Categories
AI Insights, Insights

There are countless benefits of incorporating AI into your business, including company efficiency, customisation and innovation. However, we understand that adopting AI may be challenging at first if you have never done it before. You need to collect the right data, find the right AI tools for your organisation, and train team members to be able to understand AI models. 

In some more cases, AI mistakes can result in lost productivity, money or reputation. For instance, Zillow recently faced huge financial losses due to improperly set up AI. 

To prevent this from happening in your company, we have rounded up five common AI mistakes and how you can avoid them:

 

Focusing on the tech

AI is inevitably technical, but it must also be linked to business needs in order to have the impact you’re looking for.

Before onboarding, outline the main business aims of your AI project with all key stakeholders. For example, if you’re adopting AI to predict customer churn, quantify the ideal outcomes (e.g. improve retention rate by 5%), before getting caught up in the technicalities of implementation.

 

Overlooking architectural-fit

Despite temptations to get started with AI, it can be hard to reap the rewards if you don’t have the right data infrastructure in place.

An organisation must be able to collect, store, and process data before even considering adding AI into the mix. Those that can’t do this may implement analytics that aren’t mature, which leads to a whole range of problems.

 

Limiting access to AI

Limiting the number of individuals or teams that access AI insights can also be a mistake. By giving all relevant teams access to key AI insights, the organisation can make better use of the data and understand what it means for the organisation and its customers.

When onboarding AI, you should identify each of the internal stakeholders who will benefit from the data and insights, and ensure that data can be shared timely and seamlessly.

 

Not optimising AI models

Business expectations and regulatory requirements can change as quickly as data itself. This results in models becoming degraded, even obsolete. AI optimisation maintains a model’s performance and empowers businesses to enhance their models on-demand. As outlined by Rick Hao, AI optimisation leads to more efficient code and greener, cheaper, and fairer AI. Without optimisation, AI can be costly and difficult to scale. 

 

Forgetting to think green

The UN’s climate change agenda has increased businesses’ urgency to maximise sustainability; AI must follow.

The average carbon footprint of AI is equivalent to five times the lifetime emissions of an average car. By optimising AI, you can lower memory and energy consumption, simultaneously reducing carbon emissions and supporting your business’s sustainability goals.

By putting these five steps in place, your organisation is more likely to avoid mistakes and reap the many rewards of well-implemented AI.