AI development, like the technology itself, is still in its early stages. This means that many development tools are also emerging and advancing.  Over the past several months, we’ve seen the rise of a new technology stack when it comes to AI application development, as the focus shifts from building machine learning models to building AI solutions, says Maryam Ashoori, director of product management for watsonx.ai at IBM, in an email interview. “To navigate exponential leaps in AI, developers must translate groundbreaking AI research into real-world applications that benefit everyone.”  Essential Tools  Current AI tools provide a comprehensive ecosystem supporting every stage of the AI development process, says Savinay Berry, CTO and head of strategy and technology at cloud communications services provider Vonage, in an online discussion. A wide array of tools helps developers create and test code, manage large datasets, and build, train and deploy models, allowing users to work efficiently and effectively, he notes. “They also facilitate the interpretation of complex data, ensure scalability through cloud platforms, and offer robust management of data pipelines and experiments, which are crucial for the continuous improvement and success of AI projects.”  Related:IT Pros Love, Fear, and Revere AI: The 2024 State of AI Report Within the current AI landscape, there are a variety of essential development tools, Ashoori states, including integrated development environments (IDEs) for efficient coding, version control tools for collaboration, data management offerings for quality input, cloud platforms for scalability and access to GPUs, and collaboration tools for team synergy. “Each is critical for streamlined, scalable AI development,” she says.  Every AI developer should have a minimum set of tools that cover various aspects of development, advises Utkarsh Contractor, vice president of AI at generative AI solutions firm Aisera and a generative AI senior research fellow at Stanford University. “These include an IDE such as VS Code or Jupyter Notebook, a version control system like GitHub, and open-source frameworks like PyTorch and TensorFlow for building models.” He believes that data manipulation and visualization tools, like Pandas, Matplotlib, and Apache Spark, are essential, along with monitoring tools, such as Grafana. Contractor adds that access to compute resources and GPUs, either locally or in the cloud, are also critical for quality AI development.  GitHub Copilot, an AI-assisted programming tool, isn’t essential but can enhance productivity, Contractor says. “Similarly, MLflow excels in tracking experiments and sharing models, while tools like Labelbox simplify dataset labeling.” Both are valuable additions, but not required, he observes.  Related:Keynote Sneak Peek: Forrester Analyst Details Align by Design and AI Explainability When it comes to cloud services, Berry notes that tools such as AWS SageMaker, Google Cloud AI Platform, Google Colab, Google Playground, and Azure Machine Learning offer fully managed environments for building, training, and deploying machine learning models. “These platforms provide a range of automated tools like AutoML, which can help developers quickly create and tune models without deep expertise in every aspect of machine learning,” he says. “They are particularly valuable for developers who want to focus more on model development and less on infrastructure management.” Berry adds that these tools add value by streamlining processes, enhancing collaboration, and improving the overall user experience, even if they aren’t strictly required for all AI projects.  When it comes to scaling AI development at the enterprise level, organizations should look beyond disparate development tools to broader platforms that support the rapid adoption of specific AI use-cases from data through deployment, Ashoori advises. “These platforms can provide an intuitive and collaborative development experience, automation capabilities, and pre-built patterns that support developer frameworks and integrations with the broader IT stack.”  Related:Sidney Madison Prescott Discusses GenAI’s Potential to Transform Enterprise Operations Fading Away  As AI evolves and new tools arrive, several older offerings are falling out of favor. “Some libraries, such as NLTK and CoreNLP for natural language processing, are losing relevance and becoming obsolete due to innovations like generative AI and transformer models,” Contractor says.  “Once the go-to for data analysis, Pandas and NumPy, two popular Python libraries for data analysis and scientific computing, are losing adherents,” observes Yaroslav Kologryvov, co-founder of AI-powered business automation platform PLATMA via email. “Theano, replaced by TensorFlow and PyTorch, has suffered a similar fate.”  As AI development continues to advance rapidly, staying updated with the latest tools and frameworks is crucial for maintaining a competitive edge, Berry says. “While some older tools may still serve specific purposes, the shift toward more powerful, efficient solutions is clear,” he states. “Embracing innovations ensures that AI developers can tackle increasingly complex challenges with agility and precision.”  