According to VentureBeat, while there is a general agreement that artificial intelligence (AI) offers transformative benefits, recent findings from Gartner reveal that nearly 60% of surveyed companies have not fully leveraged AI's potential. Even more strikingly, only slightly over 10% of the companies have implemented any form of AI solution. This highlights a significant gap between the optimism surrounding AI and the actual adoption by businesses.
The survey results indicate that many organizations are still in the early stages of their AI journey. A large portion of companies prefer to purchase pre-packaged AI solutions or use AI features already embedded within their existing applications. This makes sense as end users aim to solve real business problems rather than simply acquiring AI technology for its own sake.
One key reason for this preference is the shortage of internal expertise. Many companies lack the necessary skills to develop custom AI solutions, making it easier to rely on off-the-shelf options. Gartner’s analysis suggests that the technology gap remains the main barrier to widespread AI adoption. Most AI projects are still in the initial phase, with many firms struggling to move beyond descriptive analytics into predictive and prescriptive machine learning.
Another interesting finding is that companies deploying AI are not always those labeled as "aggressive" or tech-forward. In fact, over half of the respondents indicated that these companies see themselves as "mainstream," often waiting for more mature technologies before taking action. This reflects a cautious approach to AI implementation.
AI is still in the knowledge-gathering stage for many businesses. There has been a dramatic increase in interest since 2015, with discussions about AI growing fourfold among our customers. In 2016, “artificial intelligence†wasn’t even in the top 100 search terms, but by 2017, it had climbed to the seventh position. This shows a growing curiosity about how AI can be integrated into digital business strategies.
However, despite this enthusiasm, about one-third of respondents report challenges in defining their AI strategy. This is understandable, as 59% of companies are still in the knowledge-gathering phase. Additionally, 30% and 27% of companies cite AI security and integration as major hurdles. Surprisingly, only 23% consider measuring AI’s value a challenge—likely because they are still in the early stages and haven’t yet focused on ROI.
It’s time to go to school!
We’ve noticed that while companies struggle to find experienced data scientists, it’s even harder to find employees who are proficient in using AI tools like deep learning. Many AI breakthroughs occur in academic settings, with graduates joining big tech firms or starting their own ventures. As a result, companies are increasingly looking to upskill their workforce.
Some are partnering with system integrators to transfer knowledge internally, while others are hiring students with practical experience from local universities. Ideally, companies should seek individuals with advanced degrees in data science and machine learning. They should also invest in employee training and use rapid prototyping to build team capabilities and demonstrate AI’s value to leadership.
Build your own strategy
Companies should start by collaborating with internal executives to define how AI will be used, focusing on improving decision-making and operational efficiency. Before launching any AI initiatives, they should establish clear metrics to evaluate success. Once in production, continuous monitoring and optimization are essential. Communicating these results to senior management is crucial for securing ongoing support and demonstrating return on investment.
Additionally, companies should assess their existing applications to identify opportunities for integrating AI. AI enables businesses to add intelligence to apps, services, and digital assets. Leaders must determine when and how to implement AI, while also addressing the potential impact on customers and employees.
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