Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More AI has evolved at an astonishing pace. What seemed like science fiction just a few years ago is now an undeniable reality. Back in 2017, my firm launched an AI Center of Excellence. AI was certainly getting better at predictive analytics and many machine learning (ML) algorithms were being used for voice recognition, spam detection, spell checking (and other applications) — but it was early. We believed then that we were only in the first inning of the AI game. The arrival of GPT-3 and especially GPT 3.5 — which was tuned for conversational use and served as the basis for the first ChatGPT in November 2022 — was a dramatic turning point, now forever remembered as the “ChatGPT moment.”  Since then, there has been an explosion of AI capabilities from hundreds of companies. In March 2023 OpenAI released GPT-4, which promised “sparks of AGI” (artificial general intelligence). By that time, it was clear that we were well beyond the first inning. Now, it feels like we are in the final stretch of an entirely different sport. The flame of AGI Two years on, the flame of AGI is beginning to appear. On a recent episode of the Hard Fork podcast, Dario Amodei — who has been in the AI industry for a decade, formerly as VP of research at OpenAI and now as CEO of Anthropic — said there is a 70 to 80% chance that we will have a “very large number of AI systems that are much smarter than humans at almost everything before the end of the decade, and my guess is 2026 or 2027.” Anthropic CEO Dario Amodei appearing on the Hard Fork podcast. Source: https://www.youtube.com/watch?v=YhGUSIvsn_Y  The evidence for this prediction is becoming clearer. Late last summer, OpenAI launched o1 — the first “reasoning model.” They’ve since released o3, and other companies have rolled out their own reasoning models, including Google and, famously, DeepSeek. Reasoners use chain-of-thought (COT), breaking down complex tasks at run time into multiple logical steps, just as a human might approach a complicated task. Sophisticated AI agents including OpenAI’s deep research and Google’s AI co-scientist have recently appeared, portending huge changes to how research will be performed.  Unlike earlier large language models (LLMs) that primarily pattern-matched from training data, reasoning models represent a fundamental shift from statistical prediction to structured problem-solving. This allows AI to tackle novel problems beyond its training, enabling genuine reasoning rather than advanced pattern recognition. I recently used Deep Research for a project and was reminded of the quote from Arthur C. Clarke: “Any sufficiently advanced technology is indistinguishable from magic.” In five minutes, this AI produced what would have taken me 3 to 4 days. Was it perfect? No. Was it close? Yes, very. These agents are quickly becoming truly magical and transformative and are among the first of many similarly powerful agents that will soon come onto the market. The most common definition of AGI is a system capable of doing almost any cognitive task a human can do. These early agents of change suggest that Amodei and others who believe we are close to that level of AI sophistication could be correct, and that AGI will be here soon. This reality will lead to a great deal of change, requiring people and processes to adapt in short order.  But is it really AGI? There are various scenarios that could emerge from the near-term arrival of powerful AI. It is challenging and frightening that we do not really know how this will go. New York Times columnist Ezra Klein addressed this in a recent podcast: “We are rushing toward AGI without really understanding what that is or what that means.” For example, he claims there is little critical thinking or contingency planning going on around the implications and, for example, what this would truly mean for employment. Of course, there is another perspective on this uncertain future and lack of planning, as exemplified by Gary Marcus, who believes deep learning generally (and LLMs specifically) will not lead to AGI. Marcus issued what amounts to a take down of Klein’s position, citing notable shortcomings in current AI technology and suggesting it is just as likely that we are a long way from AGI.  Marcus may be correct, but this might also be simply an academic dispute about semantics. As an alternative to the AGI term, Amodei simply refers to “powerful AI” in his Machines of Loving Grace blog, as it conveys a similar idea without the imprecise definition, “sci-fi baggage and hype.” Call it what you will, but AI is only going to grow more powerful. Playing with fire: The possible AI futures In a 60 Minutes interview, Alphabet CEO Sundar Pichai said he thought of AI as “the most profound technology humanity is working on. More profound than fire, electricity or anything that we have done in the past.” That certainly fits with the growing intensity of AI discussions. Fire, like AI, was a world-changing discovery that fueled progress but demanded control to prevent catastrophe. The same delicate balance applies to