Gather AI

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Logging is essential for successful DevSecOps teams. Logs are filled with the information needed to monitor and understand systems. Tracking down a defect? Trying to understand a sudden burst in questionable logins from a new region? Need to figure out why an app is crawling? Logs are that single source of truth for understanding what’s really happening. But there’s a problem that comes along with logs: the sheer amount of data. The information logged by services and applications just keeps on growing. And growing. It doesn’t take long for it to become more — much more — than can be managed. The data becomes overwhelming. Alert fatigue sets in. Data keeps growing. Human resources can’t. But there’s hope on the horizon. Innovations in AI have revolutionized the process of continuous log monitoring. AI algorithms can analyze and detect patterns within vast datasets, translate raw logs into actionable insights, and proactively alert teams to problems — all at a scale and at higher precision to assist humans. Let’s look at 8 ways that AI can maximize the value of logs. #1: Handling massive amounts of data First, the most obvious. Cloud-native environments, with their dozens (or hundreds) of distributed components, emit a massive volume of log data. In turn, this data requires high levels of expertise to sift through and analyze it all. But most organizations already face a shortage of people with the skills needed to tease out the insights from this data. Companies could train more people — but training is slow. And the growing complexity of the data tends to outpace any new skills. Here is where AI shines: It’s a scalable way to handle log data, no matter the volume and no matter the time. Humans need breaks; they clock out for the day, get sick, take PTO, and play Galaga when bosses aren’t looking. But log data still arrives in massive volumes, regardless of the time of the day or day of the week. An AI-based system with analysis, detection, and alerting is always on. #2: Automated security and access control When dealing with logs, it’s important to protect any sensitive user information and ensure that the data is only accessible to authorized team members. SecOps managers often implement fine-grained access control to ensure that the approved team members have access to (and only to) the data or metrics intended for use. AI systems can automatically identify and redact sensitive information — such as personal identifiers, financial details, or confidential business information — from log data before it’s accessed by humans. Or, as part of automated preprocessing, AI can de-identify or mask sensitive parts of data. #3: Collating data from disparate sources Merging data from various sources is a complex task. But it’s essential for effective security and operations. When logs are properly aggregated and correlated, the resulting data and metrics can give the context needed for better visibility and better troubleshooting. But this is a menial and time-consuming task … which makes it perfect for AI. AI can automatically gather information from various sources and identify patterns within the data, making its analysis easier. AI correlates data from different sources far more efficiently than a human. Modern log analytics tools leverage AI to gather log data from cloud services and on-premises environments. Log analysis and issue resolution become proactive, preventing negative impacts on the health of applications and systems. #4: Transforming raw log data Organizations depend on skilled professionals to handle and analyze log data, yet they often face overwhelming resource constraints. This is where AI can contribute significantly, by automating repetitive tasks and enhancing human capabilities. Before analysis, log data often required cleaning and preprocessing to remove errors, duplicates, or irrelevant information. AI can automate this process, ensuring the accuracy and standardization of all the data. AI can also organize log data into clusters based on similarities or classify them into predefined categories. This helps manage data more efficiently, making it easier for humans to understand and act upon the insights derived. #5: Analyzing log data A clear use case for AI within a DevSecOps strategy is the automation of repetitive and time-consuming tasks — such as data cleaning, feature selection, and model training. With AI taking on these tasks, developers can focus on other tasks. AI can sift through a mountain of data to spot duplication and anomalies — like subtle signs of a cyberattack or unusual traffic patterns — that might easily slip past human scrutiny. This yields enhanced security and operational insights. It’s more than just about handling the volume; AI is adept at detecting patterns that are too complex or too faint for the human eye to catch. For example, let’s consider logging and monitoring for a network to catch signs of data exfiltration. This kind of anomaly might manifest as an unusually high volume of data being sent to an unfamiliar external IP address during off-hours; that’s a pattern that might not immediately raise flags for a human amid thousands of legitimate data transfers happening every day. On the other hand, an AI-based system that’s trained on vast datasets of normal and malicious network behavior can identify this subtle pattern by correlating different indicators: The timing of the data transfer
The volume of data
The destination IP address
The type of data being transferred For a human, recognizing such a complex pattern requires painstaking analysis and might be missed entirely due to the sheer volume of log data. But an AI system can continuously monitor for these patterns across the entire network, detecting potential threats with precision and speed that far surpasses human capability. #6: Reducing alert fatigue Traditional infrastructure and service monitoring solutions are notoriously noisy, often generating alerts for events that don’t signify a genuine threat. Excessive unactionable alerts lead to alert fatigue. AI-based alerting intelligently filters alerts and reduces the noise, ensuring that the alerts generated are relevant and actionable. For example, traditional monitoring can’t adjust for seasonality — so a crossed threshold in the middle of a high-season weekday afternoon gets as much attention as one in the middle of the night during what should be a slow week. AI-based alerting uses historical data to continually train its models, factoring seasonality into its baselines. The result is fewer false positives and no more alert fatigue. Of course, organizations using AI-driven alerting need to rigorously test results in order to tune specificity and sensitivity. This ensures that critical events are captured effectively. #7: Proactive monitoring Given the large amounts of data an organization generates, teams often struggle to monitor all the organization’s resources proactively. AI is well-equipped to address this issue at scale. By continuously monitoring and aggregating logs from across entire environments, an AI-based tool can identify anomalies before they become widespread, allowing teams to detect potential threats in their initial stages. For example, the threat detection and investigation from Sumo Logic provides the visibility to address advanced threats before they affect operations. AI features such as this enable real-time monitoring, alerting, and data analysis across security tools, cloud infrastructures, and SaaS applications. This enables a DevSecOps team to investigate and respond to cyber threats swiftly. #8: Efficient incident response AI-driven alerting improves incident response by facilitating automatic resource allocation and gathering contextual information about an incident. This helps identify potential security threats faster, which in turn helps organizations respond more quickly. When AI-powered logging and observability platforms provide automated remediation features, teams can connect the dots: from continuously monitored logs to incident detection to remediation playbooks. Automated playbook execution means near-immediate response to an incident, whether that’s eliminating the root cause or alerting an engineer to begin an investigation. Remember, with every delay in responding to a security incident, the window for impact widens. Minimizing that delay with AI directly minimizes the impact of an incident. Privacy concerns related to AI adoption One last note: as we’ve seen, AI shows great promise as a tool for improving logging. But remember that it’s still an emerging technology. As a recent GitLab report makes clear, there are serious privacy concerns around AI adoption. While 83% of the teams surveyed said implementing AI in their development process was essential, 79% said they were highly concerned about privacy and IP when dealing with AI. But in many business contexts, AI tools need access to that private data for analysis. So take advantage of AI, but be aware and guard against any privacy concerns. Drowning in log data? AI is here to help. Logs are crucial. They’re a rich source of data for monitoring applications and infrastructure. But the multiple data sources and volume of log data — along with the sensitivity of some of that data in the logs — lead to some big challenges in managing log data, security, and privacy. AI is here to help. Modern log management and SIEM solutions are leveraging AI to automate analysis, enhance monitoring, and improve incident response. AI is making DevSecOps more efficient. And as AI solutions evolve, their role in log analysis will only grow, offering smarter, faster insights into logs. Logging is essential for successful DevSecOps teams. Logs are filled with the information needed to monitor and understand systems. Tracking down a defect? Trying to understand a sudden burst in questionable logins from a new region? Need to figure out why an app is crawling? Logs are that single source of truth for understanding what’s really happening. filled But there’s a problem that comes along with logs: the sheer amount of data. The information logged by services and applications just keeps on growing. And growing. It doesn’t take long for it to become more — much more — than can be managed. The data becomes overwhelming. Alert fatigue sets in. Data keeps growing. Human resources can’t. But there’s hope on the horizon. Innovations in AI have revolutionized the process of continuous log monitoring . AI algorithms can analyze and detect patterns within vast datasets, translate raw logs into actionable insights, and proactively alert teams to problems — all at a scale and at higher precision to assist humans. continuous log monitoring Let’s look at 8 ways that AI can maximize the value of logs. #1: Handling massive amounts of data First, the most obvious. Cloud-native environments, with their dozens (or hundreds) of distributed components, emit a massive volume of log data. In turn, this data requires high levels of expertise to sift through and analyze it all. massive But most organizations already face a shortage of people with the skills needed to tease out the insights from this data. Companies could train more people — but training is slow. And the growing complexity of the data tends to outpace any new skills. could Here is where AI shines: It’s a scalable way to handle log data, no matter the volume and no matter the time. Humans need breaks; they clock out for the day, get sick, take PTO, and play Galaga when bosses aren’t looking . But log data still arrives in massive volumes, regardless of the time of the day or day of the week. An AI-based system with analysis, detection, and alerting is always on. play Galaga when bosses aren’t looking #2: Automated security and access control When dealing with logs, it’s important to protect any sensitive user information and ensure that the data is only accessible to authorized team members. SecOps managers often implement fine-grained access control to ensure that the approved team members have access to (and only to) the data or metrics intended for use. AI systems can automatically identify and redact sensitive information — such as personal identifiers, financial details, or confidential business information — from log data before it’s accessed by humans. Or, as part of automated preprocessing, AI can de-identify or mask sensitive parts of data. #3: Collating data from disparate sources Merging data from various sources is a complex task. But it’s essential for effective security and operations. When logs are properly aggregated and correlated, the resulting data and metrics can give the context needed for better visibility and better troubleshooting. But this is a menial and time-consuming task … which makes it perfect for AI. AI can automatically gather information from various sources and identify patterns within the data, making its analysis easier. AI correlates data from different sources far more efficiently than a human. Modern log analytics tools leverage AI to gather log data from cloud services and on-premises environments. Log analysis and issue resolution become proactive, preventing negative impacts on the health of applications and systems. #4: Transforming raw log data Organizations depend on skilled professionals to handle and analyze log data, yet they often face overwhelming resource constraints. This is where AI can contribute significantly, by automating repetitive tasks and enhancing human capabilities. Before analysis, log data often required cleaning and preprocessing to remove errors, duplicates, or irrelevant information. AI can automate this process, ensuring the accuracy and standardization of all the data. AI can also organize log data into clusters based on similarities or classify them into predefined categories. This helps manage data more efficiently, making it easier for humans to understand and act upon the insights derived. #5: Analyzing log data A clear use case for AI within a DevSecOps strategy is the automation of repetitive and time-consuming tasks — such as data cleaning, feature selection, and model training. With AI taking on these tasks, developers can focus on other tasks. AI can sift through a mountain of data to spot duplication and anomalies — like subtle signs of a cyberattack or unusual traffic patterns — that might easily slip past human scrutiny. This yields enhanced security and operational insights. duplication It’s more than just about handling the volume; AI is adept at detecting patterns that are too complex or too faint for the human eye to catch. For example, let’s consider logging and monitoring for a network to catch signs of data exfiltration. This kind of anomaly might manifest as an unusually high volume of data being sent to an unfamiliar external IP address during off-hours; that’s a pattern that might not immediately raise flags for a human amid thousands of legitimate data transfers happening every day. On the other hand, an AI-based system that’s trained on vast datasets of normal and malicious network behavior can identify this subtle pattern by correlating different indicators: The timing of the data transfer The volume of data The destination IP address The type of data being transferred The timing of the data transfer The volume of data The destination IP address The type of data being transferred For a human, recognizing such a complex pattern requires painstaking analysis and might be missed entirely due to the sheer volume of log data. But an AI system can continuously monitor for these patterns across the entire network, detecting potential threats with precision and speed that far surpasses human capability. #6: Reducing alert fatigue Traditional infrastructure and service monitoring solutions are notoriously noisy, often generating alerts for events that don’t signify a genuine threat. Excessive unactionable alerts lead to alert fatigue. AI-based alerting intelligently filters alerts and reduces the noise, ensuring that the alerts generated are relevant and actionable. For example, traditional monitoring can’t adjust for seasonality — so a crossed threshold in the middle of a high-season weekday afternoon gets as much attention as one in the middle of the night during what should be a slow week. AI-based alerting uses historical data to continually train its models, factoring seasonality into its baselines. The result is fewer false positives and no more alert fatigue. Of course, organizations using AI-driven alerting need to rigorously test results in order to tune specificity and sensitivity. This ensures that critical events are captured effectively. #7: Proactive monitoring Given the large amounts of data an organization generates, teams often struggle to monitor all the organization’s resources proactively. AI is well-equipped to address this issue at scale. By continuously monitoring and aggregating logs from across entire environments, an AI-based tool can identify anomalies before they become widespread, allowing teams to detect potential threats in their initial stages. For example, the threat detection and investigation from Sumo Logic provides the visibility to address advanced threats before they affect operations. AI features such as this enable real-time monitoring, alerting, and data analysis across security tools, cloud infrastructures, and SaaS applications. This enables a DevSecOps team to investigate and respond to cyber threats swiftly. threat detection and investigation #8: Efficient incident response AI-driven alerting improves incident response by facilitating automatic resource allocation and gathering contextual information about an incident. This helps identify potential security threats faster, which in turn helps organizations respond more quickly. When AI-powered logging and observability platforms provide automated remediation features, teams can connect the dots: from continuously monitored logs to incident detection to remediation playbooks. Automated playbook execution means near-immediate response to an incident, whether that’s eliminating the root cause or alerting an engineer to begin an investigation. Remember, with every delay in responding to a security incident, the window for impact widens. Minimizing that delay with AI directly minimizes the impact of an incident. Privacy concerns related to AI adoption One last note: as we’ve seen, AI shows great promise as a tool for improving logging. But remember that it’s still an emerging technology. As a recent GitLab report makes clear, there are serious privacy concerns around AI adoption. While 83% of the teams surveyed said implementing AI in their development process was essential, 79% said they were highly concerned about privacy and IP when dealing with AI. But in many business contexts, AI tools need access to that private data for analysis. GitLab report privacy and IP So take advantage of AI, but be aware and guard against any privacy concerns. Drowning in log data? AI is here to help. Logs are crucial. They’re a rich source of data for monitoring applications and infrastructure. But the multiple data sources and volume of log data — along with the sensitivity of some of that data in the logs — lead to some big challenges in managing log data, security, and privacy. AI is here to help. Modern log management and SIEM solutions are leveraging AI to automate analysis, enhance monitoring, and improve incident response. AI is making DevSecOps more efficient. And as AI solutions evolve, their role in log analysis will only grow, offering smarter, faster insights into logs.

