Ready or Not, Digital Doubles Are Here
Imagine this: You need a new pair of running shoes. Instead of driving to the store or starting a search online, you turn to your artificial intelligence agent that already knows you wear a women’s size 8 with average support. You ask for a price point of under $140 with delivery by Friday and make sure it knows you prefer sustainable brands. Within seconds, the agent has found and completed the transaction, applied your membership discount, and arranged for delivery at no additional charge.
This isn’t science fiction. It’s the emerging reality of digital doubles—personalized AI agents that act autonomously on behalf of consumers in the marketplace. These AI agents go far beyond chatbots. They can learn individual preferences, behaviors, and values; based on these insights, they can then make decisions, shop, book services, and interact with companies without constant human oversight.
For CRM professionals, this represents a fundamental shift in how customer relationships work. Importantly, and perhaps frighteningly, this isn’t a question of whether significant transformation is coming but whether your CRM infrastructure is ready to adapt, migrate, mutate...or die.
“Digital doubles will fundamentally shift brands from direct customer engagement to competing for algorithmic favor,” says Hannah Choi, CEO and founder of Spotlite, a professional model booking service provider. The impact of this shift on companies will be significant. Instead of focusing on persuading human customers to make a purchase, companies will also need to consider how they can attract the attention of and win over AI agents that will take on a gatekeeper role for many consumer purchasing decisions.
Maryna Hradovich, cofounder and chief operating officer of Maestra, a marketing personalization platform provider, explains how these relationships are likely to evolve. “In the past, customer-brand relationships worked like a one-way broadcast,” she says, noting that companies sent out offers, and customers made purchase choices and decisions.
The rise of digital doubles, though, will involve agent-to-agent interactions. “A customer’s digital agent will make very specific requests—size, budget, style, timing, and more—and the brand’s task will be to provide the most detailed and relevant offer possible,” Hradovich explains.
The impact of AI-driven search is already being felt. Choi points to data from Adobe indicating that traffic from AI browsers has already increased 4,700 percent year over year. In addition, more than 50 percent of consumers expect to use AI shopping assistants by the end of this year. “We’re in the experimentation phase where 25 percent of enterprises deploy agents this year, growing to 50 percent by 2027,” Choi says.
Furthermore, the Business Research Company valued the current autonomous agents market at $4.4 billion and expects it to grow to more than $103 billion by 2034. Analysts predict that, by 2028, roughly 68 percent of customer interactions will be handled by autonomous tools.
The Impact on Brand Relationships
The transformation extends beyond simple automation. “Digital doubles will fundamentally invert the power dynamic between brands and consumers,” explains Devidas Desai, senior vice president of product management at ASAPP, a provider of AI-powered contact center software. “For the first time, the customer’s proxy, not the brand, will define the terms of engagement. Instead of companies personalizing experiences based on their own data models, consumers will bring AI agents trained on their unique preferences, values, motivations, and intent.”
This shift moves commerce from one-to-many marketing to one-to-one negotiation. “Brands will no longer compete for impressions; they’ll compete for alignment with each consumer’s agent,” Desai says.
MJ Kaufmann, a cybersecurity author and instructor at O’Reilly Media, frames it more directly: “Digital doubles will act as gatekeepers, shifting the customer-brand relationship from direct persuasion to agent-mediated trust. Brands will need to prioritize transparency and machine-readable signals of value and reliability, as it’s the AI agent, not the human, that makes the initial judgment.”
Choi offers an important caveat, though. Companies that can’t separate themselves in meaningful and impactful ways from their competitors will be virtually invisible. “Just as we’ve seen in fashion, brands with genuine creative vision and unique messaging will actually strengthen their position, using AI agents to handle repetitive commerce while preserving bandwidth for what truly matters: cultural relevance and human connection,” Choi predicts.
The Infrastructure Challenge: Are CRM Systems Ready?
But here’s the uncomfortable truth: Most CRM systems are far from prepared for robust agent-to-agent commerce that will delight instead of dismay consumers.
Making agent-to-agent commerce possible, Hradovich explains, requires technology that’s able to connect a deep understanding of customers with relevant products that will meet their needs. That means rich, structured product information. Systems will require the following:
Unified customer profiles that “bring together all available data, from age and gender to browsing history, purchase records, and even the agent’s queries.” When the information is structured and easy to use, Hradovich says, “typically through a customer data platform (CDP), it enables brands to deliver more accurate and personalized offers.”