AI today. A discovery of immense power, fire transformed civilization by enabling warmth, cooking, metallurgy and industry. But it also brought destruction when uncontrolled. Whether AI becomes our greatest ally or our undoing will depend on how well we manage its flames. To take this metaphor further, there are various scenarios that could soon emerge from even more powerful AI: The controlled flame (utopia): In this scenario, AI is harnessed as a force for human prosperity. Productivity skyrockets, new materials are discovered, personalized medicine becomes available for all, goods and services become abundant and inexpensive and individuals are freed from drudgery to pursue more meaningful work and activities. This is the scenario championed by many accelerationists, in which AI brings progress without engulfing us in too much chaos. The unstable fire (challenging): Here, AI

學茶人 洪錦鉉 「手打青檸鴨屎香茶」不僅在「龍騰觀塘夜市」熱銷，在潮州更是隨處可見。「鴨屎香」這個聽起有些俗氣的名字，引發了眾人的好奇：它究竟是怎樣一種茶，為何叫如此特別的名字？ 鳳凰單叢屬於六大茶類的青茶或稱烏龍茶，憑藉「一樹一品，一品一香」的獨特魅力，成為茶中珍品｡生長在潮州鳳凰山雲霧繚繞的茶園裏的「鴨屎香」單叢，得天獨厚的環境賦予了它非凡的氣質﹐是鳳凰單叢茶中的佼佼者｡而「鴨屎香」名字背後，藏著饒有趣味的故事。 一種說法是，茶樹生長在一種黃土壤上，潮汕人稱之為「鴨屎土」，這種富含礦物質的土壤滋養出的茶葉品質上乘，因其葉色墨綠、形似鴨脚木葉，便被稱為「鴨屎香」。另一種說法是，一位魏姓村民從烏崬山引種茶樹，精心呵護，培育出的茶香氣四溢、韵味悠長。當旁人好奇詢問茶名時，他出於保護茶樹的心思，隨口說出「鴨屎香」，沒想到却流傳開來。 有茶農及專家覺得「鴨屎香」名不雅，難登大雅之堂，認為該茶沖泡時冒出的香氣近似金銀花盛開時的香味，將「鴨屎香」更名為「銀花香」。可是，人們更鍾情「鴨屎香」。它充滿鄉土氣息，獨特又好記，在信息爆炸的時代，一下就能抓住人們的眼球，勾起大家的好奇心。其背後的傳說承載著潮州的風土人情和歷史文化，是當地茶文化的生動體現，能引發强烈的情感共鳴。再加上市場的宣傳推廣，「鴨屎香」系列茶飲流行起來，知名度越來越高，品牌認知度也不斷提升，已然成為了茶葉市場的熱門之選。 「鴨屎香」的特徵是乾茶條索緊卷、烏褐油潤，其製作工序極為考究，采青、曬青、晾青、做青、炒青、揉拈、烘焙，每一步都凝聚著茶農的心血。其中做青最為關鍵，碰青、搖青、發酵循環往復，茶葉的香氣在這奇妙的過程中被慢慢激發出來。當「鴨屎香」泡入杯中，茶湯金黃明亮。瞬間，多種香氣交織在一起，縈繞在鼻尖，歷經十泡香氣依舊。入口滋味醇厚濃烈，有些微微的甘苦，但是回甘强，韵味獨特、悠長。 在潮州的山水間，「鴨屎香」早已超越了飲品的範疇，既有歲月的沉澱，又是文化的象徵。韓愈在《潮州鳳凰山遊記》中以「疑是天仙遺玉露，人間何幸得此鮮」形容當地風景，用以讚譽「鴨屎香」單叢茶是大自然的珍貴饋贈也很貼切。它帶著鳳凰山的靈氣，走進了每一位愛茶人的心裏。 LinkedIn Email Facebook Twitter WhatsApp The post 你品嘗過「鴨屎香」嗎？ appeared first on VeriMedia. source

The Challenge: Many IT procurement processes focus on short-term cost-cutting rather than long-term business value. AI in Action: AI transforms procurement into a strategic function by optimizing spend, improving supplier collaboration, and aligning purchasing decisions with your business goals. Breaking Barriers with AI Traditional procurement models, designed for an era of predictable IT investments, can’t keep pace with the breakneck speed of today’s software development. If you want to stay competitive, you need smarter, faster ways to evaluate, select, and deploy new technology. source

Asian banks need technology investment to continue their growth momentum. The industry is robust, with nineteen of the top 50 global banks being Asia-based. If these banks do not continue investing in emerging technologies to drive innovation and productivity, they may face increased resilience risks in the long run. The Asian banking sector has faced numerous challenges and is rapidly evolving due to geopolitical tensions, rising interest rates, growing needs for inclusion and microfinance, increasing demand for hyper-personalized services, a worsening risk environment, operational efficiency issues, and tighter regulatory oversight. Each of these challenges require technology investments. In this context, the bank must decide on what its priority investments will be. Let’s take a look at some of the potential winners for 2025 and beyond. Banks need to be agile in their transformation initiatives. Agility depends on a combination of technology infrastructure and a strategy that supports quick deployment of new capabilities. This could involve a platform strategy, microservices for easy integration, or a mix of adopting and building. Choosing the right approach is both an art and a science. What is needed is an infrastructure that facilitates innovation. All of us who have struggled with the “Technology Bill of Material” understand that the legacy infrastructure setup processes are in months in an age where innovation, POC, and A/B tests are required within days. That is where cloud computing is important, and it must be in the mix. Cloud computing is also important as more of the enhancements require extensive data computing. While agility helps build an architecture for fast innovation deployment, banks still need to decide on enhancements. Three factors are coming into play in today’s digital age. These are functionalities that increase revenue, automation opportunities to improve efficiency, and, finally, build trust through resiliency and avoiding financial crime. In IDC’s survey (June 2024), 41% of the banks stated that they require new products and services to generate revenues. Launching any new product requires data, which may comprise of a mix of synthetic data generation and data management techniques. It also requires effort to build the right models, whether that may be techniques like graph and RAG or models such as agentic, generative, predictive, or interpretative. Servicing, operational excellence, and risk management remain the main areas of deployment. Embedded finance and new product development are good use cases for revenue enhancements through AI deployment. Climate risk is