By Susan Saltzstein, Mark Foster and Tansy Woan  ( December 20, 2024, 3:55 PM EST) — In 2024, securities litigation remained active with filing trends in line with historic averages. What made this year somewhat different was the U.S. Supreme Court had four securities cases on its docket. What was even more unusual is that the Supreme Court then dropped two of those cases from its docket after oral argument…. 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

Just in time for Christmas, OpenAI is generating buzz with its o3 and o3-mini models, claiming groundbreaking reasoning capabilities. Headlines like ‘OpenAI O3: AGI is Finally Here’ are starting to show up. But what are these ‘reasoning advancements,’ and how close are we really to Artificial General Intelligence (AGI)? Let’s explore the benchmarks, current shortcomings, and broader implications.  o3’s Benchmarks Shows Progress In Reasoning And Adaptability  OpenAI’s o3 builds on its predecessor, o1, with enhanced reasoning and adaptability. I blogged about o-1 in September. The o3 models show notable performance improvements, including:  ARC-AGI Benchmark (Visual Reasoning): With 87.5% accuracy, o3 showcases significant visual reasoning gains. This addresses prior models’ shortcomings in reasoning over physical objects, contributing to the AGI hype.  AIME 2024 (Math): With 96.7% accuracy, o3 far surpassing o1’s 83.3%. Mathematics is another important benchmark because it demonstrates the model’s ability to understand abstract concepts that underpin the science of our universe.  SWE-bench Verified (Coding): This benchmark is 71.7%, up from o1’s 48.9%. This is a very large improvement in the model’s ability to produce software. Think of software coding as the equivalent of hands and fingers. In the future, autonomous agents will manipulate the digital world using code.  Adaptive Thinking Time API: This is a standout feature of o3, enabling users to toggle between reasoning modes (low, medium, and high) to balance speed and accuracy. This flexibility positions o3 as a robust tool for diverse applications.   Deliberative Alignment: o3 improves safety by detecting and mitigating unsafe prompts. Meanwhile, o3-mini demonstrates self-evaluation capabilities, such as writing and running scripts to refine its own performance.   Reasoning Holds The Key To More Autonomous Agents- And To AI Progress  Reasoning models like o3 and Google’s Gemini 2.0 represent significant advancements in structured problem-solving. Techniques like “chain-of-thought prompting” help these models break down complex tasks into manageable steps, enabling them to excel in areas like coding, scientific analysis, and decision-making.   Today’s reasoning models have many limitations. Gary Marcus openly criticizes OpenAI for what amounts to cheating in how they pretrained o3 on the ARC-AGI benchmark. Even OpenAI admits o3’s reasoning limitations, acknowledging that the model fails on some “easy” tasks and that AGI remains a distant goal. These criticisms underscore the need to temper expectations and focus instead on the incremental nature of AI progress.   Google’s Gemini 2.0 on the other hand differentiates from Open AI through multimodal reasoning—integrating text, images, and other data types—to handle diverse tasks, such as medical diagnostics. This capability highlights the growing versatility of reasoning models. However, reasoning models only address one set of skills needed to approximate human-equivalent abilities in agents. Today’s best models lack critical:   Contextual Understanding: AI doesn’t intuitively grasp physical concepts like gravity or causality.  Learning Adaptability: Models like o3 cannot independently ask questions or learn from unanticipated scenarios.  Ambiguity Navigation: AI struggles with nuanced, real-world challenges that humans navigate seamlessly.   Moreover, while research into model reasoning has produced techniques that are well-suited for today’s transformer-based models, the three skills mentioned above are expected to pose significantly greater challenges.  Tracking and discerning the truth in announcements like this coupled with learning how to better work with more capable machine intelligences are important steps for enterprises. Enterprise capabilities like platforms, governance and security are as important because foundation model vendors will continue to leapfrog each other in reasoning capabilities. The Forrester Wave™: AI Foundation Models For Language, Q2 2024 points out that benchmarks are just one chapter in the story and models need enterprise capabilities to be useful. AGI Is A Journey, Not a Destination – And We’re Only At The Beginning  AGI is often portrayed as a sudden breakthrough, as we have seen depicted in the movies. Or an intelligence explosion as philosopher Nick Bostrom imagines in his book, Superintelligence. In reality, it will be an evolutionary process. Announcements like this mark milestones, but they are just the beginning. Ultimately as agents become more autonomous, the resulting AGI will not replace human intelligence but rather will enhance it. Unlike human intelligence, AGI will be machine intelligence designed to complement human strengths and address complex challenges.   As organizations navigate this transformative technology, success will depend on aligning AGI capabilities with human-centric goals to foster exploration and growth responsibly.  The rise of advanced reasoning models in this journey presents both opportunities and challenges for responsible development and deployment. These systems will amplify your firm’s automation and engagement capabilities, but they demand increasingly rigorous safeguards to mitigate ethical and operational risks.  source

