by Csongor Fekete | Apr 3, 2025 | AI, Business, Machine Learning
Title: The Importance of Inclusive AI Models: A Holistic Approach for Business Value
A recent article published by Science | AAAS titled "AI models miss disease in Black and female patients" highlights a critical challenge in the field of artificial intelligence and machine learning in healthcare: biased data leads to biased models. The investigation reveals that several AI-driven diagnostic tools, widely used in medical evaluation, show significantly lower accuracy in detecting diseases for Black patients and women.
Key points from the article:
- Many Machine Learning models in healthcare are largely trained on data from white male patients.
- This lack of diversity in training data results in lower detection rates of disease in marginalized groups, including women and racially diverse populations.
- These performance inconsistencies can lead to misdiagnosis, delayed treatments, and ultimately worsened patient outcomes.
- The current state underscores the urgent need for more inclusive data practices and custom AI models that accurately serve all segments of the population.
The key learning from this research is clear: AI tools are only as good as the data used to build them. Bias in training inputs leads to bias in predictions, which drastically impacts the model’s performance and its commercial or clinical efficacy. It also impairs customer satisfaction and trust in AI systems intended to improve outcomes.
What this means for AI consultancy and martech:
This article acts as a cautionary example for organizations across industries—not just healthcare. For martech businesses looking to leverage next-gen AI tools, it emphasizes why a holistic view is essential when designing AI systems. AI agencies and AI experts must ensure that customer data encompasses all relevant demographics and behavioral diversity. Companies like HolistiCrm offer expertise in building inclusive, custom AI models tailored to the unique customer base of each business.
Use-Case: Inclusive AI in Healthcare CRM
Consider the application of a customer relationship management (CRM) system for a healthcare provider. By using a holistic and inclusive Machine Learning model, the CRM can recommend personalized health content, flag at-risk patients, and improve triage—all with demographic fairness. This not only enhances operational performance but also drives higher customer satisfaction and loyalty. More importantly, it ensures that equitable care is promoted across all patient groups, a clear step toward ethical AI practices.
The takeaway for business leaders: Bias isn't just a technical flaw—it’s a strategic liability. Inclusive AI is not just a moral imperative; it’s a source of long-term business value.
Original article: https://news.google.com/rss/articles/CBMijAFBVV95cUxObGtjX1hSSjJqcF92NkYyS2RFNjhmQkxvb3BrWEVGN0x4SS1pdzJfbW9pUVhoMEp5MERzdDNvd3YxcWI4RTY0Vk5kdk5fRl9nQTZ2N0xyNTBaT0tISURXVDUxMndJSDRFOHhGX09KdVBJdlRoNjlNX0FBaUs1Mnp2b2pvYTZFcHNaZWZKMw?oc=5
by Csongor Fekete | Apr 2, 2025 | AI, Business, Machine Learning
Title: Turning AI Into Business Value: Key Takeaways from Netguru’s “How to Make an AI Model”
As organizations increasingly seek to create differentiation through digital capabilities, mastering the foundations of AI is becoming critical. Netguru’s recent article, How to Make an AI Model: A Step-by-Step Guide for Beginners, provides clear practical guidance on how businesses and individuals can begin their journey toward building effective machine learning systems.
Key Takeaways:
- Define the Problem: Developing a successful Machine Learning model starts with a clearly defined business problem. Misaligned goals can lead to costly iterations and poor performance.
- Data Collection & Preparation: High-quality, relevant data is the backbone of any custom AI model. Data needs to be clean, labeled, and representative to build an accurate solution.
- Model Selection & Training: Choosing the right algorithm depends on the specific use-case—classification, regression, etc. Training and validating the model iteratively is essential to achieving optimal performance.
- Evaluation & Deployment: The model must be evaluated using performance metrics such as accuracy, precision, recall, or F1 score. Once it meets business expectations, it is deployed into production.
- Continuous Improvement: AI models naturally degrade over time as external conditions change. Regular maintenance and retraining are necessary to maintain customer satisfaction and business value.
Applying This to Business: A Martech Use-Case
For businesses in the marketing and CRM space, such as HolistiCrm, understanding this foundation accelerates the development of holistic AI strategies. For example, consider a use-case where a CRM platform uses a custom AI model to predict which leads are most likely to convert based on engagement behavior and demographic data. With this predictive model:
- Marketers can target potential clients more effectively, reducing cost-per-acquisition
- Customer satisfaction increases due to more personalized communication
- Sales cycles shorten, improving revenue velocity
This holistic approach—supported by AI experts or an AI consultancy—represents the future of marketing technology. Companies that invest in in-house competencies or partner with an AI agency can leverage their customer data into real-time strategic insights, ultimately driving long-term business value.