Maximizing Log Value with AI: 8 Ways to Revolutionize DevSecOps Monitoring

Today, the online search is being flipped on its head. Thanks to the magic of (LLMs), we have a better alternative for finding information: large language models Instead of drowning in a sea of blue links that often lead to a maze of irrelevant content, now we have a one-stop . chat box It can tackle all our hefty queries with highly relevant and comprehensive AI-generated answers. That's the face of AI Search today. And it’s becoming our new normal. If you're hustling online, you might wonder: how will this shift mess with your organic traffic? Better yet, how can you take advantage of it without being disrupted? That’s the core we're diving into here. The full scope of AI Search is still a big mystery. Though, we already have something solid to bounce off. First, let’s look at how AI Search stands now (in Q4 of 2023). The Synergy of Large Language Models and Search Engines Until not long ago, the limitations of LLM datasets were a topic of concern. Take ChatGPT as an example - its utility was hampered by a specific cut-off date, making it less reliable for real-time data needs. However, its recent with the Bing search engine has completely turned the tables. Since then, ChatGPT's capacity has been augmented with access to the entire internet. integration Another duo worth mentioning is Google and Bard, Google’s native Conversational AI. And soon, Google is about to launch its (SGE), melding its search engine prowess with its . Search Generative Experience multimodal LLM, Gemini So, in each of these AI Search scenarios, we got the killer convenience of LLM conversational power paired with real-time data backup from indexes. As HubSpot recently , the goal is to furnish users with holistic AI-generated answers to their queries, eliminating the need to sift through multiple links or type in a new query to find what you’re looking for. highlighted Quite the game-changer, right? But, what does this search evolution signify for online businesses? User Convenience at the Cost of Organic Traffic - Here’s What Studies Predict . For a myriad of online businesses, marketers, and creators reliant on organic traffic, the emerging AI Search could cause a major drop Recently, I came across an interesting by Search Engine Land that provides insights into how Google's SGE could potentially impact websites. study Specifically, the study examined 23 websites in the tech industry, assessing traffic for informational keywords. It estimated an aggregate organic traffic drop of 18-64% as a result of SGE. Quite the range, but not surprising. If we look at the SGE beta model today, it's clear where organic links are placed. They sit , right below Ads and an AI-generated answer with the AI snapshot at the right corner. at the very bottom of the SERP Given the well-known fact of the short attention span of modern users, how many would even scroll that far to click the organic links? 🤔 So in this new setup, winners will likely be those who land a spot in the AI snapshot - that carousel linking to 3 websites backing up the generated answer. The study assumes: If you don’t rank in the SGE snapshot carousel but you do rank in the traditional 10 blue links, you won’t get any traffic. It seems like a race for the AI snapshot, and Ads is on the horizon, favoring companies with larger budgets and resources. Meanwhile, here are a few more assumptions from the study: If your page ranks in the snapshot carousel, you will receive (some) organic traffic. Traffic from the snapshot carousel will be than the same rank on Google today. equal to or less It’s unlikely that the traffic plunge we’re talking about here will happen overnight. As people are inclined to stick to their habitual browsing patterns, they will likely continue to scroll through organic links for a while. However, as users get accustomed to AI-generated answers over time, their engagement with organic links will unavoidably dwindle. Thus, figuring out ways to adapt to this new AI Search paradigm now, rather than getting disrupted later, is the way to go. And that’s what we’re gonna do next. But before, let's first examine how AI Search might change the dynamics of query-text matching compared to traditional search engines. AI Search Goes Beyond Just Mechanical Keyword-Text Matching That’s what Fabrice Canel, Bing's Principal Product Manager, pointed out in a recent (slightly rephrased): interview " ." We do care about keywords, but it’s less about matching a query keyword with data containing the same keyword. Now, it's about aligning responses with user intent and context So, based on this comment, Bing is now integrating a more nuanced, semantic approach where understanding user intent and context is the focus. I'm not sure about Bing's past algorithms, but Google introduced its - a semantic search algorithm back in 2013. Hummingbird And that marked the search evolution far beyond a simple keyword-text matching engine. Since the release of Hummingbird (and later with , , and ), Google has been all about semantic search - aiming to match user intent with query context leveraging its semantic database . BERT MUM RankBrain Knowledge Graph So, the point here is that we got with the rise of AI Search. It's been evolving in the search engine landscape for nearly a decade. semantic search isn’t something new This is why the online chatter claiming the revolutionary aspect of AI Search is its focus on semantics and user intent can be confusing. But undoubtedly, by now the algorithms have evolved and become more sophisticated. They can better decode user intent in a more nuanced way by analyzing past behavior and broader web patterns, local and seasonal context (if there’s any), improved understanding of language nuances, and correction of typos, among other things. Overall, journey we are on. AI Search represents not a beginning, but a progression of the semantic search But the question remains: how do we excel in it? What's the New Game Plan for Excelling in AI Search? It's no revelation to say that, as of now, the inner workings of AI search systems—how they select and favor content for generating responses—remain somewhat of a black box. And, frankly, it might always stay that way. Nonetheless, we can still attempt to crack the AI Search code by examining it through two lenses: Digital PR and SEO. In the upcoming issues of my newsletter , I will go more granular on each in the context of AI Search - so make sure to . AImplifier sign up For now, let’s skim the surface of both strategies briefly. How Does Digital PR Score in AI Search Ranking? For clarity, Digital PR roughly means a marketing strategy leveraging external high-authority channels to boost brand awareness and online presence. Such channels can be media outlets, industry publications, reference websites,  influencer domains, etc. For brands, being mentioned by such channels and getting backlinks from them has always been one of the most effective ways to organically top-rank on search engines. Now, let’s assess Digital PR from the AI Search angle. The Index Side of AI Search As indexes integrate with AI Search, Digital PR's role in boosting brand online visibility stays crucially important. For ChatGPT to feature your brand, for instance, you've got to be on good terms with Bing. Like with any search engine, to get ranked on Bing, your brand needs to secure positive media coverage, reputable backlinks, and mentions from authoritative sources. What’s more, such links and mentions will be advantageous for ranking in both brand and non-brand queries, which can be highly pertinent to your brand. From the LLM Angle of AI Search As we know, another part of AI Search involves large language models (LLMs) that generate responses based on their static datasets. These are periodically updated. According to AI developers I had a chance to talk with, during these updates, they curate data for quality based on the trustworthiness of the source, among other factors. Roughly, sources are typically categorized into high, medium, or low trust levels. Highly trusted sources may include media outlets, reputable reference sites, academic journals, and industry reports—essentially, classical top-tier Digital PR channels. The takeaway here? To increase your brand's chances of getting included in the next LLM update, you need to be present external authoritative resources. across And this is where Digital PR again comes in. Further to this, let me share an experiment I’ve done with ChatGPT to explore how it selects sources to shape its recommendations in response to queries. How ChatGPT Comes Up with Recommendations - My Experiment I harnessed ChatGPT to scout the best crypto cold wallet for me. To avoid a screenshot galore here, in short: I prompted: " ." I'm looking for the best crypto cold wallet. Help me pick one After consulting with its dataset and Bing, it presented a list of wallets, backing each with a rationale based on security, price, fees, and so on. Next, I requested a list of sources that corroborated these recommendations. Here are a few interesting observations: First, none of the sources in the list was a crypto wallet website. They were all Digital PR channels. In particular, reference websites with high domain authority, a lot of user reviews, ratings, product comparisons, etc. Second, not all these resources were top-ranked on Bing for my query. The last one got me wondering: Why did ChatGPT choose resources that weren't at the top of Bing's rankings? So, I dug deeper. Turns out, rank isn't the only thing ChatGPT considers when deciding where to pull its answers from. If ranking isn't the decisive factor, then which one is? 🤔 As you see from the screenshot, ChatGPT evaluated in selecting sources to back up its recommendations. a mix of criteria Among the key ones were relevance, recency, authority, comprehensiveness, and uniqueness. And, guess what? It chose Digital PR channels that fit these criteria to suggest a crypto wallet to me. There was no brand website among them. Now it doesn’t mean that brand websites are excluded from ChatGPT's selections. If they fit all the necessary criteria, they have a shot. But that requires going all-in on the website's authority growth and ranking, which is always a tough and costly marathon. If limited resources and time are the case, it's more pragmatic to prioritize being present ACROSS multiple already established external channels rather than putting all eggs in one website basket. Wrapping it up: In the context of AI Search looks like Digital PR is the primary direction brands need to go to fit both index and LLM parts of chatbots. Now, onto the SEO aspect of AI Search - is it dead, or do we need to do it differently? AI Search and SEO Again, since we have that index part in AI search systems, we still need SEO to optimize for it. That’s why all those . cries of ‘SEO is dead with AI’ seem out of place There's also chatter about a “new” SEO wave—AI SEO—that’s deemed to be new due to its focus on user intent, context, and conversational language in content. However, as we covered above, this isn't fresh off the boat. It's been a decade-old standard. In the AI era, it just remains valid and evolving. So if SEO is part of your marketing mix, sticking to the fundamentals still makes sense, regardless of the AI aspect of search: Thoroughly follow search engine guidelines. Get your website properly structured. Cultivate a healthy backlink profile for your site. Optimize your content for conversational language, and constantly keep it updated. That already accounts for 50% of your SEO success. In the above-mentioned , Bing's Fabrice Cannel confirmed this. interview Now, what will need to be done differently in SEO? 1. Double down on voice search optimization (VSEO) While voice search has been around well before the AI surge, its accuracy and adoption have profoundly increased. With Statista the global voice recognition market to rise from $10 billion in 2020 to $50 billion by 2029, VSEO is becoming more crucial than ever. projecting I’ll be exploring VSEO strategies in the upcoming issues of my newsletter . Sign up so you don’t miss out. AImplifier 2. Learn from Chatbots Pay special attention to how chatbots address potential user queries. Interrogate them on why and how they’ve generated particular responses, what criteria they use when coming up with recommendations, etc. So far, chatbots serve as the most vivid examples of how AI processes and responds to user queries. 3. Leverage Chat-Specific Webmaster Tools Optimizing for ChatGPT? Use Bing Master Tools. Their latest includes chat-specific metrics like CTRs for Bing bot answers. Performance Report With ChatGPT integrated into Bing, this data could be invaluable in boosting your brand's visibility there. To sum up, the mantra for SEO in the context of AI Search doesn’t seem to be radically different: delivering relevant, comprehensive, in-depth, and updated content remains at the core of optimization, AI-infused or not. So What’s the Best Way to Adapt to AI Search: Digital PR or SEO? The combo of both. Together, Digital PR and SEO have always been the best strategy for brands to score organic traffic. However, in the AI era, Digital PR needs to take center stage. Why? It's more effective at resonating with LLMs by signaling them trust, authority, and credibility through reputable external channels. In other words, PR alone without SEO would have a higher potential to get your brand to AI Search ranking than SEO without PR. That’s why I believe Digital PR needs to be the priority. On a Last Note, a Case That Puts All This AI Search Talk Into Hard Numbers As we wrap this up, let's pivot to a real-world example: a brand that tapped into ChatGPT as a new revenue channel. The company Logikcull has a to tell here. story They revealed that since March 2023, the company started seeing an uptick in new clients who discovered Logikcull through ChatGPT-generated answers. Their CEO, Andy Wilson, commented that now they’re projecting that ChatGPT would bring them $100,000 in monthly subscription revenue. Note: this started in March 2023, i.e., before ChatGPT had even integrated with Bing. While now it's uncertain if such ROI stories will become the new norm anytime soon, it's still a good idea to start thinking about how to become their heroes. Conclusion AI Search is still in its infancy, and we're far from having its full picture. Figuring out how to adapt, we can only piece together traditional marketing wisdom and emerging insights we can gather. That’s what this article has been doing. So here are : the key takeaways The transition to AI Search is likely to disrupt businesses that depend heavily on organic traffic, especially with the upcoming launch of Google's SGE. Digital PR should take center stage in your marketing mix. By leveraging high-ranking and authoritative platforms, your brand stands a better chance of getting picked up by both the Index and LLM components of AI Search. SEO isn't going anywhere and should work in tandem with your Digital PR efforts. While AI SEO is buzzing, the fundamentals of semantic optimization remain largely the same—though with an added focus on voice search and a more conversational style. In sum, by ramping up your Digital PR and complementing it with strategic SEO, you'll increase your brand’s chances of succeeding in the impending AI Search transformation. The rest is experimentation. More on this topic in the next issues of my newsletter, . If you enjoyed it, make sure to sign up. AImplifier Feel free to share your thoughts or correct me. And if you're interested in collaborating on the next piece, I'm open. I'd also love to connect on and LinkedIn . Twitter Thanks for reading! Your time and attention are priceless. ❤️

