Three Steps To Using Next-Generation AI Tools With A Risk-Based Approach

We currently have remarkable technology available thanks to companies in the life sciences. We now have ever-more powerful tools in our hands thanks to the remarkable advancements made in the last several years, and it is up to leaders to decide how to best utilise them.

Many of us are currently concentrating on the best method to implement these most recent developments in AI, including generative AI and massive language models, to mention a few.

When it comes to AI, it can be easy to fall victim to blue-sky thinking. Our natural tendency is to use the most recent technology to immediately make whatever change we can, but doing it correctly will take years of meticulous implementation.

It is worthwhile for us to take a risk-based approach to the development and implementation of AI because it has the potential to remove much of the human touch from routine procedures, for better or worse. Bigger-picture concepts are more likely to become reality if we take slow, deliberate steps forward.

Here are some actions businesses in the life sciences and other tightly regulated sectors may take to advance AI adoption in a deliberate and significant way.

1. Begin with easy projects

I recently heard a great rule of thumb for any company trying to implement AI into new business channels: start with something that’s relatively straightforward yet provides significant value to your customers.

For a risk-based strategy, it’s crucial to start with an idea that simply requires simple data, since without a trustworthy data stream, your AI project won’t ever accomplish what you set out to do. Getting the data part right early will enable you to broaden your focus later.

Second, you will notice a quick, positive return and start with early success if you begin your AI development with something that thrills clients. Laying the groundwork for future issues by creating straightforward, fresh products that you can execute well right away

A project that anticipates when customers will run out of your goods based on past purchases is a fantastic example of a low-complexity, high-value project. It’s not the end of the world if you’re wrong. It provides you with a cheap cost of failure to test the model and then notify your consumers about it.

Applying AI at the front of your processes, where you can rely more heavily on human intervention to identify issues early on, can help you find appropriate, early implementations. In the early stages of discovery, when the risk is minimal, using AI will help ensure that any errors are discovered by human checks and balances.

2. Recognise how to lessen the impact of negative events
The fact that AI can be quite unpredictable is one of the crucial things we are learning about it. AI methods may produce accurate findings 97% of the time, but the remaining 3% of the time, they produce catastrophically incorrect outcomes.

That unregulated 3% is actually a life-or-death issue for the biological sciences. Even if it happened just 3% of the time, a pharmaceutical or gene therapy business would never take the chance of hurting a patient.

The unpredictable nature of AI solutions presents tech leaders in life science organisations with a twin challenge to overcome. They need enough information to be able to trust the 97%, and they also need to take steps to lessen the effects of the 3% of the time when it’s incorrect.

Companies using a risk-based strategy must have a clear understanding of what it means when their AI projects fail and how to handle it. Even more information and, often, highly specialised treatments for each problem are needed to reduce that risk.

3. Hold off on attempting to run before you can walk
Many software businesses approach new implementations as a race because the winner is frequently the first to market. They have huge goals and advance as quickly as they can.

AI is still seen by many businesses as a problem without a solution because it is inapplicable to the life sciences. Adopting the technology poses a greater existential danger than potential benefit until your company has a clear understanding of how AI may add new value to your consumers in a secure manner.

Our strategy should be comparable to how automakers have approached the development of autonomous vehicles. The technology has been under development for many years, but before governments approved the next steps, careful proof of concept and real-world testing were required.

The only way ahead will be to introduce safe uses of AI with plenty of safety nets, especially when doing so with something that is meant to completely replace human interaction. In other words, you wouldn’t use it in place of a stage 3 clinical trial.

Development that is methodical and intentional will create best practises. When you start working on the bigger, more significant ideas, you’ll be better prepared for success if you learn as you go with smaller, lower-risk ventures.

In the end, there is no doubt that AI will be here.
If we adopt a risk-based strategy, AI will enable great improvements in the life sciences. By starting small, minimising errors, and making changes along the way, we can win over our clients’ trust and pave the way for a promising future for technology.