Author: Cal Al-Dhubaib / Originally posted to AI News

If you ask ten different data practitioners to define AI, you’ll get ten different answers. In its simplest form, AI is software that recognizes and reacts to complex patterns—but the way in which businesses derive value from those patterns can vary drastically. 

In recent years, we’ve seen a number of incredible AI applications in healthcare, manufacturing, finance, and beyond. So, why is it that up to 92% of AI projects still fail to yield business results? 

AI has significantly evolved over the last several decades, making it more challenging than ever for businesses to clearly understand and design AI successfully. Continue reading to learn more about top AI challenges shared by companies today—and how to solve them. 

Four solutions to top AI challenges 

Despite the complexity of AI, there are a number of ways companies can position themselves for success with machine learning models. Here are a few. 

1. Cultivate data and AI literacy

In the 2000s, companies were most focused on digital literacy (think: word processing and spreadsheets). In the 2010s, industries shifted their focus to data literacy—can we acquire the data and can we build models with that data? Today, AI literacy is top-of-mind.

According to Harvard Business Review, fewer than 25% of the workforce would consider themselves data literate. Defined as the ability to assess, understand, and utilize data, data literacy is a skill that directly enables individuals to work with tools like machine learning models.

Cultivating data and AI literacy within your organization, through educational workshops or insightful articles, will significantly improve AI adoption rates and employee trust in AI-based initiatives.  

2. Clearly define your business value

With AI, the path to defining and deriving business value is often unclear. Oftentimes, companies will have the right data, design an adequate model, and identify the level of accuracy the model can achieve, but the team does not consider the actual human or group of humans that will be making decisions based on the model. This is one area where we see a high failure rate.

When developing your AI strategy, be sure to account for how the AI’s recommendations will be interpreted and used by your team. Will your team need a dashboard explaining the results? How else can you ensure your team trusts and accurately uses the information? 

3. Understand the journey to AI is iterative

AI strategy and design can often be broken down into two processes:

1. Design. Where you are working to build a statistically valid model that can solve your problem. This process often requires experimentation with data and redefined requirements based on revealed constraints. 
2. Develop. Where you are developing the solution and translating it into the hands of the end user(s). 

One of the most important phases of AI design is building resilience. You will likely encounter instances where data in the real world doesn’t match the training data used to build the model. Or, you may realize decision makers or other end users don’t trust the model enough to use it. Working through these challenges to design a resilient, trustworthy model will result in higher success rates compared to companies that ignore the complexity of the AI process. 

4. Mitigate unintended bias and risk

Risk mitigation and bias prevention must be at the forefront of your AI strategy in order to truly generate business value with AI. Involve diverse humans in your feedback loop, test your AI against unexpected situations, and understand the costs of undetected bias in your solution. 

Reducing the chance of negative bias in your solution protects end users from harm, and cultivates a deeper level of trust between your organization, your solution, and stakeholders. 

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