The 6 Dos of AI Product Development -

With its rapid expansion, the AI market is predicted to reach $280 billion by 2025. AI adoption is increasing across industries and business sizes, with an astounding 87% of global firms thinking that AI technology would give them a competitive advantage.
It’s evident that artificial intelligence (AI) technology is changing the way businesses operate, and many are attempting to figure out how to develop AI products themselves. Due to the rise in demand for AI solutions, both those that power customer-facing applications and those that power internal business applications,
In addition to outlining the main obstacles you must overcome, this post offers 12 dos and don’ts to guide you through the process of creating your own AI product in 2024.

Table of Contents

Key Challenges in Building AI Systems

Undoubtedly, artificial intelligence (AI) has revolutionized a number of industries by offering cutting-edge solutions and previously unheard-of capabilities. However, there are a number of obstacles in the way of achieving the full potential of AI-driven goods, such as:

choosing the ideal application to connect with people and attain the desired product-market fit
Making sure AI systems are resistant to cyberattacks and protecting user data
obtaining top-notch data that will be sufficiently extensive to deliver reliable, correct findings
Increasing the transparency and interpretability of AI models, especially in sectors governed by regulations
Handling moral conundrums that may arise when integrating AI into programs that users interact with
It is not a simple effort to overcome these hurdles in this continuously expanding industry. Here are 12 dos and don’ts to guide you through the challenges:

How to Build an AI Product: 6 Dos

1. Refine your AI product concept

It’s important to develop your AI product concept before delving into the technical details to make sure it solves a genuine issue or offers significant value. This first stage is crucial to the development process’s success since it establishes the framework for the entire undertaking.

First, the AI product should be truly valuable to users.

Integration and practicality: ensure that your AI solution fits in naturally with your users’ current processes or connects easily with the tools they currently use. Usability and practicality are crucial for gaining users’ acceptance.
Efficiency and automation: Think about how your AI solution can help users by automating processes, saving time, or requiring less effort. Increased efficiency is a strong argument in favor of adopting new technologies.
Personalization: By providing pertinent information or assistance that is suited to each user’s needs, try to offer individualized support. User pleasure and engagement are increased via personalization.
Anticipation: Examine how your AI solution can predict user requirements. Here’s where AI may differentiate itself from conventional software programs and offer genuinely unique value.

Second, the AI product needs to provide sufficient accuracy to build users’ trust.

The foundation of any successful AI product is trust. It is insufficient to have a brilliant concept. You need to pick a use case that you can execute accurately enough to gain the trust of your users. This is an important factor that frequently affects how successful AI applications are in the long run.
The data you have access to has a major impact on the accuracy level you can attain. Consider carefully how data restrictions may affect the functionality of your product. High accuracy is usually easier to achieve in focused, well-defined business applications with lots of high-quality, targeted data.
On the other hand, large-scale applications could need a lot of training data and have more difficulty staying accurate. Building artificial, narrow intelligence is generally the best course of action (focused on a single area)

2. Evaluate data availability and quality

When developing an AI system, data is invaluable. You will need to create a data strategy early on in the project, which will specify the data source as well as the amount of data required to reach the necessary level of accuracy.

Let’s first discuss the data’s scale.

You won’t be creating an AI from scratch for the majority of commercial applications; instead, you’ll probably take advantage of an already-existing LLM (like GPT-4 or LiaMA) for its general intelligence and natural language processing skills.

Thus, the only thing to consider when discussing data scale is what business data you will require to supplement these LLMs. Broadly speaking, a greater scope of data is required to yield reliable results, the broader the application.

Now think about the source of your data. There are numerous choices to think about, such as:
Data your product already has: You could want to use structured or unstructured data that your product has acquired if AI features are added to an already-existing product. In this instance, organizing and data cleansing to make it machine learning compatible would be all that is required.
Automated data collection: This entails scraping data from websites and other sources using automated tools and programs. Although the cost may be lower than with manual data collection, the quality of the data can be lower.
Purchased datasets: Purchasing datasets from outside data providers might be an affordable approach to obtaining a lot of data fast, but cleaning the data of any inaccurate or unnecessary information may need more work.

3. Carefully weigh AI technology tradeoffs

You will have to carefully weigh a number of trade-offs while creating your own AI system, including:.