Albert Einstein published his groundbreaking “light quantum” paper in 1905, birthing his theory that light consists of tiny energy packets known as photons. This idea — along with findings from Niels Bohr and Max Planck — laid the foundation for quantum physics, a field that is shaping the future of computing today.  What Is Quantum Computing?  Quantum computing aims to tackle problems that traditional computers struggle with — either due to complexity or speed. By leveraging the principles of quantum physics, quantum computers can unlock new possibilities.  Instead of the binary bits (zero or one) used in traditional computing, quantum computers utilize qubits. A qubit can represent both zero and one simultaneously due to a phenomenon called superposition, allowing quantum computers to process multiple possibilities at once. This offers unprecedented efficiency for specific mathematical challenges. While this makes the potential vast, it’s important to note that quantum computers won’t replace everyday computing purposes — like office work, media consumption, or gaming. Instead, they excel in niche areas such as solving specific mathematical problems and simulating quantum states, which is important for research on quantum physics.   Related:Facing the Specter of Cyber Threats During the Holidays And we’ve come a long way — quantum computing has started to revolutionize various industrial sectors and leading organizations, from Google to IBM, along with research institutions and startups, are making significant strides in the field.   And while exciting, these developments might also enable them to decode encryption methods that are hard to break using currently available computing clusters.   Is Quantum Computing a Threat to Cybersecurity?  As quantum technology progresses, one major area of concern is looming: cybersecurity. Could quantum computers crack the encryption systems we rely on today?   Luckily, the short answer is not yet. Still, the potential threat is real, with McKinsey citing that capable quantum systems could be ready by 2030.  Though functional quantum computers already exist — and some companies even provide access to them, today’s quantum computers are still limited, with the most powerful systems containing only around 1,200 qubits.   This limited number of qubits is not yet enough to solve problems that are too complex for existing computers or super computers. In fact, experts predict that breaking the most secure encryption methods would require a quantum computer with 20 million qubits — a benchmark that still gives us time to prepare.  Related:Forrester Panel: Government Cybersecurity Leaders Discuss Next Steps for Zero Trust However, with each advancement, the power of quantum computing inches closer to the point where it could potentially unravel protections that currently safeguard everything from personal data to state secrets.   Can We Quantum-Proof Our Future?  Preparing for a quantum future means rethinking our encryption methods.   There are mathematical problems that are just as difficult for quantum computers to solve, and cryptography experts are currently building schemes based on these problems. This type of encryption is called post-quantum cryptography (PQC).  However, we can also make the encryption methods we use today quantum resistant. For instance, a method called RSA encrypts a large portion of internet traffic. It uses prime factors which are hard for traditional computers to compute.   Today’s encryption algorithms — like RSA — rely on the difficulty of factoring large prime numbers. While that is a challenge for classical computers, it’s much easier for quantum systems. Before quantum computers become powerful enough, organizations must pivot to quantum-resistant algorithms.  One solution lies in increasing the number of bits. For instance, RSA encryption using 2048-bits is currently safe, but doubling it could make decryption — even for quantum computers much more complex. Other encryption schemes may require similar adjustments to stay ahead of the quantum threat.  Related:Ransomware Attack on Rhode Island Highlights Risk to Government Some actors are already storing encrypted data with the intent to decrypt it in the future when quantum computers are more powerful — a tactic known as “harvest now, decrypt later.”  The data they’re storing might be old by then, but it can still be critical. Think of intelligence services, for example. This makes it essential to transition to post-quantum encryption sooner rather than later.  How do IT Professionals Prepare?   To prepare, IT professionals can start by identifying sensitive data and encryption use across your organization — VPNs, external server access or remote access are key areas to focus on. Determine which cryptographic methods you’re using and explore the implementation of post-quantum standards for the future.  In the coming years, many operating systems and browsers will incorporate quantum-safe cryptographic libraries, making it easier for organizations to adopt post-quantum encryption. It’s crucial to stay updated and ensure your systems are patched and compatible with these new standards.  Be Prepared, But Don’t Forget the Basics   We’ve come a long way since Einstein first published his paper, and quantum-safe encryption is becoming a critical focus for the cybersecurity world. Yet, while the quantum threat is on the horizon, do not neglect the basics. The probability of your network being attacked, due to an outdated system, is still much higher than the threat of quantum computers breaking your encryption. So, for now, the focus should remain on protecting your network from threats present today, while beginning conversations and thinking through a five-year plan that includes PQC.  And remember, as quantum computing advances, having a robust security foundation today will make it easier to quantum-proof your IT systems tomorrow.   source

