Generative AI offers personalization and loyalty across the enterprise—if you get it right

Generative AI (GenAI) has exploded onto the business agenda with a promise of next-generation personalization. With it, enterprises could be on the verge of huge improvements in customer experience and employee experience. It enables more granular interactions and eventually makes every interaction a conversation—not just a message. To unlock the value, enterprise leaders must carefully manage how they access and apply personal data. Get this wrong, and hyper-personalization can result in creeped-out customers and employees running from you rather than buying into you.

Gain consumer and employee loyalty with hyper-personalization

Using consumer behavior and historical data, personalized marketing has focused on moving beyond the one-size-fits-all messaging of traditional broadcast advertising toward messaging for individual consumer preferences. Increased relevance adds utility, with messaging about the right thing at the right time for the right person perceived as useful. It typically drives increased customer satisfaction and loyalty.

Until the advent of generative AI, this personalized approach had been limited to digital prompts of a “you-did-this-so-I-suggest-you-do-that” nature in a narrow cause-effect cycle. Iterations, real-time interaction, and meaningful automated conversations with the consumer have not been possible. Likewise, the technique has remained largely limited in application to the marketing department. Generative AI is making hyper-personalization possible, as personal messaging and assistance can become a mainstay across the enterprise landscape (HR, finance and operations, customer service, and marketing) to boost productivity, alignment, insight, value, and loyalty.

We are already seeing the first announcements in marketing: Salesforce recently claimed its Marketing GPT can “deliver customized commerce experiences at every step of the buyer’s journey with auto-generated insights and recommendations based on unified real-time data.”

The essence of hyper-personalization lies in using generative AI to make every communication personal and every piece of work better informed. With a GenAI personal assistant for employees, every employee will have a catered experience to better understand their productivity challenges, corporate goals, and situational ambiguities, just as every manager will get better and deeper employee satisfaction reports from generative AI conversational check-ins. If done right, this approach could increase production and sales and create a much stronger alignment between employees, consumers, and enterprises, resulting in increased loyalty.

The imperative is clear, but so are the challenges. Effective hyper-personalization requires access to personal data, and you need to ensure your personalization does not get creepy. The dividing line between relevance and overreach can result in the recipient feeling intruded upon. In essence, the challenges remain the same as they were in 2022 (see Exhibit 1).

Exhibit 1: Alienation, misinterpretation, and a lack of data access are among the greatest risks, according to customer experience (CX) decision makers

Sample: 300 customer experience decision makers, The Future of Experience in a Hybrid Reality, 2022
Source: HFS Research, 2023

Get personal quickly: Build use cases from the ground up

As we have previously pointed out, the modern enterprise needs to come to terms with new leadership roles. They urgently need to create chief customer and employee experience officers to lay down strategic goals for engaging and implementing hyper-personalization across the enterprise and gain the most value from this new opportunity. They must quickly define simple use cases; maturation will not happen overnight. Most use cases will eventually develop from being public-data-trained assistants to enterprise-data-trained assistants before finally becoming personal-data-trained assistants, and the appearance and design of these assistants will not be uniform. Creativity, open minds, and cross-silo collaboration are all top virtues in the pursuit of hyper-personalization.

Build trust to get permission for access to personal data

The key prerequisite for generative-AI-assisted hyper-personalization is to get data and consent for the outcome you want to achieve, keeping adequately in line with data regulations like GDPR. Gathering this data will require increased trust from your data subjects. Building this trust will encompass every part of the enterprise and enterprise narrative: product quality and production methods, brand integrity, ESG (environmental, social, and governance), and CSR (corporate social responsibility), cybersecurity, and privacy protection.

Enterprises should also be prepared for a near future in which personal data remains in the ownership of the individual, to be shared with the enterprise only intermittently and for specific short-term purposes.

Enterprises must adopt long-term value propositions to consumers and employees and ensure relevance spanning the entire value chain. Recalibrating enterprise strategies to include meaningful and trustworthy relationships across the board is an imperative for yesterday.

Bias is the ghost in the machine: Build new controlling roles

AI is not faultless. The real dangers of bias and unintended actions from using AI technologies requires the creation of a new AI controller function in every enterprise. Guarding against unintended AI behavior will require significant investments into systems that analyze behavior patterns, detect anomalies, and ensure adequate and meaningful transparency across AI training and implementation. We already highlighted these requirements in 2021, and the EU is already putting them into regulation as well. Hyper-personalization is only personal through a machine-human interface, and you need to control how the machine interacts with your human endpoints (see our previous highlight on AI for HR for reference). Paving the way for and implementing AI controlling functions needs to be on every agenda in 2023.

The Bottom Line: Data and trust are the keys to unlocking hyper-personalization.

Generative AI will empower hyper-personalization efforts across the entire enterprise value chain, in turn enabling improved sales, loyalty, alignment, and productivity. To get ahead with this new opportunity, enterprises must ensure they work on the right use cases across organizational siloes and immediately start working on how to build sufficient trust with their customers and employees to be deemed worthy of their data. Likewise, retaining trust post-implementation is just as critical, and enterprises must look to build AI controlling functions sooner rather than later. The evergreen imperatives of data, cross-silo organizations, and trust are unchanged, but the potential rewards just increased significantly.