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Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More A new report from AI data provider Appen reveals that companies are struggling to source and manage the high-quality data needed to power AI systems as artificial intelligence expands into enterprise operations. Appen’s 2024 State of AI report, which surveyed over 500 U.S. IT decision-makers, reveals that generative AI adoption surged 17% in the past year; however, organizations now confront significant hurdles in data preparation and quality assurance. The report shows a 10% year-over-year increase in bottlenecks related to sourcing, cleaning, and labeling data, underscoring the complexities of building and maintaining effective AI models. Si Chen, Head of Strategy at Appen, explained in an interview with VentureBeat: “As AI models tackle more complex and specialised problems, the data requirements also change,” she said. “Companies are finding that just having lots of data is no longer enough. To fine-tune a model, data needs to be extremely high-quality, meaning that it is accurate, diverse, properly labelled, and tailored to the specific AI use case.” While the potential of AI continues to grow, the report identifies several key areas where companies are encountering obstacles. Below are the top five takeaways from Appen’s 2024 State of AI report: 1. Generative AI adoption is soaring — but so are data challenges The adoption of generative AI (GenAI) has grown by an impressive 17% in 2024, driven by advancements in large language models (LLMs) that allow businesses to automate tasks across a wide range of use cases. From IT operations to R&D, companies are leveraging GenAI to streamline internal processes and increase productivity. However, the rapid uptick in GenAI usage has also introduced new hurdles, particularly around data management. “Generative AI outputs are more diverse, unpredictable, and subjective, making it harder to define and measure success,” Chen told VentureBeat. “To achieve enterprise-ready AI, models must be customized with high-quality data tailored to specific use cases.” Custom data collection has emerged as the primary method for sourcing training data for GenAI models, reflecting a broader shift away from generic web-scraped data in favor of tailored, reliable datasets. The use of generative AI in business processes continues to expand, with notable increases in IT operations, manufacturing, and research and development. However, adoption in areas like marketing and communications has slightly declined. (Source: Appen State of AI Report 2024) 2. Enterprise AI deployments and ROI are declining Despite the excitement surrounding AI, the report found a worrying trend: fewer AI projects are reaching deployment, and those that do are showing less ROI. Since 2021, the mean percentage of AI projects making it to deployment has dropped by 8.1%, while the mean percentage of deployed AI projects showing meaningful ROI has decreased by 9.4%. This decline is largely due to the increasing complexity of AI models. Simple use cases like image recognition and speech automation are now considered mature technologies, but companies are shifting toward more ambitious AI initiatives, such as generative AI, which require customized, high-quality data and are far more difficult to implement successfully. Chen explained, “Generative AI has more advanced capabilities in understanding, reasoning, and content generation, but these technologies are inherently more challenging to implement.” The percentage of AI projects making it to deployment has steadily declined since 2021, with a sharp drop to 47.4% in 2024. Similarly, the mean percentage of deployed projects showing meaningful ROI has fallen to 47.3%, reflecting the growing challenges businesses face in achieving successful AI implementations. (Source: Appen State of AI Report 2024) 3. Data quality is essential — but it’s declining The report highlights a critical issue for AI development: data accuracy has dropped nearly 9% since 2021. As AI models become more sophisticated, the data they require has also become more complex, often requiring specialized, high-quality annotations. A staggering 86% of companies now retrain or update their models at least once every quarter, underscoring the need for fresh, relevant data. Yet, as the frequency of updates increases, ensuring that this data is accurate and diverse becomes more difficult. Companies are turning to external data providers to help meet these demands, with nearly 90% of businesses relying on outside sources to train and evaluate their models. “While we can’t predict the future, our research shows that managing data quality will continue to be a major challenge for companies,” said Chen. “With more complex generative AI models, sourcing, cleaning, and labeling data have already become key bottlenecks.” Data management emerged as the leading challenge for AI projects in 2024, with 48% of respondents citing it as a significant bottleneck. Other obstacles include a lack of technical resources, tools, and data, highlighting the increasing complexity of AI implementation. (Source: