Since its launch, there has been a rush among tech company leaders to incorporate AI features into their products. However, true business value comes from delivering product capabilities that align with user needs, rather than merely using cutting-edge technology. In order to achieve a 10x return on engineering effort with AI, it is important to start with the basic principles of what users require from your product, develop an AI capability that supports that vision, and measure adoption to ensure its success.
The Importance of User-Centric AI
The first AI feature implemented by our company was not aligned with this user-centric approach, resulting in a disappointing 0.5% adoption rate among returning users within the first month. However, after refocusing on our core principles and understanding what our users truly needed, we decided to adopt an “AI as an agent” approach. This involved developing a new AI capability that reached a 5% adoption rate in its first week. This formula for success in AI can be applied to almost any software product, as long as the focus remains on user needs.
The Pitfalls of Hasty Integration
Many startups, including ours, often fall into the trap of integrating the latest technology without a clear strategy in place. In our case, we were enticed by the release of OpenAI’s pre-trained generative transformer (GPT) models and eagerly incorporated large language model (LLM) AI technology into our product. However, the initial AI feature we introduced, which involved a small summary function using GPT to describe uploaded files, had no significant impact on our user experience or key metrics. Only 0.5% of returning users interacted with the feature during the first month, and there were no improvements in user activation or registration rates.
Understanding the Value Proposition
Upon reflection, we realized that generating a few words about the uploaded file did not provide any meaningful analytical insights, which was the core value proposition of our product. In our rush to deliver something AI-related, we missed an opportunity to deliver real value to our users. This realization prompted us to reevaluate our approach and develop a more effective AI capability.
Success with an “AI as an Agent” Approach
The AI approach that ultimately led to our success was the “AI as an agent” principle. This principle allows our users to interact with our product data through natural language, providing a more intuitive and user-friendly experience. This concept can be applied to almost any software product built on API calls.
After our initial AI feature, although we had checked off the box, we knew there was room for improvement. Inspired by traditional hackathons, we gathered as a team to brainstorm and implement an AI agent that acts on behalf of the user. This agent uses our own product by making API calls to the same endpoints that our web interface calls. It builds API calls based on a natural language conversation with the user and performs actions on their behalf, which are then manifested in our web UI as if the user had performed the actions themselves.
Maximizing Profitability through User-Centric AI
This “AI as an agent” approach proved to be highly successful for our company, resulting in a 10x increase in profitability. By focusing on user needs and allowing them to interact with our product using natural language, we created a more seamless and intuitive experience. This approach not only increased user adoption but also improved key metrics such as user activation and registration rates. The success of this AI capability demonstrates the importance of putting users at the forefront of AI development and ensuring that the technology aligns with their needs.
Conclusion
Incorporating AI into your product can be a valuable strategy for driving growth and improving user experiences. However, it is crucial to approach AI development with a user-centric mindset, starting with a clear understanding of what your users need and how AI can support those needs. By focusing on user needs, developing AI capabilities that align with those needs, and measuring adoption and success metrics, companies can achieve significant returns on their engineering efforts with AI. The “AI as an agent” principle has proven to be an effective approach in maximizing profitability, but it is important to constantly evaluate and adapt AI capabilities to ensure ongoing success.
—
Since the advent of AI, many companies have been eager to incorporate AI features into their products. However, it is important to remember that true success lies in delivering product capabilities that align with user needs, rather than simply relying on the latest technology. This article explores the key principles of user-centric AI and how they can lead to a significant return on engineering effort.
## The Importance of User-Centric AI
Startups often fall into the trap of integrating new technology without a clear strategy. In the rush to incorporate AI into their products, they often miss the mark in delivering real value to their users. This article emphasizes the need to start with the basic principles of what users require from a product and develop AI capabilities that support that vision.
## The Pitfalls of Hasty Integration
Many startups, including the author’s own company, have experienced the pitfalls of hasty integration. In an attempt to leverage the latest AI technology, they implemented AI features that did not align with the core value proposition of their product. This lack of alignment resulted in low adoption rates and no significant improvements in key metrics.
## Understanding the Value Proposition
It is important to understand the value proposition of your product before incorporating AI. In the author’s case, they realized that generating a few words about uploaded files did not provide meaningful analytical insights, which was the core value proposition of their product. This realization prompted them to refocus their approach and develop a more effective AI capability.
## Success with an “AI as an Agent” Approach
The author’s company achieved success with an “AI as an agent” approach. This approach allows users to interact with the product data through natural language, providing a more intuitive and user-friendly experience. By developing an AI agent that acts on behalf of the user, they were able to provide a seamless and personalized experience.
## Maximizing Profitability through User-Centric AI
By adopting a user-centric AI approach, the author’s company experienced a 10x increase in profitability. This approach not only increased user adoption but also improved key metrics such as user activation and registration rates. It demonstrates the importance of putting users at the forefront of AI development and ensuring that the technology aligns with their needs.
