Augmented intelligence is a human-centered partnership model of people and AI working together to enhance cognitive performance. It focuses on AI’s assistive role in advancing human capabilities. AI interacting with people and improving what they already know reduces mistakes and routine work, and can improve customer interactions, citizen services and patient care. The goal of augmented intelligence is to be more efficient with automation, while complementing it with a human touch and common sense to manage the risks of decision automation.
Chatbots are the face of AI and impact all areas where there is communication between humans such as car maker KIA, which talks to 115,000 users per week, or Lidl’s Winebot Margot that provides guidance on which wine to buy and tips on food pairings. Chatbots can be text- or voice-based, or a combination of both, and rely on scripted responses involving few people. Common applications exist in HR, IT help desk and self-service, but customer service is where chatbots are already having the most impact, notably changing the way customer service is conducted. The change from “the user learns the interface” to “the chatbot is learning what the user wants” means greater implications for onboarding, productivity and training inside the workplace.
Machine learning can solve business problems, such as personalized customer treatment, supply chain recommendations, dynamic pricing, medical diagnostics or anti-money laundering. ML uses mathematical models to extract knowledge and patterns from data. Adoption of ML is increasing as organizations encounter exponential growth of data volumes and advancements in compute infrastructure. Currently, ML is being used in multiple fields and industries to drive improvements and find new solutions for business problems. American Express uses data analytics and ML algorithms to help detect fraud in near-real-time in order to save millions in losses. Volvo uses data to help predict when parts might fail or when vehicles need servicing, improving its vehicle safety.
Organizations should not neglect AI governance. They need to be aware of the potential regulatory and reputational risks. “AI governance is the process of creating policies to fight AI-related biases, discrimination and other negative implications of AI,” says Sicular.
Identify transparency requirements for data sources and algorithms to reduce risks and grow confidence
To develop AI governance, data and analytics leaders and CIOs should focus on three areas: trust, transparency and diversity. They need to focus on trust in data sources and AI outcomes to ensure successful AI adoption. They also need to identify transparency requirements for data sources and algorithms to reduce risks and grow confidence in AI. They should ensure data, algorithms and viewpoint diversity to pursue AI ethics and accuracy.
Most organizations’ preference for acquiring AI capabilities is shifting in favor of getting them in enterprise applications. Intelligent applications are enterprise applications with embedded or integrated AI technologies to support or replace human-based activities via intelligent automation, data-driven insights, and guided recommendations to improve productivity and decision making. Today, enterprise application providers are embedding AI technologies within their offerings as well as introducing AI platform capabilities — from enterprise resource planning to customer relationship management to human capital management to workforce productivity applications. CIOs should challenge their packaged software providers to outline in their product roadmaps how they are incorporating AI to add business value in the form of advanced analytics, intelligent processes and advanced user experiences.
Read more: 6 Design Principles for Artificial Intelligence in Digital Business