• Data Science
  • Jessica Groopman
  • OCT 12, 2017

200 Artificial Intelligence Use Cases, 29 Industries, 12 Themes

Ready to learn Data Science? Browse courses like Machine Learning Foundations: Supervised Learning developed by industry thought leaders and Experfy in Harvard Innovation Lab.

As the scope and velocity of the artificial intelligence (AI) market expands, it can be challenging for suppliers and adopters alike to keep up. Dynamics or developments in one sector or technology can influence another; opportunities for multi-disciplinary collaboration or risk mitigation are coalescing; the very definition of digital transformation is evolving. In the age of colossal data and rapidly shifting customer expectations, companies must navigate the hype, adopt new capabilities and adapt their strategies, all while proving efficiencies and new revenue.

In Tractica’s analysis of more than 200 AI use cases across 29 industries, a number of overarching themes emerged, illustrating critical dynamics to watch across the broader AI market. What follows is a summary of these trends:

All AI falls into three macro categories: Big Data, vision, and language. Although most think AI is driven by Big Data analytics, larger growth areas are capabilities having 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 digitizing 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. Some examples of these combinations include machine learning, computer vision, and sensors; or deep learning and natural language processing (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; for example, 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 chatbots are very close to disguising their code-based composition.

AI’s manifestation will shift alongside other technology macrotrends: AI is not the only show in town; numerous other technologies will both leverage and influence AI’s development, adoption, and regulation. Trending technologies include the Internet of Things (IoT), augmented reality (AR), virtual reality (VR), cameras, blockchain, renewable energy, genomics, three-dimensional (3D) printing, etc.

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 data sets, 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, AI 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, 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.

As with other technological revolutions, such as the industrial revolution, personal computers, and the smartphone, AI will fundamentally redefine how work gets done. From autonomous robots to agent-based simulations for decision-making, from facial recognition to foreign language translation, from social media bots to swarming drones (and over 200 other use cases that Tractica covers), competitive shifts abound in every industry.

Originally posted at Tractica

Boston city bkg

Made in Boston @

The Harvard Innovation Lab


Matching Providers

Matching providers 2
comments powered by Disqus.