Artificial customer: How to make insights actionable and AI adoptable

It's not a chatbot, not a dashboard, not another Copilot button. It's something new: a context-aware, multi-modal, always-on artificial customer that always lives inside your key workflows. A companion that understands your business, challenges your thinking, and helps you act—not guess. Artificial customers, also known as "synthetic users" or "digital twins" in customer experience contexts, are computer-generated entities that replicate human behavior and interactions in various business scenarios. These virtual replicas are specifically designed to mirror real customers' behaviors, preferences, and decision-making processes, providing businesses with unprecedented insight into consumer mindsets. Unlike traditional analytics that simply report what happened, artificial customers can simulate what might happen, allowing for predictive rather than merely reactive strategies. 

Implementations of artificial customers range from predictive marketing systems to AI-driven customer service solutions, each showcasing unique approaches to simulating customer behaviors and enhancing organizational decision-making. 

What Is the artificial customer, really? 

Built on dynamic data and deep behavioral logic, the Artificial Customer is a simulation of your most important customer truths—continuously evolving, role-playing, and capable of empathy-infused feedback. It's trained on your customer personas, journey data, CRM insight, service usage patterns, and even product logic. It's not a generic model. It's your customer, in your pocket. 

This approach aligns with what industry experts describe as "Digital Twin for Customer Experience" - virtual replicas that allow businesses to test under varied digital scenarios, revealing behaviors and customer insights previously inaccessible. These sophisticated simulations enable organizations to anticipate customer wants before they even manifest, testing offerings and messaging before going live with real audiences. 

The technical foundation of artificial customers combines artificial intelligence, machine learning, and big data analysis to deliver reliable emulations based on past behavior patterns and predictive models. Modern implementations utilize advanced natural language processing (NLP) and large language models (LLMs) to create increasingly realistic and nuanced customer simulations. 

Three dimensions of value 

Importantly, the real power of the Artificial Customer lies not just in what it knows, but in how it collaborates with you. Unlike dashboards or copilots that react to queries, the Artificial Customer engages proactively—partnering with teams across strategy, design, marketing, and product development. It doesn’t just answer questions. It helps ask better ones. It doesn’t just analyze behavior. It simulates it, challenges it, and improves it. In doing so, it unlocks three distinct layers of value: co-creation, validation, and long-term transformation.  

These three dimensions show how artificial customers move from experimental tools to indispensable collaborators—shaping decisions and unlocking insight at the speed of work. 

1. 1. Co-creation

The Artificial Customer doesn't just answer questions—it co-creates with you. It helps teams generate ideas, stress-test messaging, and prototype features by thinking like your sharpest customer segment. It's not just a validator. It's a partner in innovation. 

This collaborative aspect represents a significant advancement over traditional market research methodologies. As demonstrated in real-world applications, AI-based ideal customer simulations allow marketing professionals to move beyond static customer personas toward dynamic, interactive models of customer behavior and preferences. These tools enable teams to gain deep insights into customer decision-making processes, such as understanding what drives a customer to pay a premium for a product. 

2. 2. Assumption validation & sparring

Every team needs a sparring partner. Someone who can call out weak points and help sharpen the thinking. The Artificial Customer plays this role in real time: refining assumptions, identifying friction points, and elevating quality by simulating real-world reaction. 

Research shows that organizations implementing AI-enabled Consumer Intelligence (AICI) are 8.5 times more likely to report annual revenue growth of 20% or more compared to those with only basic capabilities. This dramatic improvement stems from the ability to make data-driven decisions based on actionable insights derived from artificial customer interactions. For example, multinational food company Danone successfully used AI to study yogurt consumers, which led to campaign improvements that increased reach by 20% and engagement by 35%. 

3. 3. Bridge to the agentic future

Long-term, the Artificial Customer is more than a tool—it's the first agent that prepares your people to work in multi-agent systems. It builds trust, normalizes human–AI co-working, and introduces the organization to artificial collaboration—a future where AI systems don't just support humans, but collaborate with them as equals. 

This evolution mirrors the broader transition in AI applications from passive analytical tools to active participants in business processes. As customers themselves become more digitally sophisticated, businesses need equally sophisticated virtual models to understand and anticipate their needs. The artificial customer serves as a bridge to this future state of human-AI collaboration, where artificial entities play increasingly important roles in business decision-making. 

Why it works at scale: Emotion + adoption 

What makes the Artificial Customer so powerful isn't just the logic—it's the feel. It turns working with AI into an empathic interaction. Instead of prompting an AI copilot with abstract queries, you're talking to a customer. Roleplaying. Exploring. 

This emotional shift is key to adoption. People aren't scared of the Artificial Customer. They're curious about it. They come back to it. It becomes part of their flow. 

