Last week, SUSE hosted SUSECON 25 in Orlando, Florida. Although SUSE has been in the open-source game for decades, it’s been getting increased attention with some seasoned leadership coming from competitor Red Hat, its acquisition of much-loved Rancher, and changing expectations/norms in the open-source ecosystem. SUSE’s big message started with a commitment to Linux, open-source innovation, and choice. CEO Dirk-Peter van Leeuwen presented awards to 12 different customers emphasizing broad enterprise reach. The company also revealed a rebranded portfolio focused on four key platforms: Linux, cloud-native, edge, and AI. Notable rebrands include SUSE Rancher (Kubernetes management), SUSE Storage (formerly Longhorn), SUSE Virtualization (formerly Harvester), and SUSE Multi-Linux Support (formerly Liberty Linux). These changes aim to simplify navigation and address market need. The big changes announced include: Linux support. Firms operating multiple distributions of Linux in production must address Linux support issues, especially when maintenance or support has lapsed. SUSE’s rebranded Multi-Linux Support (formerly Liberty Linux) offers lifecycle management for legacy systems, providing flexibility without forced licensing changes. This package enables lifecycle management of legacy systems, offering flexible migration options according to business timelines rather than a vendor-imposed deadline. SUSE also touted deep expertise in integrating Linux across various platforms with support for customized use cases like telco and manufacturing. Cloud-native workload support. SUSE is poised to support composable multicloud platforms across cloud environments, data centers, and the edge (given the SUSE-backed K3s Kubernetes base). The rebranded SUSE Observability (formerly StackState) enhances operations by tracking application states and addressing anomalies via SUSE Rancher. Notably, SUSE Rancher now manages AWS EKS workloads directly. Coupled with the embedding of Neuvector as a container-focused security solution, Rancher has emerged as a converged platform for holistic container application operations. Edge computing. SUSE Edge, a cloud-native platform that manages edge devices at scale, interfaces with various network options and ensures management and security. The Edge Image Builder open-source project customizes SL Micro base configuration images to address network complexities including low-bandwidth or fully air-gapped environments. SUSE Edge is tailored for telecom, retail, and industrial firms. AI and edge intelligence. AI-enabled edge intelligence drives localized experiences with streaming analytics, edge ML, and real-time data management. The new SUSE AI platform supports secure deployment of AI models with observability, security guardrails, and agentic AI capabilities in industrial, retail, and healthcare, which addresses only a subset of the broader edge AI market. SUSE Is Keen To Make A Move As we look to 2025, SUSE has new opportunities to transition from its perception in the market as an important but low-profile provider of several solid IT infrastructure offerings into an enterprise IT platform vendor that gets more visibility among C-level enterprise IT leaders. That’s no easy task in an IT world dominated by multitrillion dollar hyperscalers. However, the generative AI shakeup of the tech landscape has prompted a rethink of IT strategy in big companies and government agencies. SUSE therefore has an opportunity to expand its customer, partner, and product reach among strategic enterprise technology and infrastructure decision-makers. How can the company execute on this vision? Forrester believes that to do this effectively, SUSE must make more traction in North America via its AI capabilities, up-level positioning to more senior executives, and go all in on the open-source AI ecosystem opportunity. Why It Matters: You Need Flexible, Holistic Tech Platforms Enterprise technology systems are evolving into layered platforms. That doesn’t come without cost, maintenance, or risk, which must be managed over time to address the changing needs of the business. It’s called platform engineering, and it’s the main enterprise standard. Yet, many platform providers haven’t adjusted to this way of working. SUSE’s latest efforts do. SUSE’s realignment and messaging taps into the platform-oriented model and makes a promise to improve those platforms over time to match customer needs, while avoiding the introduction of prescriptive frameworks that limit choice. That’s a good thing; it’s moving in the right direction as a product company. Most businesses are being blindsided by the rapid iteration of AI, disruptions resulting from geopolitical unrest, and core vendor strategy changes. In the face of uncertainty, SUSE’s message of technology choice and autonomy should resonate. Expect similar language from its competition over the course of the year. If your organization is faced with the difficulty of how to choose your next platform, Forrester is here to help you find the right path. If you’re already a client, you can click here to schedule a guidance session or request research to help you take the next best action. source