Appen State of AI Report 2024) 4. Data bottlenecks are worsening Appen’s report reveals a 10% year-over-year increase in bottlenecks related to sourcing, cleaning, and labeling data. These bottlenecks are directly impacting the ability of companies to successfully deploy AI projects. As AI use cases become more specialized, the challenge of preparing the right data becomes more acute. “Data preparation issues have intensified,” said Chen. “The specialized nature of these models demands new, tailored datasets.” To address these problems, companies are focusing on long-term strategies that emphasize data accuracy, consistency, and diversity. Many are also seeking strategic partnerships with data providers to help navigate the complexities of the AI data lifecycle. Data accuracy in the U.S. has steadily declined, dropping from 63.5% in 2021 to just 54.6% in 2024. The decrease highlights the growing challenge of maintaining high-quality data as AI models become more complex. (Source: Appen State of AI Report 2024) 5. Human-in-the-Loop is More Vital Than Ever While AI technology continues to evolve, human involvement remains indispensable. The report found that 80% of respondents emphasized the importance of human-in-the-loop machine learning, a process where human expertise is used to guide and improve AI models. “Human involvement remains essential for developing high-performing, ethical, and contextually relevant AI systems,” said Chen. Human

Organizational design is a forgotten art and science. This oversight is having a negative effect on today’s corporations. Sherlock’s Law states that “structure enables results.” I’ve opted to take the positive spin on this concept, but the reality is, poor structure can cripple results. What I’ve seen is that, to address the rapid change and complexity in today’s business environments, leaders have created overly complicated organizational structures. Teams, leaders, and employees want and need clarity. They’re clamoring for it. One of the major issues today is overlapping executive roles. My research reveals lack of clarity across overlapping functions has a negative impact on the workplace climate and reduces productivity by 22%. To increase efficiency, we have to place greater focus and consideration on organizational design. Peter Drucker famously said, ‘Culture eats strategy for breakfast.’ The best formulated strategy typically can’t be executed when employees aren’t engaged or there is pervasive negativity in a corporate environment. I build upon that adage by saying, ‘Structure eats culture for lunch and dinner’ because lack of organizational clarity destroys culture. Infighting, turf wars, budget wrangling, and competition are often the source of cultural discontent. If you heed Sherlock’s Law, a clear, coherent structure can support the culture, strategy, and results. source

Few technologies have captured the public imagination quite like generative AI. It seems that with every passing day, there are new AI-based chatbots, extensions, and apps being released to eager users around the world.  According to a recent Gartner survey of IT leaders, 55% of organizations are either piloting or in production mode with generative AI. That’s an impressive metric by any degree, least of all considering that the phrase ‘generative AI’ was barely part of our collective lexicon just 12 months ago.   However, despite this technology’s promise to accelerate the productivity and efficiency of its workforce, it’s also left a minefield of potential risks and liabilities in its wake. An August survey by Blackberry found that 75% of organizations worldwide were considering or implementing bans on ChatGPT and other generative AI applications in the workplace, with the vast majority of those (67%) citing the risk to data security and privacy.  Such data security issues arise because user input and interactions are the fuel that public AI platforms rely on for continuous learning and improvement. Consequently, if a user shares confidential company data with a chatbot (think: product roadmaps or customer information), that information then becomes integrated into its training model, which the chatbot might then reveal to subsequent users. Of course, this challenge isn’t limited to public AI platforms, as even a company’s internal LLM trained on its own proprietary datasets might inadvertently make sensitive information accessible to employees who are not authorized to view it.  Related:Bridge the Gap Between Business Leaders and Tech Teams To better evaluate and mitigate these risks, most enterprises who have begun to test the generative AI waters have primarily leaned on two senior roles for implementation: the CISO, who is ultimately responsible for securing the company’s sensitive data; and the general counsel, who oversees an organization’s governance, risk, and compliance function. However, as organizations begin to train AI models on their own data, they’d be remiss to not include another essential role in their strategic deliberations: the CTO.  