by Csongor Fekete | Jan 24, 2026 | AI, Business, Machine Learning
AI-powered superagents are poised to revolutionize HR by 2026, marking the most significant transformation in the industry in decades. According to a recent article from PR Newswire, these intelligent, autonomous systems will go beyond traditional chatbots, employing advanced custom AI models to handle complex HR tasks such as recruitment, onboarding, employee support, and performance management with minimal human intervention.
These superagents are built using large language models (LLMs), coupled with holistic integration across martech and enterprise platforms, enabling them to deliver personalized, context-aware interactions. Key learnings from this development include the importance of embedding AI natively into business workflows, upgrading data infrastructure, and rethinking operational processes to fully harness AI’s capabilities.
A relevant use-case for HolistiCrm lies in leveraging AI to transform customer-facing HR touchpoints into seamless, automated experiences. By deploying a Machine Learning model tailored for HR service interactions, businesses can substantially increase response speed and employee satisfaction, reduce operational costs, and free up human teams for strategic tasks. A holistic approach, combining AI consultancy with domain-specific expertise, ensures these superagents are not only scalable but aligned with brand values and business objectives.
As HR evolves into a tech-centric function, an AI agency that enables custom integrations and continuous learning loops within enterprise systems will be critical for organizations seeking long-term transformation and performance improvement.
Source: original article
by Csongor Fekete | Jan 24, 2026 | AI, Business, Machine Learning
Meta’s latest strategic move highlights the growing importance of in-house AI capabilities. According to the recent article by Reuters, Meta's newly formed generative AI team delivered its first key internal models this month. The company’s CTO emphasized that these models are already being integrated into Meta’s advertising and business tools, improving personalization and user engagement.
The key takeaway is clear: custom AI models are becoming central to enhancing product performance and operational efficiency. By developing models in-house, Meta gains tighter control over model tuning, privacy, and domain-specific performance—core principles that align with a holistic AI strategy.
A similar use-case translates directly into business value for companies in the martech domain. Imagine a B2C marketing firm leveraging a custom Machine Learning model to predict customer behavior from CRM data. By tailoring campaigns based on enriched insights, businesses not only increase campaign ROI but also elevate customer satisfaction. This AI-driven personalization can lead to reduced churn, improved targeting accuracy, and smarter budget allocation.
Meta’s approach signals a broader industry shift—AI agencies and AI consultancies that can deliver holistic, domain-specific solutions will be critical partners in this transformation. Investing in in-house or partnered AI expertise is moving from optional to essential for competitive marketing and CRM performance.
original article: https://news.google.com/rss/articles/CBMixAFBVV95cUxOdU1NVGtGaXd3bm1BT1QzRTBoTHB0VG1zWGE5c2ljWmpQQW01cWVnOUNxN1RmS2M1bjR0QklXVkxJT1o5ZUg4OXFzQ1pKVGlkcnlGblhtWjhFenNQNUZsV1hjcklUbmdSaDl0NThaM0FraXB4VDl5eVg3bkRwZlRGQkJMUXFOOGgzcjNBSVY4ejlFbno4MkozaDY1UFVCWDZCTUVnaGx3UjNJWHR5ZmN6aTZHWFpRY0J6ZnhRNnlKVDVBNTNS?oc=5
by Csongor Fekete | Jan 23, 2026 | AI, Business, Machine Learning
Ozcan’s Optical AI: A Glimpse Into Sustainable Generative AI
Energy efficiency in AI remains a hot-button issue, especially with the massive power demands of generative AI systems. The recent innovation by Professor Aydogan Ozcan and his team at UCLA introduces a transformative approach—an optical AI model that can perform generative tasks without relying on power-hungry digital components.
The core of Ozcan's work harnesses light-based computation—using diffractive optical elements to structure and manipulate light for performing computations. Unlike conventional neural networks running on GPUs, this optical model performs inference in a passive manner, consuming orders of magnitude less energy. The system, designed for generative tasks like image reconstruction, is also hardware-embedded, suggesting significant improvements in performance-per-watt over traditional machine learning models.
From a business perspective, incorporating energy-efficient models like this unlocks new potential across marketing, martech, and customer-facing applications. Imagine deploying lightweight, high-performance custom AI models within IoT devices, edge computing environments, or even retail displays, drastically reducing operational costs while boosting customer satisfaction with real-time personalization.
A potential use-case relevant to HolistiCrm could involve optical AI integration for in-store analytics or holographic customer assistants that can generate and serve personalized promotional content without intensive backend infrastructure. This would enhance the holistic customer journey while ensuring sustainability and superior performance metrics.
Forward-thinking AI agencies and AI consultancies should explore emerging paradigms like optical AI to expand their technological horizons, drive innovation, and stay competitive in a rapidly evolving martech landscape.
