According to Gartner, the global business value generated by artificial intelligence (AI) is expected to reach $1.2 trillion in 2018, a 70% increase from 2017. The business value derived from AI is expected to reach $3.9 trillion in 2022.
Gartner's forecast assessment of AI-derived business value covers the total business value of AI across all enterprise verticals. There are three distinct sources of AI business value: customer experience, new revenue, and cost reduction.
Customer Experience: Positive or Negative Impact on Indirect Costs. Customer experience is a necessary prerequisite for the widespread adoption of AI technology to realize its full potential and realize value.
New Revenue: Increase sales of existing products and services, and/or create new product or service opportunities beyond what is currently available.
Reduce costs: Reduce the cost of producing and delivering these new or existing products and services.
"AI is expected to be the most disruptive technology category over the next 10 years due to the diversity of computing power, volume, speed and data and advancements in deep neural networks (DNNs)," said John-David Lovelock, research vice president at Gartner . "One of the largest overall sources of AI-enhanced products and services acquired by businesses from 2017 to 2022 will be niche solutions to meet a need, and business executives will drive investment in these products from thousands of companies Focus on specialized suppliers with specific AI-enhanced applications.”
AI business value growth shows a typical S-curve pattern associated with emerging technologies. In 2018, the growth rate was estimated at 70%, but will slow down in 2022 (see Table 1). After 2020, the curve will flatten, resulting in low growth rates for the next few years.
“In the early years of AI, customer experience (CX) was the primary source of derived business value, as organizations saw the value of using AI technology to improve every customer interaction with the goal of increasing customer growth and customer retention. By reducing costs, organizations are Look for ways to use AI to increase process efficiency to improve decision making and automate more tasks,” Lovelock said. “However, by 2021, as companies discover the business value of using AI to increase sales of existing products and services, and Opportunities to discover new products and services, new revenue will be the main source. So in the long run, the business value of AI will bring new revenue possibilities.”
Breaking through the global business value derived from AI types, decision support/enhancement (such as DNNs) will account for 36% of the global AI-derived business value in 2018. By 2022, decision support/augmentation will surpass all other types of AI initiatives, accounting for 44% of global AI-derived business value.
"DNNs allow organizations to perform data mining and pattern recognition on large datasets that are not easily quantified or classified, creating tools to classify complex inputs and then classify traditional programming systems, enabling decision support / Augmented algorithms are able to process information directly, which previously required a human to sort through it," Lovelock said. "These have a huge impact on an organization's ability to automate decision-making and interaction processes, and this level of automation reduces costs and risk, and through better micro- Targeting, segmentation, marketing and sales achieved increased revenue.”
Virtual agents allow organizations to reduce labor costs as they take over simple requests and tasks from call centers, help desks and other service personnel, while handing off more complex issues to their personnel. They can also boost revenue, such as Roboadvisors sales in financial services or call centers. As virtual employee assistants, virtual agents can help with scheduling, scheduling, and other administrative tasks, freeing employees up for higher value-added work and/or reducing the need for human assistants. Agencies accounted for 46% of global AI-derived business value in 2018 and 26% in 2022 as other AI types mature and contribute to business value.
Decision automation systems use AI to automate tasks or optimize business processes. They are particularly useful for tasks such as translating speech to text and vice versa, processing handwritten forms or images, and classifying other rich data content that conventional systems cannot access. Since unstructured data and ambiguity are major factors in the enterprise world, decision automation will bring enormous business value to the enterprise over time. Currently, decision automation accounted for only 2% of the global AI-derived business value in 2018, but will grow to 16% by 2022.
In 2018, smart products accounted for 18% of the global AI-derived business value, but will shrink to 14% by 2022 as other DNN-based system types mature and surpass the contribution of smart products to business value. AI is embedded in smart products, often in the form of cloud systems that can integrate user preference data from multiple systems and interactions. They understand users and their preferences to create a hyper-personalized experience and drive user engagement.
Article source: Control Engineering Network