AI Search: Exploring the Threat of Traffic Drops and How to Rank on Large Language Models

এআই অনুসন্ধান: ট্র্যাফিক ড্রপের হুমকির অন্বেষণ এবং কীভাবে বড় ভাষার মডেলগুলিতে র‌্যাঙ্ক করা যায়

KI-Suche: Untersuchung der Gefahr von Traffic-Rückgängen und Ranking bei großen Sprachmodellen

Búsqueda con IA: exploración de la amenaza de las caídas de tráfico y cómo clasificar en modelos de lenguaje grandes

Recherche AI : Explorer la menace de baisse de trafic et comment se classer sur de grands modèles linguistiques

एआई खोज: ट्रैफ़िक में गिरावट के खतरे का पता लगाना और बड़े भाषा मॉडल पर रैंक कैसे करें

AI 検索: トラフィック ドロップの脅威と大規模な言語モデルでランク付けする方法を探る

AI 검색: 트래픽 감소 위협 탐색 및 대규모 언어 모델에서 순위를 매기는 방법

Pesquisa de IA: explorando a ameaça de quedas de tráfego e como classificar em grandes modelos de linguagem

Поиск с помощью ИИ: изучение угрозы падения трафика и способы ранжирования на больших языковых моделях

Yapay Zeka Arama: Trafik Düşüşü Tehditini ve Büyük Dil Modellerinde Nasıl Sıralanacağını Keşfetmek

Tìm kiếm AI: Khám phá mối đe dọa giảm lưu lượng truy cập và cách xếp hạng trên các mô hình ngôn ngữ lớn

人工智能搜索：探索流量下降的威胁以及如何在大型语言模型上排名

Practically 95% of any human reaction is a form of prediction. From how one gets dressed to the chosen commute, every decision is guesswork based on parameters such as the weather, traffic, and time of day. Yet, as inventory forecasters predict demand on multiple levels, the number of external factors impacting their decisions also skyrocket, making it extremely difficult for humans to make the predictions. Robust inventory management is the unsung hero in logistics—and it’s encouraging the global supply chain AI market to reach by 2028. But it’s dependent on one thing: Accurate forecasting. $17.5 billion providers and data teams today need to be hot on understanding the , what sources they require to meet their objectives, and how to maintain its quality. Logistics power of data With that in mind, let’s explore how paying more attention to data quality and advanced analytics can optimize inventory forecasting and improve operational efficiency. Why Accurate Data is Essential in Forecasting McKinsey reports that applying AI-driven forecasting to supply chains reduces errors between . But the limited usefulness of the data collected remains the top barrier to successful implementation, keeping the rate of AI adoption since 2018 at a low 6%. With the right data and tools, inventory forecasters can predict anything from how much time they have to release trending products to the risks and costs of missing out on the opportunity. 20% and 50% Take fast-fashion brands, for example. They distribute up to 20,000 clothing stock-keeping units (SKUs) across ten countries in the EU, where price sensitivity and sizes vary per location. Then the new Barbie movie is released, and suddenly everyone wants to wear pink. That’s over 200,000 SKUs to consider . barbie-fying While stores like Gap are ahead of the game in creating a , others are apprehensive, knowing that the merchandise will end up on sale if movie ratings are low. Gap x Barbie collection Inventory forecasters need to weigh everything from budget to the predictability of movie success—and they want these figures yesterday. With accurate data regarding operations, such as delivery speed, workers available, and vehicle capacity, alongside external inputs, including consumer trends, AI-powered analytics tools can help inventory teams make data-driven decisions instantly. AI for Data Collection and Storage Many organizations possess sufficient data to leverage the benefits of AI-driven forecasting: A quality forecast could be achieved on 80% internal and 20% external data. The trick is ensuring that data is collected, stored, and visible to inventory teams. And this truth is something data champions like and are more than aware of. For stores to better support customer fulfillment and omnichannel retailing, the two companies have started putting drones to work as a method of tracking warehouse inventory. Gather AI IFM From counting parcels to spotting at checkout, computer vision AI technology tools can detect and track objects and SKUs as they move through the warehouse or store. This helps provide the continuous and reliable updates needed for making real-time data-driven decisions while removing room for human error. retail theft Automating data collection, such as the number of items on shelves and the locations of particular pallets, helps inventory teams understand their operations and needs in real time. While storing operational, financial, and external data required to meet inventory objectives in central repositories enables data teams to capture a holistic view. That’s why retailers and companies must devote more time to their data collection and storage methods, looking at how they can increase data visibility. This includes ensuring strict access controls and encryptions to protect data and keep it in the hands of those with authorization. logistics AI for Data-Driven Inventory Forecasting At the analytical stage of the data lifecycle, AI forecasting tools, fed with continuous and reliable data, can predict sales down to the SKU level. This way, sellers can adjust production levels, manage procurement, and allocate resources effectively. This alignment of inventory with demand reduces handling and storage costs and improves overall operational and financial efficiency. Here’s how to implement forecasting models with success: What business changes could affect the inventory variables? What product expiration periods, storage space, and storage conditions are required? How could staff turnover and ship maintenance affect this? Defining reasonable ranges for these parameters is crucial to avoid creating unrealistic outputs leading to adverse business decisions. Define the critical parameters: Unexpected events will likely skew AI predictions. The smoothing technique is a statistical approach to eliminating outliers from datasets. It helps to minimize the variation in data between periods where an anomaly occurred, creating a more representative data set for AI-powered forecasting models to learn from. Apply data smoothing: Take Covid-19, for instance. The data captured during this time falsely illustrate business as usual and would require smoothing. If data is incomplete, logistics teams struggle to have an accurate historical representation, and assumptions go awry. For this reason, that is ‘synthesized’ or ‘artificial’ information generated from existing data—is used to train AI-powered forecasting models with potential scenarios. The machine can define more data classifications and increase predictability by reading a broader data set. Train what-if simulations with augmented data: augmented data— What-if scenarios are critical in cases of limited data samples, which diminishes confidence in forecasting with accuracy. They are also valuable for addressing distant future uncertainty where it is challenging to predict unexpected events. Depending on the size of your data, you may choose a simpler AI model or a more complex one where larger data sets and many iterations are available to train numerous parameters. Harnessing the power of data-driven inventory forecasting is the key to unlocking logistics companies’ full potential in today’s complex supply chain landscape. With robust forecasting models, inventory managers can ensure the right products are available when customers need them, leading to enhanced customer satisfaction, loyalty, and a competitive edge. Organizations can go on to assess market opportunities, plan new product launches, manage product lifecycles, and make optimal supply chain decisions aligned with business objectives. This article was originally published by Hristo Hadjitchonev on . 150sec