## Conclusion
Incorporating AI into a product can be a valuable strategy for driving growth and improving user experiences. However, it is crucial to approach AI development with a user-centric mindset and align the technology with user needs. By focusing on user needs, developing AI capabilities that support those needs, and measuring adoption and success metrics, companies can achieve significant returns on their engineering efforts with AI.
—
Investing in AI technology can be a game-changer for businesses, but it’s crucial to approach it strategically and with a user-centric mindset. Here are a few key takeaways from the article:
1. Start with user needs: Before integrating AI into your product, it’s important to understand what users truly need from your product. AI should serve as a tool to enhance user experiences and deliver real value.
2. Avoid hasty integration: Incorporating the latest technology without a clear strategy can lead to wasted efforts. Take the time to evaluate how AI can align with your product’s core value proposition and improve key metrics.
3. Focus on the value proposition: Ensure that the AI capabilities you develop align with the core value proposition of your product. Generating insightful analytical data should be a priority, as it provides meaningful value to your users.
4. Adopt an “AI as an agent” approach: This approach allows users to interact with your product through natural language, creating a seamless and intuitive experience. Developing an AI agent that acts on behalf of the user can drive user adoption and improve key metrics.
5. Measure success and adapt: Continuously evaluate the adoption and success of your AI capabilities. By measuring user engagement and monitoring key metrics, you can make informed decisions and adapt your AI strategy accordingly.
In conclusion, user-centric AI development is key to maximizing profitability and delivering products that truly serve users’ needs. By focusing on user requirements, avoiding hasty integration, and aligning AI capabilities with your product’s value proposition, you can harness the full potential of AI technology.
—
**Summary:**
The article discusses the importance of user-centric AI development and its potential impact on a company’s profitability. It highlights the pitfalls of hasty integration and emphasizes the need to align AI capabilities with user needs and the core value proposition of the product. The author shares their own experience of initially implementing an AI feature that did not provide significant value to users, leading to low adoption rates. They then describe their successful transition to an “AI as an agent” approach, which improved user engagement and resulted in a 10x increase in profitability. The article concludes by stressing the importance of continuously evaluating and adapting AI capabilities to ensure ongoing success.
—————————————————-
| Article | Link |
|---|---|
| UK Artful Impressions | Premiere Etsy Store |
| Sponsored Content | View |
| 90’s Rock Band Review | View |
| Ted Lasso’s MacBook Guide | View |
| Nature’s Secret to More Energy | View |
| Ancient Recipe for Weight Loss | View |
| MacBook Air i3 vs i5 | View |
| You Need a VPN in 2023 – Liberty Shield | View |
Since launch From ChatGPT, a stampede of tech company leaders has been chasing the rumor: everywhere you look, another company is touting its pioneering AI feature. But true business value comes from delivering product capabilities that matter to users, not just from using cutting-edge technology.
We achieve a 10x return on engineering effort with AI by starting with basic principles of what users need from your product, developing an AI capability that supports that vision, and then measuring adoption to make sure it hits the mark.
The first feature of our AI product was not aligned with this idea and it took us a month to reach a disappointing 0.5% adoption among returning users. After refocusing on our core principles of what our users need from our product, we developed an “AI as an agent” approach and launched a new AI capability that reached 5% adoption in the first week. This formula for success in AI can be applied to almost any software product.
The waste of excessive haste
Many startups, like ours, are often tempted by the lure of integrating the latest technology without a clear strategy. So, after the groundbreaking release of the various incarnations of OpenAI’s pre-trained generative transformer (GPT) models, we began looking for a way to use large language model (LLM) AI technology in our product. Very soon, we secure our place on the hype train with a new AI-powered item in production.
This first AI capability was a small summary function that uses GPT to write a short paragraph describing each file our user uploads to our product. It gave us something to talk about and we did marketing content, but it didn’t have a significant impact on our user experience.
Many startups are often tempted by the lure of integrating the latest technology without a clear strategy.
We knew this because none of our key metrics showed an appreciable change. Only 0.5% of returning users interacted with the description during the first month. Additionally, there were no improvements in user activation or changes in the pace of user registration.
When we thought about it from a broader perspective, it became clear that this feature would never change those metrics. The core value proposition of our product is about big data analytics and using data to understand the world.
Generating a few words about the uploaded file won’t result in any meaningful analytical insights, which means it won’t help our users much. In our rush to deliver something AI-related, we missed an opportunity to deliver real value.
Success with AI as an agent: 10 times greater profitability
The AI approach that made us successful is an “AI as Agent” principle that allows our users to interact with our product data through natural language. This recipe can be applied to almost any software product built on API calls.
After our initial AI feature, we checked the box, but we weren’t satisfied because we knew we could do better for our users. So we did what software engineers have been doing since the invention of programming languages: get together for a hackathon. Starting from this hackathon, we implemented an AI agent that acts on behalf of the user.
The agent uses our own product by making API calls to the same API endpoints that our web interface calls. It builds API calls based on a natural language conversation with the user, attempting to accomplish what the user asks it to do. The agent’s actions are manifested in our web UI as a result of API calls, as if the user had performed the actions themselves.
—————————————————-