Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More In the enterprise of the future, human workers are expected to work closely alongside sophisticated teams of AI agents.  According to McKinsey, generative AI and other technologies have the potential to automate 60 to 70% of employees’ work. And, already, an estimated one-third of American workers are using AI in the workplace — oftentimes unbeknownst to their employers.  However, experts predict that 2025 will be the year that these so-called “invisible” AI agents begin to come out of the shadows and take more of an active role in enterprise operations.  “Agents will likely fit into enterprise workflows much like specialized members of any given team,” said Naveen Rao, VP of AI at Databricks and founder and former CEO of MosaicAI. Solving what RPA couldn’t AI agents go beyond question-answer chatbots to assistants that use foundation models to execute more complex tasks previously not considered possible. These natural language-powered agents can handle multiple tasks, and, when empowered to do so by humans, act on them. “Agents are goal-based and make independent decisions based on context,” explained Ed Challis, head of AI strategy at business automation platform UiPath. “Agents will have varying degrees of autonomy.” Ultimately, AI agents will be able to perceive (process and interpret data), plan, act (with or without a human in the loop), reflect, learn from feedback and improve over time, said Raj Shukla, CTO of AI SaaS company SymphonyAI. “At a high level, AI agents are expected to fulfill the long-awaited dream of automation in enterprises that robotic process automation (RPA) was supposed to solve,” he said. As large language models (LLMs) are their “planning and reasoning brain,” they will eventually begin to mimic human-like behavior. “The wow factor of a good AI agent is similar to sitting in a self-driving car and seeing it steer through crowded roads.” What will AI agents look like? However, AI agents are still in their formative stages, with use cases still being fleshed out and explored.  “It’s going to be a broad spectrum of capabilities,” Forrester senior analyst Rowan Curran told VentureBeat.  The most basic level is what he called “RAG plus,” or a retrieval augmented generation system that does some action after initial retrieval. For instance, detecting a potential maintenance issue in an industrial setting, outlining a maintenance procedure and generating a draft work order request. And then sending that to the end (human) user who makes the final call.  “We’re already seeing a lot of that these days,” said Curran. “It essentially amounts to an anomaly detection algorithm.”  In more complex scenarios, agents could retrieve info and take action across multiple systems. For instance, a user might prompt: “I’m a wealth advisor, I need to update all of my high net worth individuals with an issue that occurred — can you help develop personalized emails that give insights on the impact on their specific portfolio?” The AI agent would then access various databases, run analytics, generate customized emails and push them out via an API call to an email marketing system.  Going further beyond that will be sophisticated, multi-agent ecosystems, said Curran. For example, on a factory floor, a predictive algorithm may trigger a maintenance request that goes to an agent that identifies different options, weighing cost and availability, all while going back and forth with a third-party agent. It could then place an order as it interacts with different independent systems, machine learning (ML) models, API integrations and enterprise middleware.  “That’s the next generation on the horizon,” said Curran.  For now, though, agents aren’t likely to be fully autonomous or mostly autonomous, he pointed out. Most use cases will involve human in the loop, whether for training, safety or regulatory reasons. “Autonomous agents are going to be very rare, at least in the short term.” Challis agreed, emphasizing that “one of the most important things to recognize about any AI implementation is that AI on its own is not enough. We see that all business processes are going to be best solved by a combination of traditional automation, AI agents and humans working in concert to best support a business function.”  Helping with HR, sales (and other functions) One example use case for AI agents that nearly every industry can relate to is the process of onboarding new employees, Challis noted. This typically involves many people, including HR, payroll, IT and others. AI agents could streamline and speed up the process as it receives and handles contracts, collects documents and sets up payroll, IT and security approval.  In another scenario, imagine a sales rep using AI. That agent can collaborate with procurement and supply chain agents to work up pricing and delivery terms for a proposal, explained Andreas Welsch, founder and chief AI strategist at consulting company Intelligence Briefing.  The procurement agent will then gather information about available finished goods and raw materials, while the supply chain agent will calculate manufacturing and shipping times and report back to the procurement agent, he noted.  Or, a customer service rep can ask an agent to gather relevant information about a given customer. The agent takes into account the inquiry, history and recent purchases, potentially from different systems and documents. They then create a response and present it to a team member who can review and further edit the draft before sending it along to the customer. “Agents carry out steps in a workflow based on a goal that the user has provided,” said Welsch. “The agent breaks this goal into subgoals and tasks and then tries to complete them.” How FactSet put AI agents to work While agent frameworks are relatively new, some companies have been using what Rao called compound AI systems. For instance, business data and analytics company FactSet runs a finance platform that allows analysts to query large amounts of financial data to make timely investments and financial decisions.  The company created a compound AI system that