Adaptability and Streamlining  In the rapidly evolving AI universe, developers must maintain a high degree of adaptability, continuously reassessing and optimizing their toolsets, Contractor says. “As innovation accelerates, tools that are essential today may quickly become outdated, necessitating the adoption of new cutting-edge technologies and methodologies to enhance workflows and maximize project efficiency and effectiveness.”  To simplify and streamline the AI development experience, organizations should seek platforms that provide developers with optionality, customization and configurability at every layer of the AI stack, Ashoori concludes.  source

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To the average eye, extended reality is starting to look bleak. The metaverse has bombed, the Apple Vision Pro has flopped, and Sony has all but abandoned the PSVR. Sadly for Mark Zuckerberg, consumers rarely want to strap computers to their faces. But there is one place where business is booming: the military. XR has diffused across the armed forces since 2021, when Microsoft signed a contract with the US Army worth up to $21.9bn (€19.6bn). Under the deal, the tech giant would develop training programmes for HoloLens-based headsets. Despite a shaky start — literally, for the nauseated soldiers — the partnership continues to this day. But it might not last much longer: around 80 firms are now vying for the contract.  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! As the market has expanded, new use cases have emerged. You can now find XR in endless military applications, from combat training and battlefield tactics to vehicle exercises and helmet systems. And that’s just on the ground. Arguably the fastest-growing military application for XR is pilot training. In this segment, there’s an undisputed European champion: Varjo. From its headquarters in Finland, Varjo (pronounced “var-yo”) is building a thriving business in synthetic flight training. The company’s CEO, Timo Toikkanen, says a “paradigm shift” has begun.  The trigger was evolving needs for aircraft simulators. Advances in XR paved a path towards compelling new systems. One was recently delivered to Ukraine. It will support the country’s latest aerial weapons: F-16 fighter jets. Flight preparations After years of lobbying western allies, Ukraine finally received its first F-16 fighter jets in August. The shipment marked a milestone for the country’s air forces, which have hitherto relied on Soviet-era jets. F-16s add a powerful upgrade to the fleet. But there’s a problem with the order: Ukraine doesn’t have enough pilots who can fly the planes.  Traditional solutions to this problem come with their own issues. Aircraft training is extremely expensive, seats are limited, and courses often require long-distance travel. The conventional alternative is dome simulators, but these have hefty price tags too. They’re also vast machines that require their own dedicated buildings. XR can hurdle these barriers. The simulators are cheaper than domes and can operate in offices. They can also integrate systems from across the armed forces. But their biggest strength today is their speed. “The training time of a fighter pilot is compressed by 30 to 50%,” Toikkanen says. “When every year costs millions, that’s a very significant change.” Ukraine has an urgent need for this fast and affordable training. The new F-16s could intercept enemy jets and establish areas of air superiority — but only with enough pilots to fly them. To train them, the country recently acquired its first fully-functional XR F-16 system.  Czech firm Dogfight Boss built the simulator, while Varjo supplied the XR tech. After entering the cockpit, pilots learn the controls, fine-tune their techniques, and fly virtual missions. Ukraine’s air forces can then reap the benefits. But for Varjo, the country is a tiny addition to a ballooning global market. “The F-16 in Ukraine is one example of a much broader phenomena,” Toikkanen said. “And the phenomena is traditional means of training pilots being replaced by mixed reality technology.” XR takes off One of Varjo’s closest partners is Aechelon Technology. The American company creates geo-specific visualisations of the real world, which have been integrated with Varjo’s XR system. Together, the partners have crafted headsets for the US Air Force. “We are one of the big players in the US. But Varjo is the player,” Javier Castellar, the co-founder of Aechelon, tells TNW. Castellar estimates that Varjo has captured at least 95% of the XR flight training market. He calls the firm “the Tesla of Finland.”  This reputation in military aviation has blossomed rapidly. A few years ago, XR wasn’t technically capable of replacing air force simulators. Fast forward to today, and Varjo is in over 80 military synthetic training programs across NATO’s footprint. Orders for XR, Castellar says, now outstrip those for domes by at least eight to one. The reason for this turnaround is a big leap in tech.  