Read the original article for more: https://news.google.com/rss/articles/CBMiqwFBVV95cUxPWEpKZFN1dnZqQXFuMFJLOGNwbUJEcnVPLU1CdVRtRHJjMDRocUhmb3JObk9DMnpuY0VWUl9NSzV0WUlVeGMxVXNxRWNvWE5HaWIwYXZYcHUxdV9POHpUSFRKWHYwaTU3cHlGcGxGUTh4aXR2OWhDdm5mY1ZjcjItRkpRLTFjYTlrUFJuNWRrVW84bGVFQ0tTNlZNd3Z3ZWI4Sk85NWZGNFU4TVE?oc=5 (original article)
by Csongor Fekete | Jan 23, 2026 | AI, Business, Machine Learning
Despite clear advantages such as transparency, cost-effectiveness, and adaptability, open-source AI models are not gaining mainstream traction in business settings, according to a recent MIT Sloan article. The key challenges include lack of technical expertise, incomplete documentation, complex deployment, and insufficient alignment with business-centric metrics such as ROI or time-to-market. Companies often default to closed, commercial AI platforms that offer polished interfaces, robust support, and easy deployment—appealing features for teams lacking dedicated AI expertise.
A major learning from the article is that open models can unlock strategic value when paired with the right AI consultancy or AI agency that provides tailored implementation strategies. Businesses capable of customizing open models to their specific martech or operational needs can achieve superior flexibility and performance compared to off-the-shelf solutions.
Consider a use-case in marketing automation: By adapting an open-source Machine Learning model through a holistic approach, a company could build highly specialized customer segmentation tools, leading to more personalized and impactful campaigns. This results in improved customer satisfaction and better conversion metrics, all while maintaining transparency in data workflows and reducing vendor lock-in.
For businesses ready to invest in custom AI models with support from AI experts, the open model ecosystem offers a path to differentiated performance and long-term business value.
Source: original article
by Csongor Fekete | Jan 22, 2026 | AI, Business, Machine Learning
In the recent article "AI cannot automate science – a philosopher explains the uniquely human aspects of doing research," The Conversation explores the intrinsic limits of artificial intelligence in replicating true scientific inquiry. The key message emphasizes that while AI, including Machine Learning models, can accelerate data analysis and hypothesis generation, it lacks the deeper human capabilities of creative insight, moral judgment, and philosophical reasoning essential to the advancement of science.
Among the core takeaways:
- AI thrives in pattern recognition and processing large datasets but does not possess the capacity for value-driven inquiry or contextual nuance.
- Scientific innovation often demands divergent thinking and the questioning of foundational assumptions—tasks where human researchers still outperform even the most advanced custom AI models.
- The practice of science involves social collaboration, ethical considerations, and tacit knowledge that cannot be codified or automated by algorithms.
For businesses and martech companies, this serves as a strategic reminder that machine learning should be viewed as an augmenting tool, not a replacement for human expertise. A powerful use-case, for example, lies in enhancing marketing performance through hybrid human-AI collaboration. At HolistiCrm, AI consultancy efforts focus on building holistic solutions where AI supports customer behavior modeling, campaign optimization, and satisfaction prediction, while marketing professionals make strategic, creative, and ethical decisions. This innovation synergy between AI models and human expertise drives measurable business value, strengthening outcomes across CRM, retention, and personalization strategies.
Ultimately, sustainable AI adoption in business demands not complete automation but curated augmentation—a philosophy deeply aligned with holistic martech and AI agency practices.
Original article: https://news.google.com/rss/articles/CBMiyAFBVV95cUxNSmxybUpBQjNteWVTYnQ0NFdsb3pkTVlnaTdiVk9KdmdUZVZyckdKSC1hV3dVSHJoRmlGYkcweWROWlJWMmVib2FYTW92aVRVNE9nTXJfOUZQV0dYck5YV0MwcUplMGp2ZFN1U3dRRk80WHF3bzdaNUFYVXVWRDFkd2RsekVlZFF2S0RxQ2xRRFZ4dlQxXzVPZ3pzYkVkSDRrU3hqdEoxSklaaU5aTk55cjE2QjhqQ3ZRMG5jMTY3LXFobGtJTkU5RA?oc=5
by Csongor Fekete | Jan 22, 2026 | AI, Business, Machine Learning
The recent revealing of VoidLink, an advanced AI-generated malware, by Check Point Research signals a turning point in cybersecurity. The report highlights how AI is now being weaponized to create polymorphic malware capable of bypassing traditional detection systems. VoidLink’s code mutates upon every execution, making it nearly impossible for signature-based security solutions to keep up. Built using AI-generated bytecode obfuscation, it demonstrates a new era of machine learning-assisted cyber threats.
This development stresses a critical lesson: while AI and Machine Learning models offer transformational potential for business optimization and martech innovation, they also introduce new vulnerabilities. For AI consultancies and martech-driven organizations, this is a call to adopt a more holistic view of performance—not just in terms of ROI or customer satisfaction, but also in technical resilience and digital hygiene.
A use-case that creates business value in this context lies in leveraging custom AI models to fortify cybersecurity layers in CRM platforms. Embedding anomaly detection algorithms trained on behavioral patterns helps proactively detect unusual actions signaling possible breaches. For marketing operations, this means preserving integrity in customer data management and ensuring that martech systems are not exploited as attack vectors.
With threats now being generated by AI, only systems empowered by equally adaptive intelligence—trained, maintained, and monitored by experienced AI experts and agencies—can effectively mitigate the risk. Investing in robust, explainable, and secure ML architectures becomes not just a technical requirement, but a business imperative.
Source: original article
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