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The food services industry might seem an unlikely candidate for AI adoption, yet the market, which includes full-service restaurants, quick-service restaurants, catering companies, coffee shops, private chefs, and a variety of other participants, is rapidly recognizing AI’s immediate and long-term potential.  AI in food services is poised for widespread adoption, predicts Colin Dowd, industry strategy senior manager at Armanino, an accounting and consulting firm. “As customer expectations shift, companies will be forced to meet their demands through AI solutions that are similar to their competitors,” he notes in an email interview.  Mike Kostyo, a vice president with food industry consulting firm Menu Matters, agrees. “It’s hard to think of any facet of the food industry that isn’t being transformed by AI,” he observes via email. Kostyo says his research shows that consumers want lower costs –making it easier to customize or personalize a meal — and faster service. “We tell our clients they should focus on those benefits and make sure they’re clear to consumers when they implement new AI technologies.”  Seeking Insights  On the research side, AI is being used to make sense out of the data deluge firms currently face. “Food companies are drowning in research and data, both from their own sources, such as sales data and loyalty programs, and from secondary sources,” Kostyo says. “It’s just not feasible for a human to wade through all of that data, so today’s companies use AI to sift through it all, make connections, and develop recommendations.”  Related:IT Pros Love, Fear, and Revere AI: The 2024 State of AI Report AI can, for example, detect that spicy beverages are starting to catch on when paired with a particular flavor. “So, it may recommend building that combination into a new menu option or product,” Kostyo says. It can do this constantly over time, taking into account billions of data points, creating innovation starting positions. “The team can take it from there, filling their pipeline with relevant products and menu items.”  Data collected from multiple sources can also be used to track customer preferences, providing early insights on emerging flavor trends. “For example, Campbell’s and Coca-Cola are currently using AI in tandem with food scientists to create new and exciting flavors and dishes for their customers based on insights collected from both internal and external data sources,” Dowd says. “This approach can also be applied to restaurants and other locations that rely on recipes.”  Management and Innovation  AI can also optimize inventory management. “AI is being used to determine when to order, and how much inventory a company needs to purchase, by analyzing historical data and current trends,” Dowd says. “This allows the restaurant to maintain ideal inventory levels, reduce waste and better ensure that the restaurant always has the necessary ingredients.”  Related:Inside The Duality of AI’s Superpowers When used as an innovation generator, AI can inspire fresh ideas. “Sometimes, when you get in that room together to come up with a new menu item or product, just facing down that blank page is the hardest part,” Kostyo observes. “You can use AI for some starter ideas to work with.” He says he loves to feed outlandish ideas into AI, such as, ‘What would a dessert octopus look like?’ “It may then develop this really wild dessert, like a chocolate octopus with different-flavored tentacles.”  Customer Experience  AI promises to help restaurants provide a consistently positive experience to consumers, says Jay Fiske, president of Powerhouse Dynamics, an AI and IoT solutions provider for major multi-site food service firms, including Dunkin’, Arby’s, and Buffalo Wild Wings. He notes in an email interview that AI and ML can be used to flag concerning data, indicating potential problems, such as frozen meat going into the oven before it should, or predicting a likely freezer breakdown sometime within the next two weeks. “In these situations, facility managers have time to quickly preempt any issues that could cost them money, as well as their reputations with consumers,” he says.  Related:Keynote Sneak Peek: Forrester Analyst Details Align by Design and AI Explainability Another way AI is transforming the food services industry is by providing more efficient and reliable energy management. “This is important, because restaurants, ghost kitchens, and other food service businesses are extremely energy intensive,” Fiske says. Refrigerators, freezers, ovens, dish washers, fryers, and air conditioners all consume massive amounts of power that can be controlled and optimized by AI.  Future Outlook  The sky is the limit for food services industry AI, Kostyo states, noting that market players are taking various approaches. Some are excited about AI, and afraid to get left behind, so they’re jumping right into these tools, while others are a little more skittish, concerned about ethical and privacy issues.  Kostyo urges AI adopters to periodically monitor their customers’ AI acceptance level. “In some ways, customers are very open to AI,” he says. “Forty-six percent of consumers told us they’re already using AI to assist with food decisions in some fashion, such as deciding what to cook or where to eat.” Kostyo adds that 59% of surveyed consumers believe that AI can develop a recipe that’s just as delicious as any human chef could create.  On the other hand, people still often crave a human touch. Kostyo reports that 66% of consumers would still rather have a dish that was created by a human chef. “Consumers frequently push back when they see AI being used in a way that would take a human job.”  Service First  Kostyo urges the food industry to use AI in ways that will enhance the overall consumer experience. “At the end of the day, we are the hospitality industry, and we need to remember that.”  source