Castellar has become a big  believer in XR. Credit: Varjo Varjo’s breakthrough headset was the XR-4 series. Released last year, the devices blend a 360-degree view of the synthetic environment with the cockpit interior. Castellar says the system “crossed a threshold of human vision.” Inside the headset, foveated rendering tracks the pilot’s eyes and maximises the resolution where they’re looking. By applying this technique, the XR-4 can boost visual quality while cutting computation needs. Dual 4K x 4K displays can then deliver photorealistic scenes at 90 frames per second. To integrate the pilot’s surroundings, two 40-megapixel cameras align the visual focus with the pilot’s gaze. When their eyes shift from the digital surroundings to the physical cockpit, the pass-through system shifts the view from the virtual to the real. TNW got to test the tech last year and found the transition seamless. As the uptake grows, new capabilities are emerging. “It’s not just a display system,” says Castellar. “It has major implications on defence.” Flying higher Modern aircraft are expensive to update. In domes, the costs are cut, but the changes still don’t come cheap. They can also involve laborious implementations. XR promises a simpler solution. ”The architecture can be continuously adapted, because it becomes more of a software problem,” says Castellar.  The enhancements are potentially endless. On an F-16 simulator, you could add night vision goggles, new weapon systems, or the latest aircraft helmet. All these components can then enter mission rehearsals. But that’s only for aircraft. Varjo expects XR to now spread across armed forces and into complex military operations. The headsets will connect planes in the sky with ships at sea and vehicles on the ground. Numerous simulators will

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Hugging Face today has released SmolLM2, a new family of compact language models that achieve impressive performance while requiring far fewer computational resources than their larger counterparts. The new models, released under the Apache 2.0 license, come in three sizes — 135M, 360M and 1.7B parameters — making them suitable for deployment on smartphones and other edge devices where processing power and memory are limited. Most notably, the 1.7B parameter version outperforms Meta’s Llama 1B model on several key benchmarks. Performance comparison shows SmolLM2-1B outperforming larger rival models on most cognitive benchmarks, with particularly strong results in science reasoning and commonsense tasks. Credit: Hugging Face Small models pack a powerful punch in AI performance tests “SmolLM2 demonstrates significant advances over its predecessor, particularly in instruction following, knowledge, reasoning and mathematics,” according to Hugging Face’s model documentation. The largest variant was trained on 11 trillion tokens using a diverse dataset combination including FineWeb-Edu and specialized mathematics and coding datasets. This development comes at a crucial time when the AI industry is grappling with the computational demands of running large language models (LLMs). While companies like OpenAI and Anthropic push the boundaries with increasingly massive models, there’s growing recognition of the need for efficient, lightweight AI that can run locally on devices. The push for bigger AI models has left many potential users behind. Running these models requires expensive cloud computing services, which come with their own problems: slow response times, data privacy risks and high costs that small companies and independent developers simply can’t afford. SmolLM2 offers a different approach by bringing powerful AI capabilities directly to personal devices, pointing toward a future where advanced AI tools are within reach of more users and companies, not just tech giants with massive data centers. A comparison of AI language models shows SmolLM2’s superior efficiency, achieving higher performance scores with fewer parameters than larger rivals like Llama3.2 and Gemma, where the horizontal axis represents the model size and the vertical axis shows accuracy on benchmark tests. Credit: Hugging Face Edge computing gets a boost as AI moves to mobile devices SmolLM2’s performance is particularly noteworthy given its size. On the MT-Bench evaluation, which measures chat capabilities, the 1.7B model achieves a score of 6.13, competitive with much larger models. It also shows strong performance on mathematical reasoning tasks, scoring 48.2 on the GSM8K benchmark. These results challenge the conventional wisdom that bigger models are always better, suggesting that careful architecture design and training data curation may be more important than raw parameter count. The models support a range of applications including text rewriting, summarization and function calling. Their