Machine-readable product data that is complete and always up to date. “That includes real-time inventory, detailed specifications, delivery terms, and warranties. A person might not always have the time or desire to explore all these details, but their digital double will,” Hradovich says.
API-first architecture to create experiences that can be readily navigated by both people and machines. “If your platform can’t serve the AI agent clean, timely, personalized data, you’re out of the conversation,” warns Bryan Cheung, chief marketing officer of Liferay, a digital customer experience platform provider. “The challenge isn’t flashy AI. It’s foundational—unified data, composable architecture, and experiences designed to be navigated by both people and machines,” he says.
Tom Richardson, senior vice president and regional technology lead at RAPP, a precision marketing agency, adds that success will require “clean, discoverable product data, clear policies, and simple ways for agents to compare, configure, and buy.”
The Opportunity: Beyond Efficiency to Strategic Advantage
Despite the infrastructure challenges, the opportunities are substantial for organizations that get this right.
“The biggest upside is reducing acquisition waste and purchase friction,” Hradovich notes. “Today, companies spend heavily on broad Meta/Google campaigns to find their audiences and then nurture them. Digital doubles actively search and study products themselves, bringing high-intent demand and sometimes completing the purchase.”
Richardson sees additional benefits. “Opportunities include smoother journeys from discovery to delivery, richer personalization, and lower service costs because agents handle routine steps,” he says. “New growth will come from being ‘agent-ready.’”
Retention improvements matter too. “Fewer mismatches between what customers want and what they receive mean fewer returns and happier buyers,” Hradovich explains. “Routine re-orders can be delegated to an agent. In service, agent-initiated order status, exchanges, and warranty checks shorten time to resolution and reduce costs to serve.”
The Biggest Barrier to Adoption: Security Issues
Security concerns represent the most critical obstacle to mainstream digital double adoption, and most organizations are dangerously unprepared.
Jagannadh Mellacheruvu, head of product and policy at Mirror Security, an AI security company, has conducted extensive research on AI infrastructure vulnerabilities. “Our research across the AI ecosystem shows that current AI infrastructure operates with security assumptions that adversaries can easily exploit,” Mellacheruvu says. “Approximately 73 percent of organizations are currently using AI agents without any runtime protection.”
AI agents present different vulnerabilities than traditional systems. “AI agents introduce attack vectors that traditional security tools simply weren’t designed to address,” Mellacheruvu explains. For instance: prompt injection attacks. In such attacks, he says, “malicious actors can manipulate AI agent behavior through carefully crafted inputs, knowledge extraction where proprietary business logic embedded in AI models can be stolen, and context manipulation where attackers influence agent decisions by controlling the information they consider relevant.”
Abhay Bhargav, CEO and founder of SecurityReview AI, shares these concerns: “AI agents have had a bad track record, so far, in terms of security and privacy. Most AI tools are very easily vulnerable against attacks like prompt injection, where, for example, a sleazy product company might use product marketing that would force the agent to use or suggest their product to the user every single time.”
Protecting data will be equally challenging. “About 87 percent of organizations lack proper data encryption for their AI systems,” Mellacheruvu notes. He points to a paradox. “If you encrypt customer data using conventional approaches, your AI agents can’t perform semantic understanding, similarity matching, or personalized recommendations. The moment you decrypt that data for AI processing, you expose it to breaches.”
For consumers, the stakes are equally high. “Digital doubles introduce a new attack surface,” Kaufmann warns. “If compromised, an agent could be manipulated to make fraudulent purchases or leak sensitive personal data.”
A Balanced Perspective: Promise and Peril
Vanitha Swaminathan, Thomas Marshall Professor of Marketing at the University of Pittsburgh’s Katz Graduate School of Business, and the author of Hyper-Digital Marketing: Six Pillars of Strategic Brand Marketing in an AI-
Powered World, offers important perspective on both the potential and limitations of digital doubles.
“By making shopping decisions on behalf of customers, they can help companies by taking away some of the frictions inherent in the process of making a purchase,” she notes. However, she cautions: “It is possible that the digital doubles lack the ability to negotiate or decide on behalf of customers in high-risk contexts such as financial decision making and purchasing of big-ticket items.”