Best overall facial recognition software: Amazon Rekognition Best for KYC verification: BioID Best AI-powered ID verification for travel and border checks: Paravision Best for law enforcement use cases: Cognitec Best for AI developers using Java, .NET, C++ for facial recognition technology development: Luxand Best for transparent pricing for different business types: Kairos Best for small to mid-sized businesses: Sky Biometry Best for grocery store theft prevention: FaceFirst Best for skeleton detection: Face++ Best for flexible deployment options: Trueface Biometric security technologies such as facial recognition, are becoming more sophisticated due to the rise in cybersecurity threats. Facial recognition software employs artificial intelligence and machine learning to scan human faces and match them against existing biometric data to confirm if an individual should be granted access to an application, computer system, or environment. Here’s a look at the current top facial recognition software vendors, as well as use cases for the technology. ManageEngine Log360 Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Micro (0-49 Employees), Small (50-249 Employees), Medium (250-999 Employees), Large (1,000-4,999 Employees), Enterprise (5,000+ Employees) Micro, Small, Medium, Large, Enterprise Features Activity Monitoring, Blacklisting, Dashboard, and more Graylog 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 Activity Monitoring, Dashboard, Notifications ManageEngine Desktop Central 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 Activity Monitoring, Antivirus, Dashboard, and more Top facial recognition software: Comparison table The comparison table below highlights some of the features of facial recognition software, which product has what and a snippet of what the pricing looks like. Product Liveness Detection ID verification for KYC Video analytics Spoofing detection Pricing Amazon Rekognition Yes Yes Yes Yes Pricing varies depending on region and usage. BioID Yes Yes No Yes Contact vendor for a quote. Paravision Yes No Yes Yes Contact vendor for a quote. Cognitec Yes Yes Yes Yes Contact vendor for a quote. Luxand Yes No Yes Yes Contact vendor for a quote. Kairos Yes Yes Yes Yes Starts at $19/month plus a 14-day free trial. Sky Biometry Yes No Yes Yes Starts at $55 per month. FaceFirst Yes No Yes No Contact vendor for a quote. Face++ Yes No Yes Yes Starts from $100/ Day, depending on the number of requests. Trueface Yes No Yes Yes Contact the vendor for quote. Amazon Rekognition: Best overall facial recognition software Amazon Rekognition is a popular provider of facial recognition services around the world. The Rekognition software has facial search and facial analysis features that help record facial detection, user verification, and public security use. In addition, the software has a huge database at its disposal, enhancing its accuracy in object recognition. With the Amazon Rekognition software, image, and video hosting providers can easily apply content moderation capabilities to their applications and websites. Content moderation helps to identify inappropriate or unsafe images or videos. Furthermore, this software can label a wide range of objects and detect custom logos, celebrities, and texts. Why I chose Amazon Rekognition I chose Amazon Rekognition as my best overall for its scalability, extensive database, and transparent pricing. It’s also got the advantage of being an Amazon product, which enables easy integration with AWS services. This alone makes it a prime consideration for a number of businesses and enterprises. Pricing Amazon Rekognition is available as part of AWS Free Tier. Organizations can use this tier for up to 12 months. On the free tier package, users can analyze up to 5,000 images and store up to 1,000 face metadata objects per month. The paid tier depends on the region where the service is hosted and the volume of usage. For the paid tier, the cost of image processing starts at $0.001 per image for the first one million images analyzed, while the cost of storing face metadata is $0.00001 per metadata per month. For more complex needs, AWS provides a pricing calculator. Features Offers image and video moderation through image and video detection and analytics. Face liveness detection. Face compare and search support. Face detection and analysis. Content moderation (recognition of inappropriate content for moderation). Pros and cons Pros Cons Free 12-month usage. You can easily scale up and down depending on your need for the product. Businesses can easily connect via an API. Pricing categories are transparent. The pricing could be complicated for a quick buyer. BioID: Best for KYC verification BioID offers cloud-based facial recognition software that can be accessed from anywhere using APIs. BioID has two major features: liveness detection and photoverify facial recognition. Liveness detection is a wonderful tool for fighting online frauds. This feature can easily detect live persons, spoofing attacks and differentiate humans from avatars, but requires some level of human interaction to function. The photoverify feature is a solution for Know Your Customer verification, facial log-in use cases, and other facial recognition needs. Why I chose BioID I chose BioID for its effective KYC verification process and impressive liveness detection feature. Pricing For pricing contact BioID support Features Offers spoofing attack detection. Supports ‘Proof of life” verification for recipients of pensions and benefits. Supports mask