compact size enables deployment in scenarios where privacy, latency or connectivity constraints make cloud-based AI solutions impractical. This could prove particularly valuable in healthcare, financial services and other industries where data privacy is non-negotiable. Industry experts see this as part of a broader trend toward more efficient AI models. The ability to run sophisticated language models locally on devices could enable new applications in areas like mobile app development, IoT devices, and enterprise solutions where data privacy is paramount. The race for efficient AI: Smaller models challenge industry giants However, these smaller models still have limitations. According to Hugging Face’s documentation, they “primarily understand and generate content in English” and may not always produce factually accurate or logically consistent output. The release of SmolLM2 suggests that the future of AI may not solely belong to increasingly large models, but rather to more efficient architectures that can deliver strong performance with fewer resources. This could have significant implications for democratizing AI access and reducing the environmental impact of AI deployment. The models are available immediately through Hugging Face’s model hub, with both base and instruction-tuned versions offered for each size variant. source

By Nadia Dreid ( October 31, 2024, 6:39 PM EDT) — One-half of the Federal Communicatiions Commission’s Republican minority is coming out strong against the majority’s plans to restrict bulk billing for broadband services, saying that the commission was under pressure by the Biden administration to “raise the price of Internet service for Americans living in apartments by as much as 50%.”… Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

The outcome of the US presidential election will not be the end of cyberthreats from bad actors who might be backed by aggressor nation states. Geopolitical tensions will persist on the domestic and international stages with the potential for enterprises to be targets. Denial of service attacks, ransomware, and other forms of digital malice stand to be in play for the sake of political agendas, though money can play as much a role in hackers’ motivations as ideology.     Hacktavists and other bad actors backed by aggressor states will continue to be in play well after the election as geopolitical tensions continue. What types of organizations might find themselves to be targets (perhaps again) after the election? This episode of DOS Won’t Hunt brings together Carl Wearn, (upper left in video) head of threat intelligence analysis and future ops at Mimecast; Robert Johnston, (lower right) co-founder and CEO of Adlumin; Mike Wiacek, (lower center) CEO of Stairwell; Armaan Mahbod, (lower left) vice president of security and business Intelligence with DTEX Systems; and Adam Darrah, (upper center) vice president of Intelligence with ZeroFox. They discussed ways organizations might orient their cybersecurity and defenses for the post-election world, the prevalent types of attacks launched on behalf of aggressor states, and how the current cybersecurity infrastructure measures up to the potential threats that are in play. Related:2024 Cyber Resilience Strategy Report: CISOs Battle Attacks, Disasters, AI … and Dust Listen to the full podcast here. source

We (still) have an incomplete view of DEX. For years, enterprises have approached digital employee experience (DEX) initiatives in silos — IT is doing one thing, HR another, and the business something totally different — all of which has made it difficult for organizations to successfully drive DEX outcomes for employees. At Forrester, we hear the symptoms of a fractured approach to DEX all the time from clients. “If only HR knew how we could help improve DEX,” remarked one IT leader, while an HR leader said, “IT thinks they own DEX, but they aren’t responsible for any employee outcomes like talent attraction and retention.” At Forrester, we’re seeing signs that this may be changing. Organizations are starting to bring various stakeholders to the table to tackle DEX at the executive level, focusing on improving the daily work of employees. There’s an additional problem, though: Not only are enterprise teams siloed when it comes to DEX, so are their tools — one tool for monitoring end user computing and the service desk, another for corporate communications, and yet another for digital adoption … and that’s just the start. So many tools, so little visibility. As we move into the end of 2024, clients are consistently asking for their DEX tools to do more in order to help them: Illuminate blind spots in tech experience. Understand and drive adoption of new technologies. Diagnose the user experience of key business applications. Measure employee outcomes using experience-level agreements. Improve search and application access. That need for a consistent experience is hard to do when your organization has so many tools in its arsenal, none of which are integrated. The good news? Vendors understand the problem. Vendors in the end-user experience management space have responded with a slew of innovations in the past year, driving the beginning of a “DEX-pansion” in which vendors strive to obtain a more holistic and accurate view of DEX. For example: 1E acquired Exoprise in October 2024, significantly bolstering 1E’s application monitoring capabilities with in-depth real user monitoring, comprehensive synthetic testing, and improved support for unified-communications-as-a-service monitoring. The company also announced a new partnership with Goliath, a virtual desktop monitoring tool focused on electronic health record systems such as Epic and Cerner. 