With apologies to author William Gibson, who originally pointed out that "the future is already here, it's just not very distributed," we can say that the impact of artificial intelligence (AI) is here, but that's just not so very evenly distributed either. But that's okay, for now. That's because when it comes to AI, every industry has its own unique story to tell.
Microsoft CTO Kevin Scott, for one, recently marveled at the thousands of different applications for AI emerging in different forms across all industries -- many of which are also linked to the Internet of Things. "Two of the most inspiring things that I’ve seen technologically over the past year are the developments in precision medicine and precision agriculture," he related to Mariya Yao in an interview published at FreeCodeCamp.
"Precision agriculture, for instance, we are entering an era where this intelligent edge, like having these AI-capable devices everywhere including and being able to mount them in drones, is allowing you to gather more interesting data about agricultural operations," Scott says. In manufacturing, he adds, the combination of AI and IoT enables companies to employ computer vision "to identify your inventory and your parts that you are using in your manufacturing processes."
The current and potential impact of AI varies from industry to industry, and this was recently documented in a recent report from PwC. The productivity implications of AI could be astounding, according to the report's authors, Dr. Anand S. Rao and Gerard Verweij. "Global GDP will be up to 14% higher in 2030 as a result of the accelerating development and take-up of AI – the equivalent of an additional $15.7 trillion," they point out. AI-driven growth will be the result of three factors:
- "Productivity gains from businesses automating processes (including use of robots and autonomous vehicles)."
- "Productivity gains from businesses augmenting their existing labor force with AI technologies (assisted and augmented intelligence)."
- "Increased consumer demand resulting from the availability of personalized and/or higher-quality AI-enhanced products and services."
PwC's Rao and Verweij went on to outline the variations in AI from industry to industry. Here are some of the distinctions they identified:
Manufacturing
AI's potential:
- Enhanced monitoring and auto-correction of manufacturing processes
- Supply chain and production optimization
- On-demand production
AI barrier: All supply chain parties need to have the necessary technology and be ready to collaborate
AI use case: Enhanced monitoring and auto-correction.
Technology, communications and entertainment:
AI's potential:
- Media archiving and search – bringing together diffuse content for recommendation
- Customized content creation (marketing, film, music, etc.).
- Personalized marketing and advertising
AI barrier: "Cutting through the noise when there is so much data, much of it unstructured."
AI use case: Media archiving and search
Retail
AI's potential:
- Personalized design and production.
- Anticipating customer demand
- Inventory and delivery management
AI barrier: "Adapting design and production to this more agile and tailored approach. Businesses also need to strengthen trust over data usage and protection."
AI use case: Personalized design and production
Financial services
AI's potential:
- Personalized financial planning
- Fraud detection and anti-money laundering
- Process automation
AI barrier: Consumer trust and regulatory acceptance
AI use case: Personalized financial planning
Automotive
AI's potential:
- Autonomous fleets for ride sharing
- Semi-autonomous features such as driver assist
- Engine monitoring and predictive, autonomous maintenance
AI barriers: "Technology still needs development – having an autonomous vehicle perform safely under extreme weather conditions might prove more challenging. Gaining consumer trust and regulatory acceptance."
AI use case: Autonomous fleets for ride sharing.
Healthcare
AI's potential:
- Supporting diagnosis
- Early identification of potential pandemics and tracking incidence of disease
- Imaging diagnostics (radiology, pathology)
AI barrier: Concerns over the privacy and protection of sensitive health data. The complexity of human biology.
AI use case: Data-based diagnostic support
Transport and logistics
AI's potential:
- Autonomous trucking and delivery
- Traffic control and reduced congestion
- Enhanced security
AI barrier: Technology for autonomous fleets still in development.
AI use case: Traffic control and reduced congestion.
(Source: PwC)