However, Salesforce isn’t the only agentic AI provider that is taking the approach of launching basic agents which could be tweaked to suit a variety of use cases. Microsoft’s corporate vice president Bryan Goode, who leads products such as Copilot Studio and Dynamics 365, told CIO.com during the launch of its AI agents that it was releasing 10 pre-built agents that would act as templates for enterprises to help them develop agents for a variety of use cases. Salesforce, and rivals such as Google, Microsoft, AWS, and IBM, are also partnering with other software vendors, such as Workday, DocuSign, and Neuron 7, to create more agents that can be accessed via their marketplaces. source

Australia has announced plans to develop a national AI strategy to strengthen its AI capabilities and attract investment, aiming to unlock the economic productivity potential of these technologies. The strategy will focus on building AI skills, sovereign capabilities, and infrastructure, positioning Australia to seize the $600 billion boost to GDP that the government expects AI will deliver by 2030. Set to be developed in consultation with industry, the AI plan is expected to be released in late 2025 — after the next federal election, which is scheduled for the year’s first half. SEE: The Challenges of Demonstrating AI ROI In Australian Organisations Australia’s Federal Minister for Industry and Science, Ed Husic, emphasized the plan’s role in driving AI investment, supercharging industries and creating well-paid jobs nationwide. “We need to scale up our capabilities in critical technologies in ways that work for businesses and their workers,” Husic said in a statement announcing the new AI plan. What will be contained in Australia’s new AI capability plan? The AI plan aims to develop a comprehensive strategy focusing on AI investment, existing strengths and advantages, skills development, and sovereign infrastructure and capabilities. Australia wants to grow AI investment The government plans to review how existing Australian state and federal government support mechanisms work together to hinder or enable Australia’s AI technology ecosystem. It will also identify ways to boost private sector innovation and investment in AI capabilities. A focus on strengthening AI capabilities The plan will identify areas of research and innovation strength within Australia’s universities and businesses that can support the growth of the AI industry. Additionally, the government will explore new opportunities for comparative advantage across key sectors of the economy, including agriculture, mining, and renewable energy. A strategy for AI skills and training The government wants to accelerate AI literacy by identifying new skills and training and re-training approaches. Efforts will also focus on enabling workers to reskill throughout their careers, helping them seize opportunities in new AI-driven jobs or retrain as AI automates parts of existing roles. Sovereign capabilities and infrastructure The government will consider where sovereign capability or infrastructure may be required for Australia to maximize AI technologies. It will also consider opportunities and risks of AI and digital inclusion in Australia and how AI will impact communities and workers. More must-read AI coverage How will Australia’s new plan be developed and finalised? Because AI will impact almost every industry, the government has said the strategy will be developed in consultation with various stakeholders — both in industry and the general public. As a result, the government will undertake both a targeted and public consultation period before finalising the plan. Australia’s Department of Industry, Science and Resources will conduct the process. When will the AI capability plan be finalised? The plan will not be released until late 2025. Given the fast pace of AI development, the Australian Information Industry Association declared this pace too slow to seize new AI opportunities. It argued that the government should accelerate its timetable. A federal election will take place during this period, which could impact the process if there is a change in government. Why has Australia embarked on the creation of an AI Capability Plan? The Australian Government has outlined its intention to build on the country’s existing comparative advantages in AI, emphasizing an “Australian-first” approach to grow the local AI industry for the future. In announcing the plan, the government highlighted that around 650 AI companies are already headquartered in Australia. Data released by the government indicates that, in the five years leading up to 2023, foreign investors contributed AUD $7 billion