Lean product development: Differentiate your AI startup

othmane khadri

Jul 22, 2024 • 8 min read

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As companies face the monumental challenge of innovating while minimizing risk, the principles of lean product development have never been more valuable.

With a staggering 70% of projects stumbling in the product development stage, it is imperative for startups to adopt a customer-centric development approach that not only drives efficiency growth but also ensures the delivery of value-adding solutions.

For this you need data and a lean approach to product development. AI startups face big challenges today and this is one of the major issues: getting the usage data needed for insights on product improvement.

However, AI has been a game changer by offering lean startups a wealth of creative potential and data analysis, enabling them to sidestep the needs for a big budget in order to gather insights from their users.

An example of this is our text analytics tool, Phospho. Our open source platform automates insights extraction and monitors the performance of your AI app in real-time for visibility into your users’ interactions.

Phospho’s capabilities in conjunction with lean principles helps AI startups streamline data driven development cycles, manage resources more efficiently, and get to market faster with concentrated fine tuning and optimization. These are pivotal in the AI startup field where velocity is essential.

In this article, we’ll discuss the challenges AI startups face today and how a lean development approach using the right AI product analytics tools can help you overcome those obstacles and differentiate yourself in a competitive market.

Understanding the Real Challenge for AI Startups

Creating a new product is hard. That’s why some of the most successful companies in the world use a lean product development approach to turn market needs and innovation into products that customers genuinely want - quickly and efficiently.

Let’s first briefly identify and go over the common bottlenecks that AI startups face in trying to emulate that:

1) The High Cost of Data

Data costs are a big worry for AI startups. They need lots of good usage data to train and fine tune their models, but getting and processing this data is expensive and can quickly use up their funds and eat into their runway.

2) Decoding User Needs

Getting usability insights from traditional methods is difficult and not suited to AI products. This often leads to products that don't meet customers' needs quickly enough, which feeds into the next bottleneck below.

3) Slow Iteration Cycles

Without adequate usage data on your AI app, it takes a long time to iterate accurately in accordance with user needs. This slow pace can prevent a startup from building fast and adapting to any market changes.

4) Balancing Innovation and Practicality

It’s difficult to balance the two when pushing the boundaries with cutting edge AI technology but a balance is key. Usage data is the leverage to nudge innovation in the right direction towards real market needs and ensure that you minimize any wasted effort or resources on development that doesn’t add real value.

5) Customer Satisfaction Struggles

Customers get unhappy if AI products don't live up to expectations or change fast enough. This can cause many customers to leave and spread negative reviews. AI product analytics are key in fixing these problems, helping startups quickly identify the pain points and areas for the most focused improvement.

To confidently iterate according to user needs and thereby minimize wasted development efforts, sign up here and try out Phospho to extract insights from your user's interactions.

Introducing Lean Product Development

Lean product development (LPD) and lean manufacturing both originated at Toyota Motor Company. Toyota established this method to identify and eliminate waste through continuous improvement focused on what customers actually want.

Just as lean manufacturing focuses on eliminating waste and maximizing efficiency in the production process, lean product development aims to do the same in the realm of innovation.

It encourages startups to test their assumptions, learn from customer feedback, and iterate quickly to create products and services that deliver real value to their customers. This approach asks the simple question: “How much of what we’re doing truly benefits end users or customers?”, and any work that does not produce a benefit to stakeholders is regarded as waste and should be cut to the absolute bare minimum.

This results in a faster time to market and less overall risk assumed in development, all without sacrificing the quality of the end product itself or using up a lot of resources which most startups will likely not have access to.

In recent years, the concept of lean product development has become increasingly popular across all industries. An example of a startup that champions lean product development is Spotify. They employed an agile, rapid customer feedback loop to release music streaming features that resonated with its audience, growing from a modest startup to a dominant industry player.

Traditional vs Lean Product Development

Lean product development prioritizes iteration towards the final product based on quick cycles, rapid feedback, and minimizing effort by identifying low-priority development. This leads to quicker design times and more successful launches.

Traditional development uses a ‘waterfall’ methodology, which can be seen as a completely opposite approach. Actually, Waterfall is a typical hardware development method applied to software engineering, and most researchers agree that it has three main principles: low customer involvement, strong documentation, and sequential project structure.

Strong documentation is a requirement because Waterfall developers don’t contact their customers during the project realization. That is why they have to document all requirements.

Each Waterfall project has 5 or 7 stages that should be performed one after another. The team cannot change their places. It also cannot return to the previous stage of the project after it is finished.

Lean product development and agile philosophy were brought in out of necessity for the demands of fast moving markets and the need for quicker development cycles. Startups require a fast and validated market entry with their products and it’s even more so important for AI products with the constant rapid evolution of LLMs.

If you want to get to market faster through data-led iterations, sign up here to integrate Phospho into your LLM app and leverage untapped insights from your users’ interactions.