From Instinct to Insights: Data-Driven Inventory Forecasting in Logistics

Why AI Will Bring an Explosion of New Jobs

**DUBAI, United Arab Emirates, September 19th, 2024/Chainwire/--**Crypto Content Creator Campus, a groundbreaking initiative designed to empower and inspire the next generation of crypto influencers, is thrilled to announce its official launch in Dubai this fall. Taking place from November 8th to 10th, CCCC will serve as the premier annual gathering for the crypto community. It offers a unique platform for crypto content creators, influencers, and key opinion leaders (KOLs) to learn, network, and grow together, shaping the future of the industry. As a team of industry experts and visionaries, CCCC is dedicated to fostering a thriving ecosystem for content creators within the Web3 and crypto space. Our mission is to provide an educational retreat that equips creators with the tools to drive crypto adoption and expand the crypto ecosystem. CCCC: Learn x Mingle x Grow CCCC will offer sponsors and participants an unparalleled opportunity to: Masterclass With World-Class Celebrities: Discover the secrets to personal branding and content strategies from world-renowned celebrities and influencers.
Mingle With Founders of Major Ecosystems: Connect with ecosystem pioneers and discover how they are shaping the future of blockchain and Web3.
Learn From Top Executives: Learn from industry leaders including top executives from YouTube, TikTok, and X, alongside over 200 global content creators.
Showcase Your Skills: Participate in the Content Creator Hacker House with top content creators and global KOLs and compete for a $70,000 prize while contributing to the future of crypto.
Elevate Your Brand: Partner with CCCC as a sponsor and gain unparalleled exposure to a highly engaged crypto community. The team is proud to announce that CCCC has already received overwhelming support from the blockchain industry. Leading crypto exchanges have signed on as title sponsors, while key players from the Layer 2 and DeFi ecosystems have also shown their support by joining as sponsors with varying levels of participation. "CCCC will provide a thought-provoking and transformative experience for crypto influencers," said Phoebe Peng, spokesperson for CCCC. "We provide a supportive community and platform that empowers creators to not only expand the future of the crypto industry but to redefine what's possible in the digital age." CCCC is now accepting sponsorship applications and ticket purchases. For more information or to register, users can visit https://www.cccc.buzz/ About Crypto Content Creator Campus (CCCC) Crypto Content Creator Campus (CCCC) is led by a team of industry experts and visionaries dedicated to shaping the future of content creation within the Web3 and crypto sphere. Driven by a shared passion for creating a high-value community, CCCC curated a campus that promises an experience unlike any other. For more details about CCCC, users can visit: https://www.cccc.buzz/ For inquiries, users can contact: hello@cccc.buzz Contact PR Rep Tony Au Crypto Content Creator Campus tony.au@bybit.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here **DUBAI, United Arab Emirates, September 19th, 2024/Chainwire/--**Crypto Content Creator Campus, a groundbreaking initiative designed to empower and inspire the next generation of crypto influencers, is thrilled to announce its official launch in Dubai this fall. Taking place from November 8th to 10th, CCCC will serve as the premier annual gathering for the crypto community. It offers a unique platform for crypto content creators, influencers, and key opinion leaders (KOLs) to learn, network, and grow together, shaping the future of the industry. As a team of industry experts and visionaries, CCCC is dedicated to fostering a thriving ecosystem for content creators within the Web3 and crypto space. Our mission is to provide an educational retreat that equips creators with the tools to drive crypto adoption and expand the crypto ecosystem. CCCC: Learn x Mingle x Grow CCCC will offer sponsors and participants an unparalleled opportunity to: Masterclass With World-Class Celebrities: Discover the secrets to personal branding and content strategies from world-renowned celebrities and influencers. Mingle With Founders of Major Ecosystems: Connect with ecosystem pioneers and discover how they are shaping the future of blockchain and Web3. Learn From Top Executives: Learn from industry leaders including top executives from YouTube, TikTok, and X, alongside over 200 global content creators. Showcase Your Skills: Participate in the Content Creator Hacker House with top content creators and global KOLs and compete for a $70,000 prize while contributing to the future of crypto. Elevate Your Brand: Partner with CCCC as a sponsor and gain unparalleled exposure to a highly engaged crypto community. Masterclass With World-Class Celebrities: Discover the secrets to personal branding and content strategies from world-renowned celebrities and influencers. Mingle With Founders of Major Ecosystems: Connect with ecosystem pioneers and discover how they are shaping the future of blockchain and Web3. Learn From Top Executives: Learn from industry leaders including top executives from YouTube, TikTok, and X, alongside over 200 global content creators. Showcase Your Skills: Participate in the Content Creator Hacker House with top content creators and global KOLs and compete for a $70,000 prize while contributing to the future of crypto. Elevate Your Brand: Partner with CCCC as a sponsor and gain unparalleled exposure to a highly engaged crypto community. The team is proud to announce that CCCC has already received overwhelming support from the blockchain industry. Leading crypto exchanges have signed on as title sponsors, while key players from the Layer 2 and DeFi ecosystems have also shown their support by joining as sponsors with varying levels of participation. "CCCC will provide a thought-provoking and transformative experience for crypto influencers," said Phoebe Peng, spokesperson for CCCC. "We provide a supportive community and platform that empowers creators to not only expand the future of the crypto industry but to redefine what's possible in the digital age." "CCCC will provide a thought-provoking and transformative experience for crypto influencers," said Phoebe Peng, spokesperson for CCCC. "We provide a supportive community and platform that empowers creators to not only expand the future of the crypto industry but to redefine what's possible in the digital age." CCCC is now accepting sponsorship applications and ticket purchases. For more information or to register, users can visit https://www.cccc.buzz/ https://www.cccc.buzz/ https://www.cccc.buzz/ About Crypto Content Creator Campus (CCCC) Crypto Content Creator Campus (CCCC) is led by a team of industry experts and visionaries dedicated to shaping the future of content creation within the Web3 and crypto sphere. Driven by a shared passion for creating a high-value community, CCCC curated a campus that promises an experience unlike any other. For more details about CCCC, users can visit: https://www.cccc.buzz/ https://www.cccc.buzz/ https://www.cccc.buzz/ For inquiries, users can contact: hello@cccc.buzz hello@cccc.buzz hello@cccc.buzz Contact PR Rep Tony Au Crypto Content Creator Campus tony.au@bybit.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Crypto Content Creator Campus (CCCC) Launches As The Premier Annual Gathering For Crypto Influencers

Crypto Content Creator Campus (CCCC) запускаецца ў якасці прэм'ернага штогадовага збору для Crypto Influencers

El campus Crypto Content Creator (CCCC) es llança com a reunió anual principal per a influencers criptogràfics

Crypto Content Creator Campus (CCCC) startet als wichtigstes jährliches Treffen für Krypto-Influencer

Se lanza el Crypto Content Creator Campus (CCCC), la principal reunión anual para influencers de criptomonedas

क्रिप्टो कंटेंट क्रिएटर कैंपस (CCCC) क्रिप्टो इन्फ्लुएंसर्स के लिए प्रीमियर वार्षिक सभा के रूप में लॉन्च हुआ

Crypto Content Creator Campus (CCCC) viene lanciato come il principale incontro annuale per gli influencer delle criptovalute

暗号コンテンツクリエイターキャンパス（CCCC）が暗号インフルエンサーのためのプレミア年次集会として発足

Crypto Content Creator Campus (CCCC) криптографиялық әсер етушілерге арналған жыл сайынғы басты жиын ретінде іске қосылды.

Kripto turinio kūrėjų miestelis (CCCC) prasideda kaip pagrindinis kasmetinis kriptovaliutų įtakojančių žmonių susirinkimas

Kripto satura veidotāju pilsētiņa (CCCC) tiek atklāta kā galvenā ikgadējā kriptovalstu ietekmētāju sanāksme

Fillon Kampusi i Krijuesit të Përmbajtjes së Kriptove (CCCC) si Mbledhja Vjetore Premier për Influencuesit e Kriptove

Crypto Content Creator Campus (CCCC) Crypto Influencers க்கான முதன்மையான வருடாந்திர கூட்டமாகத் தொடங்குகிறது

