by Csongor Fekete | Apr 11, 2025 | AI, Business, Machine Learning
📌 Blog Post Title: Public vs. Expert Perceptions of AI — Business Takeaways for High-Performance AI Strategies
As the integration of artificial intelligence continues to accelerate, understanding how different groups perceive this technology is essential. A recent study by Pew Research Center titled “How the U.S. Public and AI Experts View Artificial Intelligence” sheds new light on a key divide between public sentiment and expert perspectives regarding AI’s impact, potential, and societal value.
🔍 Key Findings from the Pew Research Study
- Public Skepticism: Only 37% of the U.S. public believe artificial intelligence will improve life, while 45% think it will make things worse or a mix of both.
- Expert Optimism: In contrast, a large majority of AI experts are significantly more bullish, citing potential benefits for healthcare, education, climate modeling, and discovery.
- Common Concerns: Both groups share apprehensions over AI misuse, data privacy, and bias—a reflection of real ethical and governance challenges.
- Polarizing Impact: The divide illuminates an urgent need to bridge gaps between technical capability and public trust through transparent, ethical AI development.
💡 Business Applications: Aligning AI Use-Cases with Customer Perceptions
In the martech and CRM space, aligning technological capabilities with customer expectations isn't optional—it's essential. Companies leveraging AI to improve customer satisfaction and marketing performance must reflect both technical feasibility and public sentiment.
Use-Case Insight: Holistic AI for CRM Optimization
A practical and high-value use-case drawn from the article's lessons is the deployment of a custom AI model to optimize customer journey personalization. By integrating ethical AI strategies and transparency, businesses can strengthen trust while increasing retention and lifetime value.
Benefits for businesses include:
- Improved Marketing ROI: By leveraging AI models trained on customer behavior, organizations can execute hyper-personalized campaigns.
- Enhanced Satisfaction: Predictive analytics enable smarter, real-time engagement—responding with relevance, not repetition.
- Responsible Innovation: Addressing concerns raised in the survey—like inclusivity and data transparency—helps build trustworthiness and brand equity.
🔧 How AI Agencies and Consultancies Can Help
To implement AI responsibly and effectively, partnering with an AI consultancy or AI agency experienced in holistic machine learning model development is vital. These experts help ensure alignment with both performance goals and ethical guidelines—solving business problems while supporting public trust.
👥 Bridging the Gap
The Pew study underscores a vital message: Custom AI doesn't just need to perform—it must also be trusted. A holistic approach to martech development, guided by AI experts and grounded in customer sentiment data, helps organizations future-proof their initiatives.
Read the full original article: How the U.S. Public and AI Experts View Artificial Intelligence – Pew Research Center
https://news.google.com/rss/articles/CBMirwFBVV95cUxOVlNTUHoxVHlZLXNLNW9QSFYweGtidUplcWhLbndiVk1NMXZTaWc0TVo5VVUydlp5WncyVktQdTZpbHRSQzZzTWU5UWhEa0w4aEk1dUVSX2RVSktwbU5jWW45NXJLUkxvZ2ZCOW1IeWtWY1d4YXAxQmFSZEJIM2FXQW5oT2lpaXhzTnNvaEVUbGZMY2JfUUF4d1J6ZWswTlhsNmJfSTFqMWxidWR0N1RZ?oc=5
by Csongor Fekete | Apr 10, 2025 | AI, Business, Machine Learning
In a strategic move to strengthen its customer relationship management (CRM) capabilities, ServiceNow has announced the acquisition of Logik.ai’s AI-powered Configure, Price, Quote (CPQ) technology. This acquisition signals a growing trend where top-tier enterprise software providers are tapping into advanced Machine Learning models to boost performance and customer satisfaction through automation and personalization.
Key Highlights from the Acquisition:
- Logik.ai brings a best-in-class, AI-powered CPQ solution that simplifies complex product configurations and pricing processes.
- The integration with ServiceNow's existing platform aims to digitize and streamline end-to-end sales workflows.
- The CPQ solution leverages custom AI models to deliver faster and more accurate sales quotes, improving sales velocity and customer engagement.
- With this technology, ServiceNow moves closer to offering a holistic CRM suite that aligns with the evolving needs of modern enterprises.