1E also announced a partnership with B2M Solutions to create a mobile endpoint experience product, further demonstrating 1E’s commitment to expanding its DEX visibility. Riverbed released Aternity Mobile earlier this year, expanding its vision to better serve an often forgotten segment of the workforce: frontline employees. Aternity Mobile features multivendor mobile device, application, networking, and sentiment monitoring capabilities, one of the only dedicated mobile DEX solutions in the market today and more evidence of DEX-pansion. Nexthink acquired AppLearn, a digital adoption platform vendor, in January 2024, expanding the company’s visibility into employee application usage with built-in guidance and learning. A top priority among digital workplace leaders, driving digital adoption helps bring Nexthink even closer to understanding DEX holistically. While expansions like these pose tremendous opportunities for both vendors and customers, they’re also risky. Customers should ensure that they: Review the quality of integration between converging product sets. Ask questions about changing pricing and packaging models. Prepare for any gaps in support as vendors seek to up-level their expertise in unfamiliar DEX territory. My prediction? The DEX-pansion will begin at full force in 2025. As the need for holistic DEX grows, organizational silos break down, and interest rates fall, consolidation will increasingly characterize the DEX market. Mergers, acquisitions, full-scale product releases, and net-new partnerships will dominate the 2025 DEX market, resulting in the most exciting year for DEX since 2020. If you’re interested in learning more about my thoughts on the future of this market, reach out to me on LinkedIn. Forrester clients can also submit an inquiry request at [email protected]. If you’re a vendor that’s doing cool things in this space, submit a briefing request. source

On Oct. 28, Apple users can begin using Apple Intelligence, the new suite of generative AI features coming to iPhones, iPads, and MacOS. These tools aim to deliver on Siri’s original promise of a humanlike assistant that makes device interactions more intuitive. Additionally, they introduce popular generative AI functions, such as on-device image generation, enhancing the capabilities of Apple’s ecosystem for a more dynamic user experience. What does Apple Intelligence do? Apple Intelligence is an umbrella term for various generative AI features that can operate on Apple devices. It includes: Generative writing tools. Automatic summarization. Smart prioritization of notifications and emails. Photo editing. Automatic photo sorting. The ability to control some of the phone’s settings with voice commands. A more conversational and versatile attitude for Siri. How does Apple Intelligence work? Apple Intelligence is enabled by Apple’s own A17 Pro, A18, A18 Pro, M1, or M2 chips, which include a neural network component that runs on-device generative operations. Some of the more processing-intensive generative AI tasks on iPhones will be offloaded to Private Cloud Compute, a gateway between the phone and OpenAI’s ChatGPT. SEE: Our cheat sheet has everything you need to know about iPhone 16. Which devices have Apple Intelligence? Apple Intelligence runs on iPhone, iPad, and Mac with iOS 18.1 or later. The full list of devices is: iPhone 16 (A18)iPhone 16 Plus (A18)iPhone 16 Pro Max (A18 Pro)iPhone 16 Pro (A18 Pro)iPhone 15 Pro Max (A17 Pro) iPhone 15 Pro (A17 Pro)iPad Pro (M1 and later)iPad Air (M1 and later)MacBook Air (M1 and later)MacBook Pro (M1 and later) iMac (M1 and later)Mac mini (M1 and later)Mac Studio (M1 Max and later)Mac Pro (M2 Ultra) Apple Intelligence is not available in China and may be limited in the EU. Will Apple Intelligence come to older phones? No. Devices with hardware older than the ones listed above don’t have suitable chips to run Apple Intelligence. How much does Apple Intelligence cost? Apple Intelligence is free with a software upgrade on the supported devices. Must-read Apple coverage When did Apple Intelligence launch? Apple Intelligence launched with limited features, including writing tools, typing to Siri, the image Clean Up tool, and natural language search in Photos on Oct. 28. Both Apple Mail and phone notifications will be sorted by priority. AI writing tools work in Mail, Messages, Notes, Pages, and