by Csongor Fekete | May 16, 2025 | AI, Business, Machine Learning
As businesses continue to embrace AI-driven strategies, a common stumbling block is integrating these powerful systems with real-world data in a way that truly drives performance. A recent article from The Drum, “Here’s how to seamlessly connect an AI model with your company’s data,” outlines essential considerations for organizations aiming to unlock the full potential of AI within their operations.
The article emphasizes the importance of building infrastructure that supports real-time data flows, ensuring that custom AI models work with the most relevant and up-to-date information. Clean, structured, and accessible data is foundational. It also highlights the need for cross-department collaboration—between marketers, IT teams, and data scientists—so that the AI implementation aligns with actual business goals such as customer satisfaction and operational efficiency.
Key learnings include:
- Data readability and integration are crucial.
- Domain-specific Machine Learning models deliver the most value.
- Real-time feedback loops improve outcomes over time.
- Cross-functional input enhances system design and deployment.
At HolistiCrm, these insights reinforce the critical role of holistic martech strategies, where tailored AI solutions are not just layered on top of legacy systems but integrated at the core of customer experience and marketing analysis. A practical use-case stemming from this is the creation of a custom churn prediction model. When seamlessly connected with CRM and support ticket data, this model can help preemptively identify which customers are most likely to leave, enabling personalized retention campaigns—ultimately boosting long-term satisfaction and revenue retention.
Harnessing AI effectively is not just about having a powerful model, but about orchestrating a seamless flow between the model and the business’ own dynamic datasets. That’s where strategic AI consultancy, like an experienced AI agency, can unlock true martech advantages.
original article
by Csongor Fekete | May 16, 2025 | AI, Business, Machine Learning
The landscape of digital marketing is undergoing a fundamental shift. Traditionally focused on clicks and conversions, the rise of advanced generative AI tools like ChatGPT is repositioning success metrics around brand mentions. According to Fortune's latest article, marketers now face a new battlefield: the AI-generated conversation.
The key message is clear—being part of AI-driven dialogues is the next competitive edge. As large language models like ChatGPT increasingly become consumer-facing tools, these models reference brands based on publicly available information. In this emerging dynamic, visibility and share of voice in the AI ecosystem are directly linked to innovation in martech and content strategy.
For businesses, aligning with this shift is not optional. It’s imperative to rethink how message delivery and brand positioning integrate with algorithmic structures. This opens a new frontier for AI agencies and consultancies.
A high-impact use-case is training custom AI models tailored to industry-specific brand sentiment tracking. By deploying machine learning models that monitor and optimize brand mentions across generative tools and conversational AIs, businesses can proactively influence perception. This holistic approach increases customer satisfaction, ensures higher brand relevance, and boosts marketing performance in the age of AI-native interaction.
AI experts and marketing teams working together under strategic AI consultancy can thus unlock measurable business value—extending far beyond the outdated metrics of clicks and impressions.
Read the original article here: original article
by Csongor Fekete | May 15, 2025 | AI, Business, Machine Learning
Stripe’s latest innovation marks a significant leap forward in the evolution of financial infrastructure by introducing stablecoin accounts and a new custom AI model tailored for payment optimization. This launch focuses on increasing cross-border payment efficiency and reducing transaction failures, a critical pain point in global commerce.
Key highlights from the announcement include:
- Businesses can now hold, manage, and convert USDC stablecoins within their Stripe accounts, enabling near-instant and low-cost international transactions.
- Stripe’s proprietary AI model is designed to proactively reduce payment declines by leveraging a large dataset of transaction behaviors, recurrence patterns, and up-to-date issuer trends.
- The solution operates seamlessly across more than 150 countries, serving both traditional and crypto-native businesses.
For organizations leveraging martech and embracing a holistic business strategy, this development is a case study in how custom AI models can be embedded directly into core infrastructure to enhance end-to-end performance. Applying such Machine Learning models in payment gateways helps reduce friction, improve conversion rates, and ultimately boost customer satisfaction.
A comparable use-case for HolistiCrm could involve deploying a custom AI model within a CRM ecosystem that predicts transaction success or flags potential friction points in customer journeys. This insight provides actionable guidance for sales and marketing teams, enabling smarter routing, offer personalization, and proactive issue resolution.
