Here’s the truth. Your tech skills might be the best in your circle. But what if you one-up that and launch your own AI software? In just 8 weeks.

Surprised? Don’t be. This isn’t about spending money you don’t have. It’s about smart strategies that don’t cost an arm and a leg.

Think of AI as your secret weapon to revenue growth. One that doesn’t need an arsenal to back it up. Ready to change everything without breaking the bank?

But, before we get started, if you're looking for a deeper dive into rapid AI software launch strategies that will accelerate your business model on my LinkedIn.

Choose The Right Problem

It all starts with the problem. Never the product. You need to first approach AI from a wholistic approach to understand if it is even needed at all. If you are attempting to operate AI in resource-poor environments it may not be the place for AI at all. Traditional solutions may work fine.

But if you are dead set on AI then identifying the right problem involves understanding the unique constraints and needs of a low-resource environment. This means selecting AI applications that do not require large-scale data or computational power.

One technical example is the use of decision tree algorithms, which are less data-intensive than deep learning models.

Use Existing Solutions

You don’t need to invent something new every time. One of the things that sets the tech world apart from others is it’s open source idealogy. You may have heard companies reference this saying that they “build in public”. Solo engineers and large companies are share the code they write. Allowing you to build with the most difficult of problems already solved.

Leveraging existing solutions involves utilizing pre-built AI models and platforms, which can drastically reduce the need for computational resources and expertise. An example is the use of Google’s TensorFlow Lite, which allows the deployment of machine learning models on mobile and IoT devices.

Optimize Your Data

A quick story to help drive the point of data optimization home. A startup working on handwriting recognition for languages with limited datasets used data augmentation to artificially expand their training data. By altering existing characters slightly, the system could learn from a more varied set of examples. Leading to a plethora of early adopters, massive growth, and a successful sale of the company.

In data optimization, the goal is to enhance the data’s utility. Techniques like data augmentation — where existing data is altered to create new variants — can help overcome the scarcity of datasets.

Simplify Your Models

Train and retrain! Just like with the rest of software development, your end product should constantly be in a state of change. Always trying to squeeze a little more performance out of your AI models. With the end results usually being a more straightforward and simpler model.

Simplifying models can involve using algorithmic techniques to reduce the complexity and size of AI models. Model pruning, where connections within neural networks that contribute little to the output are removed, can result in smaller and more efficient models without a significant loss in accuracy.

Evaluate Your Results

Evaluating the results in a low-resource environment might mean using metrics that are less dependent on vast amounts of validation data and more on robustness and reliability over time. Techniques like cross-validation can be invaluable when the available data is scarce.

Want a free guide to nail product-market fit so that you can launch your own AI software in just 8 weeks? Head over to my LinkedIn and grab a free copy

All the best,

-Adrian