to Australian AI technologies. SEE: Dovetail CEO Argues for Balanced Approach to Innovation, Regulation “This plan will look to harness our AI know-how to secure our supply chains and strengthen our critical infrastructure,” Husic said. “This is something business is calling for and we’re delivering. We will work closely with firms, and with communities and workers, to drive investment in our AI capabilities.” What else is Australia doing to enhance its AI prospects? The government has been criticised for focusing on regulation and guardrails to protect citizens from AI, rather than supporting AI innovation, investment, and adoption. The AIIA called Australia a “slow adopter of AI across its economy by global standards,” due what it called “concerns and fears” with the technology. However, the government pointed out AUD $1 billion has been committed for critical technologies under the National Reconstruction Fund, while the Research and Development Tax Incentive supported nearly AUD $500 million worth of AI, computer vision, and machine learning projects from 2022 to 2023. The government has also established network of “AI Adopt” centres to upskill SMEs who want to understand and adopt AI within their businesses. The National AI Centre has also released a micro skill course, “Introduction to Artificial Intelligence,” delivered through TAFE NSW. source

According to the 2024 Global Startup Ecosystem Report by Startup Genome, the Netherlands ecosystem is now ranked number 13 in the world — placing it ahead of both Paris and Berlin. In 2023, Dutch startups raised $2.2bn. While there have been fewer startup deals this year, overall investment is up, according to figures from the Dutch Startup Association. And for some startups and scaleups, 2024 was truly a monumental year. Picnic raises one of Europe’s largest rounds  Having grown its business 40% in 2023 following international expansion across France and Germany, Dutch online supermarket Picnic kicked off the year in style as it announced a €355mn funding round in January. The Bill and Melinda Gates foundation participated in the round, which brought the company’s total raised to €1.3bn. Founded in 2015, Picnic, its fully automated fulfilment centres, and delivery algorithms have defied the mass collapse of online grocery delivery startups that befell the likes of Getir and Flink after the pandemic. In 2018, the year before hitting 1,000,000 shoppers in the Netherlands, the company’s CTO Daniel Gebler took the stage at TNW Conference to talk about the tech that is disrupting the “everywhere commerce” space. Gebler also closed the year with a bang, as he was named CxO of the year by Computable.nl.  How Startup Amsterdam Boosts Innovation and Growth at TNW Conference Discover how the City of Amsterdam partnered with TNW to amplify its startup ecosystem, attract global talent, and foster innovation that drives economic impact. DataSnipper reaches unicorn status  Its Series B $100mn raise in February saw Amsterdam-headquartered auditing platform DataSnipper valued at $1bn, aka achieving the mythical status of unicorn. The round was led by Index Ventures and the funds are helping DataSnipper, which already counts Hilton, Siemens, and Frontier Airlines among its clients, to expand across more verticals including forensic accountants and tax advisors. DataSnipper was founded by Maarten Alblas, Jonas Ruyter, and Kai Bakker in 2017. In 2023, the company appointed a new CEO in Vidya Peters (on the featured image along with the founding team). Peters was previously Chief Operating Officer at payment solution provider Marqeta, helping the company go public in 2021. She sees the long term objective of DataSnipper as connecting unstructured data across industries, and believes there is tremendous opportunity for growth and expansion globally.  Mews becomes a unicorn, €100mn fund by Carbon Equity March was a month of celebration for current and former TNW Spaces member startups. Hotel management software provider Mews hit a €1.1bn valuation after a €101mn raise, led by Swedish investment company Kinnevik. The good news for Mews, founded in 2012 by former hotelier Richard Valter, did not stop there. In September, the company bagged another €90mn from Vista Credit Partners. Having already purchased nine other startups in the sector, the funds will allow Mews to continue its buying spree, consolidating its place as a market leader in redefining the hospitality industry with its cloud offerings. Meanwhile, leading climate fund investment startup Carbon Equity raised €100mn for its Climate Tech Portfolio Fund II — exceeding an initial target of €75mn and more than doubling its first fund from 2022. Founded only in 2021, Carbon Equity has quickly become a force to be reckoned with for investments in curated clean tech solutions.  In October, Wired dubbed Carbon Equity one of the hottest startups in Amsterdam, and at the beginning of December, co-founder Jacqueline van den Ende was awarded the title of Changemaker of the Year by Change Inc, rounding off a momentous year. Let’s hope climate tech investment continues to thrive in 2025.  First ever tech fund by Dutch Ministry of Defence  It is perhaps an unfortunate sign of the times we live in, but there is no denying that defence tech startups — from Ukrainian drone developers to German AI darling Helsing — are on a roll. In October, the Dutch Ministry of Defence announced a €100mn fund to provide early-stage financing to the country’s startups, scaleups, and SMEs that meet specific innovation needs.  The fund will invest up to €5mn per company. It will focus on dual-use technologies, meaning tech that can be used both for civilian and military purposes. It is expected to open in 2025, so keep your eyes peeled for the first investments. We can’t wait to see what 2025 will bring as Amsterdam celebrates its 750th anniversary and TNW Conference returns to NDSM island in June. Join us as we bring together the whole Dutch tech ecosystem and discover what is truly next in tech! source