As AI consultancy becomes a foundational layer of modern business solutions, the Stripe initiative sets a critical example of how AI agencies can convert raw transaction data into strategic value drivers.
original article: https://news.google.com/rss/articles/CBMiqwFBVV95cUxQOWJNWTlPS01Gb0daZ1E5YUJGRUxWaUxNS2stejk4NV93blNTYTk4Wll4Ykc1dnRJVUx4ZUw0d1RINjRHTzdCLXkyZUx6MmJWcmFaZE9NWW9xa1JVdm1aMEp2RVhKQmplRWU4cDRQLU5SRm9fdVN5bk41UDZ6TF9jNGpNblhqaHJONFowQUt4LU5oWnNYbjFac3pYZTZLYWtNX3ZRMGxEaS1uOWc?oc=5
by Csongor Fekete | May 15, 2025 | AI, Business, Machine Learning
Zalando, one of Europe's leading fashion platforms, is leveraging advanced AI tools to deliver faster, more cost-efficient marketing campaigns, according to a recent Reuters article. By integrating a large language model and image-recognition technologies into its marketing operations, the company has begun automating content creation and streamlining personalized outreach at scale.
Key takeaways from the article include:
- Zalando uses AI to reduce the administrative load and time required to launch marketing campaigns.
- Custom AI models generate text, images, and even entire campaigns in multiple languages across markets.
- Automation has significantly reduced project turnaround times and improved targeting, leading to cost reductions.
- The AI tools are also being used to declutter product databases and streamline searchability, increasing overall performance and customer satisfaction.
This use case exemplifies how custom AI models in martech can revolutionize customer engagement, content velocity, and ROI. For an AI consultancy like HolistiCrm, the learnings are clear: businesses that embed tailored Machine Learning models into their marketing stack not only enhance operational efficiency but also unlock new dimensions of personalization and scalability.
Enterprises seeking to boost campaign performance, reduce costs, and increase agility must adopt a holistic approach to AI integration. Partnering with an AI expert or AI agency that understands both strategy and deployment can maximize the business value of automation across the marketing lifecycle.
original article: https://news.google.com/rss/articles/CBMiswFBVV95cUxQOF9acV84VEZMV2RMUlgxVlBRSkM3d0ZNOWVFbzUyb0xLbDNteFBKS1ZoYWhuSFRONHNsYmlIamFKOWQwV2pXM1I0NExMQ2dtSmxETzNSN2VOdzVxWDdCc3NuT19LRjJPa09MOUdNb3Y4QzNUZzNZaEJDdk5yWnNIT0FHWk45dzAwaUxQUjBVd1l5VV9WU0pyeUExdFNtbkhsZ1JGY0F4S2hSaENtODVmNk1wVQ?oc=5
by Csongor Fekete | May 14, 2025 | AI, Business, Machine Learning
Mount Sinai has developed a powerful AI model that significantly improves the prediction of delirium in hospitalized patients. This condition, a serious and often preventable complication, is notoriously difficult to diagnose early. By leveraging a Machine Learning model trained on electronic health records, the hospital system achieved increased accuracy in identifying high-risk patients — even before symptoms surface.
Key takeaways from the article include:
- The AI model processed structured clinical data from over 100,000 patient encounters, offering real-time, high-performance risk scoring.
- The system integrates directly into the electronic health record workflow, allowing clinicians to take preventative action.
- Early results show measurable improvements in patient outcomes, including reduced ICU stays and improved recovery rates.
- Interpretable AI techniques ensure clinical usability, aligning predictive power with human decision-making.
This use-case provides a blueprint for how custom AI models can deliver tangible business value beyond healthcare. In sectors like marketing and martech, integrating predictive Machine Learning models can drive customer satisfaction by anticipating user behavior, improving targeting accuracy, and optimizing campaign timing through holistic analysis.
AI consultancy and AI agency experts can extract similar value across verticals: for example, reducing churn in subscription services, improving resource allocation in logistics, or personalizing user experience in retail.
For businesses aiming to transform operations with AI, this case highlights the benefits of embedding tailored ML solutions into existing workflows—boosting performance while improving outcomes.
Original article: https://news.google.com/rss/articles/CBMi3AFBVV95cUxOZGxRQmVCcjc1aUstQUYwZnJyXzFqcWNGYTkza0ZqRUdOM2FhRGhOQjVaZG9Cc0F0eWdKdFE2TlNqSGR3ZWNPaUNfNkFROTRpQVgydUt6VkpXRE8tWXJjOVp0SUNuLTFIcXBzSjhuRzZEYVpLTkNSVXhXMEpXUThQY1ZfUkZJUGdoN2VydXpOUXNRdnpfRzdmb244NThjdGVydlUtWXA4ZVNXVEhDWHR0OHVieWFWUGltUEt5MlRWN2VaMWVZZkxDbGRxMU96T2dwdlRMZmZVTUg0QVFo?oc=5
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