(IBW), the country’s flagship series of blockchain and Web3 events held in Bangalore from Dec.4-10, is delighted to announce that the attendees are going to have unparalleled opportunities for networking, collaboration, and discussion at the weeklong festival. BANGALORE, India, November 29th, 2023/Chainwire/-- India Blockchain Week There will be over 60 conferences, hackathons, networking events, workshops, a demo day and more. The IBW will be a platform where blockchain builders, investors, TradFi executives, policymakers, and crypto-curious newcomers from India and around the world come together to discover the future of DeFi, crypto, dApps, and explore the impact of Web3 on finance and culture. IBW presents an unparalleled gateway for participants to engage with their most-admired thought leaders, experts and influencers in the blockchain ecosystem. For Web3 companies, this is a unique opportunity to engage with emerging talent, participate in the IBW Demo Day and Hackathon, attract new developers to the blockchain arena, and expand the ecosystem in India. Besides the IBW VIP Dinner, IBW After Party, and Hashed Founders Night organized by IBW and Hashed Emergent, here are some of the key highlights of the industry gathering: : Hosted on Dec.6-7, 2023 at the Sheraton Grand Whitefield, it features keynote speeches and panel discussions on the latest developments and opportunities in the web3 space, and examining the policy landscape. The headline conference also includes a demo day and a hackathon. IBW’23 Conference : The Demo-Day will be held on 7th December from 2-6 PM to spotlight 12 notable Indian Web3 startups at the IBW'23 Conference venue. This 4-hour demo day will cater to the broad audience  including investors, founders, builders and others. It is organized in partnership with elite companies, VC funds and accelerators including Aelf Ventures, Hashed Emergent, Graviton, Coinswitch, and Google Cloud. Demo Day Hackathon: The virtual hackathon on Dec.6 is expected to see the participation of over 2,500  enthusiastic individuals. Organized by IBW and HackerEarth, the hackathon will conclude on a 250-member stage at the IBW'23 Conference venue. : Organized by Devfolio and ETHGlobal from Dec.8-10, it is one of the world's largest Ethereum hackathons, hosting over 1,500 builders and 25+ workshops. ETHIndia : Set for Dec.7, Polygon Connect India is the celebration of this region’s leg of DevX Global Tour, a global gathering of tech builders and innovators. Polygon Connect : Another prominent side-event on Dec.7. It's a one-day conference filled with Polkadot innovations, thought-provoking conversations, and community gathering. Polkadot Pulse : ​The Indian edition of Filecoin’s global event series. Spanning across Dec.3-4, it focuses on providing developers the tools, resources and guidance to build for the decentralized future. FILBangalore : Organized by ETHGlobal, Pragma is a one-day, single-track summit with intimate, high-quality talks by industry leaders for a global audience. Pragma India : Powered by STAN, it’s an exclusive journey into the thrilling realm of Web3 Gaming! This event unites industry leaders, developers, and passionate Web3 enthusiasts to discuss groundbreaking ways of forging connections and creating value in Web3 Gaming. Confirmed speakers are Sebastian Borget from The Sandbox, Vinayak Rao from Aptos, Neeraj Khandelwal from Okto, Parth Chadha from STAN and more. IBW Gaming Mixer Here’s the . With over 60 registered side events, attendees can expect a week brimming with engagement and insight. IBW has also onboarded the popular NFT artist Punk6529 as the official NFT Art Partner. Artworks from the Memes by 6529 and the 6529 Museum collections will be displayed at the event. full list of side events The IBW organizers would like to thank the sponsors for their help in making it the most important event in the country for conversations about crypto, blockchain, and Web3. Avalanche is IBW’s top-flight title sponsor while the roster of gold sponsors includes Polkadot, Internet Computer, Glip, Ledger, Aptos and Hunch. The silver sponsors are Bharatbox, Saber, FireDrops, dYdX, Supra, Orbs, Zoth, Persona, Berachain, and Shemaroo. About India Blockchain Week (IBW) India Blockchain Week (IBW) is a series of blockchain and web3 events, spanning from 4-10 December 2023 across Bangalore, India. IBW is a week-long array of 60+ web3 events, including flagship events such as the headline IBW Conference, ETHIndia, Polygon Connect, Polkadot Pulse, FILBangalore and many other side-events, networking events, workshops, parties etc hosted by Indian & global blockchain industry stakeholders. The IBW Conference is the two-day headline event anchoring India Blockchain Week (IBW) on 6-7 December 2023 at the Sheraton Grand, Bangalore and hosted by Hashed Emergent - a team and brand dedicated to accelerating the mass adoption of Web3 in India. Buy Tickets Discover Sponsors Check out FAQ For more information about IBW, visit: | | | Website Twitter LinkedIn Telegram Contact Irshad Ahmed gm@indiablockchainweek.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program . here

India Blockchain Week Gears Up For The Country’s Biggest Web3 Gathering With Over 60 Side Events

GATHERING CLOUDS.

Black Friday is nearly here, which means customers will be swarming stores to snag the best deals — and retailers will be bracing for the corresponding boost in thefts. But even aside from the Black Friday bump, theft is a growing problem for many retailers. How could AI reduce the risk? 1. Enabling Automatic Cart Audits Some retailers have employees standing near store exits to check shoppers’ receipts as they leave, verifying that the list of items matches their cart contents. However, this process often becomes cumbersome during busy periods, requiring people to wait in lines. Leaders at Sam’s Club believe AI can do the task faster. As of April 2024, more than 120 locations used AI exit scanning technology, and the overall figure matched executives’ goal of adding AI to 20% of its stores by the end of the first quarter. Sam’s Club represents the largest deployment of such customer-facing technology in the retail industry. Customers who engage with it can leave stores 23% faster, and internal data shows more than 50% of shoppers use it. Notably, before rolling out this solution, Sam’s Club employees got feedback from customers about current pain points and what they wanted from their experiences. Participants mentioned they’d like faster and more convenient shopping trips and consistently brought up the waiting times associated with the previous cart audits as a primary downside.  Now, computer-vision cameras and digital technologies work in the background to check that cart contents match a customer’s payment records. This high-tech approach aligns with the company’s other improvements, such as its Scan & Go offering, which allows people to bypass the traditional checkout process when buying their items. The market value for AI products in retail was approximately $5 billion in 2023, and it’s expected to grow. Decision-makers are even more likely to reap the benefits if they do as Sam’s Club leaders did and gather customer feedback. 2. Curbing Potentially Harmful Employee Preferentiality Most people think of customers as the main people behind retail theft, but workers can exacerbate the problem, too. Sometimes, they might slip small items into their pockets during slow periods, believing no one will notice and they will never get caught. The circumstances can also become more complicated. One example is what some in the retail industry call “sweethearting.” It happens when employees give discounts or free items to friends or relatives. One tech company, Corsight AI, began offering AI technology to detect such interactions. More specifically, it spots unusual behavior by analyzing how close customers stand to employees. Because the tool tracks engagements over time, it can also determine if specific shoppers keep going to the same employee’s cash register or otherwise show a preference. The system flags unusual behaviors to the relevant store decision-makers, letting them decide whether to act. Critics have pointed out that regularly engaging with the same employees when shopping at a store doesn’t necessarily mean anything suspicious. Perhaps a customer appreciates a worker’s friendliness, efficiency or product knowledge, causing them to naturally interact when opportunities arise. However, increasing theft problems have caused some retail leaders to scrutinize things they might have previously ignored. That is particularly true for retailers selling high-value items. For example, jewelry companies’ crime-related losses exceeded $129 million in 2022. These retailers usually have high-tech security systems, but those defenses probably won’t catch occasional instances of store workers offering unusually generous discounts. AI tools might. 3. Providing Better Awareness Although circumstances vary, some potential shoplifters target locations before attempting to steal things. During each visit, they gather information such as the number of workers in the store at particular times, whether certain departments or products have dedicated security measures and the locations of entrances and exits. Someone who goes to a well-staffed store, notices numerous security cameras and sees how employees seem continually aware of their surroundings may decide against proceeding with theft attempt plans. Conversely, stores that seem virtually deserted and feature few or no visible security cameras are much more appealing to would-be thieves. Because AI products can work in the background and detect unusual occurrences, they help loss prevention officers and others become more aware of what happens in individual stores. For example, Everseen creates AI tech that can be used to help reduce self-checkout theft. Part of its approach involves processing footage from ceiling-mounted cameras, often gathering tens of millions of customer interactions daily. Another helpful aspect of the technology is that it can scan shelves to determine when specific products are almost out of stock. Additionally, retail leaders can use this product to get near-real-time analytics about what’s happening in a store and receive prompts about handling certain situations. For example, if the AI spots a frustrated customer, it might prompt a worker to engage with them to learn what’s wrong. Many retailers already use artificial intelligence creatively by incorporating it into their demand-planning processes. For example, automating supply chain activities could reduce costs by as much as 10%, making that goal a worthy pursuit for cost-conscious parties. In any case, improved awareness provided by specialty tech products could help retailers manage theft. 