by Csongor Fekete | Dec 31, 2025 | AI, Business, Machine Learning
The recent article "We might finally know what will burst the AI bubble" by BBC Science Focus Magazine raises critical concerns about the AI industry’s sustainability. As excitement around generative AI reaches new heights, economists and technologists are beginning to question whether the pace of hype matches real-world value creation. One of the article’s key assertions is that AI’s performance improvements may begin to stagnate in areas such as large language models, while their costs – especially around training and compute – continue to rise.
The piece highlights how early promises of revolutionary change have not always translated into measurable productivity gains across industries. Much of AI’s perceived value today stems from investor enthusiasm rather than demonstrated operational efficiency. This growing disconnect calls for a more holistic approach. Businesses must move beyond general-purpose tools and begin exploring the real performance benefits of deploying targeted, custom AI models aligned with specific customer and workflow needs.
At HolistiCrm, this underlines a critical opportunity for martech teams. Many CRM platforms boast generic AI capabilities, but these often fail to account for the subtle nuances in customer behavior, segmented campaigns, or regional market preferences. A use-case that searches for patterns in customer churn using a machine learning model—trained on a company’s own CRM data—can outperform generalized models. When refined through AI consultancy that understands both data science and customer experience, such solutions boost customer satisfaction, optimize marketing spend, and increase lifetime value.
With growing scrutiny over AI ROI, businesses now need the guidance of an AI agency to cut through the noise and focus on applications with definable outcomes. Tailored solutions built with domain context—rather than one-size-fits-all approaches—will be the key to remaining competitive as the bubble of AI expectations meets practical business needs.
Read the original article: https://news.google.com/rss/articles/CBMiekFVX3lxTE9WaG1WX2RMbENjTldZeU1MRE5RWlBGZEtleFoxdDFYM09NSTFfa1VidWR3X01lZ3pvaXl2STJDbWdPaFNpYlpLdXQ2RmlDdURsTjc2RXRaTktaQ2c4TjZNSjZ0aVg4M0YwUE9VbGpzY1VqZ0ZxN3hPTFZn?oc=5 (original article)
by Csongor Fekete | Dec 31, 2025 | AI, Business, Machine Learning
The crossover between artistic creativity and artificial intelligence is no longer theoretical—it’s being actively explored. In the recent article from the San Francisco Chronicle, theater artists are stepping into AI roles, leveraging their storytelling, empathy, and multidisciplinary skills to carve out unique value in the tech landscape. These non-traditional AI contributors are finding roles in prompt engineering, user experience design, and model training—areas requiring intuition, improvisation, and emotional intelligence.
The key insight from the article is that the AI industry needs more than just technical prowess. Emotional nuance, creative problem-solving, and human-centered design—qualities honed by artists—are becoming essential in shaping AI tools that truly resonate with users. This shift is especially relevant in marketing and martech, where understanding human behavior and crafting compelling narratives are central to enhancing customer satisfaction and performance.
For AI consultancies like HolistiCrm, this trend underscores the importance of building custom AI models that not only analyze data but also consider the human context behind it. Imagine a Machine Learning model trained by creatives to identify emotional tones in customer feedback or generate dynamic, brand-aligned marketing copy. Such solutions blend holistic data strategy with artistic finesse, transforming AI into a tool of deeper connection rather than just automation.
This convergence opens up new dimensions of business value—more personalized campaigns, empathetic chatbots, and enriched customer journeys—powered by AI experts who think like artists. It’s a martech evolution driven by diversity of thought as much as technical innovation.
original article: https://news.google.com/rss/articles/CBMinwFBVV95cUxOOUJIMjZxMGMyMGotVlVuaV9WZThsT1pnSDRpMHQ5WlZnVVhiTGQ3T0pZTFJNQjZfZFZSNzlUM0EtaldWRFlUQW1KVTYtV1piejFEWlRURmJPSW1uZ2doNXZnZkJ5QjFKNElnY2RfbW5pMENLb0I4WVo3NmhGenVMVUU1OUxnRlJncGxXdVJsMUtaODRIU1Q5cGJtLXp4eVU?oc=5
by Csongor Fekete | Dec 30, 2025 | AI, Business, Machine Learning
Despite the widespread hype around artificial intelligence, its tangible impact on scientific discovery has not yet met expectations. The New York Times article, "Where Is All the A.I.-Driven Scientific Progress?", emphasizes a key disconnect: while machine learning models have shown immense promise in controlled lab settings, their practical application to real-world scientific breakthroughs is still limited.
The article discusses several factors hindering progress: overreliance on data-heavy environments, difficulty transferring AI success from simulations to complex real-world systems, and the lack of domain expertise in many AI development efforts. Even impressive AI models, such as those used in protein folding or materials discovery, often struggle when data is noisy or incomplete—conditions typical in natural sciences.
For the martech and CRM industries, this analysis offers a valuable lesson. Building AI-driven marketing or customer experience systems requires more than just powerful technology; it demands contextual, domain-specific customization. At HolistiCrm, the approach centers on building holistic, custom AI models trained on real, high-precision business data with embedded performance metrics. This ensures that marketing and customer satisfaction efforts are not just automated—but deeply aligned with actual market behavior.
