Osman Koç is the CEO of UserGuiding, a platform that helps teams improve user onboarding and build in-app experiences that drive adoption.getty​For years, SaaS people have been optimizing product experiences for speed (and fast results). We’ve been trying to reduce learning curves, build no-code tools, remove technical barriers and simplify in-app experiences with onboarding.All in all, the goal was always the same: decrease friction and shorten time to value (TTV). The faster users could complete a meaningful action, the more likely they were to adopt the product.For a long time, that worked, but generative AI has changed the equation. When an AI tool can produce drafts, designs, summaries and analyses instantly, TTV approaches zero. The output appears immediately, and the task is technically “done.”So, if value can be generated instantly, what exactly should we be optimizing for?​What Time To Value Meant In Traditional SaaSTTV, in simple terms, measures how quickly a user completes a meaningful action inside a product. The specific action and timeline vary across products and segments, but fundamentally, TTV shows how quickly users interact in a meaningful way.Before, that interaction required active participation. Users had to configure settings, input information, make choices and move through a structured process before reaching a meaningful outcome. Even when onboarding was optimized and friction was reduced, some level of cognitive and practical effort was needed. That effort was also a part of the value formation process. By working through the steps themselves, users developed a clearer understanding of how the product functioned and how it could fit into their workflow. This is why onboarding is important for value realization and activation, too. On one hand, onboarding shortens the path to value and reduces unnecessary friction. On the other hand, it still requires users to invest a certain level of attention and effort to reach that value themselves.Guided flows and product tours simplify the journey, but they do not eliminate participation. Users still click, adjust, experiment and complete steps, and they are still involved in producing value. This is why TTV is never purely about speed. It’s also about creating familiarity and a subtle bond with the tool. By the time users reached their first meaningful result, they had already invested something of themselves into the process. ​From Result-Focused To Engagement-FocusedIn many AI releases, speed is heavily emphasized. Marketing messages repeatedly highlight how much can be done in seconds. You can send hundreds of personalized emails instantly. You can generate long-form content within moments. You can analyze large datasets and receive insights almost immediately.If you view it strictly through a traditional time-to-value lens, this looks exceptional. The meaningful action happens right away, the delay is gone and, in some cases, the user barely needs to interact at all. It is often presented as “zero-click value”—the ultimate product achievement.However, zero-click value comes with its own risks. When output is produced with minimal involvement, the results can easily feel superficial. In some cases, they may be sloppy or generic. But even when the output is objectively strong, the “easy come, easy go” effect can emerge. What I mean here is that when users have not invested effort into creating something, they are less likely to feel connected to it. Thus, they become more likely to stop using it. I’ve already stated that in the traditional road to value, user participation is as important as speed or the smoothness of that road. Being on the road, experiencing the road and getting to know the road is important. With instant AI generation, that gradual bonding phase can disappear. The tool delivers a result, but the user has not meaningfully engaged with how it was produced or how it should be refined. The interaction remains shallow, so the product stays somewhat external. This dynamic also resembles what behavioral psychology refers to as the IKEA effect, where people tend to value things more when they have participated in creating them. The point is not that users should struggle unnecessarily, nor that AI-generated outcomes are inherently inferior. AI capabilities are widely appreciated and increasingly expected. However, AI products need to intentionally reintroduce forms of involvement elsewhere. The sense of ownership that once emerged naturally through feature workflows and interactions must now be designed into the refinement and integration stages.That is why I propose shifting from a result-focused TTV mindset to one that prioritizes integration and further engagement. You need to measure how long it takes for an AI-generated output to move beyond the screen and into actual use—not just when a draft appears but when it’s edited, applied or trusted in a real workflow.It’s true that some of these behaviors are difficult for product teams to track directly. For example, it’s challenging to measure how an AI-generated dashboard is actually used during meetings or informs decision-making. That said, there are still meaningful indicators that reveal how much users are actively participating in the creation and refinement of AI-generated outputs. Edits, reformulations, shares, collaboration invites, applied filters—these are all signals of engagement. Depending on the nature of your AI tool, you can identify actions that reflect active involvement in value creation and, ultimately, integration into daily workflows.​Final Tips For Optimizing TTV For AI Tools ​​As someone who has spent many years in the SaaS world working on improving TTV and user engagement, I’ve seen that there are many ways to guide users toward a meaningful value journey. The key points, however, are always clear communication and information sharing. As final tips, I recommend the following:• Design onboarding to include refinement or iteration steps after AI outputs.• Surface real-world application examples and/or templates via email. • Share best practices and tips that are specific to use cases in webinars.• Organize one-on-one customer success meetings to go over specific workflows with your important accounts.• Create user forums and communities for your power users to share their insights with new users. When AI completes tasks in seconds, you should redirect that saved time into helping users master your product and get better results from it. Finally, be sure to track value-anchoring metrics, like edits, reformulations, shares and workflow adoption, instead of only generation speed.​Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?