Swaminathan identifies another critical limitation: “Autonomous agents may not be able to understand a customer’s need for variety or have a good understanding of changing needs of customers. AI agents are inherently amoral—they are neither immoral nor moral—thus can never really fully replace a human being in certain aspects of decision making.”
The key, she suggests, is understanding where digital doubles add value vs. where human judgment remains essential. “In certain mundane and frequently bought categories, such as your repeat purchases of coffee, toothpaste, and detergent, AI agents may be able to place orders by anticipating customers’ purchases. In these cases, the products may be purchased and delivered even prior to the customer noticing that these are needed.”
What CRM Professionals Should Do Now
Given the complexity of this transformation and the significant security challenges, what practical steps should CRM professionals take today? The experts recommend the following.
Audit your data infrastructure. An important first step is assessing whether customer data is unified, structured, and accessible. Companies need “clean, discoverable product data, clear policies, and simple ways for agents to compare, configure, and buy,” Richardson stresses.
It will be important to focus on creating structured data formats that AI agents can efficiently process. This includes not just human-readable descriptions but machine-parseable specifications, real-time inventory status, pricing rules, delivery terms, and eligibility requirements.
Implement security foundations. The time to address AI agent security isn’t after a breach has occurred. It’s now. Then, Richardson suggests, “implement runtime protection for your AI agents, and develop strategies to protect sensitive data while maintaining AI functionality.”
Data governance is also critically important, including decisions on which data to collect and why it needs to be collected. “Collect only what’s necessary and explain clearly what is shared and with whom,” Hradovich advises. In addition, she suggests maintaining auditable logs of what agents access or do, providing users with a way to review their digital double’s actions at any time, and making consent revocation immediate and easy.
“Customers should choose which data powers their agent and be able to change or revoke access at any time, with easy portability,” Richardson adds.
Restructure product and policy information. Product and policy information must be accurate, up to date, consistent, and machine-readable. Product specifications, inventory status, pricing rules, delivery terms, return policies, and eligibility requirements must all be structured and designed to be read by AI agents efficiently.
Rethink attribution and measurement. Attribution and measurement will continue to be an important element of managing and monitoring the impact of AI agents, but this will need to be done in new ways.
“As agent steps reshape the funnel, analysts will need new ways to track and understand customer journeys,” Hradovich points out. New key performance indicators will also be needed, Richardson says. “We must add metrics such as task completion, time saved, satisfaction, safe automation rate, and quality of human handoff, alongside the more standard revenue outcomes.”
Start small with high-value, low-risk use cases. Rather than attempting a wholesale transformation, begin with specific, bounded use cases. Richardson notes that in the near term, “expect agents to assist with high-value, repeatable tasks, such as customer service, travel planning, subscriptions, and reorders which are triggered by intentional consumer actions and protected by clear guardrails.”
Certain companies will be impacted more and sooner than others, Kaufmann notes. “Early adoption will happen within the next two to three years in sectors like travel and retail, but true mainstream use across industries will likely take three to five years as trust, standards, and regulation mature.”
The Bottom Line: Start Preparing Now
Digital doubles represent a fundamental transformation in how commerce operates and how customer relationships function. The shift from human-to-company to agent-to-agent commerce will reshape everything from marketing attribution to product data management to customer service.
For CRM professionals, the message is clear: AI agents are here to stay. The companies that begin preparing now by unifying data, implementing security controls, and creating machine-readable content will be positioned to capture the opportunities that digital doubles represent.
Those who wait risk becoming invisible to the next generation of commerce.
As Choi puts it: “Brands must decide now whether they’ll use agents to deepen relationships or become mere background utilities in someone else’s marketplace.”
The timeline is compressed. Mainstream adoption in certain sectors is just two to three years away. But as Mellacheruvu warns, “The bottleneck isn’t consumer willingness or technical capability; it’s security infrastructure.”
The future belongs to CRM organizations that can successfully engage the prospects and customers they can’t see—the ones operating through digital doubles. Start building that capability today.
Linda Pophal is a freelance business journalist and content marketer who writes for various business and trade publications. Pophal does content marketing for Fortune 500 companies, small businesses, and individuals on a wide range of subjects, from human resource management and employee relations to marketing, technology, healthcare industry trends, and more.