How to assess the AI suitability of your product?

Everything a Product Owner Needs to Know Before an AI Project

Swarb

12/3/20236 min read

As a product owner, it's helpful to gain a thorough understanding of AI, including its mechanisms, advantages, challenges, and potential solutions, before deciding on its incorporation into your product.

But first, let's clarify some essential definitions.

What is AI?

AI means creating smart machines or their computing components that can do tasks requiring human-like thinking, like learning, solving problems, and understanding language. It includes different things, from simple rule-based systems to more advanced machine learning methods.

Machine learning, a part of AI, is about using algorithms to teach computers to learn from data on their own, getting better without being explicitly programmed. There are different types, like supervised learning and reinforcement learning.

Data science is about finding useful information from data using stats, machine learning, and what we know about a specific area. It covers everything from getting the data and cleaning it to exploring it, making models, and understanding the results.

How do these connect?

AI is the big idea, machine learning is a specific way to do AI, and data science is a wide field covering everything about dealing with data.

What is GenAI?

A subset of AI is "GenAI", which refers to Generative AI, a subset of artificial intelligence focused on creating new content, such as images, text, or other forms, often using neural networks and machine learning techniques to generate creative outputs.

So back to the question - why we need AI?

There's a simple answer.

AI-driven products over 3-5 times more functionality for users while signicantly reducing maintenance, costs, and team size, and delivering results that are 5-10 times more efficient.

With fewer components, AI powered products require less code, fewer people to manage, and with far less components to test before rolling out to the users.

By leveraging AI technologies, businesses can automate routine tasks, analyze vast amounts of data, make data-driven decisions, and create personalized customer experiences.

The current global revenue of the AI software market has surpassed $100 billion. According to a survey, 9 out of 10 multinational corporations think AI technology will give them a competitive advantage. This represents a 12% increase from the prior year. [Source].

"We all know that if you swing for the fences, you're going to strike out a lot, but you're also going to hit some home runs. The difference between baseball and business, however, is that baseball has a truncated outcome distribution. When you swing, no matter how well you connect with the ball, the most runs you can get is four. In business, every once in a while, when you step up to the plate, you can score 1,000 runs. This long-tailed distribution of returns is why it's important to be bold. Big winners pay for so many experiments."

— Je Bezos

Crafting each conditional statement by hand is a laborious task, followed by extensive testing by multiple engineering teams before public release.

Smaller software projects typically take around 4-6 months, while larger projects, including testing and support, can span 1-2 years [Source].

Even then, traditional software proves to be inflexible, demanding considerable effort to shift from its initial purpose to a different one.

The intricacies and exceptions in human languages pose challenges for traditional software, leading to inaccuracies in handling edge cases.

AI is tailor made to solve these kinds of problems.

"When the tasks are very complex, the lines of decision- making murky, the picture is uncertain, the rules vague, the exceptions to the rule are too many to consider, or there are way too many edge cases, it's for tasks such as these where AI becomes indispensable."

An AI algorithm will learn patterns automatically without you having to tell it what to learn.

And once you have set up your AI system, collected enough data to train and test it, and streamlined and stabilized the entire process of training, testing, and deployment, you can just sit back and chill.

Let your AI system work on the data, learn the patterns to be gleaned from it, and provide you with the desired output.

It will come together like a perfect little machine. Simple, clean and efficient. And not too expensive.

What kinds of tasks is AI good at solving?

AI works with text, images, sounds, and raw data. In fact, AI works with any kind of data. Any complex task that uses data can be solved through AI.

If there is any pattern at all, a well crafted AI or data science algorithm will be able to pick it.

Once you have found an algorithm that can pick the patterns that exist in all real world data, you can build intelligent systems on top of such an algorithm.

And big businesses around such intelligent systems.

"It is not the strongest of the species that survive, nor the most intelligent, but the one most responsive to change."

— Charles Darwin

AI Checklist To Test AI Suitability

As a software startup founder or IT product owner venturing into the world of AI, it is crucial to have a checklist to ensure a smooth and successful implementation of your custom AI products for businesses. This checklist will help you navigate the complexities of AI implementation, ensuring that you make informed decisions and maximize business growth. Here are the key points to consider:

1. Define your AI objectives:

Clearly identify the specic business problems you aim to solve using AI. Establish measurable goals and outcomes, such as improving customer satisfaction, increasing operational eciency, or optimizing revenue generation.

2. Data availability and quality:

Assess the availability and quality of data required to train and test your AI models. Identify any gaps in data collection, and establish processes to collect, clean, and store relevant data securely.

3. Ethical considerations:

Ensure that your AI models adhere to ethical guidelines and legal requirements. Address potential biases, fairness, and transparency issues to avoid any negative impact on your customers or business reputation.

4. Technical infrastructure:

Evaluate your existing technical infrastructure and determine if it can support the AI implementation. Consider factors such as computational power, storage capacity, and network bandwidth to handle the AI workloads eectively.

5. Talent and expertise:

Assess the skills and expertise within your team or consider partnering with AI experts who can guide you through the implementation process. This may include data scientists, AI engineers, or consultants who specialize in creating custom AI solutions for businesses.

6. Security and privacy:

Implement robust security measures to protect sensitive data and intellectual property associated with AI. Ensure compliance with data protection regulations and maintain transparency with your customers regarding data usage.

7. Scalability and exibility:

Plan for future growth and scalability of your AI solutions. Consider the potential need for expanding computational resources, adapting to evolving business requirements, and integrating AI capabilities into your existing IT ecosystem.

8. User acceptance and feedback:

Involve end-users early in the development process to understand their needs and expectations. Continuously seek feedback from users to rene your AI models and improve user experience.

9. Monitoring and maintenance:

Establish a system for monitoring AI performance and detecting any anomalies or issues. Develop protocols for regular maintenance, model retraining, and updates to ensure optimal performance and accuracy.

By following this checklist, you can condently embark on your AI journey. It will help you avoid common pitfalls, ensure a successful implementation, and unlock the full potential of AI for business growth.

AI is a powerful tool, and with the right approach, it can revolutionize your business operations and drive sustainable growth. AI has the potential to do a lot of good.

If you would like some help building an AI driven system that solves the entire problem for your business, talk to us.

Click here to schedule an AI strategy call now.

Tasks which are impossible at scale without AI

AI is not only about automation. There are tasks that simply cannot be achieved without AI.

For example, tasks that involve complex pattern recognition, decision making in uncertain environments, or learning from huge amount of data.

A human is often good with a handful of numbers. However, provide them with a substantial list, and they will have no clue whether there is something lurking within — a pattern? a mechanism that may open the door to something bigger?

Software engineers write software programs which basically take inputs and give outputs based on conditional artifacts -- gloried if-else statements.