On Dec. 5, a warning from vendor Deloitte alerted the state government of Rhode Island that RIBridges, its online social services portal, was the potential target of a cyberattack. By Dec. 10, Deloitte confirmed the breach. On Dec. 13, Rhode Island instructed Deloitte to shut down the portal due to the presence of malicious code, according to an alert published by the state government.   Brain Cipher, the group claiming responsibility, is threatening to release the sensitive data stolen in the attack, potentially impacting hundreds of thousands of people, according to The New York Times.   State and local government entities, such as RIBridges, are popular targets for ransomware gangs. They are repositories of valuable data, provide essential services, and are often under-resourced. What do we know about this attack so far and the ongoing cyber risks state and local governments face?   The Brain Cipher Attack  RIBridges manages many of Rhode Island’s public benefits programs, such as the Supplemental Nutrition Assistance Program (SNAP), Medicaid, and health insurance purchased on the state’s marketplace. Deloitte manages the system and Brain Cipher claims to have attacked Deloitte, BleepingComputer reports.   “We are aware of the claims by the threat actor. Our investigation indicates that the allegations relate to a single client’s system, which sits outside of the Deloitte network. No Deloitte systems have been impacted,” according to an emailed statement from Deloitte.   Related:Facing the Specter of Cyber Threats During the Holidays The information involved in the breach could “include names, addresses, dates of birth and Social Security numbers, as well as certain banking information,” according to the RIBridges alert.   Rhode Island Governor Daniel McKee (D) issued a public service announcement urging the state’s residents to protect their personal information in the wake of the breach.   “Based on the information that’s being put out there by the governor about … the steps you can take to minimize the fallout of this, that tells me that they’re unlikely to be paying the ransom,” says Truman Kain, senior product researcher at managed cybersecurity platform Huntress.   Brain Cipher appears to be a relatively new ransomware gang. “We’ve tracked five confirmed attacks so far, including this one. Two others have been on government entities as well: one in Indonesia and one in France,” Rebecca Moody, head of data research at Comparitech, a tech research website, tells InformationWeek.   In June, the ransomware group hit Indonesia’s national data center. It demanded an $8 million ransom, which it ultimately did not receive. In August, it posted Réunion des Musées Nationaux (RMN), a public cultural organization in France, to its data leak site, alleging the theft of 300GB of data, according to Comparitech.   Related:Forrester Panel: Government Cybersecurity Leaders Discuss Next Steps for Zero Trust In addition to these confirmed attacks, there are 19 unconfirmed attacks potentially linked to Brain Cipher, according to Moody. It is unclear how much the group may have collected in ransoms thus far.   “It’s always really difficult to know when people have paid because, obviously, if they pay they [threat groups] shouldn’t really add them to the data leak site, and obviously, companies are very reluctant to tell you if they’ve paid a ransom because they think it leaves them open to future attack,” says Moody.   Ransomware Attacks on Government  Government remains a popular target for threat actors. “They are vulnerable because they are a key service for people, and they can’t afford downtime,” says Moody. “It is one of the sectors that we’ve seen a consistently high number of attacks.”   Between 2018 and December 2023, a total of 423 ransomware attacks on US government entities resulted in an estimated $860.3 million in downtime, according to Comparitech. For 2024, Comparitech tracked 82 ransomware attacks on US government agencies, up from 79 last year.   Related:Cybercriminals and the SEC: What Companies Need to Know Of the 270 respondents in the state and local government sector included in The State of Ransomware in State and Local Government 2024 report from Sophos, just 20% paid the initial ransom demand. States such as Florida, North Carolina, and Tennessee, have legislation limiting or even prohibiting public entities from paying ransom demands.    That doesn’t necessarily mean threat actors will avoid targeting government entities. Even if a threat group cannot successfully extort a victim, it can still sell stolen data to the highest bidder. “Ransoms are probably higher than what they would get for leaking the data. It depends on how much data is stolen though and the value of that data,” says Moody.   Regardless of whether a government agency pays when hit with ransomware, it still must deal with the disruption and fallout.   