Video: With AI everywhere, experts advocate the need to educate users
Many scare stories around AI are designed to strike fear and uncertainly amongst people who are not familiar with the technology. For instance, robots are going to steal our jobs, steal our identities, influence election outcomes, and even detect how we are feeling right now.
But business leaders are less likely to worry about hype stories. They want practical AI applications that will work in their organisations and help them run their organisations more efficiently. They want evidence and case studies.
According to analysts Gartner, AI is currently is at the peak of the technology hype cycle, but it soon will be on its way down to the bottom of the curve. However, successful technologies, such as AI, will go on to succeed in the mainstream technology landscape.
AI is a technology enabler that will impact all areas of our lives.
Deploying AI-based applications typically requires a large investment of both development time and money, which makes them an unrealistic prospect for many businesses.
Luckily, technology is rapidly evolving in this day and age.
AI is now considered to have hit the mainstream across all verticals and industries. Whilst some AI deployments are still in pilot, many are in the mainstream.
AI deployments across industries
These AI deployments demonstrate that businesses across industries can maximise their AI investment and improve customer perception:
ABB Ability Ellipse platform: The ABB Ability Ellipse platform with AI optimises asset management across the enterprise. It has alerts to detect anomalies to minimise maintenance costs across the enterprise.
Aylesbury Vale Disctict Council: The Aylesbury Vale District Council has used AI in its residents' services team to save costs. It developed and launched a 'skill' in Amazon Echo's Alexa to access its services, working with suppliers including Amazon itself to ensure the skill fit in the Alexa skill set family. Its service team's response rate improved by 50 percent with 90-percent accuracy, reducing costs from £2.20 to £0.10 to £0.12 per query.
Ebay: eBay acquired Expertmaker for its AI platform for optimization and automation. It expects to apply the technology across its platform, to improve shipping and delivery times, pricing, and improve customer trust.
KLM Dutch Airlines: KLM Dutch Airlines uses AI for social media management. It adds automated answers to general questions from customers without the need for an intervention of a human service agent. Over 50 percent of its 130,000 social media mentions are handled with 95-percent accuracy. The AI system learns from the service agent's actions and gets smarter over time.
IBM and Local Motors: Local Motors co-c reated and produced a vehicle, Olli, enhanced by IBM's Watson Natural Language API and Internet of Things (IoT) for Automotive. Olli converses with passengers using natural language, taking them to requested destinations, providing recommendations on where to go, and answering questions about the vehicle, the journey, and the surrounding environment.
Lufthansa: Lufthansa uses AI to personalise customer experiences. It uses machine learning to personalise offers such as lounge access to people who have connecting flights. If customers do not accept the offer, after repeated notifications, Lufthansa changes its algorithm to improve the customer experience.
Magoosh: Tools for student tests provider Magoosh added AI to its Zendesk agent interface to increase efficiency for community support and its distributed teams of remote tutors. Over 83 percent of tickets are supported by AI, which provides 92 percent accuracy in tagging predictions.
Toyota: At CeMat 2018, Toyota envisaged how its horizontal transporters might communicate directly with high-level machines using AI. Its idea is that all movement should be optimised, with exactly the right type of handling machines being deployed for each task, passing loads between machines as required.
TravelBird: TravelBird used AI to manage incoming customer enquiries. After three months, it could cover over 65 percent of the 900,000 incoming queries. Pre-filling accuracy by the AI reached 95 percent, and customer satisfaction score reached 90 percent, while average handling time went down by 30 percent.
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