In new research commissioned by TheyDo and conducted by Sapio Research, 68% of enterprise decision-makers said AI had helped them make decisions faster and with greater confidence. Yet 38% reported that their organizations had run into unintended customer experience problems following AI-driven decisions. For Jochem van der Veer, Co-Founder and Chief Executive Officer of TheyDo, these findings expose a gap between the confidence to make a decision and the context needed to understand its consequences. “AI has compressed the time between question and answer,” he says. “But it has not removed responsibility for what happens next.”
Why Speed Is Running Ahead Of Confidence
The same research found that 77% of decision-makers are expected to decide faster than they can fully assess the consequences. As van der Veer puts it, “many organizations still rely on processes, structures, and governance that were never designed for decisions at this speed.”
The trouble grows when the facts behind a recommendation sit in different places. The research found that 77% of leaders say their dashboards, customer feedback, and customer relationship management data tell different stories at least some of the time, leaving them guessing which source to trust.
An AI-supported effort to reduce average handling time, for example, might improve a contact center’s efficiency metrics while leaving the underlying customer problem unresolved. Customers call back, creating additional costs and frustration elsewhere in the journey. What appears to be a successful decision for one team can create problems for another. This is why, says van der Veer, “Customers do not experience a business through isolated touchpoints or interactions.”
Among those who reported unintended customer experience problems following AI-driven decisions, 51% experienced increased complaints or support queries.
What Leaders Should See Before They Act
Van der Veer argues that AI recommendations should be open to scrutiny. “Give decision-makers access to the evidence behind AI recommendations so they can understand, question, and validate them before acting.” Having the raw data helps, and 86% of leaders say they would trust a recommendation more when the underlying data is visible to them. Seeing how the pieces fit together helps just as much, because the same share would trust it more with customer journey context.
Customer journey context connects a recommendation to the relevant customer segment, journey stage, pain point, operational metric, and business outcome. It also shows which teams own the issue and where a proposed change might have consequences elsewhere in the experience. “Look beyond individual teams, processes, and transactions to understand how a decision could affect other teams, customer segments, or stages of the journey,” he says. Organizations where AI is widely embedded across teams are more than 3.5 times as likely to report full visibility across the customer journey as those where AI remains isolated (43% versus 12%). “Trust is not created by making AI more autonomous, but by giving people and AI access to the same connected view of the customer.”
Accountability Across the Customer Journey
A shared view of the customer also raises the question of accountability. When a decision affects several teams, someone needs to coordinate how its consequences are assessed and what happens next. Van der Veer says the answer is “shared accountability for connecting insights, assessing wider consequences, and coordinating action across teams.” He adds that most companies already own many of the pieces they need, from customer feedback to support conversations and operational metrics, so the issue is not a shortage of tools or a lack of data to look at. “What matters is not simply the models an organization uses, but how well it connects customer perspectives, journey stages, situational information, business objectives, and constraints.”
The consequences of missing that context are already apparent. TheyDo’s research found that 43% of decision-makers delay or avoid decisions at least once a week, because they lack sufficient customer or business context.
“Closing the decision-confidence gap does not mean removing uncertainty altogether,” says van der Veer. “It means giving people enough connected evidence to understand that uncertainty, challenge recommendations, and make better decisions alongside AI.”
Follow Jochem van der Veer on LinkedIn for more insights on customer journey management, enterprise decision-making, and scaling AI effectively across large organizations.