also emerging as a real threat, impacting project risks and worsening personal credit. Extreme weather events affect individuals’ ability and willingness to make payments. Investing in geospatial data-based solutions could be a smart long-term strategy. Placing these bets could eventually lead to positive leverage for Asian BFSI players. Join me and my team at our 2025 Financial Services event series happening across Singapore, China, and India from June to August. We’ll explore the latest trends in DX adoption by Asian banks and demonstrate how financial leaders can build holistic ecosystems with technology. Learn how to reap AI benefits through operational efficiency and improved customer experience in a multi-polar world. Also, don’t miss our upcoming webinar on Driving Growth Beyond AI in Banking and Financial Services – register today! source

Speedify VPN fast facts Our rating: 3.1 stars out of 5.00 Pricing: Starts at $7.49 per month Key features: Dedicated free version. Can combine multiple internet connections. Servers from 55+ locations. As its name suggests, Speedify VPN focuses on speed to set it apart from the competition. It has unique functionality like a bonding feature that combines multiple internet connections for better speed and the ability to share cellular connections amongst other Speedify users. While these speed-centric features can be appealing, Speedify lacks essential security and privacy features we expect from a VPN in 2025. It’s also priced relatively higher than other VPNs with a smaller server network — making it harder to justify against other feature-rich, more secure, and more affordable options available today. Rippling IT 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, Dashboard, Data Security, and more Semperis Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Small (50-249 Employees), Medium (250-999 Employees), Large (1,000-4,999 Employees), Enterprise (5,000+ Employees) Small, Medium, Large, Enterprise Features Advanced Attacks Detection, Advanced Automation, Anywhere Recovery, and more ESET PROTECT Advanced 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 Advanced Threat Defense, Full Disk Encryption , Modern Endpoint Protection, and more Speedify VPN Pricing Annual Three-year plan Monthly Speedify Individual $7.49 per month $4.99 per month $14.99 per month Speedify for Families $11.25 per month $7.50 per month $22.50 per month Speedify Teams $7.49 per user per month N/A $14.99 per user per month Ideally, users want VPN subscriptions to be priced at around $4-$5 per month for an annual plan and between $10-$12 per month for a monthly plan. However, Speedify VPN subscriptions are on the pricier side. Speedify Individual, designed for individual users, is priced at $7.49 per month for the annual plan and $14.99 per month for the monthly plan. The plan includes up to five simultaneous device connections, but this is on the lower end compared to other VPNs — such as CyberGhost VPN which offers seven simultaneous device connections and Private Internet Access, which offers unlimited connections. Speedify also offers Speedify Teams, which comes with centralized account management. However, it doesn’t provide reduced rates per user compared to its usual monthly price, unlike Windscribe or TunnelBear’s teams implementation. While Speedify Teams offers features like the ability to purchase dedicated servers and command line access, discounted pricing could incentivize more organizations to make the spend. In addition to the monthly and annual options, Speedify has a three-year plan for both its Individual and Teams subscription. If you’re set on a Speedify subscription, their three-year Families plan is the best value. At $7.50 per month, you get five accounts, with up to five simultaneous device connections, at the same monthly fee for an Individual annual subscription. I can see this being a good option for small teams or businesses. Speedify has a full free version that doesn’t require any card or personal information. However, it’s data capped, so free users only get access to 2GB per month. Speedify VPN Free Version Speedify’s Free version. Image: Luis Millares Speedify includes a full free version that gives users access to the VPN with no email or sign up required. However, it has a 2GB data cap per month that will realistically only give you about an hour to a day’s worth of use — depending on how you use it. During my time using Speedify’s free version, I was impressed with its speed. Many free VPNs are fairly slow as a way to entice you to upgrade to the paid version. Having a fast free version could be Speedify’s way of letting users get a feel for their VPN’s fast performance — prioritizing the free tier’s speed instead of giving more data for regular use. Speedify’s free version can theoretically be useful for one-off instances where you need to switch to a different IP location in a pinch. But if you’re looking for a free VPN that doesn’t have a data limit, you should consider Proton VPN instead. Speedify VPN Pros Offers good speed and performance. Has a free version. Unique multi-internet