authentication that only needs the eyes to work. Offers selfie verification for online banking and digital onboarding. Pros and cons Pros Cons KYC verification is straightforward. It can be used for remote identity verification through live face matching. Supports biometric verification of ID ownership through PhotoVerify. There is no free trial. Pricing is not transparent. Paravision: Best AI-powered ID verification for travel and border checks Paravision is a cloud-based AI recognition solution with several quality characteristics. Founded to solve activity recognition and facial recognition issues, Paravision relies on real-time streaming and frame-based techniques to provide face detection solutions. The software easily detects and verifies faces and maps their locations during live sessions. Other features include face clustering, face comparison, spoof detection, phenotype detection, and age

Five high-flying scaleups from the Benelux region have made it into TECH5 — the “Champions League of Technology.” The classy quintet joins an exclusive group of Europe’s fastest-growing tech companies. Over the next two months, they will join six other regions — the Nordics, Southern Europe, France, the Baltic States, DACH, and the UK & Ireland — in a competition for the crown of hottest scaleup on the continent.  The contest will conclude on June 19-20, when the TECH5 champion will be announced on the main stage of TNW Conference. But first, the contenders have to win their regional title. The challengers from the Netherlands, Belgium, and Luxembourg were selected based on an analysis of their growth, impact, and future potential. Our evaluation led us to the following five finalists, listed in random order: 1. DataSnipper The 💜 of EU tech The latest rumblings from the EU tech scene, a story from our wise ol’ founder Boris, and some questionable AI art. It’s free, every week, in your inbox. Sign up now! DataSnipper has emerged as a shining star of Dutch tech. Over 500,000 audit and finance professionals across more than 125 countries now use the scaleup’s AI-powered software, which automates repetitive finance and audit procedures.  The company was profitable early on, scaled rapidly, and recently hit a $1bn valuation. Under the leadership of CEO Vidya Peters — one of the star speakers at this year’s TNW Conference — DataSnipper has be,en ranked by Deloitte as the fastest-growing tech firm in the Netherlands for two years in a row. After finding success in Europe, the company has expanded globally, opening new offices in New York, Tokyo, Kuala Lumpur, and Mexico City. “That’s been a huge component of DataSnipper’s growth,” Peters told TNW last month. “Another is investing in AI.” 2. Dexter Energy Dexter Energy plans to transform the renewable energy landscape. To achieve this grand ambition, the scaleup provides AI-driven forecasting and trading-as-a-service for the short-term trading cycle. By making renewables trading profitable, Dexter Energy wants to accelerate the transition to a cleaner, more affordable energy future. “With our products, we empower energy companies across Europe to unlock the full potential of their wind, solar, and battery portfolios, maximising efficiency, profitability, and sustainability,” the company told TNW. These plans have progressed rapidly since Dexter Energy was founded in Amsterdam in 2017. Last year, the scaleup reduced emissions equivalent to those produced by 100,000 households — a figure that’s been doubling annually. 3. Gorilla Representing Belgium, Gorilla is reinventing the operations of energy retailers. The company supplies clients with real-time data and analytics, which lead to faster pricing, smarter decisions, and full visibility from sales to hedging.  “Our ambition is to become the commercial brain of energy retail,” Gorilla told TNW. “The impact? A more agile industry that can actually keep up with climate, policy, and consumer change.” Founded in 2018, the Antwerp-based scaleup has partnered with many of the world’s top energy providers, including Shell, ScottishPower, ENGIE, and Centrica Business Solutions. Investors have been impressed by the progress. Last year, Gorilla raised €23mn in a Series B round led by US VC firm Headline. 4. Eye Security Top-tier cybersecurity is often inaccessible to SMEs. Enterprise-grade solutions can be prohibitively expensive and complex. As a result, countless companies are left vulnerable to cyber threats.  Eye Security plans to change that. The Dutch scaleup combines a 24/7 managed security operations centre (SOC), integrated cyber insurance, and expert-led incident response to streamline safeguards for SMEs. The company’s founders have deep experience with digital threats. Before launching Eye Security in 2020, all three of them were national security employees. They’re now focused on supporting the most vulnerable businesses. “Our approach simplifies protection — combining advanced threat detection with insurance in one seamless service,” the team told TNW. 5. Gain.pro In the lucrative world of private market intelligence, Gain.pro aspires to set the global standard. The Amsterdam-based fintech has developed a platform that unearths powerful insights to guide investment decisions.  