Data Security and the CTO   While the role of the CTO will vary widely depending on the organization they serve, almost every CTO is responsible for building the technology stack and defining the policies that dictate how that technology infrastructure is best utilized. Given this, the CTO has a unique vantage point from which to assess how such AI initiatives might best align with their strategic objectives.  Related:How to Submit a Column to InformationWeek Their strategic insights become all the more important as more organizations, who might be hesitant to go all-in on public AI projects, instead opt to invest in developing their own AI models trained on their own data. Indeed, one of the major announcements at OpenAI’s recent DevDay conference focused on the release of Custom Models, a tailored version of its flagship ChatGPT service that can be trained specifically on a company’s proprietary data sets. Naturally, other LLMs are likely to follow suit given the pervasive uncertainty around data security.    However, just because you choose to develop internally does not mean you’ve thwarted all AI risks. For example, consider one of the most valuable crown jewels of today’s digital enterprise: source code. As organizations increasingly integrate generative AI into their operations, they face new and complex risks related to source code management. In the process of training these AI models, organizations are often using customer data as a part of the training sets and storing it in source code repositories.   This intermingling of sensitive customer data with source code presents a number of challenges. Whereas customer data is typically managed within secured databases, with generative AI models, this sensitive information can become embedded into the model’s algorithms and outputs. This creates a scenario where the AI model itself becomes a repository of sensitive data, blurring the traditional boundaries between data storage and application logic. With less-defined boundaries, sensitive data can quickly sprawl across multiple devices and platforms within the organization, significantly increasing the risk of being either inadvertently compromised by external parties, or in some cases, by malicious insiders.   Related:How Many C-Levels Does It Take to Securely Manage Regulated Data? So, how do you take something that is as technical and as abstract as an AI model and tame it into something suitable for users — all without putting your most sensitive data at risk?   3 Ways the CTO Can Help Strike the Balance  Every enterprise CTO understands the principle of trade-offs. If a business unit owner demands faster performance for a particular application, then resources or budget might need to be diverted from other initiatives. Given their top-down view of the IT environment and how it interacts with third-party cloud services, the CTO is in a unique position to define an AI strategy that keeps data security top of mind. Consider the following three ways the CTO can collaborate with other key stakeholders and strike the right balance:  1. Educate before you eradicate: Given the many security and regulatory risks of exposing data via generative AI, it’s only natural that so many organizations might reflexively ban their usage in the short term. However, such a myopic mindset can hinder innovation in the long run. The CTO can help ensure that the organization’s acceptable use policy clearly outlines the appropriate and inappropriate uses of generative AI technologies, detailing the specific scenarios in which generative AI can be utilized while emphasizing data security and compliance standards.  2. Isolate and secure source code repositories: The moment intellectual property is introduced to an AI model, the task of filtering it out becomes exponentially more difficult. It’s the CTO’s responsibility to ensure that access to source code repositories is tightly controlled and monitored. This includes establishing roles and permissions to limit who can access, modify, or distribute the code. By enforcing strict access controls, the CTO can minimize the risk of unauthorized access or leaks of sensitive data as well as establish processes that require code to be reviewed and approved before being merged

New research by cybersecurity firm Mandiant provides eyebrow-raising statistics on the exploitation of vulnerabilities by attackers, based on an analysis of 138 different exploited vulnerabilities that were disclosed in 2023. The findings, published on Google Cloud’s blog, reveals that vendors are increasingly being targeted by attackers, who are continually reducing the average time to exploit both zero-day and N-day vulnerabilities. However, not all vulnerabilities are of equal value to attackers, as their significance depends on the attacker’s specific objectives. Time-to-exploit is falling significantly Time-to-exploit is a metric that defines the average time taken to exploit a vulnerability before or after a patch is released. Mandiant’s research indicates: From 2018 to 2019, the TTE sat at 63 days. From 2020 to 2021, it fell to 44 days. From 2021 to 2022, the TTE dropped even further to 32 days. In 2023, the TTE sat at just 5 days. SEE: How to Create an Effective Cybersecurity Awareness Program (TechRepublic Premium) Zero-day