By Roger Abbott, Adam Bartolanzo and Kathryn Carlson ( November 13, 2024, 5:40 PM EST) — The U.S. Department of Defense recently published a final rule formally implementing the Cybersecurity Maturity Model Certification program.[1] The final rule culminates five years of work to standardize the safeguards that government contractors must implement to protect federal contract information and controlled unclassified information while also bolstering compliance with these requirements…. 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

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Advanced Micro Devices announced its Versal Premium Series Gen 2 chip platform for data center customers doing AI processing and other work. The AMD Versal is an adaptive FPGA, or field programmable gate array, platform for system-on-chip customers. It delivers accelerated performance for a wide range of workloads in data centers, communications, test and measurement, and aerospace and defense markets. AMD said the Versal Premium Series Gen 2 will be the FPGA industry’s first devices featuring Compute Express Link 3.1 and PCIe Gen6 as well as LPDDR5X memory support in hard intellectual property. These next-generation interface and memory technologies access and move data rapidly and efficiently between processors and accelerators for tasks such as AI processing. CXL 3.1 and LPDDR5X help unlock more memory resources faster to address the growing real-time processing and storage demands of data-intensive applications across markets. “System architects are constantly looking to pack more data into smaller spaces and move data more efficiently between parts of the system,” said Salil Raje, SVP of adaptive and embedded computing group at AMD, in a statement. “Our latest addition to the Versal Gen 2 portfolio helps customers improve overall system throughput and utilization of memory resources to achieve the highest performance for their most demanding applications from the cloud to the edge.” Using the open-standard interconnect, AMD said the new processors enable high-bandwidth host CPU-to-accelerator connectivity. PCIe Gen6 offers a two times to four times faster line rate compared to competing FPGAs with PCIe Gen4 or Gen5 support, AMD said. CXL 3.1 also offers similar benefits as well as enhanced fabric and coherency. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Leaders of AI projects today may face pressure to deliver quick results to decisively prove a return on investment in the technology. However, impactful and transformative forms of AI adoption require a strategic, measured and intentional approach.  Few understand these requirements better than Dr. Ashley Beecy, Medical Director of Artificial Intelligence Operations at New York-Presbyterian Hospital (NYP), one of the world’s largest hospitals and most prestigious medical research institutions. With a background that spans circuit engineering at IBM, risk management at Citi and practicing cardiology, Dr. Beecy brings a unique blend of technical acumen and clinical expertise to her role. She oversees the governance, development, evaluation and implementation of AI models in clinical systems across NYP, ensuring they are integrated responsibly and effectively to improve patient care. For enterprises thinking about AI adoption in 2025, Beecy highlighted three ways in which AI adoption strategy must be measured and intentional: Good governance for responsible AI development A needs-driven approach driven by feedback Transparency as the key to trust Good governance for responsible AI development Beecy says that effective governance is the backbone of any successful AI initiative, ensuring that models are not only technically sound but also fair, effective and safe. AI leaders need to think about the entire solution’s performance, including how it’s impacting the business, users and even society. To ensure an organization is measuring the right outcomes, they must start by clearly defining success metrics upfront. These metrics should tie directly to business objectives or clinical outcomes, but also consider unintended consequences, like whether the model is reinforcing bias or causing operational inefficiencies. Based on her experience, Dr. Beecy recommends adopting a robust governance framework such as the fair, appropriate, valid, effective and safe (FAVES) model provided by HHS HTI-1. An adequate framework must include 1) mechanisms for bias detection 2) fairness checks and 3) governance policies that require explainability for AI decisions. To implement such a framework, an organization must also have a robust MLOps pipeline for monitoring model drift as models are updated with new data. Building the right team and culture One of the first and most critical steps is assembling a diverse team that brings together technical experts, domain specialists and end-users. “These groups must collaborate from the start, iterating together to refine the project scope,” she says. Regular communication bridges gaps in understanding and keeps everyone aligned with shared goals. For example, to begin a project aiming to better predict and prevent heart failure, one of the leading causes of death in the United States, Dr. Beecy assembled a team of 20 clinical heart failure specialists and 10 technical faculty. This team worked together over three months to define focus areas and ensure alignment between real needs and technological capabilities. Beecy also emphasizes that the role of leadership in defining the direction of a project is crucial: AI leaders need to foster a culture of ethical AI. This means ensuring that the teams building and deploying models are educated about the potential risks, biases and ethical concerns of AI. It is not just about technical excellence, but rather using AI in a way that benefits people and aligns with organizational values. By focusing on the right metrics and ensuring strong governance, organizations can build AI solutions that are both effective and ethically sound. A need-driven approach with continuous feedback Beecy advocates for starting AI projects by identifying high-impact problems that align with core business or clinical goals. Focus on solving real problems, not just showcasing technology. “The key is to bring stakeholders