Inside the platform, human curation and generative AI combine to find, understand, and track companies that matter to each user. The benefits have attracted an illustrious list of clients — including all the MBB (McKinsey, Bain, BCG) and Big 4 (Deloitte, EY, KPMG, PwC) advisory firms. “Our platform brings together verified public information and proprietary insights, empowering users to focus on high-value work that truly sets them apart,” Gain.pro told TNW. “In doing so, we’re making the world of M&A more transparent, efficient, and fair.” What’s next for the TECH5 scaleups? All five of these scaleups would make worthy winners for Benelux, but only one can triumph. The victor will be announced soon. Stay tuned next week to meet the next region’s challengers for the TECH5 title. TECH5 is part of a packed programme for TNW Conference, which takes place on June 19-20 in Amsterdam.Tickets for the event are now on sale. Use the code TNWXMEDIA2025 at the check-out to get 30% off the price tag. source

Microsoft is adding two new AI reasoning agents to its Microsoft 365 Copilot suite: Researcher and Analyst. These AI agents are designed to streamline workflows by handling multi-step processes that typically require significant time and expertise. The tech giant introduced these reasoning agents, which Microsoft says are “first-of-their-kind,” as part of its continued push to make AI a core part of productivity tools. Smarter AI for research and data analysis Researcher Researcher is built to handle in-depth, multi-step research projects. It combines OpenAI’s deep research model with Microsoft 365 Copilot’s advanced orchestration and deep search capabilities. It pulls information from a mix of sources, including Microsoft 365 work data (emails, meetings, chats, and files) and external platforms like Salesforce, ServiceNow, and Confluence. Microsoft says Researcher can help users build go-to-market strategies, analyze industry trends, or create in-depth client reports by integrating internal and third-party data. Analyst Analyst focuses on data analysis and operates like a skilled data scientist; it processes raw data, identifies patterns, and generates insights. It’s powered by OpenAI’s o3-mini reasoning model and can perform advanced data analysis, including Python-based calculations and visualizations. Microsoft says users will be able to watch the AI generate Python code in real time, making it easier to understand and verify results. Analyst follows a “chain-of-thought” reasoning process, meaning it works through problems iteratively to refine its conclusions, mimicking human analytical thinking. Early access — but not for everyone These tools will debut in April as part of Microsoft’s “Frontier” program, an early-access initiative for Copilot users. Businesses with a Microsoft 365 Copilot license can try these AI tools, while also acting as testers for features still in development. In addition, Microsoft is rolling out updates to Copilot Studio, its tool for building custom AI agents. It’s offering new “deep reasoning” and automation capabilities that enable companies to create, manage, and deploy agents that handle multi-step tasks. The company says this will allow organizations to develop AI-driven workflows tailored to specific business needs, while also using flows to automate processes quickly and predictably. source

A coalition of European startups has urged swift action to slash burdensome EU regulations after a landmark declaration from the D9+ group of digitally advanced nations. The declaration stressed the need for “removing barriers” and “simplifying EU rules and procedures.” Ministers from all 13 countries in the D9+ — Belgium, the Czech Republic, Denmark, Estonia, Finland, Ireland, Luxembourg, the Netherlands, Poland, Portugal, Slovenia, Spain, and Sweden — signed the statement. They emphasised the need for a “reviewed digital rulebook” that is “deregulated where possible” and “avoids unnecessary red tape.” A startup group has called for the ministers to back up their words with actions. The S9+ Coalition — founded to give startups a voice in the policy agenda of the D9+ — warned that Europe’s “excessive and fragmented regulation” has become a self-imposed barrier. 3 free tickets to TNW Conference? Get them now! For a limited time, groups can get up to three extra free tickets! Book now and increase your visibility and connections at TNW Conference “If this declaration is to matter, the next step is clear: D9+ countries must push their colleagues in the [European] Council to simplify, harmonise, and reduce the burden of EU digital regulation — and to make Europe more attractive for investment in innovation,” Peter Kofler, chairman of S9+ member Danish Entrepreneurs, told TNW. The S9+ argues that startups are the foundation of economic growth. The claim is based on solid ground. According to Dealroom.co, just 0.01% of companies create 34% of all economic value — and they all start as startups. Europe, however, has struggled to turn promising companies into big businesses. Not a single EU company started from scratch in the last 50 years has a market capitalisation over €100 billion. In the same period, all six US companies with a valuation above €1 trillion were created. Hopes and fears for EU startups In recent years, Europe has fallen further behind the US in productivity and suffered from a declining share of global tech revenue. The continent’s leading startups now often look overseas to scale. Between 2008 and 2021, almost 30% of European startups valued at over $1 billion relocated their headquarters abroad. The vast majority moved to the US.   