vs N-day As TTE continues to shrink, attackers are increasingly taking advantage of both zero-day and N-day vulnerabilities. A zero-day vulnerability is an exploit that hasn’t been patched, often unknown to the vendor or the public. An N-day vulnerability is a known flaw first exploited after patches are available. It is therefore possible for an attacker to exploit a N-day vulnerability as long as it has not been patched on the targeted system. Mandiant exposes a ratio of 30:70 of N-day to zero-days in 2023, while the ratio was 38:62 across 2021-2022. Mandiant researchers Casey Charrier and Robert Weiner report that this change is likely due to the increased zero-day exploit usage and detection rather than a drop in N-day exploit usage. It is also possible that threat actors had more successful attempts to exploit zero-days in 2023. “While we have previously seen and continue to expect a growing use of zero-days over time, 2023 saw an even larger discrepancy grow between zero-day and n-day exploitation as zero-day exploitation outpaced n-day exploitation more heavily than we have previously observed,” the researchers wrote. Zero-day vs N-day exploitation. Image: Mandiant Must-read security coverage N-day vulnerabilities are mostly exploited in the first month after the patch Mandiant reports that they observed 23 N-day vulnerabilities being exploited in the first month following the release of their fixes, yet 5% of them were exploited within one day, 29% within one week, and more than half (56%) within a month. In total, 39 N-day vulnerabilities were exploited during the first six months of the release of their fixes. N-day exploitation. Image: Mandiant More vendors targeted Attackers seem to add more vendors to their target list, which increased from 25 vendors in 2018 to 56 in 2023. This makes it more challenging for defenders, who try to protect a bigger attack surface every year. CVE-2023-28121 exploitation timeline. Image: Mandiant Cases studies outline the severity of exploitations Mandiant exposes the case of the CVE-2023-28121 vulnerability in the WooCommerce Payments plugin for WordPress. Disclosed on March 23, 2023, it did not receive any proof of concept or technical details until more than three months later, when a publication showed how to exploit it to create an administrator user without prior authentication. A day later, a Metasploit module was released. A few days later, another weaponized exploit was released. The first exploitation began one day after the revised weaponized exploit had been released, with a peak of exploitation two days later, reaching 1.3 million attacks on a single day. This case highlights “an increased motivation for a threat actor to exploit this vulnerability due to a functional, large-scale, and reliable exploit being made publicly available,” as stated by Charrier and Weiner. CVE-2023-28121 exploitation timeline. Image: Mandiant The case of CVE-2023-27997 is different. The vulnerability, known as XORtigate, impacts the Secure Sockets Layer (SSL) / Virtual Private Network (VPN) component of Fortinet FortiOS. The vulnerability was disclosed on June 11, 2023, immediately buzzing in the media even before Fortinet released their official security advisory, one day later. On the second day after the disclosure, two blog posts were published containing PoCs, and one non-weaponized exploit was published on GitHub before being deleted. While interest seemed apparent, the first exploitation arrived only four months after the disclosure. CVE-2023-27997 exploitation timeline. Image: Mandiant One of the most likely explanations for the variation in observed timelines is the difference in reliability and ease of exploitation between the two vulnerabilities. The one affecting WooCommerce Payments plugin for WordPress is easy to exploit, as it simply needs a specific HTTP header. The second is a heap-based buffer overflow vulnerability, which is much harder to exploit. This is especially true on systems that have several standard and non-standard protections, making it difficult to trigger a reliable exploitation. A driving consideration, as exposed by Mandiant, also resides in the intended utilization of the exploit. “Directing more energy toward exploit development of the more difficult, yet ‘more valuable’ vulnerability would be logical if it better aligns with their objectives, whereas the easier-to-exploit and ‘less valuable’ vulnerability may present more value to more opportunistic adversaries,” the researchers wrote. Deploying patches is no simple task More than ever, it is mandatory to deploy patches as soon as possible to fix vulnerabilities, depending on the risk associated with the vulnerability. Fred Raynal, chief executive officer of Quarkslab, a French offensive and defensive security company, told TechRepublic that “Patching 2-3 systems is one thing. Patching 10,000 systems is not the same. It takes organization, people, time management. So even if the patch is available, a few days are usually needed to push a patch.” Raynal added that some systems take longer to patch. He took the example of mobile phone vulnerability patching: “When there is a fix in Android source code, then Google has to apply it. Then SoC makers (Qualcomm, Mediatek etc.) have to try it and apply it to their own version. Then Phone makers (eg Samsung, Xiaomi) have to port it to their own version. Then carriers sometimes customize the firmware

