As artificial intelligence moves deeper into customer service, personalization and digital commerce, businesses face a growing challenge: how can they train and test advanced systems while limiting the exposure of sensitive customer information?
Synthetic data is becoming part of the answer.
Artificially generated to reflect selected patterns and characteristics found in real datasets, synthetic data can help companies simulate customer behavior, test AI systems and demonstrate new capabilities without directly exposing identifiable customer records.
Ashley Vassell is Senior Product Manager for Innovation Labs at Hydrolix, where she leads forward-looking initiatives beyond the company’s core platform and console. Her work centers on emerging capabilities including Anomaly Detection and Model Context Protocol, along with strategic integrations across Spark, Splunk, Kibana and Grafana. She focuses on taking innovative ideas from concept to market and driving adoption across cloud and SaaS environments.
Through that work, Vassell has seen how legal reviews, security requirements and governance concerns can delay promising AI projects.
“It could be a failed audit, a stalled AI project, or a slow legal review that fuels the adoption of synthetic data in customer experience environments,” she explained.
Synthetic data may be artificial, but its business application is deliberate.
“The benefit is this isn’t just made-up data; it's data that can be purposefully generated and instructed to match the data profile of your customers without putting them at risk,” Vassell noted.
Organizations can create datasets that reflect customer demographics, purchasing behavior, support histories, communication preferences and recurring service issues. They can also generate scenarios that are uncommon or inadequately represented in existing records.
“The ability to specify traits when generating synthetic data can introduce new customer dimensions such as demographic mix, behavioral patterns, or edge case frequency for training, testing, and personalization that can speed up new customer experience development,” Vassell observed.
That flexibility can support recommendation engines, journey orchestration systems, predictive analytics, and automated support tools. It can also shorten development cycles when real information is costly to collect, difficult to label, or subject to strict access controls.
“Synthetic data can be designed and generated to match whatever traits are needed to support development and testing of personalization, journey orchestration, and predictive analytics,” she explained. “Synthetic data is easy to scale compared to data collection time, labeling cost, and regulatory approval cycles for gathering real data for training and personalization.”
One of the clearest applications for synthetic data is testing sensitive, uncommon, or potentially disruptive customer scenarios.
“Synthetic data is great for testing potential customer flows or even corner cases that are uncommon, haven’t been seen before, or highly customer sensitive,” Vassell said.
A business introducing a new digital checkout system, for example, needs to understand what happens when a payment is declined, a confirmation is delayed, or a transaction is interrupted. Repeatedly creating those failures for actual customers would introduce unnecessary frustration and financial risk.
“Being able to customize the data profile makes it so that engineering can test situations that are not typical but should be tested due to their impact on the customer experience,” Vassell explained. “For example, payment failures mid-checkout- that's something that cannot be stress-tested on real customers and where synthetic data could shine.”
The same approach can be applied to account recovery, chatbot escalations, subscription cancellations, shipping issues, and other high-impact moments in the customer journey.
The advantages of synthetic data are relatively clear. Its limitations may be harder to detect until an AI system encounters real customers.
“The advantages are more clear, but the limitations are very real and somewhat less obvious,” Vassell cautioned.
It remains difficult to fully recreate the randomness, timing, and interconnected conditions that shape real-world behavior.
A regional outage, product recall, or payment processor failure may trigger a sudden wave of frustrated contacts via phone, email, live chat, and social media.
“In support environments, a regional outage or a payment processor failure would cause a sudden cluster of angry contacts across channels at once,” Vassell explained.
If a generation model does not adequately preserve those relationships, it may represent the interactions as isolated events rather than symptoms of the same underlying problem.
“But synthetic data generates events as if they're independent, so it misses the correlation that real-world conditions might impose,” she added.
That risk varies by generation method. More sophisticated systems may preserve some correlations and temporal patterns, but no artificial dataset should be assumed to capture every operational relationship found in real customer activity.
Synthetic data can also struggle to reproduce the full variety of human communication. Real conversations include slang, sarcasm, incomplete sentences, misspellings, cultural references, and emotional language.
“Training solely on synthetic data can create blind spots that are impossible to capture as statistical patterns and behavioral characteristics of real customer interactions,” Vassell warned. “The result could be a model that fails on a specific customer segment or a chatbot that breaks on phrasing.”
Synthetic data can reduce direct exposure to customer records, but companies should not assume generated information is automatically anonymous, private, or secure.
“As the adoption of synthetic data continues to scale, AI governance and privacy compliance must adapt by establishing robust frameworks to ensure these artificial datasets remain secure,” Vassell advised.
If a generative system reproduces source information too closely, sensitive characteristics may still be inferred or linked to individuals.
“The priority is twofold: preventing the inadvertent leakage or reidentification of individual identities, creating a false sense of privacy, while simultaneously ensuring that we do not wrap existing biases into ‘clean’ data environments,” she explained.
Bias presents a related challenge. If the original dataset underrepresents certain customers or reflects unequal historical outcomes, the synthetic version may reproduce those deficiencies.
“Without careful oversight, the statistical patterns and behavioral characteristics of real data can inadvertently replicate systemic prejudices within the synthetic generation process,” Vassell cautioned.
Responsible use requires companies to examine how synthetic data was created, which customer groups are represented, and whether an AI system produces different outcomes across demographics, communication channels, or customer segments.
Synthetic data also has a practical role in product demonstrations.
Companies may want to show customers or prospective buyers how a new AI feature will perform, but they may not have permission to use confidential production data. Generic sample information can make a demonstration feel disconnected from the buyer’s operating environment.
“In my work, I have been able to make great use of synthetic data for demoing new AI features with customers and prospects where I want to specifically get feedback on the customer experience,” Vassell shared. “Without risking any compliance, security, or legal restrictions, I can demonstrate the intended value of new features.”
For emerging products, realistic demonstrations can help prospective customers evaluate a capability before extensive adoption data exists.
“Use of synthetic data has helped to fuel engagement on recent demos with customers by allowing them to see outcomes with data that matches scenarios they face in their day-to-day work, making it easier to engage with the demo and ask questions,” she said.
Synthetic data could reshape how businesses test and optimize customer experiences before introducing them to the public.
Its role, however, should remain complementary.
“Relying solely on synthetic data is not best either because it could introduce a bias in representation on any number of customer dimensions, such as a specific demographic or customer support channel, so it should not replace real-world testing or staged rollouts,” Vassell emphasized.
The strongest development strategy combines synthetic datasets with human review, controlled deployment and carefully governed real customer information.
Before AI reaches the customer, synthetic data can provide a valuable testing ground. Its long-term value will depend on whether businesses understand both its potential and the point where the simulation ends.