4. Analyzing Shoppers’ Gestures Although many retailers use facial recognition technologies, some consumers dislike the perceived lack of privacy. An AI alternative is potentially less invasive because it does not analyze people’s appearance, but rather whether they make suspicious movements commonly linked to theft. Wild Duck Wine & Spirits in Boston uses an AI product from company Veesion on all 24 of its security camera feeds. The technology checks shoppers’ gestures to see if they match those frequently used during shoplifting. For example, someone might try to conceal something in a clothing pocket. Another “red flag” occurs when people hide items in baby strollers. Finally, people may eat or drink while shopping, potentially consuming what they have not paid for yet. If the AI spots something specific, it alerts workers approximately 15 seconds later, providing a video clip of the event. Those employees can then choose whether to confront the person or see if they try to skip the checkout counter. A representative from the AI company explained that this solution is about more than stopping people from stealing single items. It sends them a message that they should not try to repeat their actions because they’ll get caught. Minimizing Theft in Retail These fascinating examples show how AI can help store managers, loss prevention officers and other responsible parties reduce the frequency of theft in their locations. Choosing the appropriate tools and learning how to use them properly is crucial for success. Black Friday is nearly here, which means customers will be swarming stores to snag the best deals — and retailers will be bracing for the corresponding boost in thefts. But even aside from the Black Friday bump, theft is a growing problem for many retailers. How could AI reduce the risk? Black Friday is nearly here, which means customers will be swarming stores to snag the best deals — and retailers will be bracing for the corresponding boost in thefts. But even aside from the Black Friday bump, theft is a growing problem for many retailers. How could AI reduce the risk? 1. Enabling Automatic Cart Audits 1. Enabling Automatic Cart Audits Some retailers have employees standing near store exits to check shoppers’ receipts as they leave, verifying that the list of items matches their cart contents. However, this process often becomes cumbersome during busy periods, requiring people to wait in lines. Leaders at Sam’s Club believe AI can do the task faster. As of April 2024, more than 120 locations used AI exit scanning technology, and the overall figure matched executives’ goal of adding AI to 20% of its stores by the end of the first quarter. Sam’s Club represents the largest deployment of such customer-facing technology in the retail industry. Customers who engage with it can leave stores 23% faster , and internal data shows more than 50% of shoppers use it. Notably, before rolling out this solution, Sam’s Club employees got feedback from customers about current pain points and what they wanted from their experiences. leave stores 23% faster leave stores 23% faster Participants mentioned they’d like faster and more convenient shopping trips and consistently brought up the waiting times associated with the previous cart audits as a primary downside.  Now, computer-vision cameras and digital technologies work in the background to check that cart contents match a customer’s payment records. This high-tech approach aligns with the company’s other improvements, such as its Scan & Go offering, which allows people to bypass the traditional checkout process when buying their items. The market value for AI products in retail was approximately $5 billion in 2023 , and it’s expected to grow. Decision-makers are even more likely to reap the benefits if they do as Sam’s Club leaders did and gather customer feedback. approximately $5 billion in 2023 approximately $5 billion in 2023 2. Curbing Potentially Harmful Employee Preferentiality Most people think of customers as the main people behind retail theft, but workers can exacerbate the problem, too. Sometimes, they might slip small items into their pockets during slow periods, believing no one will notice and they will never get caught. The circumstances can also become more complicated. One example is what some in the retail industry call “sweethearting.” It happens when employees give discounts or free items to friends or relatives. One tech company, Corsight AI, began offering AI technology to detect such interactions. More specifically, it spots unusual behavior by analyzing how close customers stand to employees. Because the tool tracks engagements over time, it can also determine if specific shoppers keep going to the same employee’s cash register or otherwise show a preference. The system flags unusual behaviors to the relevant store decision-makers, letting them decide whether to act. spots unusual behavior spots unusual behavior Critics have pointed out that regularly engaging with the same employees when shopping at a store doesn’t necessarily mean anything suspicious. Perhaps a customer appreciates a worker’s friendliness, efficiency or product knowledge, causing them to naturally interact when opportunities arise. However, increasing theft problems have caused some retail leaders to scrutinize things they might have previously ignored. That is particularly true for retailers selling high-value items. For example, jewelry companies’ crime-related losses exceeded $129 million in 2022. These retailers usually have high-tech security systems, but those defenses probably won’t catch occasional instances of store workers offering unusually generous discounts. AI tools might. 3. Providing Better Awareness Although circumstances vary, some potential shoplifters target locations before attempting to steal things. During each visit, they gather information such as the number of workers in the store at particular times, whether certain departments or products have dedicated security measures and the locations of entrances and exits. Someone who goes to a well-staffed store, notices numerous security cameras and sees how employees seem continually aware of their surroundings may decide against proceeding with theft attempt plans. Conversely, stores that seem virtually deserted and feature few or no visible security cameras are much more appealing to would-be thieves. Because AI products can work in the background and detect unusual occurrences, they help loss prevention officers and others become more aware of what happens in individual stores. For example, Everseen creates AI tech that can be used to help reduce self-checkout theft. Part of its approach involves processing footage from ceiling-mounted cameras, often gathering tens of millions of customer interactions daily. Another helpful aspect of the technology is that it can scan shelves to determine when specific products are almost out of stock. Additionally, retail leaders can use this product to get near-real-time analytics about what’s happening in a store and receive prompts about handling certain situations. For example, if the AI spots a frustrated customer, it might prompt a worker to engage with them to learn what’s wrong. Many retailers already use artificial intelligence creatively by incorporating it into their demand-planning processes. For example, automating supply chain activities could reduce costs by as much as 10% , making that goal a worthy pursuit for cost-conscious parties. In any case, improved awareness provided by specialty tech products could help retailers manage theft. by as much as 10% by as much as 10% 4. Analyzing Shoppers’ Gestures Although many retailers use facial recognition technologies, some consumers dislike the perceived lack of privacy. An AI alternative is potentially less invasive because it does not analyze people’s appearance, but rather whether they make suspicious movements commonly linked to theft. Wild Duck Wine & Spirits in Boston uses an AI product from company Veesion on all 24 of its security camera feeds. The technology checks shoppers’ gestures to see if they match those frequently used during shoplifting. For example, someone might try to conceal something in a clothing pocket. Another “red flag” occurs when people hide items in baby strollers. Finally, people may eat or drink while shopping, potentially consuming what they have not paid for yet. If the AI spots something specific, it alerts workers approximately 15 seconds later , providing a video clip of the event. Those employees can then choose whether to confront the person or see if they try to skip the checkout counter. approximately 15 seconds later approximately 15 seconds later A representative from the AI company explained that this solution is about more than stopping people from stealing single items. It sends them a message that they should not try to repeat their actions because they’ll get caught. Minimizing Theft in Retail These fascinating examples show how AI can help store managers, loss prevention officers and other responsible parties reduce the frequency of theft in their locations. Choosing the appropriate tools and learning how to use them properly is crucial for success.