In November 2023, violent Atlantic storm “Domingos” struck the northern coast of Portugal, generating record-high waves and leaving a path of destruction across much of Western Europe.  People on land were grappling with flooded homes, closed roads, and landslides. But just offshore, a potentially game-changing wave energy device was happily bobbing up and down, side to side — seemingly, in its element.  Built by Swedish startup CorPower, the giant golden buoy turns the raw power of the ocean into a clean, reliable electricity source. CorPower claims its tech is at least five times more efficient than the previous state-of-the-art. “We’ve proven that our technology is both energy efficient and can survive the harshest ocean conditions — two problems that have plagued the industry for decades,” Patrik Möller, Corpower’s co-founder and CEO, tells TNW.  The 💜 of EU tech The latest rumblings from the EU tech scene, a story from our wise ol’ founder Boris, and some questionable AI art. It’s free, every week, in your inbox. Sign up now! Today, the company announced that it has secured €32mn in funding, history’s largest single investment in a wave energy startup.  In an industry haunted by the ghosts of failed projects, wasted ideas, and bankrupt ventures, has wave energy finally found its golden buoy?  CorPower’s co-founder and CEO, Patrik Möller. Credit: CorPower Huge source of baseload energy  In recent years, there has been a surge of interest in wave energy, driven by the need for more reliable sources of clean power.  “Think about wave energy as a buffer of electricity,” Amin Al-Habaibeh, wave energy expert and professor of intelligent engineering systems at Nottingham Trent University, tells TNW.  Energy from waves is available 90% of the time, compared with 20-30% for wind and solar power, and is easy to predict and forecast.  “When the wind isn’t blowing or the sun isn’t shining you still have waves rolling in from thousands of kilometres away, day and night. If we manage to harness this in a commercially viable way, we have a huge source of baseload energy,” says Al-Habaibeh.  Wave energy can be harnessed across huge swathes of the world’s coastline, where most of the world’s major cities are located. Credit: CorPower In theory, waves carry enough potential energy to power the entire planet. Yet, last year wave energy devices only generated about 1MW of Europe’s total electricity, according to Ocean Energy’s latest report. That’s only enough to supply around 1,000 homes.  CorPower is one of a small but growing number of companies looking to bring wave energy out from the depths and into the ring with renewable heavyweights like solar, wind, and hydro. And the Swedish venture believes its technology has what it takes to do just that.  Tapping the ocean’s rhythm  The inspiration for CorPower’s technology came not from the sea, but from rhythmic beating of the human heart. This vital organ only uses energy when it contracts and pushes blood out and into the body. To suck blood back in, it simply relaxes, pumping blood in two directions from one action.  In 1984, Swedish cardiologist Dr Stig Lundbäck patented the Dynamic Adaptive Piston Pump, a system that replicates the dual-action of the heart. Over the years that followed, the doctor-turned-inventor schemed elaborate ways to put the pump to good use. In 2011, he teamed up with Möller, a tech entrepreneur, and founded CorPower Ocean.  The C4 is a point absorber, a type of floating wave energy device that is anchored to the seafloor and converts the up and down motion of a buoy into electrical power. It measures 18 metres high, 9 metres across, and weighs about 70 tonnes.  The C4 being towed out to sea. Credit: CorPower As the buoy moves with the waves, a Power Take-Off (PTO) mechanism — a series of springs, gears, and pistons — converts the vertical motion into rotational energy. This then drives a generator, producing power which is transferred to shore via a subsea cable. When a wave pushes the buoy up, a specially designed “wave spring” stores up pressure in a pneumatic cylinder. When the buoy goes back down, this built up pressure provides a returning force — the C4 captures two forces from one action.   Crucially, C4 uses algorithms to predict the motion of incoming waves, boosting the amount of energy it can harness. When waves get too rough, the AI sends a signal to the power control system telling it to enter “‘storm survivability”’ mode — a de-tuned state comparable to when wind turbines pitch their blades during strong gales. Over the course of a six-month trial last year, the C4 achieved a maximum power output of 600KW, electricity it exported to the Portuguese grid.   Möller called its first commercial-scale pilot a “massive breakthrough” that tackles two key issues in harnessing this huge untapped clean energy source: efficiency and survivability.    