into the conversation early, so you’re solving real, tangible issues with the aid of AI, not just chasing trends,” she advises. “Ensure the right data, technology and resources are available to support the project. Once you have results, it’s easier to scale what works.” The flexibility to adjust the course is also essential. “Build a feedback loop into your process,” advises Beecy, “this ensures your AI initiatives aren’t static and continue to evolve, providing value over time.” Transparency is the key to trust For AI tools to be effectively utilized, they must be trusted. “Users need to know not just how the AI works, but why it makes certain decisions,” Dr. Beecy emphasizes. In developing an AI tool to predict the risk of falls in hospital patients (which affect 1 million patients per year in U.S. hospitals), her team found it crucial to communicate some of the algorithm’s technical aspects to the nursing staff. The following steps helped to build trust and encourage adoption of the falls risk prediction tool: Developing an Education Module: The team created a comprehensive education module to accompany the rollout of the tool. Making Predictors Transparent: By understanding the most heavily weighted predictors used by the algorithm contributing to a patient’s risk of falling, nurses could better appreciate and trust the AI tool’s recommendations. Feedback and Results Sharing: By sharing how the tool’s integration has impacted patient care—such as reductions in fall rates—nurses saw the tangible benefits of their efforts and the AI tool’s effectiveness. Beecy emphasizes inclusivity in AI education. “Ensuring design and communication are accessible for everyone, even those who are not as comfortable with the technology. If organizations can do this, it is more likely to see broader adoption.” Ethical considerations in AI decision-making At the heart of Dr. Beecy’s approach is the belief that AI should augment human capabilities, not replace them. “In healthcare, the human touch is irreplaceable,” she asserts. The goal is to enhance the doctor-patient interaction, improve patient outcomes and reduce the administrative burden on healthcare workers. “AI can help streamline repetitive tasks, improve decision-making and reduce errors,” she notes, but efficiency should not come at the expense of the human element, especially in decisions with significant impact on users’ lives. AI should provide data and insights, but the final call should involve human decision-makers, according to Dr. Beecy. “These decisions require a level

Cybersecurity platform provider CrowdStrike announced plans to acquire Adaptive Shield, a SaaS security posture management (SSPM) vendor. Some sources reported the purchase price to be around $300 million. If that purchase price is accurate, based on Forrester’s estimates of Adaptive Shield’s current revenue, that price represents an approximately 12–15x revenue multiplier and 6 times more than Adaptive Shield’s total funding raised. As CrowdStrike moves past its July 2024 global Windows outage and commits to improving its software quality assurance processes, the time was right for the company to expand its product portfolio. Forrester observes the following: Adaptive Shield brings needed SaaS security insights to CrowdStrike and extends its monitored endpoint range. CrowdStrike acquired Adaptive Shield for its SaaS security and posture management technology — Adaptive Shield will allow CrowdStrike to perform configuration drift detection and malware and ransomware scanning on SaaS endpoints (Box, Dropbox, OneDrive, etc.), adding to the heritage S3, Azure blob, and GCP coverage. Additionally, Adaptive Shield has differentiated configuration compliance libraries and configuration drift detection with its scalable offering. The acquisition supports CrowdStrike’s goal of building a comprehensive cloud security platform, including cloud detection and response capabilities, and follows similar steps taken by Palo Alto Networks and Zscaler. Creating a true cloud and identity security platform is hard. CrowdStrike (and its competitors Trend Micro and Wiz) have been on an acquisition spree: CrowdStrike has bought Flow Security, Bionic, Reposify, and Secure Circle to enhance its organically built heritage cloud workload protection portfolio and growing identity threat protection capabilities. Building a true platform with integrated policy management, unified architecture, generative AI copilots, and central reporting is difficult and time-consuming: Palo Alto, Trend Micro, Wiz, and other cybersecurity platform vendors have struggled to integrate these capabilities completely into a single platform, even without acquiring an SSPM vendor themselves. It is likely that it will take CrowdStrike at least 18–24 months to achieve complete integration here. More SSPM acquisitions will follow. As with any maturing technology area, large vendors start acquiring successful smaller vendors when the hockey stick of enterprise adoption begins. We are now at that point in SSPM. Forrester expects that in the next 18–24 months, AppOmni, DoControl, Spin.AI, and other SSPM vendors will be likely acquisition targets for large cloud security vendors such as Palo Alto Networks, Qualys, Tenable, Trend Micro, and Wiz for platform building. Forrester sees this as similar to the recent ITDR acquisition spree across IAM and non-IAM vendors. CrowdStrike is raising the profile of identity security. Building on its existing identity and threat detection product, with this acquisition, CrowdStrike continues to emphasize the importance of identity-centric security to drive better alignment between IAM and cybersecurity teams. The strategy initially started with CrowdStrike’s 2020 acquisition of Preempt Security for its conditional access technology. Given its cybersecurity market position, CrowdStrike’s emphasis on identity security is a boon to the overall identity security market, but it foreshadows a coming reshaping of the IAM vendor landscape and competitive dynamics over the next 24–36 months. source

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