For businesses considering their own journey into AI or looking to enhance existing models, the article is an excellent roadmap.
Read the original article here: How to Make an AI Model: A Step-by-Step Guide for Beginners – Netguru.
by Csongor Fekete | Apr 2, 2025 | AI, Business, Machine Learning
🚀 How H&M Is Stitching AI Into Fashion — And What Businesses Can Learn
Swedish clothing giant H&M is stepping into a new era of retail and marketing by integrating AI-generated digital avatars of models into its online shopping experience. According to a recent article on Inc.com, the brand is piloting AI-made digital twins of human models to display fashion products more efficiently and inclusively—while ensuring the models’ consent is obtained for synthetic usage rights.
🔍 Key Takeaways from the Article:
- H&M will begin using AI-generated “digital twins” of professional models to create synthetic fashion images.
- Consent agreements are integrated into contracts, reflecting ethical AI use and image ownership.
- This approach increases scalability, model diversity, and consistency in visual merchandising across online platforms.
- The pilot is being tested on the H&M Sweden site with future plans potentially expanding the practice globally.
This technology is expected to reduce the need for live photoshoots, streamline content production, and enhance visual personalization for customers—without compromising creative control or ethics.
💡 Business Value from This Use Case:
Creating AI-generated model images can significantly improve performance in visual marketing strategies. Businesses in retail, e-commerce, or any visually-driven sector can benefit in the following ways:
- 🧠 Holistic Personalization: Custom AI models can tailor product visuals to better reflect individual customer preferences (e.g., body type, style appeal), improving satisfaction.
- 💰 Cost Efficiency: Companies lower photoshoot expenses and speed up content delivery by generating synthetic visuals at scale.
- 📈 Martech Integration: When paired with a robust martech stack, AI-generated visuals enhance A/B testing, automate campaign delivery, and support dynamic web personalization.
- 🕵️ Audience Insights: A Machine Learning model can analyze engagement data to determine which AI-generated personas boost conversion rates, feeding a closed AI feedback loop.
🌟 Lessons for AI-Driven Businesses
This innovation from H&M illustrates that advanced AI strategies should go beyond automation to include ethical, scalable, and customer-centric solutions. Organizations seeking to unlock similar capabilities should partner with an AI consultancy or AI agency with expertise in custom AI model development and AI governance.
A retail or fashion company working with an AI expert can adopt digital twins through a holistic AI solution that merges cross-functional performance goals—from marketing efficiency to an enhanced customer journey.
This is a strong signal that the future of retail—and digital content—is synthetic, smart, and ethically governed.
Original article: https://news.google.com/rss/articles/CBMisAFBVV95cUxPOXRQakl3cHVLYzVTU2tzYVVYV0VhclBQSjBlSFZ3bkIzS1ZfeUh1Mk4tMFFwemh5TDVSWm1LV2xNLTJkbzNCLXdVVjBHZnlPdGNYWmd5WEpOZUVFWWpEd2RWRDlhUjVNVTVpU05ZSU5KTWxkSjdoZnV3b20xZ0pHSGVrMWdYX1I5RUlqLW4xQjNFLXRxY1J0ZDhBbGJ5UVBva3Q1alptNGpIZF9FUHpkcg?oc=5
by Csongor Fekete | Apr 1, 2025 | AI, Business, Machine Learning
🚀 DeepSeek’s AI Model Upgrade Signals New Wave of Custom AI Opportunities in Martech
In a rapidly evolving AI landscape, Chinese firm DeepSeek has launched an upgraded version of its open-source language model, DeepSeek-V2, directly targeting the dominance of OpenAI’s ChatGPT. With a performance benchmark that reportedly exceeds GPT-3.5, DeepSeek-V2 offers 236 billion parameters and a hybrid architecture that blends the best of LLMs (large language models) and Mixture of Experts (MoE) strategies. This new release reinforces the importance of specialized, holistic AI solutions built for targeted use-cases across industries.
Key Takeaways from the Article
- DeepSeek-V2 delivers performance improvements over existing open-source rivals like Meta’s LLaMA 2 and Mistral.
- The model uses 21B active parameters per query—reducing computational cost while maintaining high inference quality.
- DeepSeek positions itself as an innovative alternative to existing American technology giants, signaling global competition in AI development.
- Open-source availability of DeepSeek-V2 promotes broader accessibility and customization among AI developers and AI agencies.