bonding feature. Allows for cellular network sharing amongst Speedify users. Speedify VPN Cons No independent audit. Lacks OpenVPN or WireGuard security protocol. Free version is limited to 2GB per month. On the expensive end. Official site logs too much data for our liking. DNS leak protection can be buggy. Security: Is Speedify VPN safe? Speedify lacks important security features that prevent me from calling it a safe VPN. It doesn’t include leading security protocols OpenVPN and WireGuard, and instead uses its own proprietary Speedify protocol designed to provide fast performance. Not having at least OpenVPN is a big miss in the security department, as it’s widely considered the most secure VPN protocol today. While having a fast-performing protocol can be valuable, Speedify should give users the option to prioritize security over speed. For encryption, Speedify comes with the military-grade AES-256 encryption and ChaCha cipher for older devices. It also comes with a built-in kill switch and protection against DNS leaks. Speedify advertises a no-logs policy that states that their VPN does not log browsing activity, sites visited, or applications used. However, it sadly isn’t backed by an independent audit, which would help verify its security and privacy claims. One peculiar thing is that Speedify’s privacy policy contradicts their own VPN’s no-logs policy. It states: “When you use our website to learn about our services, we store your IP address, the type of browser you are using, the pages you access, and your operating system.” Speedify also says that it records “what time each connection begins, how long each connection lasts and the amount of data used.” To me, this is a significant amount of data recorded when visiting their site, especially since it concerns a VPN that’s typically used to reduce data logging in the

Keeper and LastPass are two popular password managers that act as encrypted vaults for all your credentials. They also offer additional features such as password generation, multi-factor authentication, and password sharing. Keeper shines for its impressive security and business-centered features. LastPass, on the other hand, is hampered by recent data breaches despite its generous free version and easy-to-use software. With that being said, let’s compare Keeper and LastPass to see which one is best for you and your organization. Keeper: Best for an all-around password manager with strong security and extensive password-sharing and organization capabilities. LastPass: Best for testing out password managers via its free plan. NordPass 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 Log, Business Admin Panel for user management, Company-wide settings, and more Dashlane 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 Automated Provisioning ManageEngine ADSelfService Plus 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 Access Management, Compliance Management, Credential Management, and more Keeper vs LastPass: Comparison table Features Keeper LastPass Star rating 4.4/5 3.4/5 Zero trust principles Yes Yes Multi-factor authentication Yes Yes Adherence to information security standards Yes Yes Encryption AES-256 (all levels) AES-256 (vault-level only) Unlimited password storage and sharing Yes Yes Business add-ons Keeper Secrets Manager, Advanced Reporting and Alerts, Compliance Reports, KeeperChat Advanced SSO and MFA Supported Platforms Windows, Mac, Linux, Android, iOS, Chrome, Firefox, Safari, Edge, Opera Windows, Mac, Linux, Android, iOS, Chrome, Firefox, Safari, Edge, Opera Starting price Free or $2.92 per month $3 per month Keeper and LastPass pricing Keeper and LastPass have similar pricing for their individual subscriptions — with Keeper Personal at $2.92 per month and LastPass Premium at $3 per month, both billed annually. For Family plans, LastPass is more affordable at $4 per month than Keeper’s $6.25 per month. For their business plans, Keeper and LastPass offer comparable prices, but Keeper presents more options: Keeper’s Business Starter plan for $2 per user per month is good for up to 10 people and a minimum of five users. Keeper’s Business plan priced at $3.75 per user per month is good for small-to-medium sized businesses. Keeper also has the Enterprise plan that offers customized bundles and pricing. In comparison, LastPass offers two business subscriptions divided into a Teams and a Business plan: LastPass Teams is $4 per user monthly for 50 users or fewer. LastPass Business is $7 per user monthly, allowing an unlimited number of users. LastPass and Keeper both offer free tiers; however, features are limited compared to their paid subscriptions. Keeper’s free version is only available on its mobile app and on a single device. On the other hand, LastPass’ free version is more generous in that it allows an unlimited number of passwords stored for one device — accessible on both desktop and mobile. While it’s clear that LastPass’ free version is better, its recent data breaches make it hard for me to recommend it over Keeper. Both offer free trials, with Keeper offering 30-day trials to all its plans