Artificial Intelligence is capability of computer-based systems to exhibit comprehension, problem solving and task execution ability comparable to that of a human. It is based on model building and computer programming paradigm where system can learn specific behavior from available data, without explicitly being instructed. This enables system to identify and detect hidden patterns, trends, latent meaning, anomaly and features from large volumes of data automatically. This is particularly useful as efficient decision-making over large volumes of real-time data streams are becoming common in almost all domain, which goes beyond the comprehension capabilities of human beings. Beyond automation, developments in AI has resulted in many enhanced capabilities that can be useful in wide array of practical applications in day-to-day life and business.
AI Can be applicable in industries that relies of repetitive tasks, data-driven decision-making, data-driven control and data-driven business processes. Manufacturing industry typically involve operations that are performed as a repetitive task with precision. Therefore, robotic automation is quite pervasive in manufacturing. In education domain, where large number of students should be educated and evaluated, AI can provide level of personalization as well as automation to perform such large-scale task with consistency. Healthcare domain involves handling of patient records, diagnostic images and test results etc. at massive scales. Consistent management and sharing of data, automated diagnosis and disease surveillance are some of the use cases where AI can play a vital role. Retail industry involves large volumes of retail transactions that leads to decisions regarding inventory control, demand forecast, recommendation, trend detection etc. AI help automate and optimize all such data-driven decision making and analysis tasks that retailers otherwise have to perform manually.
AI capabilities can be classified in to four groups namely: Thinking, Perceiving, Human interaction, and acting. They can be linked to a group of five capabilities. Image processing & detection capability of AI allows processing large amounts of text, audio, image, and video data to recognize faces, objects, character, text, semantics, or voice. This is particularly useful in case of search, automatic diagnosis, automated conversion, automated understanding of large volumes of data. Decision-making capability of AI can be achieved using case-based approaches that helps develop smart systems. Natural language processing capability of AI allows automated understanding of human input, sentiment analysis, and conversion. Machine learning capability allows detecting hidden patterns, hidden meaning, trends, and learning models based on various approaches. Finally, AI also enables capability for responding by determining next best action and forming response, determine control messages or issue appropriate voice message.
Three Pillars of AI
AI capability can be built on three pillars namely: Digital Core, Data Analytics and Intelligence. Digital core is concerned with infrastructure needed to support AI capabilities which is generally met by cloud and SaaS based platforms in enterprises. Digital core also includes dynamic workflows and robotic process automation. Second pillar is data analytics, which consists of data engineering, machine learning, and advanced visualization. Data is the fuel of AI system. All the models and learning are based on millions of data points collected in the real-world transactions. Data analytics pillar therefore provide requisite data management capability to prepare data for advanced analysis and learning. Third pillar is related to intelligence, consisting of advance capabilities offered by Data Science, Computational linguistics and Conversational AI.
Industry Use Cases
Power of AI can be leveraged effectively in many practical scenarios in almost all types of industries. For instance, in Baking industry, AI can help quickly screen applications for loan on multiple data points by performing risk assessment. AI can also help in rendering personalized help to customers regarding various products and services. AI can also help monitor transactions to reveal any fraudulent activities. In case of Retail industry; product recommendation, dynamic pricing, bot for ecommerce, bot for customer support are some of the typical use cases where AI can play pivotal role. In media industry, AI can be applied in recommendation, personal assistant, promotions, event management, social network analytics etc. In manufacturing, AI is typically employed in automation, predictive maintenance, safety, and demand forecasting. In insurance domain, AI can be utilized in handling claims, analyzing risks, customer service, and marketing. In IT industry, AI is typically employed to diagnose faults, detect security threats, network traffic surveillance, and support bot.
AI Technology Stack
AI technology stack in typical enterprise will be appropriately integrated with existing enterprise infrastructure and systems. With help of standards-based implementation, APIs, availability of libraries in different programming languages, it is possible to realize the same AI capabilities in different enterprise stacks. At the core, lies the cloud-based enterprise infrastructure on which present enterprise system is built. AI platform management component can be configured on top for setup, configuration, monitoring and analytics of various AI tools. Various types of enterprise adaptors can be used to access data from various channels implemented in enterprise. The integration may also enable end users to avail advanced AI features using web, social media, mobile, Chatbot, or end user application.
AI System Building Blocks
Artificial Intelligence capabilities can be realized using various Open Source tools as well as vendor products and services. Rich set of alternatives are available in AI technology ecosystem. Once can choose from technology stack from enterprise vendors like Microsoft, IBM, Amazon, Google, SAP etc. or can select tools contributed by open source community. Each vendor or open source ecosystem offer rich functionality of channel adapters, metadata handing, chatbot engine, security and other utility plug-ins. Various AI libraries can be utilized as per the requirements for target use case and application, for the purpose of image processing, text processing, NLP, deep learning, data mining, statistical analysis and other machine learning capabilities. These capabilities can be made available to end users by developing various types of solutions designed specifically or are integrated with existing applications. The solutions may include automated customer support, recommendation system, fraud detection etc.
Attune can help you embrace AI through various services and engagement offerings. Our Strategic Consulting service can help you analyze your existing systems and processes to suggest how AI can be leveraged. Our experienced team can be engaged to develop specific applications and functionality as per your requirements. We can also help you integrate AI capabilities with your existing infrastructure, applications, and services. Our AI specialists can help you developed customized algorithms, models, workflows, products and services to suit your specific needs. Our engineers can help you implement AI features and capabilities. We also offer wide range of courses in our corporate training program.
AI technology is evolving continuously with new tools, technologies, languages and packages. One can choose from multiple technology stack each having specific benefits and limitations. Our experts can help you get up to speed by helping you select the right technology stack and offering you training customized as per your requirements. Highly qualified instructors will help develop familiarity with theoretical concepts, underlying technology, whereas hands on lab that leads to proof of concept development.