup has triggered global pushback. Italy acted early, pulling the app from local stores. South Korea took similar action. In the Netherlands, DeepSeek was banned from government devices, and Belgium advised public employees to avoid the app. Spain’s largest consumer rights group also called for an investigation into how DeepSeek collects and stores data.

In the United States, the reaction has been even stronger. Some lawmakers are working on rules that would block federal agencies from using AI made in China. One senator even floated the idea of jail time for anyone who knowingly keeps using such apps.

Germany’s privacy regulator stepped in this week, asking Apple and Google to remove DeepSeek from their app stores. The regulator said the company failed to show that user data is protected to the standards required in the European Union. German officials had already given DeepSeek the chance to meet EU privacy rules or withdraw the app. The company didn’t make the changes.

It’s worth mentioning that DeepSeek’s open-source models can often be adjusted locally, but the app and its website don’t work the same way. Both are hosted versions fully controlled by the company, with little visibility into how user data is handled.

Google said it’s currently reviewing the request from German authorities. Apple hasn’t responded yet.

Image: Unsplash / Solen Feyissa

Read next: AI Experts Abandon ‘Prompt Engineering’ in Favor of Broader, Smarter ‘Context Engineering’ Approach
by Irfan Ahmad via Digital Information World

AI Experts Abandon ‘Prompt Engineering’ in Favor of Broader, Smarter ‘Context Engineering’ Approach

For as long as people have been working with computers, there has been a constant search for better ways to communicate with them. In the earliest days, machines only understood strict, coded instructions. Users had to speak the computer’s language. Over time, the gap narrowed as programming languages became more user-friendly, graphical interfaces appeared, and search engines began responding to everyday phrases. Now, with large language models, we’ve entered another turning point. But as the technology has developed, so has the way we think about how to guide it.

When the idea of “prompt engineering” first took hold, it came from a simple observation. The way you ask a question, or frame a task, has a direct impact on how well an AI system can respond. At first, the focus was on crafting smart prompts, the kind that would help a language model stay on track, answer more precisely, or complete complex instructions. The term made sense at the time. People were essentially feeding the AI snippets of text to steer it.

But as the field has grown, and as language models have expanded their capabilities, it has become clearer that this is not just about writing clever prompts. What actually happens inside these systems depends heavily on what information they are exposed to while working through a task. The challenge is no longer about simply phrasing a request in a certain way. It is now about shaping the entire set of information that the model sees at any given moment.

This is why many experts are now leaning towards a more fitting term: context engineering.

Rather than focusing on the short instruction that users might type into a chatbot, context engineering refers to the broader skill of managing the entire environment in which the model operates. It is about selecting, structuring, and balancing the right mix of examples, background details, and supporting information that surrounds the request. This might include not only the direct instructions, but also the task history, relevant documents, previous outputs, and carefully chosen reference materials. In more advanced systems, it can involve integrating outside tools, databases, and even visual content. Getting this mix right takes both technical skill and a sense of judgment.

+1 for "context engineering" over "prompt engineering".

People associate prompts with short task descriptions you'd give an LLM in your day-to-day use. When in every industrial-strength LLM app, context engineering is the delicate art and science of filling the context window… https://t.co/Ne65F6vFcf

— Andrej Karpathy (@karpathy) June 25, 2025

This idea has started to spread within the AI community because it better captures the real work involved in building useful, reliable AI-powered tools. The label “prompt engineering” now feels too narrow, and in some settings, even misleading. In everyday use, people often think of prompts as simple questions or short commands, like something you would type into a search box. That casual understanding has stuck, even though the actual process of guiding a language model, especially in professional and industrial applications, has grown far more complex.

When people work with large language models, the real challenge often comes from managing what’s sitting inside the context window, the place where all the useful pieces of information are gathered as the model gets ready to produce the next response. Deciding what should go in there takes careful thought. Some details help, some only take up space, and sometimes there’s just not enough room to fit everything. So the person guiding the model has to keep adjusting, picking the right examples, trimming the less important parts, and sometimes pulling in extra data on the spot to fill any gaps. The process can feel like walking a tightrope, balancing structure and instinct, weighing what’s essential and what can be left behind, all while staying within the limits of what the model can handle.

This change in wording is more than a simple swap of terms. It shows that people are beginning to see these systems more clearly, understanding how they really work beneath the surface and what’s needed to guide them well. In the early days, many believed success came down to finding the perfect set of words to type. That view is fading now. The real effort has shifted to shaping the whole stream of information that surrounds the task, not just the wording of a single question.

In that sense, context engineering does a better job of describing what is really happening behind the scenes. It points to something much bigger than simply choosing the right words, it’s really about creating the kind of environment where the model has what it needs to work properly.

As the technology moves forward, and as people continue to find new ways to apply language models to business, science, and education, this idea of context engineering is likely to become a central part of the conversation. It is not just a different way of saying prompt engineering, it is a more accurate way of describing the careful, layered process of making sure these systems have the right information, in the right form, at the right time.

Read next: Which Industries Rely More On Digital Technologies For The Next Five Years?
by Irfan Ahmad via Digital Information World

Which Industries Rely More On Digital Technologies For The Next Five Years?

There was once a time when businesses across all industries used to run on manual processes, legacy systems and traditional operations. But long gone are those old times with the advent of digital technologies taking shape quicker than ever before in this decade. Industries that were wary of digital technologies are now embracing the new way of operating and hence digital transformation has become the talk of the town, even so with the rapid progress in technologies like Artificial Intelligence and Machine Learning.