With Black Friday Coming Up, Retailers Are Better Off Using AI to Combat Theft

Gnosis conditional token framework (CTF): tokenizing potential outcomes in prediction markets

In today's tech world with fierce competition and tight deadlines, it is useful to explore new and exciting tools to improve the workflow. Open-source tools are there to help; with some significant advantages over other tools - they're customizable, offer more control, and are community-driven. I decided to handpick 9 powerful open-source projects that will enhance your cloud workflow, project management, user feedback, automation, and more. Whether you are a beginner or a seasoned developer there should be something for everyone to elevate your toolkit and inspire your next big project. Let’s dive in and hopefully, you will find some tools that you can use in the future! 1. StackQL – Manage cloud services with SQL StackQL offers a revolutionary approach to deploy, query, and interact with cloud services and resources using Structured query language (SQL). That means you no longer need to learn different APIs for each provider. StackQL parses your SQL statements and transpiles them into API requests that all your favorite major cloud providers would understand. Some of the best features include: SQL-based querying: unified SQL semantics for various cloud providers lets you save time and manage cloud services more efficiently.


Multi-cloud support: manage Google Cloud, AWS, Azure, Digital Ocean, etc. resources all from a single interface.


Several usage modes: use StackQL via the interactive REPL shell, via the command line utility, or execute StackQL statements in an input file (IQL file).


Powerful operations: perform live queries against cloud resources, make configuration changes, manage cloud infrastructure, and more.