Many of their issues in Europe are attributed to stifling regulation. GDPR, for instance, cost small IT companies over 12% of their profits, according to estimates by three Oxford University economists. In a declaration of its own, the S9+ warned that “Europe cannot regulate its way out of stagnation.” The declaration proposes concrete actions to tackle the problem. Among its recommendations are rapidly implementing the 28th regime — a proposed single set of rules for the entire EU — and providing tech-friendly zones, with regulatory “sandboxes” alongside a gradual increase in regulatory requirements. AI regulation is a particular concern for the S9+. The coalition urged the EU to develop a regulatory framework that fosters AI innovation while ensuring fair and open data access. New compliance frameworks, the S9+ stressed, should be restricted to what’s “absolutely necessary” — and only introduced after consulting startups. The S9+ also emphasised the need to focus on emerging companies, rather than prioritising the demands of traditional industry giants. “Startups are not asking for special treatment,” Kofler said. “We are asking for a level playing field — for access to capital, data, infrastructure, and markets. If Europe wants to close its €800 billion investment gap, it won’t happen by giving old subsidies to old players. It will happen by unleashing the capital and ideas that startups already bring.” European startups are the heartbeat of TNW Conference, which takes place on June 19-20 in Amsterdam. Tickets for the event are now on sale. Use the code TNWXMEDIA2025 at the check-out to get 30% off the price tag. source

Today’s professionals are under constant pressure to improve their efficiency as the pace of business accelerates. Burnout is a common thing as workers try to multitask across several platforms and communication channels, often simultaneously, amid personal challenges such as caregiving and in-office politics. Notably, burnout symptoms differ, depending on whether employees work from home (WFH) or have been subject to return to office (RTO) mandates.   “Burnout differs between settings and how an employee is working,” says Nicole Issa, founder and licensed psychologist at The Center for Dynamic and Behavioral Therapy. “Remote workers can experience burnout due to a lack of work-life boundaries, social isolation and the pressure to be constantly available via tech. Office-based employees often struggle with long commutes, loss of autonomy, and workplace stressors such as micromanagement or rigid schedules. Both environments become a perfect storm for burnout, but the triggers do differ.”  Justina Raskauskiene, human resource team lead at ecommerce marketing platform Omnisend, agrees.  “How burnout manifests depends on the work environment,” says Raskauskiene. “Remote workers often risk blurring the lines between work and personal life, feeling like they [must] always be ‘on’, or unable to distance themselves from work. All of this makes it more likely they’ll work overtime. Plus, it’s no secret that fewer in-person interactions often equal poorer emotional well-being. In-office employees, on the other hand, may struggle with burnout due to a demanding company culture, excessive workloads or even the stress of a mandatory RTO policy. Reduced flexibility always carries the risk of contributing to employees’ stress and dissatisfaction.”  Related:How to Get a Delayed IT Project Back on Track Many employees are now in the “sandwich generation” — caregiving for children, parents or both.  “As our population ages, more and more employees will become caregivers. This isn’t just a personal crisis; it’s a ticking time bomb for our economy,” says Jennifer Fink, community educator at Alzheimer’s Association. “Seventy three percent of employees have some sort of caregiving responsibilities. Employees with caregiving responsibilities cost their employers an estimated 8% –- an additional $13.4 billion per year! By creating a caregiving-friendly workplace, organizations can unlock employee potential, reduce frustration and boost their bottom lines. [C]reating a caregiving-friendly culture isn’t expensive especially when the return on the investment is considered.”  Related:Who Makes the Best Citizen Developers? Katie Roland, chief human resources officer at KCSA Strategic Communications, says burnout occurs when employees feel they are giving more than they are getting.   “It can happen because they are actually overprogrammed and don’t feel compensated enough, or because they are working in a hostile environment and are masking all day,” says Roland. “It can be because they have responsibilities in life and at work, and they do not have the flexibility to manage both the way they feel they need to, making them feel constantly inadequate. Essentially, burnout is exhaustion.”  Many seasoned leaders are instinctively doubling down on RTO, implementing technology to oversee productivity, and demanding respect.   “What organizations need to understand is that employees who are trusted to do their job, and manage their life as needed, will produce far more for you than someone you try to control and monitor,” says Roland. “Nobody likes to be micromanaged. Instead figure out how to partner with your employees to find solutions that work on both ends.”  