A troubled past  Moored off a harbour in the Orkney islands lies the rusted wreckage of a 180 metre-long wave energy convertor built by Scottish startup Pelamis. In 2004, the giant red sea snake-looking machine became the world’s first grid-connected wave energy device.  The sea snake was a wave energy attenuator, made up of five connected sections that flexed and bent in the waves. Hydraulic rams located in the joints harnessed the movement, driving electrical generators and sending power to the grid via a subsea cable.   Pelamis went on to build several more of the 1,350-tonne behemoths. In 2008, three machines installed off the coast of Portugal were generating enough clean energy to power 1,500 homes.  Pelamis undergoing testing at the European Marine Energy Centre (EMEC) in Scotland in 2008. Credit: Falt i det fri (Public domain) But the company’s success was short-lived. High installation and maintenance costs, frequent breakdowns, poor efficiency, and a subsequent lack of funding forced Pelamis into administration in 2014. The company’s remaining wave energy converters are now little more than scrap metal.   “Pelamis is largely symbolic of an industry that has struggled with commercial viability,” 

Customers seek a hybrid future It’s unclear to what extent SAP can help customers with their digital transformations given its strict cloud strategy. No one at DSAG disputes that the cloud is a key driver of business transformation. “From DSAG’s point of view, cloud and cloud enterprise resource planning systems are the right way to go for many use cases and industries,” said DSAG CEO Jens Hungerhausen.  But from DSAG’s point of view, on-premises systems will also remain highly relevant for SAP customers for some time to come, DSAG’s leader made clear. This applies, for example, to industries with high process complexity, or under specific legal or data protection framework conditions or requirements. “The future will therefore continue to be hybrid,” Jens Hungershausen is convinced. “Simply jumping into the cloud doesn’t work.” As for that future, SAP users still have many questions. For example, to what extent can they make use of the added value and flexibility of the cloud? They must also have greater clarity on the costs associated with increased cloud usage and what could be incurred through use of downstream services.  source

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“State of ITSM in Financial Services“ An InformationWeek Report | Sponsored by TeamDynamix Data from InformationWeek’s State of ITSM in Financial Services Report shows that there’s a wide range of maturity in how ITSM teams are dealing with the unique challenges of supporting technology stacks in today’s financial vertical. While application portfolios grow and tickets mount, ITSM teams remain fairly lean. But they’re not necessarily running efficiently, as they’re forced to cope with legacy ITSM platforms, a low level of automation, and inefficient project management capabilities. Key Findings: 40% of FS ITSM teams support 100 or more applications13% of these ITSM teams service 400 or more applications58% of FS firms manage more than 500 tickets per month40% of FS IT teams struggle with low ITSM maturity43% of FS IT Service Desks identify manual processing as top issue Download this report to see how you compare. Offered Free by: TeamDynamix See All Resources from: TeamDynamix Recommended for Professionals Like You: source

Pretty much all of the tech giants offer cloud storage nowadays. However, you can easily find yourself shelling out serious money to store your digital data. As a more affordable alternative, Koofr is earning some serious plaudits. This innovative platform lets you upload and access your files with no size limit, and you can even hook up your other online accounts. In a unique offer from TechRepublic Academy, you can pick up a lifetime 1TB subscription for only $119.97 with coupon code KOOFR40 to be used at checkout. That’s a massive 85% off. Cloud storage is really an essential tool in running any business. Whether it’s simple spreadsheets, promo videos, company logos or even customer data, having a secure online backup of your files is vital. Putting your files in the cloud also means you can work on any device. About Koofr Cloud Storage Koofr provides these benefits and more. This platform allows you to upload and view files on pretty much any device with a browser. This means you can log in on Windows, macOS, Linux and Chrome laptops along with iOS and Android mobile devices. You can even connect via WebDAV. Koofr’s desktop app makes it easy to manage your data, with smart features like duplicate removal and batch file renaming. The service uses absolutely no trackers, and you can easily connect other online accounts to import your files. Another useful feature for businesses is the ability to share files via custom branded links. This means you can easily go above the file size limit on your email, with the ability to share the same link over and over again. Order today for only $119.97 with code KOOFR40 to get your lifetime 1TB subscription, normally worth $810. Prices and availability are subject to change. source