third-party apps. Plus, audio transcription will be available in the Notes and Phone apps. In the coming months, Apple will introduce more image-editing features, enhanced personal context and on-screen awareness in Siri interactions, and world knowledge powered by ChatGPT. The upcoming features are: Custom-made “Genmoji” emoji. Image Playground. Image Wand. Expanded writing tools powered by ChatGPT. Improved Priority Notifications. Apple Intelligence will initially roll out in American English. In December, it will gain localized English for Australia, Canada, New Zealand, South Africa, and the U.K. Other languages — including Chinese, French, Japanese, and Spanish — will follow next year. Is Siri artificial intelligence? The definition of AI can vary. Before the recent Apple Intelligence upgrade, Siri could have been considered a form of artificial intelligence but not generative AI, as it performed tasks without creating or remixing content. Instead, Siri primarily executed operations based on preset functions. Some argue that these computing capabilities shouldn’t qualify as AI, as the term suggests a science-fiction level of humanlike intelligence that algorithms, no matter how advanced, can’t achieve. However, “AI” has become a widely accepted term for certain types of computing. Still, it might have been more accurate to call Siri a digital assistant rather than a true AI. After the upgrade to Apple Intelligence in iOS 18.1 or the beta, it could now be accurate to call Siri generative AI, as it gains the ability to respond more dynamically and contextually. How to add Apple Intelligence to iPhone? Apple Intelligence will arrive on applicable iPhones as an automatic software update in October. To check for software updates manually, visit Settings > General > Software Update. How do I activate Apple Intelligence? On iPads or Macs, check for the free software update to iOS 18.1, iPadOS 18.1, or macOS Sequoia 15.1. From the Apple menu, navigate to System Settings > General > Software Update. What are the competitors to Apple Intelligence? Samsung and Google have both integrated generative AI into their newest phones. The Google Pixel 9 series, which came out in August, uses Google Gemini for photo editing, summarization, and conversations. The Samsung Galaxy S24 series, which came out earlier this year, also uses Google Gemini and other Google AI products for similar capabilities. source

“Harnessing Mainframe Data for AI-Driven Enterprise Analytics“ Tuesday, November 12, 2024 at 1:00pm EDT Did you know that 92% of IT leaders are actively investing in Artificial Intelligence (AI) to advance data and analytics initiatives, with an average of 5 projects either planned or ongoing? And despite the critical importance of mainframe data, only 28% of organizations report extensive use of such data for their analytical endeavors. These initiatives require data to fuel the AI and analytical models behind them, but where does mainframe data fit into the equation? Join us for a discussion on Rocket Software & Foundry’s research findings from our study, “The Role of AI and Mainframe Data in Enterprise Analytics.” This webinar is for Data & Analytics decision makers looking for knowledge of how to better integrate AI and mainframe data, overcoming prevalent challenges to unlock the full potential of enterprise analytics. Speakers: Ray SullivanVice President, Product ManagementRocket SoftwareRay Sullivan is the Vice President of Product Management for Rocket Software’s Data Modernization business. She has spent her career in Product Management, Product Marketing and Product Strategy in the enterprise software and consumer electronics industries. Ray drives Rocket’s strategy for the Structured Data portfolio, helping customers leverage and scale their data assets to deliver valuable business outcomes throughout their data modernization journeys. She also steers business and technical strategy to drive and expand technical partnerships. Lauren ZachariasDirector, Solution & Customer MarketingRocket SoftwareLauren Zacharias is the Director of Solution & Customer marketing for Rocket Software’s Data Modernization business. She has spent her career in Product Marketing, Customer Marketing, Content, and Product Development for B2B and B2C companies of different sizes in the Technology, Software, Retail, and Financial Services industries. Her specialty is creating strategic messaging for businesses that helps connect their product solutions with key buyers. Lauren has certifications and professional certificates from the Pragmatic Institute, Google, and the Product Marketing Alliance. Moderator:Peter Krass,InformationWeek  Offered Free by: Rocket Software See All Resources from: Rocket Software source

