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9 Important Statistics on Future Use of AI

The Artificial Intelligence (AI) revolution is here. But are you ready? One way to prepare is by knowing the latest developments in the AI world. With that in mind, we’ve compiled some important statistics that give clues to the future use of AI.

We’ll also talk about how your organization can adapt to accommodate these changes. 

Most companies claim to be data-driven

In the modern world, data is worth its weight in gold. It’s the key to unlocking better customer experiences, improving internal processes, and more. AI and data are also inextricably linked. Without a constant stream of data, AI cannot function. Perhaps this is the reason that so many organizations have put resources into being data-driven. 

But, despite this, The State of AI Report by Peak shows that 30% of organizations are not data-driven. 

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The problem becomes more acute when looking at businesses that employ fewer people. Only 58% of organizations that employ 100-249 people are data-driven.   

If your organization has yet to take a data-driven approach, you risk being left behind. Below are some methods to help you become more data-driven: 

  • Promote a data culture – Adopting a data-driven approach means a significant change. Prepare your employees with a change management program. Prepare your employees with a change management program, and equip them with No Code AI skills. Additionally, you might introduce champions to promote the importance of data. Finally, to underline your focus on tech, you might purchase a .io domain name . You can use Only Domains as well.
  • Enable data-decision making – A data-driven organization uses data to clear a path forward. Team leaders should have access to data to enable better decision-making. 
  • Make data accessible – Your organization will not be able to properly adapt to data if it is only accessible to specialists. Make sure you have the right analytics tools to allow all employees to unpack data. 

Only a minority of organizations are mitigating the risks of generative AI

Generative AI is one of the most exciting new developments and is sure to feature in the future use of AI. This form of AI can create content simply by receiving a prompt. Perhaps the biggest example of generative AI is ChatGPT. The tool has recently been dominating the headlines because of its ability to give detailed answers to questions. 

Lots of businesses have been experimenting with generative AI in their projects. This might mean using AI-generated art or even asking ChatGPT to create content on their website. 

But although exciting, this technology isn’t perfect. Generative AI can frequently make mistakes, some of which can leave you red-faced. For example, whilst AI can write an article, it doesn’t always have a great sense of structure. Even more worrying, it isn’t always correct. You might find your article laden with inaccuracies.

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The image above shows McKinsey’s study. As you can see, there are many potential issues relating to generative AI. For each issue, only a small number of businesses are working to mitigate risk. 

If you are using generative AI, it’s important to have a thorough check of any content that is produced. Utilizing a tool like an online whiteboard can help you to make note of any issues that occur. 

The number of companies adopting AI has seen a marked increase 

Companies are starting to take AI seriously, and more and more organizations are adapting to AI. This ranges from the use of AI-powered phone dialing software to automated workflows. Interestingly, organizations utilizing technology are also reaping the rewards. 

But despite AI’s increasing popularity, the HAI AI index report shows that use has plateaued. The percentage of organizations utilizing AI has remained static at around 50% and 60%.  

The report explains that companies that adopt AI are starting to pull ahead. The study shows these companies are experiencing meaningful cost decreases and revenue increases. The message is clear: adopt AI now and you can stand out from the competition. 

AI tools are important for customer service

Customers don’t often have the most positive view of customer support. They find themselves waiting in long queues, often to find their problems unresolved. AI can give customer service a boost. For example, a chatbot is one way of using AI in contact centers

Using machine learning, chatbots can respond to customer queries. They can answer simple to moderate questions or forward users to human agents. And, unlike human agents, AI is always available, ensuring customers don’t have to wait. 

Aside from showing the importance of AI in customer service, the HubSpot report also shows that AI can help understand customers. As many as 62% of professionals say that AI/automation tools help them understand their customers better. The same report indicates that 79% of professionals say AI is hugely important in customer service. 

Most organizations buy in ready-made AI

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Buying a ready solution or developing in-house: which option is best for your organization? The Deloitte report observes that simultaneous technology and talent investments could help reach value early. The report argues that buying technology saves resources and talent for training and advancing tech. 

This doesn’t mean, however, that a custom solution doesn’t have some merits. Building your own solutions allows you to avoid any unnecessary features, helping reduce costs. You can also create a tool that meets your needs exactly.

For example, you might build AI to support the difficult task of Intellectual property management.

You can guarantee that your custom solution will integrate with your existing software. This reduces the risk of issues later down the line. 

More AI-related job postings are appearing 

This is another finding from the HAI AI index report. The percentage of organizations searching for employees with AI skills is slowly increasing – from 1.7% in 2021 to 1.9% in 2022.

The increase covers practically every sector. Regardless of what industry you’re in, you will likely need to start focusing on AI expertise in the hiring process

AI specialists can fulfill a number of roles, including: 

  • The data team – handles the intake, processing, and analysis of data
  • The algorithm team – is responsible for dealing with machine learning development
  • The Infrastructure team – deals with the underlying functionality of AI software
  • The research team – explores ways AI can be used to support business development, from content creation to instituting a conversational AI contact center

Embrace AI today 

There’s no use trying to escape the rise of Artificial Intelligence. AI technologies are constantly evolving. If your organization is to stay afloat in the modern world, it must accept AI. In this article, we’ve looked at statistics that give clues to the future use of AI. 

Consider what these stats mean for you. And, most crucially, how your organization can adapt to them. So, there’s no time like the present. Why not become a forward-facing, data-driven organization, by embracing AI today?

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