What extent does artificial intelligence enhance the personalisation of the customer experience and what are the limitations?

Extract

[...] In summary, international collaboration, mutual standard recognition, and incentives alignment across industries are necessary to implement a more coherent global structure on AI personalization. Failing to do so, regulatory fragmentation will continue to be a major block to responsible and scalable roll-out of AI in the domain of customer experience management. New References Used Accenture. ( 25). Generative AI for customer growth. Retrieved from https://www.accenture.com/sk-en/insights/song/generative-ai-customer-growth Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing . [...]

[...] CRM Systems have also become intelligent with the inclusion of AI features. Traditional CRMs were largely databases, storing interactions and contact information for customers. By comparison, today's AI-augmented CRMs, such as Salesforce Einstein and Microsoft Dynamics 365, now use machine learning algorithms to automatically assess leads, recommend next-best actions, and maximize timing and channels for communication (McKinsey & Company, 2023). These systems use a combination structured and unstructured data - such as purchase history, clickstream records and customer-service logs - to provide an aggregated and predictive view of the customer. [...]

[...] In light of these tensions, several authors advocate a co-regulatory model- such as that of Touzani (2024)-that would see state authorities and private interests come together in order to build adaptive yet robust models of governance. Rather than try to determine overly prescriptive rules, which risk stifling innovation, governments could promote development of industry-specific ethical codes, sandbox testing environments or voluntary certification programs. These mechanisms would permit businesses to safely experiment with responsible personalization, all within the context of shared ethical norms. This disharmony also has geopolitical consequences. Countries that are less regulated may end up as havens for AI experiments, while more regulation-heavy jurisdictions may choke off innovation. [...]

[...] From a bare-bones perspective, AI-powered personalization also makes inside processes more refined. Smart CRMs and CDPs collect and analyze data from various touchpoints (e.g., website visits, storefront behavior, and customer support logs). Data centralization prevents silos of information and better aligns departments, particularly marketing, sales, and supply chain (Accenture, 2023). For instance, by predicting the demand trends for different types of customers, firms are able to more efficiently control their inventories, and prevent both over-stocking and stockout. This is especially true in industries such as fashion or consumer electronics, where relevance and timing of the product are paramount for profit. [...]

[...] Secondly, it matches offers to the right customer through predictive analytics. Third, it allows people to personalize the tone, channel or timeframe of marketing messages. In this way, AI facilitates a "controlled serendipity" effect: the customer believes that the brand really "gets" them, or even knows what they'll want before they do, and this deepens engagement, loyalty and satisfaction. The combination of artificial intelligence, personalization, and UX are the strategic axis around which most of the current marketing strategies are spinning today. [...]