What HR/Hiring Managers Should Do About It  Susan Snipes, head of people at Remote People, says HR leaders need to be prepared to address burnout in all its forms for both in-office and remote team members.   Related:Quick Study: The Evolving Roles of CIOs and IT Leaders “Flexibility is the word of the day! Flexibility should be incorporated into all aspects of the employee experience from benefits to policies and procedures,” says Snipes. “Benefits like hybrid work, flexible schedules, and mental health days go a long way toward preventing employee burnout.”  Nicole Issa, The Center for Dynamic and Behavioral Therapy Nicole Issa, The Center for Dynamic and Behavioral Therapy The Center for Dynamic and Behavioral Therapy’s Issa suggests that chief human resources officers (CHRO) and hiring managers could approach burnout as a strategic issue rather than individual failings.   “This means being proactive about identifying risk factors and offering flexibility where possible,” says Issa. “Trying to build a company culture that prioritizes well-being should be top of the list for companies now. For remote employees, organizations should set clear expectations around availability, encourage a digital detox and provide routes for social connection. For in-office workers, offering hybrid working models, focusing on meaningful in-person collaboration and ensuring workload balance is key.”  Utilizing Data Is Also Important  “Absenteeism, tardiness, lack of vacation usage, etc. all can help identify the potential issue — there may be an issue with individuals or potentially managers,” says Fran Maxwell, global lead at business consulting firm Protiviti. “If they have a robust people analytics function, they can proactively determine which employees could start to become burnt out and can work with their managers to proactively support their employees. This would include looking at time, assuming the organization tracks time, or more simply looking at vacation time accrued and taken.”  MDR provider Expel discovered that quantifying workloads creates a common language between technical teams and business leaders. According to Amy Rossi, chief people officer at Expel, the most effective solution combines data with empathy.   “Organizations need metrics to identify burnout risks objectively, but they also need leaders who understand the human elements at play,” says Amy Rossi, chief people officer at Expel. “By adapting capacity utilization formulas to track workloads, teams can turn burnout from an abstract concern into concrete data that can inform staffing, scheduling, and resource allocation decisions. This approach has revolutionized how we manage and reduce burnout across both remote and in-office settings.”  Omnisend’s Raskauskiene says HR can monitor employee sentiment and job satisfaction through surveys and by encouraging leaders to keep an eye on employees’ moods.   “Educate them how to notice early burnout signs and react appropriately,” says Raskauskiene We also encourage managers to hold regular one-on-ones, where

By Ganesh Setty ( March 28, 2025, 6:15 PM EDT) — An AIG unit has no duty to defend a Washington-state-based firearms retailer in three underlying lawsuits accusing the retailer of knowingly selling unfinished components that could be used to assemble what are commonly known as ghost guns, a New York federal court ruled, finding the complaints do not allege accidental conduct…. 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 While many enterprises are now racing to adopt and deploy AI, credit bureau giant Experian has taken a very measured approach. Experian has developed its own internal processes, frameworks and governance models that have helped it test out generative AI, deploy it at scale and have an impact. The company’s journey has helped to transform operations from a traditional credit bureau into a sophisticated AI-powered platform company. Its approach—blending advanced machine learning (ML), agentic AI architectures and grassroots innovation—has improved business operations and expanded financial access to an estimated 26 million Americans. Experian’s AI journey contrasts sharply with companies that only began exploring machine learning after ChatGPT’s emergence in 2022. The credit giant has been methodically developing AI capabilities for nearly two decades, creating a foundation allowing it to capitalize on generative AI breakthroughs rapidly. “AI has been part of the fabric at Experian way beyond when it was cool to be in AI,” Shri Santhanam, EVP and GM, Software, Platforms and AI products at Experian, told VentureBeat in an exclusive interview. “We’ve used AI to unlock the power of our data to create a better impact for businesses and consumers for the past two decades.” From traditional machine learning to AI innovation engine Before the modern gen AI era, Experian was already using and innovating with ML. Santhanam explained that instead of relying on basic, traditional statistical models, Experian pioneered the use of Gradient-Boosted Decision Trees alongside other machine learning techniques for credit underwriting. The company also developed explainable AI systems—crucial for regulatory compliance in financial services—that could articulate the reasoning behind automated