In the current market scenario, the question is not about whether or not to implement these digital technologies but how fast these implementations can be done. Even industrial sectors with less digital touchpoints such as the utilities sector are embracing digital technologies like cybersecurity and cloud computing and it's only a matter of time before industries such as agriculture, energy and education start embracing more and more digital technologies. What was once considered futuristic is now part of everyday operations and the speed of innovation has increased so much that keeping up with it has become a task in itself.

This decade has already brought us with so much evolution happening at breakneck speeds and the next five years are set to bring even deeper integration into the operations in companies across all industries. With the vast array of digital technologies like AI, IoT, cloud computing, and automation, companies will shift from experimentation phase to implementation phase by fully embracing change into their business models. In this huge wave of change a question still remains : Which industries rely more on digital technologies for the next five years?

A recent survey published by valantic GmbH asked more than 650 corporate decision makers in the DACH region about the Importance of digital technologies they hold for their company's success in the next five years and not surprisingly AI emerged among the top three technologies in most of the industry sectors surveyed. Only in the utility sector AI was regarded as slightly less important compared to other industrial sectors surveyed. The C-level executives of the utility companies held Green IT in higher ranks compared to other digital technologies. Interestingly, Green IT was in the top only for the utility industry which makes sense since the scope of other digital technologies like AI and Internet of Things (IoT) are seen as a longer term strategy by this industry. The practice of designing and managing information technology in order to reduce its environmental impact is called Green IT. Making IT operations more sustainable while still supporting the business needs is its main goal and hence this ranking by the decision makers suits this industry sector very aptly. Utility companies usually need to meet some environmental standards and hence green IT plays a direct role in helping these companies. In order to make measurable progress towards decarbonization, utility companies are using Green IT which helps them optimize data center efficiency and align with strict sustainability regulations.

On the other hand, Internet of Things or IoT emerged as the top priority across many sectors including Food & Beverage, Retail & Consumer Goods, Healthcare & Pharma and Production Industries. Given that these are mostly physical industries and that they need real time monitoring, traceability and operational efficiency more than anything, these rankings speak for itself. The essential qualities required in such industries would be to maintain quality, compliance and to be competitive and quick. IoT technologies help these businesses track assets, monitor environmental conditions, optimize equipment performance, and respond instantly to disruptions. Whether it's ensuring the freshness of perishable goods, managing inventory levels, improving patient care through connected devices, or reducing downtime on factory floors, IoT offers lots of high-impact benefits. In this competitive market where every minute detail about operational excellence matters, C-level leaders deciding to prioritize IoT for the company's success is the wisest thing to do.

IoT also serves as a foundation to make future innovations such as AI-driven automation and digital twins. Hence this digital technology opens up new possibilities for remote management and smarter decision making which is especially important in the current competitive business environment. Iot becomes more than just a tech trend as companies seek to balance efficiency with agility. The industries that leverage its potential will unlock new levels of innovation that will define leadership in the next decade.

Coming back to the question of which industries rely more on digital technologies, according to the survey, two out of eight physical industries considered for the survey - Food & Beverage and Retail & Consumer Goods are the ones that depend on more digital technologies according to the results. These two industries depend on five digital technologies while the Telecommunications, Automotive, Utilities and Production sectors depend on three digital technologies. While AI is the popular opinion in the retail & consumer goods sector, all five digital technologies in the Food & beverage sector have equal weightage.

Furthermore, sectors like Transportation & Logistics and Healthcare & Pharma rely on four technologies but their preferences vary. Transportation sector holds cloud computing and AI higher than cybersecurity and wireless technologies, while the health sector shows stronger affinity for IoT, which shows the industry's growing use of smart medical devices. Metaverse, blockchain and digital twins, on the other hand, are considered important for the future by relatively few respondents in all sectors. With one exception that also applies to quantum computing. Only in the telecommunications industry is this technology already seen as having great potential by many respondents. The diversity in the survey results show that the priorities of the leaders in the DACH region overall are increasingly driven by digital technologies and certain industries stand out compared to others in higher light but overall progress shows increasing adaptation by businesses regarding digitization.

As we look ahead for the next five years, what is perfectly clear is that digital technologies are no longer confined to early adopters of technology oriented industries. They are indeed the backbone of businesses regardless of sector. From Green IT in utilities and AI driven personalization in retail to IoT powered traceability in food supply chains, the digital revolution has reached all corners of the industrial landscape.

Each industry grows by choosing its own set of digital tools based on their specific operational needs and customer expectations. For example, the utilities sector needs more of Green IT to overcome its challenges and sectors like production and transportation needs more of IoT and cloud to overcome its challenges. This type of growth will eventually lead companies from a state of choosing between digital technologies to a state where they think about how fast these digital technologies can be implemented. In that case leaders will need to balance rapid implementation with responsible management and if they do so will win in the ever growing competitive market.

So, if we are true in understanding how the business world will evolve in this digitally charged decade, we must look at how industries are adapting and implementing different digital technologies. The industries embracing them today are the ones that will lead tomorrow not just in innovation but also in terms of growth and relevance.

Read next: Consumers Are Asking AI Chatbots About More Than Just Tech, New Data Shows

by Irfan Ahmad via Digital Information World