Accuracy vs. speed. striking a balance between the accuracy level and the processing time of AI systems.
Complexity vs. Simplicity. Finding a balance between AI models’ complexity and their maintainability and comprehension
Explain ability vs. performance. striking a balance between the accuracy and performance of AI models and their interpretability.
Your use case requirements. Does the model have to work in your setting? Is a wider context window required? Do you speak a language other than English?
Making the crucial choice of AI technology for your product necessitates a careful assessment of a number of variables. Let’s examine a couple instances:
The public Open AI API is always the latest version, in addition to being far simpler to get going with than setting up and maintaining an Azure Open AI service.
LlaMA 2 is much faster and cheaper than GPT-4, indicating that although it might not be able to generate or process exceedingly long inputs or outputs, it is a good option for activities that demand real-time performance.
GPT-4 is a multimodal model, It implies that it is capable of producing and handling text, images, and other kinds of data. Model Llama 2 is limited to text.

4. Choose the right AI model optimization strategy

Although artificial intelligence (AI) models are educated on vast amounts of data, they are not trained on your company data, which can be sensitive or unique to the issue you are attempting to resolve. Every generative AI model optimization technique has certain advantages of its own.

Model training entails creating an AI model from the ground up, which calls for a substantial amount of data and processing power (as well as specialized knowledge of data science and programming languages). While creating AI models is a very configurable and scalable process, the data collection and training process takes a long time.
Fine-tuning strikes a balance between efficiency and customisation by concentrating on modifying an already-existing model for a particular job.
RAG (retrieval-augmented generation): improves models through the incorporation of external knowledge sources; perfect for activities requiring up-to-date or company-specific data.
Prompt engineering: This technique uses well-crafted prompts to direct pre-trained models; it requires minimum computational resources and a high level of prompt design expertise. This approach is very efficient and economical at the same time, yet its potential is often overlooked.

The best AI model optimization approach is contingent upon various elements, including the availability of data, computational capacity, task specialization, requirements for current knowledge, and necessary skills.

5. Embrace an iterative development process

When dealing with AI products, adopting an iterative development method is not just recommended but absolutely necessary because there is frequently no one “right” answer to choose from the start. In contrast to traditional software development, which frequently allows for pre-planning and scoping of the project, artificial intelligence (AI) projects necessitate a dynamic approach that entails testing several proofs of concepts (PoCs) and iterations in order to identify the best viable solution.

You can keep improving your AI product in a number of ways, even once you have a functioning concept:

AI-enhanced UX: Improve the user experience by iteratively improving the AI’s features, replies, and interaction design. Utilize behavioral insights and user feedback to improve and expedite the user experience.
Model performance: Make a commitment to continuously enhance the performance of your AI model. Evaluate its recall, accuracy, precision, and other pertinent parameters on a regular basis. Adjust the model to minimize mistakes and improve its forecasting power.
Data refinement: Make your AI better by always making improvements to the data that powers it. To improve the model, early data from user interactions should be used. To train the model iteratively with more precise, varied, and pertinent data, use this feedback loop.
Expanding the features and functionality that your AI product offers should be done gradually. It may start off only addressing one specific task, but as your knowledge of user needs expands, you can gradually add new features to fulfill changing needs.

6. From the beginning, consider usability when designing

Providing a user experience that is both entertaining and intuitive is critical to the success of any AI product. In order to do this, it’s critical to take into account the principles of human-computer interaction as well as research to determine what user-centric design priorities are. This entails being aware of users’ expectations, cognitive processes, and interactions with technology..

Before you start creating or improving an AI solution, think about these questions:

Which kind of user interface—a robust user interface with buttons and menus, speech recognition, or chat—will work best for your AI product?
To what extent do you wish to grant users autonomy over their AI interactions?
Should users be able to alter their choices and settings?
Will users or staff members serve as “checks” on the decisions and outputs of the AI?
When and how often should the AI ask consumers for feedback or direction?
How do you create questions and prompts that fit the user’s workflow or discussion naturally and are pertinent to the context?
What features are you able to offer so that consumers can examine previous interactions, manage current interactions, or change their preferences?
How will the AI tool respond to mistakes or miscommunications?