lending decisions. Most significantly, the Experian Innovation Lab (formerly Data Lab) experimented with language models and transformer networks well before ChatGPT’s release. This early work positioned the company to quickly leverage generative AI advancements rather than starting from scratch. “When the ChatGPT meteor hit, it was a fairly straightforward point of acceleration for us, because we understood the technology, had applications in mind, and we just stepped on the pedal,” Santhanam explained. This technology foundation enabled Experian to bypass the experimental phase that many enterprises are still navigating and move directly to production implementation. While other organizations were just beginning to understand what large language models (LLMs) could do, Experian was already deploying them within their existing AI framework, applying them to specific business problems they had previously identified. Four pillars for enterprise AI transformation When generative AI emerged, Experian didn’t panic or pivot; it accelerated along a path already charted. The company organized its approach around four strategic pillars that offer technical leaders a comprehensive framework for AI adoption: Product Enhancement: Experian examines existing customer-facing offerings to identify opportunities for AI-driven improvements and entirely new customer experiences. Rather than creating standalone AI features, Experian integrates generative capabilities into its core product suite.  Productivity Optimization: The second pillar addressed productivity optimization by implementing AI across engineering teams, customer service operations and internal innovation processes. This included providing AI coding assistance to developers and streamlining customer service operations. Platform Development: The third pillar—perhaps most critical to Experian’s success—centered on platform development. Experian recognized early that many organizations would struggle to move beyond proof-of-concept implementations, so it invested in building platform infrastructure designed specifically for the responsible scaling of AI initiatives enterprise-wide. Education and Empowerment: The fourth pillar addressed education, empowerment, and communication—creating structured systems to drive innovation throughout the organization rather than limiting AI expertise to specialized teams. This structured approach offers a blueprint for enterprises seeking to move beyond scattered AI experiments toward systematic implementation with measurable business impact. Technical architecture: How Experian built a modular AI platform For technical decision-makers, Experian’s platform architecture demonstrates how to build enterprise AI systems that balance innovation with governance, flexibility and security. The company constructed a multi-layered technical stack with core design principles that prioritize adaptability: “We avoid going through one-way doors,” Santhanam explained. “If we’re making choices on technology or frameworks, we want to ensure that for the most part… we make choices which we could pivot from if needed.” The architecture includes: Model layer: Multiple large language model options, including OpenAI APIs through Azure, AWS Bedrock models, including Anthropic’s Claude, and fine-tuned proprietary models. Application layer: Service tooling and component libraries enabling engineers to build agentic architectures. Security layer: Early partnership with Dynamo AI  for security, policy governance and penetration testing specifically designed for AI systems. Governance structure: A Global AI Risk Council with direct executive involvement. This approach contrasts with enterprises that have committed to single-vendor solutions or proprietary models, providing Experian greater flexibility as AI capabilities continue to evolve. The company is now seeing its architecture shift toward what Santhanam describes as “AI systems architected more as a mixture of experts and agents powered by more focused specialist or small language models.” Measurable impact: AI-driven financial inclusion at scale Beyond architectural sophistication, Experian’s AI implementation demonstrates concrete business and societal impact, particularly in addressing the challenge of “credit invisibles.” In the financial services industry, “credit invisibles” refers to the approximately 26 million Americans who lack sufficient credit history to generate a traditional credit score. These individuals, often younger consumers, recent immigrants, or those from historically underserved communities, face significant barriers to accessing financial products despite potentially being creditworthy. Traditional credit scoring models primarily rely on standard credit bureau data like loan payment history, credit card utilization, and debt levels. Without this conventional history, lenders historically viewed these consumers as high-risk or declined to serve them entirely. This creates a catch-22 where people cannot build credit because they cannot access credit products in the first place. Experian tackled this problem through four specific AI innovations: Alternative data models: Machine learning systems incorporating non-traditional data sources (rental payments, utilities, telecom payments) into creditworthiness assessments, analyzing hundreds of variables rather than the limited factors in conventional models. Explainable AI for compliance: Frameworks that maintain regulatory compliance by articulating