A key use-case would be personalized customer journey optimization. By developing tailored Machine Learning models that learn from dynamic CRM data, an organization can improve retention, increase conversion rates, and elevate customer satisfaction—delivering measurable business value through AI, unlike the generalized models discussed in scientific contexts.
The challenge ahead is to bridge theoretical AI capabilities with practical business outcomes. This requires AI experts who can operate at the intersection of martech, domain understanding, and custom development—a role HolistiCrm fills as an AI consultancy and performance-driven AI agency.
Read the original article for more insight: original article
by Csongor Fekete | Dec 30, 2025 | AI, Business, Machine Learning
AI-generated advertising has entered the mainstream—yet not without some turbulence. The recent article from Business Insider titled “5 AI advertising controversies that turned heads this year, from Meta's AI granny to Coca-Cola's shape-shifting trucks” highlights the ethical and creative boundaries being tested by the growing use of Machine Learning models in digital content development.
Key takeaways from the article include:
- Meta's AI-generated ad featuring a fictional grandma drew backlash for blurring the lines between realism and fabrication, causing public discomfort.
- Coca-Cola used AI to transform delivery trucks into surreal, futuristic machines, which reportedly overshadowed the brand’s message and confused audiences.
- Brands like Under Armour, Nestlé, and even major political campaigns faced criticism for deepfake-style marketing or AI-generated content that either misrepresented individuals or spread misinformation.
- The growing role of generative AI in marketing highlights flaws in transparency and authenticity, prompting fears over brand trust and customer satisfaction.
- Industry reactions stress the need for guardrails and ethical frameworks in martech use, advocating for clearer labeling, disclosure, and regulation around synthetic media.
From a business value perspective, these cases present a rich opportunity for AI experts and AI consultancies to lead with responsibility. HolistiCrm promotes the development of holistic, custom AI models tailored to both enhance marketing performance and preserve brand trust.
Rather than relying on generic generative AI, marketing and martech teams can benefit from using purpose-built Machine Learning models optimized for tone, brand alignment, and customer segmentation. These AI systems can deliver ROI through improved engagement and conversion rates while avoiding reputational risk.
A practical use-case could involve dynamic creative optimization with real-time customer satisfaction feedback loops. Instead of generic avatars or deepfakes, custom AI could generate brand-safe content variants based on audience response data—driving performance while respecting ethical standards.
When implemented thoughtfully, AI in marketing becomes more than a novelty—it becomes a strategic capability that strengthens customer relationships and brand equity.
Reference: original article. https://news.google.com/rss/articles/CBMioAFBVV95cUxOQlBPc1R3LUctZ0VmbDhvS2JfZW50ZUs1TGlOQ0ZGUl9MbXRGaWI4VHJrdU1Eamk4OFpaTE0tTkFPaWNiVFpTeGZpdDV1QTBiOG1BZWxUNnVZU2hnWkFNeDlOSEFmNVVrVHdUR3dIamZVcTF6S2YwVEd0dURtUzhVd0pEa2o3NVR3bnBKVmFGSFgtYk1yVktoT0d1UnI2b2lJ?oc=5
by Csongor Fekete | Dec 29, 2025 | AI, Business, Machine Learning
The recent decision by the Trump administration to roll back Biden-era health IT policies—particularly those involving AI "model cards"—raises significant implications for responsible AI deployment in healthcare. As reported by Healthcare Dive, these policy changes remove prior requirements aimed at increasing transparency, trust, and accountability in AI-driven tools used in clinical environments.
AI “model cards” were introduced as a way to provide structured documentation of a Machine Learning model’s purpose, performance, data provenance, and ethical considerations. They are akin to nutrition labels for AI systems and offer critical insight for clinicians, patients, and developers into how custom AI models operate—helping support informed decisions and preventing potential bias or misuse.
With the elimination of this requirement, stakeholders in health IT and beyond are left without a consistent framework to assess AI reliability. For AI expert teams, marketing strategists, and martech leaders, this rollback underscores the necessity for internal governance policies to ensure that AI adoption remains holistic, safe, and customer-centric—regardless of regulatory shifts.
Business value emerges when organizations take ownership of transparency standards, even in the absence of federal mandates. For example, a healthcare CRM provider leveraging a Machine Learning model to predict patient follow-up behavior can create custom AI model cards to document model rationale, training data demographics, and performance benchmarks. This boosts customer satisfaction, increases clinician trust, and reduces liability related to algorithmic bias.
AI consultancy and AI agency leaders should view this moment as a call to help clients build ethical, high-performance AI architectures—capable of self-regulation through documentation, transparent practices, and internal validation tools. This approach not only enhances compliance readiness but also fosters user trust in critical areas like healthcare, finance, and marketing.
Read the original article: Trump administration nixes Biden-era health IT policies, including AI ‘model cards’ – Healthcare Dive
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