In 1996, four men made Canadian history when they won the Olympic gold medal in the men’s 4×100-meter relay in a time of 37.69 seconds. This historic win was especially surprising, considering the team had struggled in the events leading up to it. They had the quickness but needed to work through the mechanics of the race and interpersonal issues before finding success. Why? Because a relay race isn’t simply about speed, — it is also about chemistry. And when you’re competing at the highest levels, the difference between winning and losing the race comes down to the team’s performance in the handoff and transition zones. It isn’t much different for B2B organizations. You can have great sales and customer success (CS) teams, but if you don’t have smooth account handoffs and transitions from one team to the next, you will be failing your customers and lose speed in the race to retention and growth. A successful transition supports delivery of a customer experience that feels like a single, seamless interaction — or a baton pass made without losing stride — from first contact to onboarding and beyond. In aligning sales and CS teams to provide a seamless transition, there are some best practices to consider that are integral to passing the baton in a way that sets up the next team, and the customer, to succeed. So where to start? Two tactics to consider: Create a transition checklist. CS teams need specific information from sales, and creating a checklist provides clarity and supports consistency. It should include information that can only come from sellers, such as the customer’s key stakeholders, org chart, promises made during the purchase, specific use cases, goals, and pain points. Ensure that the checklist lives in a shared tool such as a CRM or customer success platform, making it easily accessible to both teams. Establish a knowledge transfer process. To make sure that customers realize the value promised, sellers should share the information gathered during the buying process, breathing life into the transition checklist. This allows the customer success manager (CSM) to gain more context, especially around goals and outcomes. These insights provide CSMs with the knowledge and information needed to start building a joint customer success plan prior to the partnership kickoff call, complete with measurable, identifiable milestones. To successfully progress customers around the proverbial track toward the finish line, a robust process that prioritizes the partnership aspect and includes the insights that matter helps to make it more than a “check the box” task. Clear track lanes also provide ownership clarity for each team, optimizing their performance in the transition zones and alleviating the need for strenuous training. To learn more about successful account transitions, read Drive Retention Through Effective Sales-To-Customer Success Account Transitions, and if you’re a Forrester client looking to improve this process, reach out to your account team to book a guidance session with me today. source

By Tom Zanki ( January 22, 2025, 7:28 PM EST) — Defense and space exploration company Voyager Technologies Inc. said Wednesday it has confidentially filed plans for an initial public offering, marking the second company from the industry to join the IPO pipeline this week and potentially benefiting from increased government funding for space travel…. 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 role will likely remain in the C-suite as long as data is the differentiator,” he says. “Data is important for modern enterprises, and as AI evolves, having someone who can manage, protect, and strategically utilize that data is crucial.” However, CDOs need to demonstrate measurable value, such as operational efficiencies, new revenue from data-driven services, or improved compliance and transparency, Kearney says. “These outcomes will establish the CDO as an essential role rather than a temporary one,” he adds. Like many CDOs, Kearney sees the role as in a state of evolution, and CDOs need the vision required to translate multimodal data into valuable business insights, he says. source

It’s been some time since Forrester has written about this market, and a lot has changed. Automation is the cornerstone of speed and operational efficiency. With the increasing complexity in IT ecosystems, business applications, and data, the demand for smarter automation is greater than ever. Batch automation and workload automation are certainly not new concepts (they date back to the early days of the mainframe), but they are undergoing a renaissance as organizations optimize their processes. In our upcoming research, we will delve into why it’s time to revisit these technologies, explore the macro trends that impact this market, and how they have the potential to reshape organizations’ automation plans. We’ll help our clients understand the current state of the market, the impact of the latest technological advancements, and new emerging use cases. Why Are We Revisiting This Research? Increased client demand. Enterprises are increasingly demanding insights into the direction of this market and vendors’ ability to solve their operational and organizational requirements. Hybrid and multicloud environments. Firms today live and operate in a hybrid setup — a mix of on-premises and public cloud services. Applications, infrastructure, and data are spread across this setup, and workload/batch automation must likewise seamlessly integrate across it. Native capabilities in business applications. Some business applications have native capabilities to perform workload automation. We will explore how these impact standalone tools in the market. AI and AI agent enhancements. While AI is no secret in automation, we want to make clear how AI will help advance solutions. When should agents take over (if at all)? Demand for operational and cyber resiliency. With the growing threat of system failures and cybersecurity issues, all automation solutions must be designed with capabilities to address these challenges. Workload/batch automation can no longer be just a tool for the IT organization: Like all other types of automation, it must be a strategic enabler for modern businesses. By revisiting research in this space, we will explore new possibilities for scalability, efficiency, and resilience. Get Involved Over the next two months, we will be conducting interviews and taking briefings with vendors. If you would like to participate in our research, please contact Meg Bellavance ([email protected]). source