and LastPass having a 30-day trial for its Single and Family tiers and 14-day free trials for its Teams and Business subscriptions. As far as discounts, Keeper offers a generous 50% discount for students and a 30% discount to former military personnel and their family, first responders, law enforcement, and healthcare workers. Feature comparison: Keeper vs LastPass Overall security and encryption Winner: Keeper For overall security, I have to give it to Keeper. This is largely due to LastPass being involved in two data breach incidents back in 2022. The first breach involved a software engineer’s corporate laptop being compromised, which exposed sensitive source code and technical information. The second incident affected customer vault data itself, with both encrypted and unencrypted customer data getting leaked. LastPass user interface. Image: Luis Millares While LastPass assured users their encrypted files are secured with military-grade 256-bit AES encryption, this was still only if they carried out password best practices for their master password. If you want to learn more about LastPass’ recent breaches, check out our full LastPass review here. On the other hand, Keeper uses the same AES-256 encryption but has yet to be involved in any sort of data breach or attack. This is what we want to hear when it comes to password managers — especially since they’re supposed to store some of our most essential personal information. Keeper’s desktop UI. Image: Luis Millares I must mention that LastPass has implemented changes in response to the data breaches. These include security and privacy changes within their service, as well separating from parent company GoTo and shifting to independent ownership in 2024. Despite this, I still feel that most businesses should opt for Keeper. With sensitive data such as passwords, a clean track record is critical, and among the two, Keeper is the better choice. Standout security features Winner: Tie LastPass and Keeper have unique features that set them apart from other password managers. Keeper offers an encrypted messaging service called KeeperChat. It’s free for all Keeper Personal users and provides end-to-end encrypted messaging and fingerprint-protected private messages. KeeperChat dashboard. Image: Luis Millares While I personally don’t envision myself using this type of service, it’s nice that Keeper offers it as an option. It may be useful for incredibly sensitive communications and or file sharing — which businesses may want to avoid doing on less protected platforms. Meanwhile, LastPass has its Country Restrictions feature, which enables users to only allow access to selected logins in countries of their choice. Country Restrictions within LastPass. Image: Luis Millares I find this really useful for businesses that regularly

Many organizations have learned that AI models need to be monitored, fine-tuned, and eventually retired. This is as true of large language models (LLM) as it is of other AI models, but the pace of generative AI innovation has been so fast, some organizations are not managing their models as they should be, yet.   Senthil Padmanabhan, VP, platform and infrastructure at global commerce company eBay, says enterprises are wise to establish a centralized gateway and a unified portal for all model management tasks as his company has done. EBay essentially created an internal version of Hugging Face that eBay has implemented as a centralized system.   “Our AI platform serves as a common gateway for all AI-related API calls, encompassing inference, fine-tuning, and post-training tasks. It supports a blend of closed models (acting as a proxy), open models (hosted in-house), and foundational models built entirely from the ground up,” says Padmanabhan in an email interview. “Enterprises should keep in mind four essential functionalities when approaching model management: Dataset preparation, model training, model deployment and inferencing, and continuous evaluation pipeline. By consolidating these functionalities, we’ve achieved consistency and efficiency in our model management processes.”  Related:Breaking Through the AI Bottlenecks Previously, the lack of a unified system led to fragmented efforts and operational chaos.   Rather than building the platform first during its initial exploration of GenAI, the company focused on identifying impactful use cases.   “As the technology matured and generative AI applications expanded across various domains, the need for a centralized system became apparent,” says Padmanabhan. “Today, the AI platform is instrumental in managing the complexity of AI model development and deployment at scale.”  Senthil Padmanabhan, eBay Senthil Padmanabhan, eBay Phoenix Children’s Hospital has been managing machine learning models for some time because predictive can models drift.  “We’ve had a model that predicts malnutrition in patients [and] a no-show model predicting when people are not going to show up [for appointments],” says David Higginson, executive vice president and chief innovation officer at Phoenix Children’s Hospital. “Especially the no-show model changes over time so you have to be very, very conscious about, is this model still any good? Is it still predicting correctly? We’ve had to build a little bit of a governance process around that over the years before large language models, but I will tell you, like with large language models, it is a learning [experience], because different models are used for different use cases.”  Related:How AI is Transforming the Music Industry Meanwhile, LLM providers, including OpenAI and Google, are rapidly adding new models turning off old ones, which means that something Phoenix Children’s Hospital built a year ago might suddenly disappear from Azure.  “It’s not only that the technical part of it is just keeping up with what’s being added and what’s being removed. There’s also the bigger question of the large language models. If you’re using it for ambient listening and you’ve been through a vetting process, and everybody’s been using a certain model, and then tomorrow, there’s a better model, people will want to use it,” says Higginson. “We’re finding there are a lot of questions, [such as], is this actually a better model for my use case? What’s the expense of this model? Have we tested it?”  How to Approach Model Management  EBay’s Padmanabhan says any approach to model management will intrinsically establish a lifecycle, as with any other complex system. EBay already follows a structured lifecycle, encompassing stages from dataset preparation to evaluation.  “To complete the cycle, we also include model depreciation, where newer models replace existing ones, and older models are systematically phased out,” says Padmanabhan. “This process follows semantic versioning to maintain clarity and consistency during transitions. Without such a lifecycle approach, managing models effectively becomes increasingly challenging as systems grow in complexity.”  Related:How Big of a Threat Is AI Voice Cloning to the Enterprise? EBay’s approach is iterative, shaped by constant feedback from developers, product use cases and the rapidly evolving AI landscape. This iterative process allowed eBay to make steady progress.  “With each iteration of the AI platform, we locked in a step of value, which gave us momentum for the next step. By repeating this process relentlessly, we’ve been able to adapt to surprise — whether they were new constraints or emerging opportunities — while continuing to make progress,” says eBay’s Padmanabhan. “While this approach may not be the most efficient or optimized path to building an AI platform, it has proven highly effective for us. We accepted that some effort might be wasted, but we’ll do it in a safe way that continuously unlocks more value.”  To start, he recommends setting up a common gateway for all model API calls.   “This gateway helps you keep track of all the different use cases for AI models and gives you insights into traffic patterns, which are super useful for operations and SRE teams to ensure everything runs smoothly,” says Padmanabhan. “It’s also a big win for your InfoSec and compliance teams. With a centralized gateway, you can apply policies in one place and easily block any bad patterns, making security and compliance much simpler. After that, one can use the traffic data from the gateway to build a unified portal. This portal will let you manage a model’s entire lifecycle, from deployment to phasing it out, making the whole process more organized and efficient as you scale.”  Phoenix Children’s Hospital’s Higginson says it’s wise to keep an eye on the industry because it’s changing so fast.  David Higginson, Phoenix Children’s Hospital David Higginson, Phoenix Children’s Hospital “When a new model comes out, we try to think about it in terms of solving a problem, but we’ve stopped chasing the [latest] model as GPT-4 does most of what we need. I think what we’ve learned over time is don’t chase the new model because we’re not quite sure what it is or you’re limited on how much you can use it in a day,” says Higginson. “Now, we’re focusing more on models that

What are CRM reports? A CRM report is an analytics tool built into a customer relationship management (CRM) system that provides users with data and insights into their customers, activities, sales, and performance. It often uses visual elements such as charts and graphs to convert CRM data into digestible and actionable insights. A customer relationship management report summarizes a business or organization’s performance during a specified time. They also highlight opportunities that you can leverage to drive growth and sales. 