While cybersecurity threats to local and state governments are highly publicized, funding continues to be a stumbling block. Just 36% of local IT executives report that they have adequate budget to support cybersecurity initiatives, according to the 2023 Local Government Cybersecurity National Survey from Public Technology Institute.   While budgets may be limited, cybersecurity cannot be ignored, Kain argues.   “I think it’s kind of an excuse for state and local governments to say, ‘Oh, well we just don’t have the budget. So, cybersecurity is an afterthought,’” he says. “Things should really start from a cybersecurity perspective, especially when you’re dealing with sensitive data like this.”   State and local government agencies can focus on cybersecurity basics, like enabling multi-factor authentication, regular security awareness training for staff, and vulnerability patching. “It’s … those key things that don’t necessarily cost a lot,” says Moody. “Also [be] prepared for the inevitable because no one’s immune to them [attacks].” source

As enterprise IT environments continue to grow in complexity, organizations are modernizing legacy applications and workloads, amongst other strategic IT initiatives, to address various IT and business challenges—including cost optimization, simplification, time to market, technical debt, scale and more. Along the way, they are adopting new technologies and infrastructure that have to be incorporated into the application development and delivery process. Powerful DevOps and automation tools, as well as serverless infrastructure and managed container solutions, are more accessible than ever. The hard part is knowing how to effectively combine these technologies to achieve tangible results, most notably on established transactional systems like the mainframe. Recent survey data from Forrester highlights the significant impacts of IT modernization challenges: 44% of decision-makers report delayed timelines, one-third cite reduced productivity, and 40% note increased operating costs. These hurdles stem, in part, from the growing complexity of application development and deployment processes as the speed of business continues to accelerate. As organizations strive to modernize applications, an effective and automated DevOps toolchain becomes critical. 3 keys to success: visibility, automation, and orchestration Complete visibility into the end-to-end application development lifecycle is essential for organizations to be successful in their modernization journeys. It’s what enables teams to identify problems and spot misconfigured resources that detract from operational efficiency. Visibility also gives engineers a clearer sense of how new features or processes will impact the larger picture. Once end-to-end visibility exists, leaders can automate their DevOps processes with better precision. Considering the growing number of tasks, processes, and accelerated development timeframes teams must work with, automation has a critical role. Leveraging automation takes much of the burden of managing repetitive, tedious, and time-consuming processes from development teams. When companies get automation right, they can significantly reduce complexity and mitigate risk. They can catch inefficiencies, improve reliability, and accelerate new feature delivery. Automation also helps reduce costs and reallocate engineering time to more innovative endeavors. Those who use automation well can then focus on broader orchestration. Service orchestration and automation platforms (SOAP), as they’re now being referred to, can kick off automations within the DevOps pipeline in one cloud and then initiate another set of activities in a different cloud or on the mainframe. With today’s tools, the possibilities are virtually endless. DevOps orchestration tools minimize human error and remove operational bottlenecks that would otherwise slow the entire IT operation. Combined with total IT visibility and automation, orchestration is what enables the level of coordination needed to upgrade application development and delivery across large, multi-platform IT footprints. Different technology teams can collaborate more effectively and partner on building the ideal end-to-end IT environment to serve the company’s needs. Invest in an enterprise-grade workload automation solution One of the best ways to improve visibility and leverage automation and DevOps orchestration is to invest in a purpose-built solution. Platforms like Rocket Software’s Rocket Workload Automation and DevOps Orchestration were designed for this exact reason. These solutions give development teams a robust set of capabilities to assess and manage development and deployment, with access to a centralized place for viewing business applications, operating systems, platforms, and DevOps tooling. This view captures mainframe, distributed, and cloud workloads. The tools and technologies exist to elevate IT workload and DevOps automation dramatically. Companies can get more done with fewer resources than ever before by leveraging automation and orchestration. The key is being able to do this across IT footprints that are becoming increasingly complex and will continue to do so for the foreseeable future. For those with limited experience in modernizing resources and building cohesive, multi-platform IT environments, working with a company like Rocket Software can accelerate the transition while maximizing return on investment. Learn more about how Rocket Software can help you leverage the right tools to optimize the application development lifecycle, end to end. source