Business Value of a Use Case: Custom AI for Marketing Automation
This technological leap opens up exciting opportunities for martech applications when combined with custom AI models. For example, a retail brand could harness a streamlined version of DeepSeek-V2 fine-tuned by an AI consultancy for automated content generation—personalizing email campaigns, product descriptions, and customer support interactions at scale.
By building on a robust open-source foundation, businesses can deploy enhanced Machine Learning models that improve performance while staying aligned with unique domain-specific needs. This customizable approach boosts customer satisfaction through more responsive and intelligent interfaces, drives higher engagement, and reduces time-to-market for marketing teams.
An AI expert working within a holistic AI agency, such as HolistiCrm, can help companies navigate the large language model ecosystem and deploy optimal, business-aligned solutions using custom AI models adapted to niche goals.
As competition intensifies among global AI platforms, the ability for enterprises to leverage these advancements through tailored, industry-specific implementations will be a key factor in retaining digital competitiveness.
Read the original article here: DeepSeek Launches AI Model Upgrade Amid OpenAI Rivalry—Here’s What To Know – Forbes: https://news.google.com/rss/articles/CBMiwwFBVV95cUxNaExwcS1SMUw1MjBkVFI2dlNrY0lwWnFyb3NFVkN5ZGlONnVIOENtMWdUeGJtMUNYY0hwcVRMaWtsUEs5bFZqTkdoNDc5NzFxdnBHMmxYa05RaDZLeXdVNzItYld2TTlCOGl1TmRQbzB2YTVESER2bnd6OHB2M2RLRXNFdHBqaDhqVDQzdVlOcWhfc2dEZVNSVmhQaDMzYThWUU5SbHhidzBVb0ZwZ25CYkJTY2o3b2ptUVR5Z0d2eVlqT3c?oc=5
by Csongor Fekete | Apr 1, 2025 | AI, Business, Machine Learning
China’s DeepSeek Steps Up the AI Race – What It Means for Marketing Innovation
In a noteworthy development in global martech and AI research, China's DeepSeek has unveiled an upgraded release of its custom AI model, DeepSeek-V2, signaling heightened competition with leading Western players like OpenAI. As reported by Reuters, the enhanced version boasts improved performance, expanded multilingual capabilities, and boosted reasoning capacity that could position it as a formidable challenger in the AI arms race.
Key Highlights from the Article:
- DeepSeek-V2 is built upon transformer-based architecture and demonstrates enhanced code generation, reasoning, and problem-solving performance.
- The update marks China's growing investment in developing AI capabilities that rival those of Western market leaders.
- DeepSeek’s new model supports over 20 languages and integrates reinforcement learning with human feedback to elevate generative accuracy and alignment.
- The development underscores a growing trend of regional players pushing for sovereign AI technologies that align with domestic data regulations and cultural contexts.
Business Learnings and Marketing Applications:
For businesses seeking to gain an edge in digital marketing and customer engagement, the evolution of large language models like DeepSeek-V2 presents valuable lessons:
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Custom AI Models Enhance Marketing Performance:
Using custom-trained Machine Learning models, businesses can deploy AI-powered campaigns that reflect local languages, cultural nuances, and consumer behaviors. This is a gateway to more intelligent personalization in marketing strategies, driving higher customer satisfaction.
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Holistic AI Consultancy Drives Competitive Advantage:
Partnering with an AI consultancy that leverages holistic data inputs—across CRM, behavior analytics, and sales trends—enables creation of tailored AI tools that yield better conversion rates and customer experiences.
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Global AI Developments Inform Local Execution:
An AI expert must stay ahead of regional developments. As seen with DeepSeek's progress, the quality bar continues to rise globally. Competitive organizations must integrate cutting-edge innovations, ensuring their martech stacks are supported by high-performance models.
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Customer-Centric AI Design:
By incorporating multilingual and context-sensitive AI systems, companies enhance inclusivity and broaden their addressable market, especially in regions where language has been a traditional barrier. This can directly affect brand reach and loyalty.
A Use Case with Business Value:
A retail brand operating across Asia could collaborate with an AI agency to build a custom language model based on DeepSeek-V2’s open-source foundation. By training this model on local product data, customer service interactions, and regional dialects, the business can automate localized product recommendations and real-time customer support. The result? Increased engagement, improved satisfaction scores, and amplified conversion across diverse consumer segments.
The time is now for AI-driven marketing leaders to explore how next-gen models like DeepSeek can supercharge performance through culturally relevant, custom AI solutions.
Read the original article: China's DeepSeek releases AI model upgrade, intensifies rivalry with OpenAI – Reuters.
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