Nobody can deny the enthusiasm the business world is having on AI. The spending on AI research and development has constantly increased over the years.
According to a recent report by IDC, the total spending on AI and related technologies will increase from $24 billion of this year forecasted spending to $77.6 billion in 2022.
In other words, AI has gone mainstream, getting into a varsity of organization across almost all industries. However, the expansion and popularity of the technology don’t equal its maturity. The hype of AI has made many businesses rushing into AI deployment without foreseeing potential risks and mistakes that turn their huge investments into trash. If you don’t want to end up like that, beware of some common mistakes below.
Be too greedy
The power of AI and machine learning gives some people the idea that AI can transform the entire business operation overnight. It is not the case. A company should start to deploy AI in simple processes then expand the AI adoption to the entire organization.
AI deployment is more than just technology. It involves the changes in corporate culture and mindset of employees and customers. If you require the entire organization to adapt to the new processes at the same time, your employees will be shocked and overwhelmed with the novelty. When you make small changes to the business processes, you not only give the people time to get used to the technology but also gain the necessary expertise to pull off the larger transformation.
Do not have enough resources
Adopting AI is not like installing a software. You will need to have everything in its right place to start and succeed with AI. It is impossible to deploy an advanced technology like AI without having sufficient resources like IT expertise, infrastructure, and human resources.
Companies who have already adopt and make progress with cloud computing, mobile & web development, and big data and analytics have more advantages when deploying AI than those who haven’t. However, to adopt AI into business operations, companies need AI specialists, not just any general IT expert. AI deployment also requires proper investments in infrastructure and data input to create an impact on business results.
Data is the core and fuel of machine learning and AI. With high-quality data, AI algorithms are useless. Don’t underestimate the importance of data and the investments required. If your business uses the same data that your competitor, you get the same insights which don’t give you any competitive advantage. Therefore, you will need to invest in unique data that gives you the edge in the competition. The costs of collecting and cleaning data (so that it can be used for AI) are not cheap.
Plan to get rid of human workers
Many businesses plan to adopt AI and automation systems to replace humans. That doesn’t always work. AI and automation can replace humans in repetitive and manual works that don’t require creativity, imagination, and emotion. AI can provide options, recommendations, analysis to support decisions or give some simple yes/no answers. In complicated situations, it is very risky to let AI work be unsupervised. The optimal approach is to form a human-AI cooperation to exploit the power of machine learning and AI while avoiding fatal mistakes algorithms can make when humans are not watching.