Global business value derived from artificial intelligence (AI) is projected to total $1.2 trillion in 2018, an increase of 70 percent from 2017, according to Gartner. AI-derived business value is forecast to reach $3.9 trillion in 2022.
The Gartner AI-derived business value forecast assesses the total business value of AI across all the enterprise vertical sectors covered by Gartner. There are three different sources of AI business value: customer experience, new revenue, and cost reduction.
Customer experience: The positive or negative effects on indirect cost. Customer experience is a necessary precondition for widespread adoption of AI technology to both unlock its full potential and enable value.
New revenue: Increasing sales of existing products and services, and/or creating new product or service opportunity beyond the existing situation.
Cost reduction: Reduced costs incurred in producing and delivering those new or existing products and services.
“AI promises to be the most disruptive class of technologies during the next 10 years due to advances in computational power, volume, velocity and variety of data, as well as advances in deep neural networks (DNNs),” said John-David Lovelock, research vice president at Gartner. “One of the biggest aggregate sources for AI-enhanced products and services acquired by enterprises between 2017 and 2022 will be niche solutions that address one need very well. Business executives will drive investment in these products, sourced from thousands of narrowly focused, specialist suppliers with specific AI-enhanced applications.”
AI business value growth shows the typical S-shaped curve pattern associated with an emerging technology. In 2018, the growth rate is estimated to be 70 percent, but it will slow down through 2022. After 2020, the curve will flatten, resulting in low growth through the next few years.
“I am cautiously optimistic about the inspiring forecast. Machine learning and AI technologies indeed have a great potential for intelligent automation and cost reduction. For example, both cybercriminals and cybersecurity companies are already using various machine learning technologies to optimize and accelerate many time consuming processes,” Ilia Kolochenko, CEO at High-Tech Bridge told Help Net Security.
“However, one should be careful not to overestimate the potential of AI technologies. A recent example of Tesla and Elon Mask clearly demonstrates that machines are still very far from replacing humans, and many years will pass before humanity will invent Strong AI (i.e. AI capable to act like human in all areas) if ever. Therefore, companies should certainly consider how they can apply ML/AI in their businesses to reduce redundancy and ineffective business processes, however they should not count on them to replace qualified personnel. Last but not least, AI is not a panacea, and in many areas, classical algorithms can easily outperform AI technologies in terms of economic practicability and ROI.”
Global business value
Breaking out the global business value derived by AI type, decision support/augmentation (such as DNNs) will represent 36 percent of the global AI-derived business value in 2018. By 2022, decision support/augmentation will have surpassed all other types of AI initiatives to account for 44 percent of global AI-derived business value.
“DNNs allow organizations to perform data mining and pattern recognition across huge datasets not otherwise readily quantified or classified, creating tools that classify complex inputs that then feed traditional programming systems. This enables algorithms for decision support/augmentation to work directly with information that formerly required a human classifier,” said Mr. Lovelock. “Such capabilities have a huge impact on the ability of organizations to automate decision and interaction processes. This new level of automation reduces costs and risks, and enables, for example, increased revenue through better microtargeting, segmentation, marketing and selling.”
Virtual agents allow corporate organizations to reduce labor costs as they take over simple requests and tasks from a call center, help desk and other service human agents, while handing over the more complex questions to their human counterparts. They can also provide uplift to revenue, as in the case of roboadvisors in financial services or upselling in call centers.
As virtual employee assistants, virtual agents can help with calendaring, scheduling and other administrative tasks, freeing up employees’ time for higher value-add work and/or reducing the need for human assistants. Agents account for 46 percent of the global AI-derived business value in 2018 and 26 percent by 2022, as other AI types mature and contribute to business value.
Decision automation systems
Decision automation systems use AI to automate tasks or optimize business processes. They are particularly helpful in tasks such as translating voice to text and vice versa, processing handwritten forms or images, and classifying other rich data content not readily accessible to conventional systems.
As unstructured data and ambiguity are the staple of the corporate world, decision automation — as it matures — will bring tremendous business value to organizations. For now, decision automation accounts for just 2 percent of the global AI-derived business value in 2018, but it will grow to 16 percent by 2022.
Smart products account for 18 percent of global AI-derived business value in 2018, but will shrink to 14 percent by 2022 as other DNN-based system types mature and overtake smart products in their contribution to business value. Smart products have AI embedded in them, usually in the form of cloud systems that can integrate data about the user’s preferences from multiple systems and interactions. They learn about their users and their preferences to hyperpersonalize the experience and drive engagement.