The analytical engine powering all aspects of the connected economy is transitioning from rigid rule-based algorithms to flexible, intelligent ones. These are solutions that learn and evolve on their own over time, with the appropriate training data. Machines no longer simply answer the questions posed by people; they guide people to ask better questions in the first place, and offer faster and more insightful answers. This transition to AI, or machine intelligence, will profoundly affect both the nature of consumption and the structure of firms, supply chains and production. AI technologies could fundamentally impact society, along with jobs, wealth distribution and resource sustainability. Major technology companies are heavily increasing their mergers and acquisitions activities to improve their products and services, using AI solutions.
AI has achieved recent performance breakthroughs across numerous cognitive applications, from image classification to pattern recognition and ontological reasoning. This progress is due largely to convergent advances across three enablers: computing power, training data and learning algorithms. To illustrate this, automated image recognition and classification has improved in accuracy over the past decade, from 85% to 95% (a human averages 93%), allowing such algorithms to progress from being novelties to enablers of real innovations, such as autonomous transportation for warehouse order picking. Solutions are currently trained on millions of image data, a 100-fold increase compared with a decade ago. They are powered by specialized graphics processing unit chips that are more than 1,000 times faster, and five to ten times more complex (based on a 150 to 200-layer neural network) than those of previous generations. Computing and storage costs have declined commensurately by an average of 35% year on year. In the near future, AI will build on adoption enablers to unlock faster, smarter and more intuitive applications, although progress will probably be confined to broad adoption of narrow, context-aware intelligence across domains. The chasm separating narrow and general intelligence is believed to represent a fundamentally different set of learning algorithms and non-deterministic computing architecture compared with what exits currently.
Use in production.
The rise of AI-as-a-service platforms, with lower barriers to entry, will allow companies to scale cognitive solutions in a zero-marginal cost setting and reshape industry dynamics. And while it is hard to predict the specific AI technology adoption paths over the next 10 to 15 years, the overarching impact themes are easier to envision, with AI technologies creating and changing the value proposition across all domains. Products and services will compete based on hyperpersonalized, cognitive features. Firms will leverage AI to process customer preferences in real time, so as to rapidly scale personalized products and services, as consumers become brand agnostic and more willing to pay for hyperpersonalized offerings. Organizations will also become efficient hierarchies (companies typically face a trade-off between efficiency of scale and hierarchical nimbleness). Large global firms and institutions, with economies of scale that have never been unleashed due to the complex coordination required, will benefit from AI; they will use AI applications to rapidly assess, predict and simulate decisions across silos, spans and layers.
Industrial companies are moving rapidly into the AI domain, investing in R&D around the “industrial internet”. Analytics is being deployed for asset performance management and operations optimization, AI is improving safety and accessibility in the automotive industry and intelligent scheduling software is being adapted to real-time production variability. AI systems are enabling new levels of production system optimization, such as predictive maintenance and improved quality management.
Natural language processing can be adopted to create task-specialized personal assistants, as well as platforms for conversational technologies that can be provided as a service and integrated in various applications. Computer vision capabilities enhance visual navigation for self-driving cars as well as 3D scanning. Pattern recognition can identify customer preferences and be deployed to aid drug discovery. AI reasoning and optimization technologies are penetrating the value chain in various industries, such as the automotive sector, and currently inform 75% of consumer picks on Netflix. AI is used to optimize the multi-robot fulfilment system in Amazon warehouses.
Barriers to further adoption.
Key ethical, regulatory, legal and economic questions about AI remain, and these may hamper its ability to become mainstream. Concerns about cybersecurity are a further critical issue in adopting AI; moreover, cybersecurity, as an industry in itself, will need to expand in tandem with AI and analytics (and IoT), to address inevitable vulnerabilities.