Having managed and rescued dozens of projects, and helped others do so, I’ve noted that there is always one critical success factor (CSF) that has either been effectively addressed or missed/messed up: clarity around the roles and responsibilities for each project participant and key stakeholder. No matter how detailed and complete a project plan may be for any project, confusion or omission of participant roles and responsibilities will cause major problems. Enter the RACI matrix. The simplest and most effective approach I’ve seen and used to define and document project roles and responsibilities is the RACI model. Integrating the RACI model into an organization’s project life cycle (PLC) creates a powerful synergy that enhances and improves project outcomes. What is a RACI matrix? The RACI matrix is a project role and responsibility assignment chart that diagrams every task, milestone, or key decision to assign team roles across four categories: Responsible, Accountable, Consulted, and Informed. These categories indicate whether a team member is Responsible for an action item, is Accountable for it, should be Consulted on it, or simply be Informed of the action, milestone, or decision. The acronym RACI stands for the four roles that stakeholders might play at any point in a project. source

Chi Onwurah, chair of the Science, Innovation and Technology Select Committee. Image: UK Parliament/Flickr/Creative Commons Legislation that mandates safety testing of artificial intelligence technologies is at risk of being pushed aside by the U.K. government, the head of the tech select committee says. Labour’s Chi Onwurah warned that the delay may reflect political efforts to align more closely with the United States, particularly the Trump camp’s outspoken opposition to AI regulation. One key focus of the AI Safety Bill is to legally mandate that companies uphold their voluntary agreements to submit frontier AI models for government safety evaluations before deployment. Nine companies, including OpenAI, Google DeepMind, and Anthropic, made such agreements with a number of international governments in November 2023. SEE: UK Report Shows AI is Advancing at Breakneck Speed In November 2024, technology secretary Peter Kyle said he would implement the legislation in the next year. At the time, Chi Onwurah, the Labour chair of the Science, Innovation and Technology Select Committee which is in charge of examining tech policy, was under the impression it was “coming soon,” she told The Guardian, but now she’s worried about whether that is really the case. More must-read AI coverage Political influences and transatlantic ties “The committee has raised with Patrick Vallance [the science minister] the lack of an AI safety bill, and whether that is in response to the significant criticism of Europe’s approach to AI, which J.D. Vance and Elon Musk have made,” she added. In a speech at February’s Paris AI Action Summit, U.S. Vice President Vance disparaged Europe’s use of “excessive regulation” and said that the international approach should “foster the creation of AI technology rather than strangle it.” Europe has solidified a pro-regulation reputation through the AI Act and numerous ongoing regulatory battles with major tech companies — resulting in hefty fines. It is no secret that Trump is not happy about this, referring to the fines as “a form of taxation” at the World Economic Forum in January. SEE: Meta to Take EU Regulation Concerns Directly to Trump, Says Global Affairs Chief U.K. ministers do not plan to publish the AI Bill before the summer in an attempt to please the Trump administration, anonymous Labour sources told The Guardian last month. But this is not the only recent evidence that the country is trying to keep the States on side. Safety vs. innovation — The UK’s strategic shift Last month, the U.K.’s AI oversight body was renamed from the AI Safety Institute to the AI Security Institute, a rebranding seen by some as a shift away from a risk-averse stance and toward national interest farming. In January, Prime Minister Keir Starmer released the AI Opportunities Action Plan which put innovation front and centre and made little mention of AI safety. He also skipped the Paris AI Summit, where the U.K. declined to sign a global pledge for “inclusive and sustainable” AI, as did the U.S. The shift toward innovation-first policymaking comes with economic implicationsLimiting AI innovation in the U.K. could have a significant economic impact, with a Microsoft report finding that adding five years to the time it takes to roll out AI could cost over £150 billion. Stricter regulations could also deter major tech firms like Google and Meta from scaling in the U.K., prompting concern from investors. A spokesperson for the Department for Science, Innovation and Technology told The Guardian: “The government is clear in its ambition to bring forward AI legislation which allows us to safely realise the enormous benefits and opportunities of the technology for years to come.” “We are continuing to refine our proposals which will incentivise innovation and investment to cement our position as one of the world’s three leading AI powers, and will launch a public consultation in due course.” source