By Christopher Cole ( January 24, 2025, 8:25 PM EST) — Federal Communications Commission Chair Brendan Carr on Friday announced several staff appointments, including acting officials to lead international affairs, engineering, economics and media relations…. 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

WHSmith just launched a retail media network (RMN) to bring retail media to its airport stores. Like every other RMN launched since Amazon, Walmart, and Best Buy pioneered retail media over a decade ago, WHSmith promises “more exciting and engaging retail experiences for consumers” and is “tailored to the needs of … supplier brands.” Our take: WHSmith’s network is yet another addition to the long tail of Amazon Ads copycats — and Amazon Ads’ scale leads other RMNs to project unrealistic growth. In the US, Amazon Ads is larger than all other RMNs combined. It’s growing faster than others and now selling advertising technology as a service, signaling sustained dominance. For smaller RMNs, operational realities interfere with execution. Advertisers tell us that they lack media know-how, mask trade promotion as media spend, and struggle to prove performance. Here are the facts: When retail media grows, trade funding declines. More than half of retail media ad spend comes from existing trade and shopper marketing budgets. Rather than earning incremental revenue, RMNs divert dollars that would have funded temporary price reductions, featured endcaps, and in-store demos into advertising. Retailers obscure RMNs’ inability to tap into digital and national media budgets by consolidating trade and retail media when reporting revenue publicly. Cannibalizing co-op funds remains a chief concern of executives at large RMNs, especially for multicategory, multibrand retailers. RMN execution is weaker than it should be. RMNs struggle to demonstrate incrementality, power real-time results, and offer self-service platforms, making it difficult for brands and agencies to plan, buy, and optimize ads. In fact, most RMNs remain mostly manual. Furthermore, despite in-store ads earning more attention than any other format, according to Forrester’s Consumer Benchmark Survey, 2024, in-store ads remain constrained by their difficulty to buy and measure. The few retailers that have invested in smart carts and digital displays have yet to roll them out nationally due to the capital expenditure that they require and their uncertain return on ad spend. Going forward, RMNs should prioritize self-service. Retail media is run by several ex-agency staff hired by RMNs to manage campaigns. Each RMN has tons of advertisers, so when media management is manual, it creates a lot of low-level labor that could be better spent on capabilities such as analytics. Resource-intensive, white-glove service may satisfy retailers’ largest first-party sellers, but there’s a long tail of first- and third-party sellers interested in allocating performance media budgets to self-serve highly relevant, revenue-generating ads. When they’re more self-service, RMNs have bigger budgets for sales, marketing, product, and engineering to focus on maximizing onsite profitability, full-funnel measurement, and making retail media programmatic. To learn what else RMNs should prioritize, check out The State Of Retail Media, 2025, by Sucharita Kodali and myself. We clarify retail media’s potential and challenges and advise how retailers can sell more ads. As always, feel free to schedule time to discuss. source

By Julie Manganis ( January 24, 2025, 3:16 PM EST) — Thomson Reuters has settled a lawsuit claiming it wrongly fired a white data scientist in its Boston office for criticizing the Black Lives Matter movement on a company messaging system, according to a filing in federal court…. 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

By Peter Jeydel ( January 23, 2025, 4:23 PM EST) — The U.S. Department of Commerce’s Bureau of Industry and Security has issued the final rule that will determine how its Information and Communications Technology and Services regulations will work going forward.[1]… 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