After more than a year of investigations, the Italian privacy regulator – il Garante per la protezione dei dati personali – issued a €15 million fine against OpenAI for violating privacy rules. Violations include lack of appropriate legal basis for collecting and processing the personal data used for training their genAI models, lack of adequate information to users about the collection and use of their personal data, and lack of measures for collecting children’s data lawfully. The regulator also required OpenAI to engage in a campaign to inform users about the way the company uses their data and how the technology works. OpenAI announced that they will appeal the decision.  This action obviously impacts OpenAI and other genAI providers, but the most significant long-term impact will be on companies that use genAI models and systems from OpenAI and its competitors — and that group likely includes your company.  So here’s what to do about it: Job #1: Obsess about third party risk management Using technology that is built without due regard for the protection and the fair use of personal data poses significant regulatory and ethical questions. It also increases the risk of privacy violations in the information generated by the model itself. Organizations understand the challenge: in Forrester’s surveys, decision-makers consistently list privacy concerns as a top barrier for the adoption of genAI in their firms. However, there is more on the horizon: the EU AI Act, the first comprehensive and binding set of rules for governing AI risks, establishes a range of obligations for AI and genAI providers and for companies using those technologies. By August 2025, General-purpose AI (GPAI) models and systems providers must comply with specific requirements such as sharing with users a list of the sources they used for training their models, results of testing, copyright policies, and providing instructions about the correct implementation and expect behaviour of the technology. Users of the technology must ensure they vet their third parties carefully and collect all the relevant information and instructions to meet their own regulatory requirements. They should include both genAI providers and technology providers that have embedded genAI in their tools in this effort. This means: 1) carefully mapping technology providers that leverage genAI; 2) reviewing contracts to account for the effective use of genAI in the organization; and 3) designing a multi-faceted third party risk management process that captures critical aspects of compliance and risk management, including technical controls. Job #2: Prepare for deeper privacy oversight From a privacy perspective, companies using genAI models and systems must prepare to answer some difficult questions that touch on the use of personal data in genAI models that runs much deeper than just training data. Regulators might soon ask questions about companies’ ability to respect users’ privacy rights, such as data deletion (aka, “the right to be forgotten”), data access and rectification, consent, transparency requirements, and other key privacy principles such as data minimization and purpose limitation. Regulators recommend that companies use anonymization and privacy preserving technologies like synthetic data when training and fine tuning models. Firms must also: 1) evolve data protection impact assessments to cater for traditional and emerging AI privacy risks; 2) ensure they understand and govern structured and unstructured data accurately and efficiently to be able to enforce data subject rights (among other things) at all stages of model development and deployments; and 3) carefully assess the legal basis for using customers’ and employees’ personal data in their genAI projects and update their consent and transparency notices appropriately. Forrester can help: Here’s what to read, and if you have questions, let’s talk! If you have questions about this topic, the EU AI Act, or the governance of personal data in the context of your AI and genAI projects, read my research —  How To Approach The EU AI Act and A Privacy Primer On Generative AI Governance – and schedule a guidance session with me. I would love to talk to you. source

Before choosing between a predictive dialer and a progressive one for your outbound call strategy, you should decide your business’s priorities. Are you looking to increase productivity? Do you want to drive up customer satisfaction scores? This tradeoff is implicit in the design of each system. The key difference between predictive and progressive dialers is how they start a call. A predictive dialer dials several numbers simultaneously, assigning each rep a number as soon as they end the previous call. A progressive dialer only dials one number at a time, which gives the rep time to research the potential client who will pick up the phone. In this post, we’ll cover the vital advantages and tradeoffs that come with using both dialers. Plus, we’ll look at alternative types of call center dialers, if neither a predictive or progressive dialer sounds like the right fit for your business. Both types of dialers are available with the leading business phone services and call center software. Typically, auto dialing capabilities are available as an add-on feature. You may also find standalone auto dialer software that integrates directly with your CRM software. 