Artificial intelligence (AI) has been defined in many ways, often with varying degrees of accuracy. Tractica defines AI as an information system that is inspired by a biological system designed to give computers the human-like abilities of hearing, seeing, reasoning, and learning. A range of technologies enable AI, including machine learning (ML), deep learning (DL), computer vision (CV), natural language processing (NLP), machine reasoning (MR), and strong AI, all of which fall under the AI umbrella.
No longer just a concept or pipe dream, AI is powering applications and use cases across consumer, enterprise, and government markets around the world. The combination of vast amounts of data, faster processing power, and increasingly smarter algorithms are enabling the use of AI to solve real problems, and open up new opportunities for efficiency gains, increased speed, and more profitability.
Based on our research and forecasting, Tractica believes the future opportunity for AI spans a wide range of industries and geographies and is particularly disruptive in highly domain-specific markets with high-volume data needs and ontologies, as well as those with growing applications for machine perception. From autonomous robotics to algorithmic news stories, from product recommendations to processing patient data, and from virtual assistants to voice recognition, AI is likely going to be the driver of the next big technological shift, on par with past shifts like the industrial revolution, the computer age, and the smartphone revolution.
Tractica’s research into AI across 30 industries has identified more than 200 use case categories, each of which is explored in this report. The report defines, contextualizes, and offers real-world examples and revenue forecasts for each use case organized by industry. It serves as a referential compendium to Tractica’s ongoing market forecasting of the AI space, offering an overview and analysis for each use case included in the model.
AI Expands Across Industries
Due to the convergence of three independent trends, AI has now become a driving force in the market. More data, faster hardware, and better algorithms are accelerating research, development, and commercial investment in AI applications at lightning speeds, with many organizations rapidly scaling from a pilot deployment to a full-scale, enterprise rollout of AI technology within months or quarters, rather than years. Those sectors that were pioneers in the digital space are now accelerating in AI adoption, as the question of how to better use and monetize data persists. Tractica’s quantitative market assessment forecasts that annual revenue generated from the direct and indirect application of AI software will increase from $5.4 billion in 2017 to $105.8 billion by 2025.
The breadth and velocity of the AI market, however, has led to increasing challenges for both technology adopters and suppliers to maintain the same pace of innovation with their products and integration plans. Further, the dynamics or developments in one sector or technology can influence another; opportunities for multi-disciplinary collaboration or risk mitigation are coalescing; and the very definition of digital transformation is evolving.
[This article is from research firm Tractica’s report on artificial intelligence use cases. View full report details.]
As the creation of data continues to increase exponentially, and customers’ expectations of AI technology shifts, companies must navigate the hype, adopt new capabilities, and adapt their strategies, all while proving efficiencies and new revenue.
Tractica’s in-depth analysis of more than 200 use cases highlights the emergence of a number of overarching themes, illustrating critical dynamics to watch across the broader AI market. A summary of these trends includes:
- All AI falls into three macro categories: Big Data, vision, and language. Although most think AI is driven by Big Data analytics, the larger growth area has to do with vision and language perception capabilities, which will feed longer-term growth and strong AI.
- AI applications mark the next evolutionary step in digital transformation: Computing, sensing, networking, and data generation were only the beginning. The ability to process data more quickly and intelligently across systems, leveraging hardware, sensors, and cameras, and to digitize language itself marks the next era of organizational transformation.
- AI is shorthand for a combination of technologies: Use cases most often consist of multiple types of AI applied or configured in conjunction with one another and other technologies. For example, ML, CV and sensors; or DL and NLP.
- AI can be overt and visible or implicit and invisible: For end users, AI interactions like robotics or autonomously moving machines are obvious, even tangible; but AI can also support Big Data analysis, real-time responses, systems management, and many other invisible means of processing data.
- AI-driven personalization and operations automation will become interconnected: Advanced AI deployments will be marked by the ability to infuse both user-facing services and interactions with back-end or enterprise process and supply chain optimization, such as in retail, financial services, energy, and healthcare.
- AI maturity is highly fragmented: Maturity and the metric for success vary widely from application to application. Relatively low-stakes applications, such as movie recommendations, are widely accepted and optimized, while others like credit scoring or medical treatment recommendations remain regulatory grey areas and face significant barriers to widespread adoption.
- AI’s ability to pass the Turing Test is also fragmented: When it comes to machines’ abilities to seamlessly interact as a human would, the jury is still out. While social media bots have effectively passed for millions of Twitter or Facebook users, neither robots nor virtual digital assistants (VDAs) are able to disguise their code-based composition.
- AI’s manifestation will shift alongside other technology macrotrends: AI is not the only show in town; numerous other technologies (e.g., the Internet of Things (IoT), augmented reality (AR), virtual reality (VR), cameras, blockchain, renewable energy, genomics, three-dimensional (3D) printing, etc.) will both leverage and influence AI’s development, adoption, and regulation.
- AI is an extension of brand interactions: As more companies deploy AI, specifically virtual agents to power consumer-facing functions, services, products, and touchpoints, brands must balance unprecedented opportunities for personalization with significant risk of failure, faux pas, or backlash.
- AI is alluring, particularly in hyper-competitive markets: It is not just greater automation and operational efficiencies that AI suppliers promise adopters, it is the ability to illuminate hidden patterns and big “dark” unstructured datasets, to simulate scenarios for decision-making, and enable altogether new products. Beware the many ways AI is oversold.
- AI promises both diverse benefits and diverse challenges. Across use cases, profound opportunities lie in forecasting, empirical decision-making, operations automation, product optimization, new business models, greater access to services, targeted services, enhanced user experiences, and even improved environmental and public health. Simultaneously, it poses urgent challenges: data integrity, re-skilling workforces, diverse ethical uncertainties, privacy concerns, unchartered legal and regulatory questions or standards, and the explainability and accountability of deep neural networks (DNNs), among others.
- AI will have a complex relationship with humans that will change over time: While certain jobs will become automated, AI is more often poised to augment human labor and decision-making. Longer-term, many applications will be designed to empower humans with non-human capabilities, memory, experiences, and knowledge. Many ethical, philosophical, cultural, societal, and business norms will be forced into re-assessment.
Tractica’s market forecast is focused on identifying the software, hardware, and services revenue opportunity for AI. Using a bottom-up, use case-based model that classifies and estimates the revenue potential for each use case, rolled up by industry, technology, and world region, Tractica estimates overall AI market revenue from 2017 to 2025, and calculates the compound annual growth rate (CAGR) for the 8-year period.
The revenue for each use case described in this report represents software revenue, which is accounted for as direct or indirect revenue. Direct revenue represents the income derived from the sales of an AI-led solution, where AI is the key value being sold and marketed.
For example, emotion analysis, legal contract analysis, or cybersecurity threat estimation are services where AI is being sold as the key value proposition. Indirect revenue is counted in cases where AI is not necessarily the key value proposition, but AI is a layer or plugin that enhances an existing application or service. In other words, for indirect revenue, the use cases are AI-enabled rather than AI-led. For example, Google search, Amazon product recommendations, and Facebook news feeds are existing services where AI does not define the end product, but is a way of enhancing it.
The forecasts throughout this report are snapshots from Tractica’s 3Q 2018 edition of the Artificial Intelligence Market Forecasts report.