Security researchers have discovered a new malicious chatbot advertised on cybercrime forums. GhostGPT generates malware, business email compromise scams, and more material for illegal activities. The chatbot likely uses a wrapper to connect to a jailbroken version of OpenAI’s ChatGPT or another large language model, the Abnormal Security experts suspect. Jailbroken chatbots have been instructed to ignore their safeguards to prove more useful to criminals. Must-read security coverage What is GhostGPT? The security researchers found an advert for GhostGPT on a cyber forum, and the image of a hooded figure as its background is not the only clue that it is intended for nefarious purposes. The bot offers fast processing speeds, useful for time-pressured attack campaigns. For example, ransomware attackers must act quickly once within a target system before defenses are strengthened. The official advertisement graphic for GhostGPT. Image: Abnormal Security It also says that user activity is not logged on GhostGPT and can be bought through the encrypted messenger app Telegram, likely to appeal to criminals who are concerned about privacy. The chatbot can be used within Telegram, so no suspicious software needs to be downloaded onto the user’s device. Its accessibility through Telegram saves time, too. The hacker does not need to craft a convoluted jailbreak prompt or set up an open-source model. Instead, they just pay for access and can get going. “GhostGPT is basically marketed for a range of malicious activities, including coding, malware creation, and exploit development,” the Abnormal Security researchers said in their report. “It can also be used to write convincing emails for BEC scams, making it a convenient tool for committing cybercrime.” It does mention “cybersecurity” as a potential use on the advert, but, given the language alluding to its effectiveness for criminal activities, the researchers say this is likely a “weak attempt to dodge legal accountability.” To test its capabilities, the researchers gave it the prompt “Write a phishing email from Docusign,” and it responded with a convincing template, including a space for a “Fake Support Number.” A phishing email generated by GhostGPT. Image: Abnormal Security The ad has racked up thousands of views, indicating both that GhostGPT is proving useful and that there is growing interest amongst cyber criminals in jailbroken LLMs. Despite this, research has shown that phishing emails written by humans have a 3% better click rate than those written by AI, and are also reported as suspicious at a lower rate. However, AI-generated material can also be created and distributed more quickly and can be done by almost anyone with a credit card, regardless of technical knowledge. It can also be used for more than just phishing attacks; researchers have found that GPT-4 can autonomously exploit 87% of “one-day” vulnerabilities when provided with the necessary tools. Jailbroken GPTs have been emerging and actively used for nearly two years Private GPT models for nefarious use have been emerging for some time. In April 2024, a report from security firm Radware named them as one of the biggest impacts of AI on the cybersecurity landscape that year. Creators of such private GPTs tend to offer access for a monthly fee of hundreds to thousands of dollars, making them good business. However, it’s also not insurmountably difficult to jailbreak existing models, with research showing that 20% of such attacks are successful. On average, adversaries need just 42 seconds and five interactions to break through. SEE: AI-Assisted Attacks Top Cyber Threat, Gartner Finds Other examples of such models include WormGPT, WolfGPT, EscapeGPT, FraudGPT, DarkBard, and Dark Gemini. In August 2023, Rakesh Krishnan, a senior threat analyst at Netenrich, told Wired that FraudGPT only appeared to have a few subscribers and that “all these projects are in their infancy.” However, in January, a panel at the World Economic Forum, including Secretary General of INTERPOL Jürgen Stock, discussed FraudGPT specifically, highlighting its continued relevance. There is evidence that criminals are already using AI for their cyber attacks. The number of business email compromise attacks detected by security firm Vipre in the second quarter of 2024 was 20% higher than the same period in 2023 — and two-fifths of them were generated by AI. In June, HP intercepted an email campaign spreading malware in the wild with a script that “was highly likely to have been written with the help of GenAI.” Pascal Geenens, Radware’s director of threat intelligence, told TechRepublic in an email: “The next advancement in this area, in my opinion, will be the implementation of frameworks for agentific AI services. In the near future, look for fully automated AI agent swarms that can accomplish even more complex tasks.” source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More DeepSeek’s release of R1 this week was a watershed moment in the field of AI. Nobody thought a Chinese startup would be the first to drop a reasoning model matching OpenAI’s o1 and open-source it (in line with OpenAI’s original mission) at the same time. Enterprises can easily download R1’s weights via Hugging Face, but access has never been the problem — over 80% of teams are using or planning to use open models. Deployment is the real culprit. If you go with hyperscaler services, like Vertex AI, you’re locked into a specific cloud. On the other hand, if you go solo and build in-house, there’s the challenge of resource constraints as you have to set up a dozen different components just to get started, let alone optimizing or scaling downstream. To address this challenge, Y Combinator and SenseAI-backed Pipeshift is launching an end-to-end platform that allows enterprises to train, deploy and scale open-source generative AI models — LLMs, vision models, audio models and image models — across any cloud or on-prem GPUs. The company is competing with a rapidly growing domain that includes Baseten, Domino Data Lab, Together AI and Simplismart. The key value proposition? Pipeshift uses a modular inference engine that can quickly be optimized for speed and efficiency, helping teams not only deploy 30 times faster but achieve more with the same infrastructure, leading to as much as 60% cost savings.  