Learnings and Implications:
AI-driven CPQ tools are becoming essential in modern martech stacks by reducing manual input errors, enabling dynamic pricing strategies, and shortening sales cycles. The ability to apply a Machine Learning model across product catalogs, pricing tiers, and customer demand data creates high-impact value—both in increased revenue and improved customer satisfaction.
Use-Case for Business Impact:
A B2B SaaS firm could benefit from adopting a custom CPQ solution powered by a tailored Machine Learning model. By leveraging an AI agency or AI consultancy like HolistiCrm, firms can implement holistic AI solutions that integrate with their CRM, enabling real-time quote generation, personalized pricing based on behavioral data, and seamless integration into marketing and sales workflows. Such a system not only boosts internal performance metrics but also elevates the end-customer experience—positioning businesses for long-term success.
This strategic shift in the CRM landscape highlights the critical role of AI experts and martech innovation in achieving operational excellence and customer-centric growth.
Read the original article: ServiceNow to Boost CRM Offering With Acquisition of Logik.ai’s Best-in-class, AI-powered CPQ Solution (Business Wire)
https://news.google.com/rss/articles/CBMi7gFBVV95cUxNaE9KQnFhLTltMTByc0tUdU5EQXgwUEJ2T1JKa3pQYUFaeHJnNkNEdXZOdTE5TE9sUkh1NnB6ZHMtQWxsclB1Mk5qYVhQTzJ1OE0yVTMxcTZvR04zdG9lRlhqa29NNUtDZ3haWVRUcmFxMHhoUWhaQ2h1OWtwRVJrSWsxdlhnZldvS2FyM08xZ1RVSlZvUHRPQktOUFVZMFFZcDgtSFp6MWxNVEJTMGpETTFCVlFRd2U3MGhib2gxLW4tY3lBVnRiYW5vLWYwWEdYelJWbGxUU0tJZ0NUbDJ4NVVLSWNwSGV5d3RQMHBR?oc=5 (“original article”)
by Csongor Fekete | Apr 10, 2025 | AI, Business, Machine Learning
Blog Post Title: Empowering Users Through AI Data Ownership – A Game-Changer for Martech
In a groundbreaking move, Vana is pioneering a new AI paradigm where users can gain ownership in the AI models trained on their personal data. According to MIT News, Vana allows individuals to not only control access to their data but also participate in the value generation of the custom AI models that use it. This shift could have profound implications for the future of marketing, customer satisfaction, and holistic AI development.
Key Learnings from the Article:
- Data as an Asset: Vana sees user data as a valuable, owned asset rather than a one-sided transaction benefiting only tech platforms.
- Decentralized AI Ownership: Users plug their data into Vana's platforms and can co-own the Machine Learning models trained on this data, potentially sharing in future financial benefits or applications.
- User Control & Consent: Data contributors can choose what data they wish to share, creating a trust-first foundation for AI development.
- Marketplace Ideation: Vana envisions a future "data wallet" economy where users can opt to contribute datasets to AI projects and receive compensation or model ownership in return.
Why This Matters for Business and Martech
As the boundaries of data privacy tighten and consumers become more informed, ethical AI practices are transitioning from a "nice-to-have" to strategic imperatives. Businesses leveraging martech and customer analytics can derive significant value from adopting a similar philosophy in their AI initiatives.
Here’s how a use-case modeled after Vana’s framework can generate business value:
Use Case: AI-Driven Personalized Marketing with User-Owned Data
Imagine a retail brand collaborating with an AI agency or AI consultancy to train a custom Machine Learning model for personalized product recommendations. Customers voluntarily contribute their browsing behavior, preferences, and purchase history via a transparent opt-in platform. In return, they gain a share of the performance gains generated by the model—such as discounts, tailored product launches, or loyalty rewards.
This creates a virtuous cycle:
- Improved AI performance: Access to quality, willingly contributed data ensures the creation of more accurate and effective predictions.
- Increased customer satisfaction: Transparent data use and tangible benefits boost trust and engagement.
- Holistic martech strategy: Integrates ethical data practices into marketing AI, aligning brand values with customer expectations.
Vana’s model offers valuable insight into how businesses and AI experts can rethink the AI value chain—putting people at the center while still optimizing technological performance.
