Outcome Driven UX + No Code Will Transform Product Discovery

Combine AI’s ability to understand context and outcomes with no-code development to decrease and automate development cycles, test, learn, and iterate faster than ever before.

Back to updating my blog after resetting it last month. Here's the first post, with more to come!

When I started writing this, I'm not quite sure if this is an article about how AI is driving a shift in UX design or how AI can help build successful products—probably a bit of both. AI brings an exciting but somewhat fuzzy future, filled with potential. Let's dive into this intersection and see where it goes. 

Introduction

Artificial Intelligence (AI) is transforming various facets of our lives, I’ll focus here on one of the big impacts to user experience (UX) design and how this paradigm shift can help companies build successful products. As we explore the capabilities of AI, we’ll focus on a couple of key areas that have a major impact on companies that launch tech products and how AI will greatly increase the probability of successful launches. This article explores how AI is poised to change the landscape of UX design and product success by enhancing speed and efficiency.

Continuous Discovery: The Foundation of Success

Continuous discovery is crucial for ensuring that a product stays aligned with user needs and market demands. Teresa Torres, a well-known figure in the field, emphasizes that continuous discovery helps product teams make better decisions by regularly infusing their processes with customer input. Adopting this mindset can bridge the gap between how companies work today and the ideal of constantly evolving with customer feedback​ (Product Talk)​​ (Mind the Product)​.

The Impact of Continuous Discovery

Continuous discovery not only ensures alignment with user needs but also significantly reduces the risk of failure. According to CB Insights, 42% of startups fail because there is no market need for their product. By continuously validating assumptions and iterating on product features based on real user feedback, companies can avoid this pitfall​ (Userpilot)​.

Story: Slack's Iterative Approach to Product-Market Fit

Take Slack, for example. Initially developed as a gaming communication tool, Slack’s creators realized that the messaging platform they built for internal use could serve a much broader market. Through continuous feedback and iterations, Slack refined its product to meet the needs of various industries. This process involved constant user testing and rapid incorporation of feedback, which helped Slack grow from an internal tool to a widely-used communication platform with millions of users worldwide. “Slack use a discovery-driven approach to continuously learn, build and measure its product. They understand the company’s goals as circumstances change, and coming up with assumptions that need to be satisfied to reach their goals. They operate like scientists, coming up with a hypothesis to be tested, not in the lab but in an ever-changing environment.” (HackerNoon)

Automating Continuous Discovery with AI

Looking forward, in a world where AI understands desired outcomes, much of the continuos discovery process can be automated.  AI can rapidly generate, implement, and test hypotheses, analyze user feedback, and optimize solutions in real-time. This will significantly speeds up the discovery process, allowing startups and established companies alike to quickly learn, validate, and optimize based on AI-driven insights.

The Challenge of Long Engineering Cycle Times

Engineering Bottlenecks: A Barrier to Success

Long engineering cycle times are a significant barrier for many startups. According to Startup Genome, 90% of startups fail, with one of the key reasons being the inability to pivot or iterate quickly enough. Delayed product launches can cost companies millions in lost revenue. For instance, McKinsey & Company found that a six-month delay in a product launch can result in a 33% reduction in profit over five years​ (Userpilot)​.

Case Study: The Fall of Quibi

Quibi, the short-form streaming service, is a stark example of how slow development cycles and a lack of iterative testing can lead to failure. Despite raising $1.75 billion, Quibi failed to gain traction and shut down just six months after launch. Critics pointed to a lack of market testing and slow response to user feedback as key factors in its downfall.” In other words, Quibi didn’t fail because of flaws in the final product or bad user experience—it failed as an idea for a product. The way to avoid this? Product discovery.” (Maze.co)

No-Code Programming: Empowering Non-Technical Founders

The Power of No-Code

No-code programming exemplifies how AI can democratize technology. For instance, a startup founder with a business background but no technical expertise can now leverage AI to create a prototype without needing a development team. AI can handle different tasks—designing the UX, coding the application, and more—potentially outperforming both the founder and many outsourced teams.

Story: Bubble’s Role in Startup Success

Bubble, a no-code development platform, has empowered countless non-technical founders to bring their ideas to life. For example, Dividend Finance, a fintech startup, used Bubble to build their MVP. This allowed them to quickly launch and iterate based on user feedback, ultimately raising over $365 million in funding​ (Airdev)\

The Future: Natural Language Coding

While Bubble represents a significant advancement, it still involves a learning curve. The true power of AI in no-code programming lies in natural language coding. This approach allows founders to describe their app and the desired outcomes using simple language. AI then designs and builds the app automatically, enabling rapid launching and real-time data collection for continuous feedback and improvement.

Imagine a scenario where a founder explains the concept of their app to an AI, which then translates that vision into a fully functional prototype. This prototype can be iterated upon based on user feedback, optimizing the app to achieve the founder’s goals. This method not only reduces the time and cost associated with traditional development but also significantly increases the chances of achieving product-market fit quickly. 

Naysayers of natural language programming will highlight the fact that people cannot come up with good enough prompts to get the flexibility and precision required for the design of software. That may be true in many cases, but there will be a number of cases where the AI can be the expert making the design and engineering decisions optimizing for the desired outcome (e.g. order size, conversion, engagement, etc.). Initially, we'll see the ability to rapidly prototype working software and likely grow to natural language no-code production software in the future as AI continues to improve.

The Paradigm Shift to Outcome-Based UX Design

Coming from a product management background, we obsess over "outcomes." As AI evolves, it is shifting from merely providing recommendations to automatically implementing and testing solutions to optimize for desired outcomes. Today, we see early iterations of this with AI image generation tools like Midjourney and DALL-E, where users provide context and desired results through prompts. However, the future lies in AI that intrinsically comprehends context and outcomes, continuously refining and enhancing the product to meet and exceed desired outcomes.

Transforming User Interaction

The integration of AI in UX design is not just about efficiency; it’s about fundamentally changing how users interact with technology. Consider the startup founder scenario again: with AI-driven continuous discovery, the founder could iterate and improve their product in real-time, based on AI-generated insights. This level of interaction and speed was previously unimaginable and can significantly enhance the likelihood of a startup's success. The startup ecosystem can grow, enabling founders with expertise in their area and a great idea to test their way into product market fit and start getting traction, without needing a technical co-founder or raising money to hire a development team. 

Conclusion

AI is set to revolutionize UX design by making it more accessible, efficient, and outcome-oriented. The ability to use natural language to have AI understand context and desired outcomes is a game-changer. This example of how it can help founders or companies greatly increase their probability of launching successful products is just one instance of its vast potential. As we continue to explore and develop these technologies, the potential for innovation in UX design is limitless. The future of UX is not just about getting better at incorporating AI into our existing tools and paradigms but about exploring new ways to leverage AI within user experiences to enhance productivity, outcomes, and innovation by integrating AI deeply into the fabric of design processes to achieve optimal results.

This was a bit of a ramble into numerous topics I've been diving into. Probably a springboard into more detailed articles on a few different topics when I have the time. If you made it this far, thanks for reading my ramblings. I'll try to get more focused in the future.

Call to Action

How do you see AI transforming your field? Share your thoughts and experiences in the comments below. Let’s explore the future of UX design together. I enjoy diving into these topics. If you have other related thoughts or ideas, let's connect and DM me.

For more insights on continuous discovery, check out Teresa Torres’ book, Continuous Discovery Habits, and her extensive work on Product Talk.