In today’s fast-paced digital landscape, the integration of artificial intelligence (AI) into IT operations (ITOps) is revolutionizing the way that organizations manage and optimize their technology infrastructure. This innovation, known as AIOps (AI for IT operations), is rapidly gaining traction across enterprises worldwide and cutting across industries. The transformative power of AIOps is staggering; it will play a critical role in enhancing efficiency, reducing downtime, and driving innovation. This blog is part of a four-part series of blogs. The series delves into the intersection of AIOps with: The future of AI-driven IT operations (Carlos Casanova). DevOps and agile (Devin Dickerson and Andrew Cornwall). Autonomous networks and business-optimized networks (Andre Kindness and Octavio Garcia Granados). Edge, IoT, and OT computing (Michele Pelino). Proactive Issue Resolution One of the most significant advantages of AIOps is its ability to predict and resolve IT issues before they impact business operations. Traditional IT operations often rely on reactive measures, addressing problems only after they have occurred. AIOps, however, leverages AI algorithms to analyze data in real time, identifying potential issues before they escalate. This proactive approach ensures seamless continuity and minimizes disruptions, allowing businesses to maintain high levels of service availability and performance. By preventing outages and reducing downtime, AIOps helps organizations save time and resources, ultimately enhancing their operational efficiency across the IT estate. Enhanced Decision-Making In IT operations, making informed decisions quickly and accurately is crucial. AIOps excels in this area by analyzing vast amounts of data to provide actionable insights. AI algorithms sift through logs, metrics, and events to identify patterns and anomalies, offering IT teams a comprehensive understanding of their infrastructure. These insights enable IT professionals to make data-driven decisions, optimizing performance and addressing issues with precision. Enhanced decision-making capabilities not only improve the efficiency of IT operations but also support strategic planning and innovation. Automation And Efficiency Automation is a cornerstone of AIOps, driving efficiency across IT operations. By automating routine tasks, AIOps frees up IT personnel to focus on more strategic initiatives. Tasks such as monitoring, alerting, and incident response can be handled by AI, reducing the manual workload and minimizing the risk of human error. This shift toward automation allows IT teams to allocate their time and resources more effectively, fostering a culture of innovation and continuous improvement. As a result, organizations can achieve higher levels of productivity and operational excellence. Scalability And Flexibility As businesses grow and evolve, their IT operations must be able to scale and adapt to changing demands. AIOps provides the scalability and flexibility needed to meet these challenges. AI-driven ITOps can seamlessly integrate with existing infrastructure, scaling up or down based on business needs. This adaptability ensures that IT operations remain efficient and effective, regardless of the size or complexity of the organization. By providing flexibility to incorporate new technologies and respond to market changes, AIOps supports long-term business growth and success. Security And Compliance In an era when cyberthreats are constantly evolving, enhancing security measures is paramount. AIOps can help security operations detect and mitigate issues in real time that could be exploited by operational weaknesses. AI algorithms continuously monitor network traffic, user behavior, and system activities, identifying potential weaknesses before they are used to harm the enterprise. Additionally, AIOps ensures compliance with industry standards and regulations, protecting organizations from legal and financial repercussions. By embedding principles of security and Zero Trust into the fabric of IT operations, AIOps helps organizations safeguard their data and maintain trust with their stakeholders. Embracing AIOps Is A Strategic Imperative For Every Tech Leader The integration of AI into IT operations via the practice of AIOps is transforming the way organizations manage and optimize their technology infrastructure. By providing proactive issue resolution, enhancing decision-making, driving automation and efficiency, offering scalability and flexibility, and strengthening security and compliance, AIOps is a game-changer for businesses in the digital age. As the adoption of AIOps continues to grow, its impact on IT operations will only become more profound, enabling organizations to achieve new levels of innovation and operational excellence. Embracing AIOps is not just a technological advancement; it is a strategic imperative for any organization looking to thrive in today’s dynamic digital landscape. Starting in January of 2025, for Forrester clients, we’ll offer a series of webinars that align with this series of blogs. Be sure to mark these dates in your calendar for the upcoming webinars. Follow the analysts below for notification when the registration links are available.   Be sure to look for the other blogs in this series coming this week: source