Available for various OS: available to download for Windows, MacOS, and Linux. Ready to reduce setup times and simplify your cloud configurations? Try StackQL yourself and explore all the awesome features it comes with! ⭐ Star StackQL on GitHub: github.com/stackql/stackql 🌎 Website: stackql.io Thanks to the StackQL team for sponsoring this article. 2. Flagsmith – Feature flag management Flagsmith is an open-source feature flag management tool. You can use hosted API, deploy to your private cloud, or run as on-premise software. It enables gradual feature rollouts for safer deployments and easier A/B testing, reducing the risk of bugs and ensuring faster iteration cycles. 👨‍💻 GitHub Repository: https://github.com/Flagsmith/flagsmith 🌎 Website: flagsmith.com 3. Formbricks – User feedback and analytics Formbricks is an open-source tool for collecting user feedback and form analytics. It helps devs understand user behavior, allowing them to improve performance, reduce friction, and enhance the overall UX. 👨‍💻 GitHub Repository: https://github.com/formbricks/formbricks 🌎 Website: formbricks.com 4. Dub – URL shortener with analytics Dub is an open-source URL shortener with built-in analytics. It simplifies link management for websites, marketing, and projects. By allowing developers to manage and analyze shortened URLs, Dub streamlines project workflows, making link tracking and data gathering much easier. 👨‍💻 GitHub Repository: https://github.com/dubinc/dub 🌎 Website: dub.co 5. AppFlowy – Project & notes management AppFlowy is an open-source, privacy-focused alternative to Notion. It provides a flexible workspace for managing tasks, notes, and general knowledge. AppFlowy offers teams control over their data and customizations, allowing them to create a tailored workspace while benefiting from privacy and scalability. 👨‍💻 GitHub Repository: https://github.com/AppFlowy-IO/AppFlowy 🌎 Website: appflowy.io 6. APITable – Visual database for collaborative apps APITable combines the ease of spreadsheets with the power of databases. It helps teams collaborate on data-driven projects with visual and interactive tables. APITable simplifies data management and collaboration, allowing developers to organize and automate workflows with minimal setup and technical knowledge. 👨‍💻 GitHub Repository: https://github.com/apitable/apitable 🌎 Website: aitable.ai 7. Flowise – Build LLM workflows with flowcharts Flowise is an open-source visual tool that simplifies building workflows for large language models (LLMs). It’s designed to integrate AI with apps easily. Flowise allows developers to visually manage complex LLM workflows, speeding up the integration of AI into their applications without deep coding knowledge. 👨‍💻 GitHub Repository: https://github.com/FlowiseAI/Flowise 🌎 Website: flowiseai.com 8. Jan – Use AI with full control and privacy Jan is an open-source tool; a ChatGPT alternative that runs 100% offline on your device but with more control, flexibility, and no privacy concerns. Jan enhances productivity by allowing you to ask questions, brainstorm, and learn from the AI running on your device to be more productive. 👨‍💻 GitHub Repository: https://github.com/janhq/jan 🌎 Website: jan.ai 9. Docsify – Documentation site generator Docsify is an open-source documentation site generator that creates beautiful, responsive docs from markdown files. It simplifies the process of generating documentation, making it easy for developers to maintain and update their docs without complex builds. 👨‍💻 GitHub Repository: https://github.com/docsifyjs/docsify 🌎 Website: docsify.js.org Did you like the resources? Here is more 👇 Join 6000+ others to receive the best DEV resources, tools, productivity tips, and career growth advice I discover by subscribing to my newsletter! Also, connect with me on Twitter, LinkedIn, and GitHub! Writing has always been my passion and it gives me pleasure to help and inspire people. If you want to get featured or partner up, feel free to get in touch! Also, published here. In today's tech world with fierce competition and tight deadlines, it is useful to explore new and exciting tools to improve the workflow. Open-source tools are there to help; with some significant advantages over other tools - they're customizable, offer more control, and are community-driven. I decided to handpick 9 powerful open-source projects that will enhance your cloud workflow, project management, user feedback, automation, and more. Whether you are a beginner or a seasoned developer there should be something for everyone to elevate your toolkit and inspire your next big project. Let’s dive in and hopefully, you will find some tools that you can use in the future! 1. StackQL – Manage cloud services with SQL StackQL StackQL offers a revolutionary approach to deploy, query, and interact with cloud services and resources using Structured query language (SQL). That means you no longer need to learn different APIs for each provider. StackQL parses your SQL statements and transpiles them into API requests that all your favorite major cloud providers would understand. Some of the best features include: Some of the best features include: SQL-based querying: unified SQL semantics for various cloud providers lets you save time and manage cloud services more efficiently. Multi-cloud support: manage Google Cloud, AWS, Azure, Digital Ocean, etc. resources all from a single interface. Several usage modes: use StackQL via the interactive REPL shell, via the command line utility, or execute StackQL statements in an input file (IQL file). Powerful operations: perform live queries against cloud resources, make configuration changes, manage cloud infrastructure, and more. Available for various OS: available to download for Windows, MacOS, and Linux. SQL-based querying: unified SQL semantics for various cloud providers lets you save time and manage cloud services more efficiently. SQL-based querying : unified SQL semantics for various cloud providers lets you save time and manage cloud services more efficiently. SQL-based querying Multi-cloud support: manage Google Cloud, AWS, Azure, Digital Ocean, etc. resources all from a single interface. Multi-cloud support: manage Google Cloud, AWS, Azure, Digital Ocean, etc. resources all from a single interface. Multi-cloud support: Several usage modes: use StackQL via the interactive REPL shell, via the command line utility, or execute StackQL statements in an input file (IQL file). Several usage modes: use StackQL via the interactive REPL shell, via the command line utility, or execute StackQL statements in an input file (IQL file). Several usage modes: Powerful operations: perform live queries against cloud resources, make configuration changes, manage cloud infrastructure, and more. Powerful operations: perform live queries against cloud resources, make configuration changes, manage cloud infrastructure, and more. Powerful operations: Available for various OS: available to download for Windows, MacOS, and Linux. Available for various OS : available to download for Windows, MacOS, and Linux. Available for various OS Ready to reduce setup times and simplify your cloud configurations? Try StackQL yourself and explore all the awesome features it comes with! ⭐ Star StackQL on GitHub: github.com/stackql/stackql Star StackQL on GitHub: github.com/stackql/stackql github.com/stackql/stackql 🌎 Website: stackql.io 🌎 Website: stackql.io stackql.io Thanks to the StackQL team for sponsoring this article. 2. Flagsmith – Feature flag management Flagsmith Flagsmith is an open-source feature flag management tool. You can use hosted API, deploy to your private cloud, or run as on-premise software. It enables gradual feature rollouts for safer deployments and easier A/B testing, reducing the risk of bugs and ensuring faster iteration cycles. 👨‍💻 GitHub Repository: https://github.com/Flagsmith/flagsmith GitHub Repository: https://github.com/Flagsmith/flagsmith 🌎 Website: flagsmith.com 🌎 Website: flagsmith.com 3. Formbricks – User feedback and analytics Formbricks Formbricks is an open-source tool for collecting user feedback and form analytics. It helps devs understand user behavior, allowing them to improve performance, reduce friction, and enhance the overall UX. 👨‍💻 GitHub Repository: https://github.com/formbricks/formbricks GitHub Repository: https://github.com/formbricks/formbricks 🌎 Website: formbricks.com 🌎 Website: formbricks.com 4. Dub – URL shortener with analytics Dub Dub is an open-source URL shortener with built-in analytics. It simplifies link management for websites, marketing, and projects. By allowing developers to manage and analyze shortened URLs, Dub streamlines project workflows, making link tracking and data gathering much easier. 👨‍💻 GitHub Repository: https://github.com/dubinc/dub GitHub Repository: https://github.com/dubinc/dub 🌎 Website: dub.co 🌎 Website: dub.co 5. AppFlowy – Project & notes management AppFlowy AppFlowy is an open-source, privacy-focused alternative to Notion. It provides a flexible workspace for managing tasks, notes, and general knowledge. AppFlowy offers teams control over their data and customizations, allowing them to create a tailored workspace while benefiting from privacy and scalability. 👨‍💻 GitHub Repository: https://github.com/AppFlowy-IO/AppFlowy GitHub Repository: https://github.com/AppFlowy-IO/AppFlowy 🌎 Website: appflowy.io 🌎 Website: appflowy.io 6. APITable – Visual database for collaborative apps APITable APITable combines the ease of spreadsheets with the power of databases. It helps teams collaborate on data-driven projects with visual and interactive tables. APITable simplifies data management and collaboration, allowing developers to organize and automate workflows with minimal setup and technical knowledge. 👨‍💻 GitHub Repository: https://github.com/apitable/apitable GitHub Repository: https://github.com/apitable/apitable 🌎 Website: aitable.ai 🌎 Website: aitable.ai 7. Flowise – Build LLM workflows with flowcharts Flowise Flowise is an open-source visual tool that simplifies building workflows for large language models (LLMs). It’s designed to integrate AI with apps easily. Flowise allows developers to visually manage complex LLM workflows, speeding up the integration of AI into their applications without deep coding knowledge. 👨‍💻 GitHub Repository: https://github.com/FlowiseAI/Flowise GitHub Repository: https://github.com/FlowiseAI/Flowise 🌎 Website: flowiseai.com 🌎 Website: flowiseai.com 8. Jan – Use AI with full control and privacy Jan Jan is an open-source tool; a ChatGPT alternative that runs 100% offline on your device but with more control, flexibility, and no privacy concerns. Jan enhances productivity by allowing you to ask questions, brainstorm, and learn from the AI running on your device to be more productive. 👨‍💻 GitHub Repository: https://github.com/janhq/jan GitHub Repository: https://github.com/janhq/jan 🌎 Website: jan.ai 🌎 Website: jan.ai 9. Docsify – Documentation site generator Docsify Docsify is an open-source documentation site generator that creates beautiful, responsive docs from markdown files. It simplifies the process of generating documentation, making it easy for developers to maintain and update their docs without complex builds. 👨‍💻 GitHub Repository: https://github.com/docsifyjs/docsify GitHub Repository: https://github.com/docsifyjs/docsify 🌎 Website: docsify.js.org 🌎 Website: docsify.js.org Did you like the resources? Here is more 👇 Join 6000+ others to receive the best DEV resources, tools, productivity tips, and career growth advice I discover by subscribing to my newsletter ! my newsletter Also, connect with me on Twitter , LinkedIn , and GitHub ! Twitter LinkedIn GitHub Writing has always been my passion and it gives me pleasure to help and inspire people. If you want to get featured or partner up, feel free to get in touch ! get in touch Also, published here . here

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