1 RingCentral RingEx Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Medium (250-999 Employees), Large (1,000-4,999 Employees), Enterprise (5,000+ Employees) Medium, Large, Enterprise Features Hosted PBX, Managed PBX, Remote User Ability, and more 2 Talkroute Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Any Company Size Any Company Size Features Call Management/Monitoring, Call Routing, Mobile Capabilities, and more Predictive dialers reach more people (with a catch) A predictive dialer is highly efficient because it can reach more people and reduce the amount of time agents spend waiting. It uses algorithms and predictive analytics to anticipate when agents will be available for the next call. The system automatically dials multiple numbers simultaneously and filters out unproductive connections, such as busy signals and voicemails, ensuring that agents are only connected to live calls. The system adjusts its dialing pace based on real-time call center metrics like agent availability and call success rates, which help to minimize downtime and increase agent productivity. By anticipating when agents will finish their current calls, the predictive dialer moves quickly to assign the next call, keeping agents busy without requiring manual input. This means that agents spend more time talking to customers and less time waiting for the next call, which can significantly increase call volume compared to manual or progressive dialing systems. Predictive dialers can lead to significant improvements in call volume, with some vendors claiming up to a 300% increase in productivity over manual dialing. However, the actual impact depends on factors like the quality of the contact list and agent readiness. In general, predictive dialers help ensure that agents are always connected to live calls, leading to more efficient use of their time. Hidden costs of predictive dialers Despite the benefits, predictive dialers come with hidden costs, including: Higher call abandonment rates: Due to faster dialing, there’s a greater risk of calls being dropped before an agent can answer, which may negatively impact customer satisfaction. Potential harm to customer satisfaction: A higher call abandonment rate may be particularly detrimental to existing customers, as they may feel neglected in favor of reaching new prospects. Compliance risks: The Federal Communications Commission (FCC) mandates that call abandonment rates must not exceed 3% over 30 days. Exceeding this threshold can lead to legal consequences, requiring businesses to carefully balance dialing speed and compliance. While predictive dialers offer the potential for greater efficiency, businesses must weigh these productivity gains against the potential downsides, ensuring they maintain a positive customer experience and stay within legal requirements. Progressive dialers have lower call abandonment (at a cost) Unlike predictive dialers, which dial multiple numbers at once, a progressive dialer calls one number at a time. It waits until the current call is completed before dialing the next one, giving agents more control over the calling process. One of the main advantages of a progressive dialer is its lower call abandonment rate. By dialing only one number at a time, it minimizes wait times for customers, making it more likely they will stay on the line. When customers hear a live agent right away, they are less likely to hang up. In contrast, if they are met with a recorded message or a long pause, the chances of abandonment increase. This improved customer satisfaction is another major benefit. With progressive dialers, customers are connected to agents more quickly, leading to a smoother experience and higher satisfaction rates. For businesses that prioritize customer relationships or work in complex sales environments, progressive dialers allow reps to handle calls more thoughtfully and attentively. In addition to customer benefits, progressive dialers offer compliance advantages. Because they only connect agents to live callers, they lower the risk of violating telemarketing regulations. Progressive dialers are inherently more compliant with the Telephone Consumer Protection Act (TCPA), which governs automated calling systems. These dialers ensure agents are always speaking to a real person, helping businesses stay within legal limits for things like prior consent and abandoned call rates. For businesses that value personalization, deal with more intricate sales processes, or are looking to enhance contact center CX, a progressive dialer is a solid choice. Its lower call abandonment rate and higher level of control for agents make it ideal for creating a more tailored and compliant customer experience. The hidden cost behind progressive dialers Owing to the step-by-step approach to making calls, progressive dialers tend to exhibit lower total call volumes and productivity when contrasted with predictive dialers. The result is slower lead conversion rates and decreased operational efficiency for businesses that heavily depend on high call volumes. So, predictive dialers may be a more efficient choice if you work in telemarketing or lead generation companies or any business that requires many outbound calls to be made in a short span. Comparing predictive dialers vs. progressive dialers Let’s compare these