Imagine running inferences worth four GPUs with just one. The orchestration bottleneck When you have to run different models, stitching together a functional MLOps stack in-house — from accessing compute, training and fine-tuning to production-grade deployment and monitoring — becomes the problem. You have to set up 10 different inference components and instances to get things up and running and then put in thousands of engineering hours for even the smallest of optimizations.  “There are multiple components of an inference engine,” Arko Chattopadhyay, cofounder and CEO of Pipeshift, told VentureBeat. “Every combination of these components creates a distinct engine with varying performance for the same workload. Identifying the optimal combination to maximize ROI requires weeks of repetitive experimentation and fine-tuning of settings. In most cases, the in-house teams can take years to develop pipelines that can allow for the flexibility and modularization of infrastructure, pushing enterprises behind in the market alongside accumulating massive tech debts.” While there are startups that offer platforms to deploy open models across cloud or on-premise environments, Chattopadhyay says most of them are GPU brokers, offering one-size-fits-all inference solutions. As a result, they maintain separate GPU instances for different LLMs, which doesn’t help when teams want to save costs and optimize for performance. To fix this, Chattopadhyay started Pipeshift and developed a framework called modular architecture for GPU-based inference clusters (MAGIC), aimed at distributing the inference stack into different plug-and-play pieces. The work created a Lego-like system that allows teams to configure the right inference stack for their workloads, without the hassle of infrastructure engineering. This way, a team can quickly add or interchange different inference components to piece together a customized inference engine that can extract more out of existing infrastructure to meet expectations for costs, throughput or even scalability.  For instance, a team could set up a unified inference system, where multiple domain-specific LLMs could run with hot-swapping on a single GPU, utilizing it to full benefit. Running four GPU workloads on one Since claiming to offer a modular inference solution is one thing and delivering on it is entirely another, Pipeshift’s founder was quick to point out the benefits of the company’s offering.  “In terms of operational expenses…MAGIC allows you to run LLMs like Llama 3.1 8B at >500 tokens/sec on a given set of Nvidia GPUs without any model quantization or compression,” he said. “This unlocks a massive reduction of scaling costs as the GPUs can now handle workloads that are an order of magnitude 20-30 times what they originally were able to achieve using the native platforms offered by the cloud providers.” The CEO noted that the company is already working with 30 companies on an annual license-based model.  One of these is a Fortune 500 retailer that initially used four independent GPU instances to run four open fine-tuned models for their automated support and document processing workflows. Each of these GPU clusters was scaling independently, adding to massive cost overheads. “Large-scale fine-tuning was not possible as datasets became larger and all the pipelines were supporting single-GPU workloads while requiring you to upload all the data at once. Plus, there was no auto-scaling support with tools like AWS Sagemaker, which made it hard to ensure optimal use of infra, pushing the company to pre-approve quotas and reserve capacity beforehand for theoretical scale that only hit 5% of the time,” Chattopadhyay noted. Interestingly, after shifting to Pipeshift’s modular architecture, all the fine-tunes were brought down to a single GPU instance that served them in parallel, without any memory partitioning or model degradation. This brought down the requirement to run these workloads from four GPUs to just a single GPU. “Without additional optimizations, we were able to scale the capabilities of the GPU to a point where it was serving five-times-faster tokens for inference and could handle a four-times-higher scale,” the CEO added. In all, he said that the company saw a 30-times faster deployment timeline and a 60% reduction in infrastructure costs. With modular architecture, Pipeshift wants to position itself as the go-to platform for deploying all cutting-edge open-source AI models, including DeepSeek R-1. However, it won’t be an easy ride as competitors continue to evolve their offerings. For instance, Simplismart, which raised $7 million a few months ago, is taking a similar software-optimized approach to inference. Cloud service providers like Google Cloud and Microsoft Azure are also bolstering their respective offerings, although Chattopadhyay thinks these CSPs will be more like partners than competitors in the long run.