1 monday CRM 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 Calendar, Collaboration Tools, Contact Management, and more 2 Pipedrive CRM 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 Calendar, Collaboration Tools, Contact Management, and more 3 HubSpot CRM 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) Micro, Small, Medium, Large How does CRM reporting work? CRM reports allow you to drill down and filter CRM data in various ways to derive actionable insights needed to make data-driven decisions. You can analyze several business key performance indicators (KPIs) and metrics, including individual rep and team performance, deal status, revenue forecasts, and progress toward sales goals. Business managers and executives can make pivotal decisions based on information derived from CRM reports. For example, a report shows low agent productivity across the whole team. They can address this issue by investing in a sales automation tool that can help boost their productivity. Marketing teams can also revise the direction of their email and online advertising campaigns based on conversions and clicks shown in the reports. Benefits of CRM reporting CRM reporting can pave the way for several business advantages. Data-driven decisions CRM reports provide actionable insights derived from consolidated customer and sales data. Businesses can make intelligent decisions that drive sales growth based on this information. For example, a lead source analysis report tells you that your most lead generation channel is email, while social media ranks the lowest. Based on this information, you can focus more on your email campaigns and work on improving your social media presence. Benchmarked performance Using CRM reports, you can evaluate your overall business performance and set standards to determine your success or opportunities for improvement. A sales pipeline report, for example, can tell you how your team is performing against their target revenue. If the cutoff date is approaching and the team still has a long way to go before hitting their quota, you can double down on efforts to generate more sales. Efficient operations CRM reports are stored in a central and easily accessible location, saving time and helping your sales, marketing, and service teams work more efficiently. A sales funnel report, for instance, points you in the right direction when you need to address bottlenecks in your CRM cycle. If you see only a few deals at the top of the funnel, you can focus your efforts there to ensure that enough deals push through the funnel later. Increased customer satisfaction Customer service teams can provide relevant and personalized service based on reports on client behavior and trends over time. Surveys after a customer interaction can help you gauge how satisfied customers are with your service. For instance, you deploy chatbots after reading customer feedback that access to a live agent takes too long. Doing this helps improve customer experience and satisfaction, ultimately leading to better retention and loyalty. Accelerated innovation Investors can use CRM reports to understand their return on investment and get insights into the business trends. For example, reports indicate that the majority of your target audience expressed an interest in buying a gadget from your store if it were available in colors other than black. Investors can use this information to direct resources into creating the variations of the product your prospects are interested in. What’s the difference between CRM reports and dashboards? The terms “CRM report” and “CRM dashboard” are related and are often used interchangeably, but they are distinct from one another. Understanding the difference between the two can help you better leverage your CRM data for business growth. A CRM report is a static document that contains data for a specific time that you can use to pinpoint trends and changes in patterns over time. You can customize a CRM report to only display specific data points within a period, sort functions, and even add graphics. Plus, you can leverage your CRM’s automation capabilities to run reports periodically and automatically send them to pre-determined recipients. Pipedrive sample CRM report. Image: Pipedrive On the other hand, a CRM dashboard is a visual snapshot of specific data points that reflect your current standing, real-time metrics, and progress toward your goals — all in one window. They typically use charts, gauges, and graphs to make information easily digestible. Most CRM platforms allow you to drill down on granular data, include several reports in one dashboard, and share dashboards with relevant stakeholders. Pipedrive sample CRM dashboard. Image: Pipedrive 5 essential CRM reports CRM reporting involves a wide range of metrics and KPIs, and each business must tailor its metrics monitoring to its unique objectives for success. Here are seven common CRM reports that you can use as part of your business strategy. Sales pipeline report A sales pipeline report provides salespeople or managers with an overview of their pipeline health. Its key metrics include the total number of deals, average deal size, deal velocity, and deal status changes. HubSpot Sales Hub sample deal pipeline waterfall summary. Image: HubSpot Sales activity report The sales activity report helps managers and salespeople track activity goals to identify issues hindering reps from hitting their daily, weekly, or monthly quotas. It indicates quota progress, the number of contacts created, the number of follow-ups sent, and the close ratio. Pipedrive sample

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