The humanization of data through artificial customers addresses one of the fundamental challenges of data-driven decision making: relevance and engagement. Traditional data analytics often fail to create emotional connections with decision makers, resulting in unused insights. By contrast, the conversational and interactive nature of artificial customers transforms cold data into meaningful dialogues3. This approach makes customer insights more accessible and compelling to teams across an organization, from marketing to product development. 

From contextual tool to ritual companion 

At first, people use the Artificial Customer when they need to validate an idea or check a decision. But soon, something shifts. It becomes a morning habit. A check-in partner. A ritual. Just like you might scan your inbox, open Slack, or review your calendar to start your day, the Artificial Customer becomes part of that loop. Not because it demands to be, but because it earns the right to be. 

That shift—from occasional utility to daily ritual—is what makes this different from any insight platform or AI copilot. It's not a button. It's a relationship. 

This integration into daily workflows represents a crucial evolution in how businesses operationalize AI. Research indicates that effective AI adoption requires technologies that complement existing processes rather than disrupting them entirely. The artificial customer achieves this by fitting naturally into decision points where customer perspective is valuable—which is virtually everywhere in modern business operations. Companies implementing these solutions report a transformation from siloed customer data programs to unified, real-time views of consumer behavior that enable confident decision-making and first-mover advantages. 

That shift—from occasional utility to daily ritual—is what makes this different from any insight platform or AI copilot. It’s not a button. It’s a relationship. 

A day in the life with the artificial customer 

The true test of any technology is how naturally it integrates into your daily flow. The Artificial Customer is designed not as a tool you visit, but as a partner that moves with you—providing insight at every critical moment, from morning planning to afternoon strategy. What follows is a glimpse into how this artificial collaborator can reshape a typical workday, making customer perspective instantly accessible, actionable, and continuous. 

Here’s a glimpse into a day in the life with the Artificial Customer: 

No dashboards. No second-hand reports. Just you and a artificial customer designed to think with you, not just for you. 

Insight-driven business, finally made real 

Everyone wants to be insight-driven. But most insights today is passive and reactive—they live in tools and reports. The Artificial Customer acts on these insights in real time. It makes the data conversational. The friction visible. The decisions smarter. You don't need to "check the numbers"—you just ask your customer. 

This transformation addresses one of the most persistent challenges in market research: translating data into action. Traditional approaches often create a disconnect between insight generation and application. In contrast, artificial customers bridge this gap by embedding insights directly into decision-making contexts.  

The technical foundation for this capability comes from advances in AI that enable more sophisticated behavioral modeling. Today's artificial customers can be programmed with crucial profile data and configured for complex tasks including web browsing, image interpretation, and nuanced response generation. These capabilities create increasingly realistic simulations that provide valid, actionable feedback. 

Personas vs artificial customers – what’s the difference? 

Understanding your customer is nothing new in business. It has long been the cornerstone of great design and effective marketing. Traditionally, this understanding has been guided by personas—fictional, research-based profiles that represent typical customer segments. 

However, unlike personas, artificial customers are AI-generated simulations that learn and adapt in real-time, offering businesses new ways to test, predict, and personalize experiences at scale. While they share a common goal—helping businesses better serve their audiences—they operate in fundamentally different ways.

The table below illustrates the key differences between personas and artificial customers:  

Built with you, built on truth 

Implementation of artificial customer technology follows a progressive path that typically begins with focused applications before expanding across the organization. This approach aligns with research showing that AI adoption is most successful when it builds on existing organizational strengths and processes. 

The most effective artificial customer implementations combine multiple data sources to create comprehensive models. Organizations like L'Oréal demonstrate the power of this approach, using AI to analyze millions of online conversations, images, and videos from thousands of sources to develop nuanced customer understandings. The result is a artificial customer model grounded in real-world behaviors rather than hypothetical constructs. 

The technology is ready. We are ready to start building. Let's make your Artificial Customer real. Start small. See the difference. Then scale into every team, every decision, every offering.

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About the author

Petri Lattu Innovation Lead at Siili Solutions LinkedIn

Petri Lattu is the Innovation Lead at Siili Solutions, where he helps businesses navigate the evolving landscape of artificial intelligence (AI) and digital transformation. With a background in design, technology, and business strategy, Petri specializes in bridging AI advancements with real-world applications, ensuring organizations maximize their AI potential. At Siili, he plays a key role in demystifying AI, developing prototypes of future AI-driven organizations, and crafting practical strategies for AI adoption. His expertise lies in identifying meaningful opportunities amid the AI hype and guiding companies toward sustainable, high-impact AI integration. Beyond his professional work, Petri is being raised by Lumen and Linus, his unruly kids and his brilliant and patient partner.