Digital giants dominate the cloud and ecommerce markets, and part of the reason for their dominance is artificial intelligence and advanced analytics. The good news is that mainstream enterprises can learn from their experiences and employ cutting-edge technologies. That's the word from R. "Ray" Wang who provides a roadmap for AI success in his latest book, Everybody Wants to Rule the World. Wang calls the capability needed to move forward "AI Smart Services" that help automate precision decisions. "To fine-tune precision decisions at scale-that is, to develop decision velocity, [data-driven enterprises] must automate the process of turning signal intelligence into a decision or action. And the way to do this is by creating AI smart services-automated processes powered by AI."

Cognitive applications run mission-critical business systems in a continuous, self-driving, self-learning, auto-compliant, self-securing, and self-healing approach. These AI driven systems intelligently automate transactional systems and processes such as campaign to lead, order to cash, procure to pay, incident to resolution, concept to market, and hire to retire. The goal of an autonomous enterprise is to continuously automate precision decisions at scale. Why? Transactional applications have run their course. Pressure to reduce margins, technical debt, and investment in core systems create tremendous pressure for automation.

While market leaders and fast followers have not yet achieved mass personalization, the next rush is focused on investments in artificial intelligence (see Figure 1). Searching for a competitive advantage and fearful of disruption, board rooms and CXO's have rushed to artificial intelligence as the next big thing. The investment in pilots for AI's subsets of machine learning, deep learning, natural language processing, and cognitive computing have moved from science projects to new digital business models powered by smart services. With the goal of precision decisions, successful AI projects require more than just great algorithms or access to data scientists. The seven success factors for AI foreshadow a world where limited players can deliver AI smart services.

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While market leaders and fast followers have not yet achieved mass personalization, the next rush is focused on investments in artificial intelligence (see Figure 1). Searching for a competitive advantage and fearful of disruption, board rooms and CXO's have rushed to artificial intelligence as the next big thing. The investment in pilots for AI's subsets of machine learning, deep learning, natural language processing, and cognitive computing have moved from science projects to new digital business models powered by smart services. With the goal of precision decisions, successful AI projects require more than just great algorithms or access to data scientists. The seven success factors for AI foreshadow a world where limited players can deliver AI smart services.

While market leaders and fast followers have not yet achieved mass personalization, the next rush is focused on investments in artificial intelligence (see Figure 1). Searching for a competitive advantage and fearful of disruption, board rooms and CXO's have rushed to artificial intelligence as the next big thing. The investment in pilots for AI's subsets of machine learning, deep learning, natural language processing, and cognitive computing have moved from science projects to new digital business models powered by smart services. With the goal of precision decisions, successful AI projects require more than just great algorithms or access to data scientists. The seven success factors for AI foreshadow a world where limited players can deliver AI smart services.

Access to huge corpus of data and massive compute power fall in the hands of the few. While market leaders and fast followers have not yet achieved mass personalisation, the next rush is focused on investments in artificial intelligence (see Figure 1). Searching for a competitive advantage and fearful of disruption, board rooms and CXOs have rushed to artificial intelligence as the next big thing. The investment in pilots for AI's subsets of machine learning, deep learning, natural language processing, and cognitive computing have moved from science projects to new digital business models powered by smart services. With the goal of precision decisions, successful AI projects require more than just great algorithms or access to data scientists.