Lean Product Development: Tools and Techniques

Some important lean tools include value stream mapping, Kanban boards, MVPs, and continuous deployment to actualize the above statement. Mapping out the value stream and building MVPs with agile software development for quick tests and learning are great examples of lean in action.

This notion of building and shipping quickly is what naturally provides the fast feedback loops which provide insights into user needs and market demands. These tools help cut resource costs and speed up market entry (with differentiation for further defensibility).

However, with AI available and accessible to today’s startups, you would be iterating with a handicap by not using the AI product analytics tools at your disposal to gather more data for further insights extraction and streamlining of your development cycles.

If you want to understand your users’ interactions and optimize your LLM app accordingly, sign up here and try out Phospho using your own data. It’s as simple as importing a CSV or Excel file!

Phospho’s approach to data gathering and analysis

We have emulated the approach of lean product development to reduce waste and streamline delivery cycles at Phospho, with our open-source text analytics platform designed specifically for startups building LLM apps.

Our platform leverages text analytics to extract rich insights from your user interactions for data driven iterations:

Real-time monitoring of user interactions lets you track and log user inputs to identify issues or trends and continuously fine-tune the performance of your LLM app.
Automated insights extraction and KPI detection so you can create your own KPIs and custom criteria to ‘flag’ for, and you can label if it was a successful or unsuccessful interaction.
A/B tests different versions of your LLM app to see which ones perform better with your users.
Continuous evaluation and iteration support. You can use our automatic evaluation pipeline that runs continuously to keep improving your AI model’s performance.

By using Phospho’s features effectively, we aim to help modern AI startups handle the complexities of obtaining usage data to inform faster development cycles without the need for big budgets or stitching together multiple tools and apps with painful learning curves.

So if you want to integrate Phospho into your LLM app, sign up here.

Implementing phospho for lean product development

The concept of lean product development may seem straightforward on paper but actioning it in real practice is not as simple. Let’s look at the core principles of lean product development and see how Phospho helps actualize these with its features:

1) Identify Assumptions

A good place to start is listing all your assumptions about your product and business model to ask yourself: What problem are we trying to solve? Who are our target customers? How will our LLM app bring them value? By clearly defining these assumptions you can focus on validating or disproving them with Phospho’s real-time monitoring. This lets you confirm your hypotheses by detecting patterns and trends in your user interactions.

2) Identify key metrics

One of the main principles of lean product development is the need for data-driven decisions to cut wasted development efforts, and to implement this, we need to identify the key metrics for product development. These metrics should be monitored to measure progress, identify bottlenecks, and make necessary adjustments. You can use Phospho to create your own custom KPIs and criteria to ‘flag’ for and fine-tune the model by labeling whether it was a successful or unsuccessful interaction.

3) Measure and Learn

Establishing metrics that are aligned with your business goals and tracking them is one thing but it is another to then actively measure them to gain insights for more confident iteration. By using Phospho’s in built A/B testing you can test each iteration version to validate changes and understand which is optimal for your users. This lets you navigate and deploy data driven iteration in line with your target market far quicker.

Integrating these strategies into any existing product development process for rich data sets is the best way to iterate fast with validation. But using multiple specialised software is usually a pain to combine or even use individually, given complicated UIs and long learning curves.

This is why we built Phospho to be comprehensive on its own and highly accessible to both developers and wider product teams. It’s intentionally as simple as possible to integrate into your LLM app - simply add Phospho to your tech stack with any popular tools and languages like JavaScript, Python, CSV, OpenAI, LangChain, and Mistral.

Here’s a quick step by step guide we’ve made to help you implement Phospho into your LLM app:

Set up and integration: Sign up for a Phospho account on our website here and configure your environment variables using the API key you have given.
Use the log function to log your LLM app’s interactions (input and output messages).
Head to the Phospho dashboard and, view your real-time analytics and insights, and evaluate your LLM app’s performance.

To adhere to lean product development, use Phospho’s tools to define targets and identify the key metrics you’ll need to monitor and evaluate. This will leverage the right data to help you spot and eliminate any waste in the product development process so you can focus your efforts on the areas that actually matter to your end users.

If you want to integrate Phospho into your LLM app and get started, sign up here. We offer plenty of documentation as well to help you get started.

Traditional bottlenecks are now opportunities for faster iteration

With the lean approach of focusing on building minimum viable products and leveraging real time feedback for improvements, the traditional startup challenges we all know and loathe like limited resources and the pressure to quickly deliver products to market, are seriously mitigated and startups are better equipped to respond to customer needs without exhausting their resources.

By using a lean approach to product development with tools like Phospho, startups can turn traditional problems into opportunities for further insights to help be more competitive and differentiated. This is an understated game changer and the unspoken promise of AI integration into not just your products but into your wider product development process.

By implementing a lean product development approach with Phospho, startups can create products faster, with fewer resources, and with a greater focus on customer needs and preferences.