Gartner Survey Reveals Organizations Have Four AI/ML Projects on Average
The Rise of Artificial Intelligence and Machine Learning Projects in Organizations
In today’s fast-paced digital world, organizations are constantly looking for ways to stay ahead of the curve and remain competitive. One of the key technologies that have been gaining traction in recent years is artificial intelligence (AI) and machine learning (ML). According to a recent Gartner survey, organizations that are working with AI or ML have, on average, four projects in place, with expectations to increase that number significantly in the coming years.
The Gartner “AI and ML Development Strategies” study, conducted in December 2018, revealed that 59% of organizations have already deployed AI in some form. This substantial acceleration in AI adoption is a clear indicator of the growing importance of these technologies in the business world. Jim Hare, research vice president at Gartner, emphasized the need for organizations to reorganize internally to ensure that AI projects are properly staffed and funded. Establishing an AI Center of Excellence can help distribute skills, obtain funding, set priorities, and share best practices effectively.
Looking ahead, organizations expect to add an average of six more AI projects in the next 12 months and an additional 15 within the next three years. This rapid pace of adoption means that by 2022, organizations anticipate having an average of 35 AI or ML projects in place.
The primary drivers for organizations to adopt AI technology include improving customer experience (CX) and automating tasks. Customer experience was cited as the top motivator by 40% of organizations, while 20% named automating tasks as a key factor. AI is being used both externally, through technologies like chatbots and virtual personal assistants, and internally, to support decision-making and provide recommendations to employees.
Despite the clear benefits of AI, organizations face challenges in adopting these technologies, including a lack of skills, understanding AI use cases, and concerns with data scope or quality. Addressing these challenges requires a strategic approach, including partnering with service providers, universities, and establishing training programs for employees. Ensuring reliable data quality is also crucial for delivering accurate insights, building trust, and reducing bias in AI projects.
When it comes to measuring success, organizations often use efficiency targets as a key metric. However, companies with aggressive adoption strategies are more likely to focus on improvements in customer engagement as a measure of success. This highlights the diverse ways in which organizations are leveraging AI and ML to drive value and innovation in their operations.
Overall, the rise of AI and ML projects in organizations signals a significant shift towards embracing advanced technologies to drive business growth and enhance customer experiences. As organizations continue to invest in AI, it is essential to address skill gaps, data quality concerns, and establish clear success metrics to ensure the successful implementation of AI projects.