As more companies consult AI agencies to build privacy-respecting ecosystems, the winners will be those that foster mutual benefit through innovation and transparency.
Read the original article on MIT News: Vana is letting users own a piece of the AI models trained on their data.
by Csongor Fekete | Apr 9, 2025 | AI, Business, Machine Learning
Alibaba Advances Custom AI Capabilities with Qwen 3 Upgrade
Alibaba is poised to release the upgraded Qwen 3 AI model in late April, continuing its push to become a global leader in generative AI. As reported by PYMNTS.com, the Qwen model—named from the Mandarin word for “deep thoughts”—is part of Alibaba Cloud’s Tongyi Qianwen large language model series. Qwen 3 aims to significantly improve performance across enterprise-grade generative AI applications, positioning it as a competitor to leading models such as GPT-4 and Google’s Gemini.
Key Highlights of the Article:
- Alibaba’s Qwen 3 is expected to enhance generative AI capabilities in multilingual contexts and complex reasoning tasks.
- The model builds upon the existing Qwen series, which has already been integrated across Alibaba’s services, from e-commerce and payments to logistics.
- This release aligns with Alibaba’s strategic shift toward AI-first cloud infrastructure and custom AI development.
Lessons and Business Opportunities:
For businesses exploring AI integration, Qwen 3 highlights the growing importance of developing or adopting custom AI models that align with specific organizational needs. By investing in purpose-built, industry-focused solutions, companies can avoid one-size-fits-all models and can deliver holistic customer experiences that improve satisfaction and performance across departments.
A potential use-case aligning with Qwen 3 involves an enterprise in the retail sector leveraging a custom Machine Learning model for personalized marketing campaigns. Using a model similar in performance to Qwen 3, the retailer can deploy advanced language processing to engage customers in multiple languages, better identify product interests through behavioral analytics, and increase conversion rates—all while optimizing marketing spend through intelligent recommendations.
By partnering with an experienced AI consultancy or AI agency such as HolistiCrm, businesses can design and deploy these custom AI-driven solutions, ensuring alignment with data privacy regulations and operational goals. Custom model integration also enables better performance monitoring and feedback loops, leading to continual model improvement, and ultimately, increased customer satisfaction.
In a maturing martech landscape, staying competitive means having access to powerful, flexible AI models that are designed around business context rather than generic capabilities. Technologies like Qwen 3 signal a broader industry shift where scalability, localization, and adaptability are key to next-gen machine learning deployments.
Read the original article here: original article.
by Csongor Fekete | Apr 9, 2025 | AI, Business, Machine Learning
💡 Blog Post: Leadership Shifts at Meta AI Signal Maturity and New Opportunities in Enterprise AI
In recent news, Meta’s head of AI research, Yann LeCun, is set to depart his leadership role in May 2024. As reported by Reuters, this shift marks the end of an era at Meta AI. While LeCun will remain with the company in a research capacity, the reorganization underscores how foundational AI efforts are evolving from pioneering research to product and business integration.
Key Takeaways from the Announcement:
- Yann LeCun, one of the "godfathers of AI," has led Meta’s AI ambitions since 2013.
- His departure reflects a broader trend of AI leadership transitions as research matures and organizations shift focus to commercialization.
- Meta aims to prioritize generative AI applications like chatbots, virtual assistants, and personalized experiences integrated into its platforms.
- The company continues to invest heavily in LLMs (Large Language Models), computer vision, and personalized content strategies.
From Research Labs to Boardrooms: The Business Shift
This headline moment is not just an internal change—it’s a signal of the broader AI ecosystem evolving. AI research is moving from labs into the core of business operations, enabling organizations to deploy custom AI models that create real business value.
For companies evaluating their martech strategies or CRM ecosystems, the opportunity lies in applying AI not as a theoretical innovation, but as a tool for performance optimization. AI consultancy services like those offered by HolistiCrm empower businesses to harness intelligent automation, customer journey personalization, and sophisticated Machine Learning models that target customer satisfaction and predict churn or purchase behavior.
Use-Case Example: AI-Powered Customer Lifecycle Optimization
A practical use-case inspired by Meta’s pivot toward personalized AI assistants could be the deployment of AI models within CRM platforms to optimize customer lifecycle stages.
