Though AI technology isn't new, its adoption is soaring to great heights. Therefore, there are chances of mistakes in any AI development project.

What to avoid when creating an AI project

There is always excitement when businesses talk about Artificial Intelligence (AI) technology and the possibilities when implementing it in their operations. According to IDC, by 2022, companies plan to spend on AI systems, deep learning, and chatbots. They also plan to spend on the required infrastructure to support these technologies. This cost is estimated to be at least 3X more than spending this year, accounting for 78 billion USD in the next two years.

If you look at the market closely, AI adoptions have gone from merely early adopters to mainstream businesses across all industries. Everyone is exploring new AI pilot projects and leveraging AI for their businesses.

However, with the growing and shifting trends in AI, there are related risks that you might want to consider. Though the technology isn’t new, its adoption is soaring to great heights. Therefore, there are chances of mistakes in any AI development project. If your business plans to apply AI to its operations, then here are a few pointers on the critical mistakes that AI developers are most likely to make and what you can do to avoid them. You want to see returns on your investment at the end of the day, not let your money go down the drain.

Taking on more than you can manage

Advancement in AI technology is opening up endless possibilities. That does’nt mean you need to start considering every possibility. You don’t want to kickstart an AI development project that transforms your entire business-decision making process overnight. It’s impossible and costly. It would be best to implement it with a clear idea of what you precisely want to achieve from the AI project.

The best way is to do this is to take small progressive steps. As you start gaining expertise, you can introduce more operations and project ideas.

Start with a low hanging fruit that you can quickly gain benefits from without putting in a lot of effort.

Investing in one-off AI systems

AI should help you create an overall process to develop further AI. If it is built to affect only a part of the designated data pipeline, it is a one-off system. Such an approach to AI may not take you too far. To succeed, you need to think of sustainability and scalability. It would be best to lay the foundation for your AI asset while considering all probabilities within each project.

This means your investment in the AI system should generate enough ROI, which you can reinvest to develop and scale the AI project further. This way, you can leverage AI capabilities that will eventually serve your entire business. It won’t be another new tool in your toolbox, serving a singular purpose.

The right infrastructure

Let’s be clear: AI is different from the core web and software development technologies available in the market. With AI projects, be prepared to invest in core and more advanced digital technologies to ensure that you create the right infrastructure. 

If you do not have exposure to cloud computing, big data, and analytics, they are likely to experience 3x more challenges than those familiar with them. Did you know that nearly 75% of organizations adopting AI depended on what they learned from building existing digital capabilities?

Begin with data

If you need Ai to work the way it should, you need data. A lot of data. Not all companies have enough in-brewed data. Such organizations resort to using the same public data, which is also used by their competitors. Though the AI models might be doing their job right, you will receive moderate results that don’t add value to the overall project. Your data must be as unique to your business as your AI models. 

To get better results than your competitors, you need to feed your AI models with better data. To get better data, you need to work on your company’s unique data. This will have to be prepared for AI by cleaning and structuring the data. You will need to invest your time and effort in collecting, cleaning data for your AI system.

Defining ways to assess and measure success

Before investing in AI, you need a hypothesis for how decisions, sales, customer support, etc., will be improved using AI technology. The hypothesis has to be tested in action and evaluated for its results.

A hypothesis is essentially a plan on how the success of a project will be measured in terms of both adoption and outcomes. The decisions backing an AI implementation should be data-driven rather than intuitions or rough estimations. If you ignore data routinely, even the smartest AI tool will fail to help you.

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