While upselling has remained constant as a strategy for businesses to increase revenue, the technology behind it has changed significantly in the past few years. What was once driven by static rules and scheduled outreach is now powered by artificial intelligence, real-time data and continuous behavioral analysis.
Five years ago, upselling in customer experience (CX) platforms relied on proxy indicators such as renewal cycles, lifecycle stages and basic usage thresholds. Today's AI-driven CX platforms analyze customer behavior across touchpoints to determine when an upsell is relevant. While AI now makes it easier to identify and time upsell opportunities, human teams still must validate those insights and decide how to act on them.
AI now identifies and times upselling
In many organizations, AI has become the primary way to find upsell opportunities. CX platforms now use machine learning models instead of predefined rules to interpret intent based on behavioral signals such as product usage, engagement patterns and signs of friction.
"Upselling has shifted away from scheduled moments and toward situational relevance driven by real-world signals," said Shauna Wager, managing director of growth and strategy at BDO USA, a business consulting firm. "It has become a real-time interpretation problem, shaped as much by customer scrutiny as by platform intelligence."
This shift reflects broader improvements in data infrastructure and integration. Five years ago, many organizations lacked the ability to connect product data, customer interactions and engagement signals into a unified view. Today, those capabilities are more mature, enabling CX platforms to surface more precise and timely recommendations.
In many organizations, AI has become the primary way to find upsell opportunities.
Organizations are finally able to operationalize data they have been collecting for years, according to Jake Johnson, principal at Alexander Group, a business consulting firm.
"For a long time, companies wanted better visibility into product usage and customer behavior, but they couldn't bring all those elements together," Johnson said. "Now, AI is helping connect those signals and making them available to customer-facing teams in real time."
That visibility allows organizations to move beyond simple triggers and toward more nuanced decision-making. AI models can detect when customers are expanding usage, adopting new features or encountering limitations. These signals may show readiness for an upsell.
Automation expands, but stops short of judgment
AI has also expanded automation across the upselling process. Modern CX platforms can prioritize opportunities, decide the best time to engage and select the most effective channel for outreach. In some cases, the ability to suppress an upsell is just as valuable as the ability to trigger one.
"Automation has taken over the parts of upselling that humans struggled to execute at scale," Wager said. "But it stops short of judgment."
That distinction is critical. While AI excels at analyzing data and surfacing opportunities, it cannot fully account for context, organizational priorities or long-term relationship considerations. "Technology surfaces opportunity; people decide how to act on it," Wager added.
This division of labor has become a defining characteristic of modern upselling strategies. AI systems increasingly handle detection, prioritization and timing, while human teams focus on interpreting context and executing the interaction.
Customer success takes on a larger role
As AI-driven insights become more accessible, upselling is also shifting within the organization. In many companies, customer success teams are taking on a larger role, particularly for simpler expansion opportunities.
"We're seeing customer success teams take on more of the straightforward upsells, like expanding usage or enabling additional features," Johnson said. "When it comes to more complex cross-sell or introducing new products, sales teams are still heavily involved."
This reflects a broader evolution in go-to-market models. Customer success teams are often closest to product usage and day-to-day customer needs, making them well-positioned to act on AI-generated insights.
At the same time, sales teams continue to play a critical role in more complex or high-value scenarios. The most effective organizations combine these functions, using insights from customer success teams to inform when and how sales engages.
Upselling becomes part of the customer experience
Another major shift is that upselling is no longer treated as a standalone sales activity. Instead, it's embedded within the overall customer experience.
Upselling is no longer treated as a standalone sales activity. Instead, it's embedded in the overall customer experience.
In more mature organizations, upsell opportunities appear throughout the customer journey during onboarding, at key usage milestones and within service interactions. This approach aligns product offers more closely with customer needs and context.
"Embedding upsell into the experience works better than handing it off to sales," Wager said. "Separating upselling from customer experience is usually a sign of organizational lag."
At the same time, customers are becoming more sophisticated. As AI-powered tools become more widely available, buyers are using them to compare options, evaluate pricing and validate recommendations before making purchasing decisions.
That dynamic raises the bar for upselling. Product offers must be personalized and clearly aligned with observable customer behavior.
"AI makes it easier for organizations to surface upsell opportunities, but it also empowers customers to scrutinize those offers more critically," Wager said.
Challenges remain as AI adoption grows
Despite the advances in AI-driven upselling, organizations still face challenges, particularly around trust and model accuracy. Johnson said companies often struggle with either over-relying on AI or not trusting it at all.
"Some organizations lean too heavily on the model without enough customer context," he said. "Others don’t trust the insights, especially if the model hasn't been updated or hasn't delivered results in the past."
Outdated models can quickly become a liability. As customer behavior and market conditions evolve, organizations must continuously update their data and algorithms to maintain relevance.
"If a model hasn't been refreshed in 12 to 18 months, there's a good chance it's no longer accurate," Johnson said.
AI's role will continue to expand
Looking ahead, AI is expected to play an even larger role in upselling strategies. Organizations are already exploring how generative and agentic AI can support customer-facing teams by synthesizing large volumes of data and surfacing insights more efficiently.
Johnson said these capabilities are helping teams better understand account activity, including support interactions and customer engagement trends.
"AI is helping teams get a clearer picture of what's happening within an account," he said. "That visibility creates more opportunities to engage at the right time with the right message."
At the same time, many organizations are placing greater emphasis on retention and expansion within existing customer bases. Ensuring customers receive value from products and services has become a prerequisite for successful upselling.
"The first priority for many companies now is preventing downsell and ensuring customers are getting value," Johnson said. "Once that foundation is in place, the opportunities for upsell and cross-sell become much stronger."
Christine Campbell is a freelance writer specializing in business and B2B technology.