For example:
- Predictive modeling can segment customers based on churn risk or purchasing behavior.
- A custom AI model can tailor email campaigns to customer microsegments, increasing open and conversion rates.
- Virtual sales assistants powered by conversational AI can enhance CRM data quality and accelerate sales cycles.
Business Value:
- Increased customer satisfaction due to personalized outreach.
- Higher marketing ROI via targeted activation strategies.
- More efficient sales operations supported by automation.
As companies adopt more holistic martech approaches, AI agencies and AI experts will play a critical role in making this transition successful.
Strategic AI leadership—whether departing or arriving—should prompt business leaders to ask: is AI a core pillar of the growth strategy, or still trapped in pilot phase?
Original article: Meta's head of AI research to depart in May – Reuters. https://news.google.com/rss/articles/CBMipwFBVV95cUxPUWFXU1FNWGhucUduSUNHa1NwWm1ESVRfc0VyeGxXaGlMZWFwbTFvbVJheE9YN1lUMUpBUkZMWDBMeXBycTI5dmg1ekdiLU1WamRxdklVRGk4MjNycE80S3FmVFhNQndJMlFhVzhDMEtENW02QkRIeV9jZjBhc3JOX1JxQTJoSG8zbjN0WWE0NVpjcncxRW5mN1FGWW8zREpIdm0zZ2hscw?oc=5
by Csongor Fekete | Apr 8, 2025 | AI, Business, Machine Learning
Alibaba's Strategic Push into Generative AI: Business Lessons for Martech Leaders
Alibaba is preparing the release of its latest flagship AI model as early as April, marking a significant milestone in global competition over cutting-edge generative AI technology. According to a Bloomberg News report shared by Reuters, this release highlights the Chinese tech giant’s ambitions to position itself alongside leading AI players in the fast-evolving landscape of artificial intelligence.
Key Takeaways from the Article:
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Flagship Release: Alibaba is expected to launch a new generative AI model in April, reinforcing its investment in foundational machine learning capabilities.
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Competitive Momentum: The move aligns Alibaba with other major global players like OpenAI, Google, and Baidu, all of whom are racing to commercialize advanced AI tools across various sectors.
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Strategic Focus: The AI models are expected to support applications in cloud services, e-commerce, and consumer interactions, enhancing user experience and operational efficiency.
Creating Business Value from AI Innovation: A Use-Case Perspective
Alibaba’s new AI model shows how custom AI models and machine learning frameworks can drive holistic value across business units. For martech and marketing leaders, AI models trained on proprietary customer and behavioral data can revolutionize how campaigns are personalized, how customer satisfaction is measured, and how marketing performance is optimized in real time.
Imagine a retail business partnering with an AI agency or consultancy to develop a custom Machine Learning model that mirrors Alibaba's approach. This model could:
- Optimize customer segmentation through advanced AI algorithms.
- Drive campaign personalization using predictive analytics based on buyer patterns.
- Improve satisfaction scores by enabling faster, automated customer support via natural language processing.
With help from an AI expert or AI consultancy like HolistiCrm, businesses can build tailored solutions that not only improve operational efficiency but also elevate brand loyalty and long-term revenue.
Companies leveraging a holistic AI integration strategy are better positioned to adapt to market dynamics and customer expectations — mirroring the agility demonstrated in Alibaba's latest move.
Original article: Alibaba prepares for flagship AI model release as soon as April, Bloomberg News reports – Reuters
Read more: https://news.google.com/rss/articles/CBMi4AFBVV95cUxNV2xtTVUxU3FpMjlTWmtrNlUzUFZPcmd6SHQteWhUV2hMYzJ3WkVtRVNwR1I2SUVOYjFxTFBWdVpCU1dRMENueHJyNkVHRDNWTWV0QmdSUVd3RXZMcmFicnNuYmlvMkpnNmprSWg2MWF0M1ktV24xV2lwMi1pVFZERHhjQ2FIck1naEJiT181Q3UtMjdoLVlsUVJVUzRfV2d0SkxXYk5IejVWX1JKb1h2UmVOaWp0T1IzREppekNPSmdYbmlFQ1NYQ1ExclUxR05SLVdlZGhTeVpvVEZ5Skd5OQ?oc=5
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