The allure of sophisticated analytics along with the potential of Artificial Intelligence (AI) and Machine Learning (ML) drive many companies to undertake data transformation projects. Once complete, the data transformation projects provide the foundation for complex data analytics and AI and ML.
Efficiency and productivity gains: AI and ML handle tasks at a pace and scale that humans can’t match. By removing such tasks from human workers’ responsibilities, AI allows those workers to move to higher-value tasks that technology can’t do.
Improved “speed of business”: AI enables shorter development cycles and cuts the time it takes to move from design to commercialization.
Enhanced customer experience: Use AI to understand, shape, customize, and optimize the customer journey.
Regulatory compliance: Decompose complex regulations, laws and policies into executable rules which can be used to test compliance.
Faster application development: Training a neural network on a large dataset of code examples, and then using the fine-tuned network to generate code that is similar in structure and function to the examples it has been trained on.
Well architected data is a pre-requisite to performing advanced analytics. Once a company has executed a data transformation project and fully organized its data, the company can run data analytics which:
AI can assess the likelihood of a lead making a purchase. Using historical customer data, AI algorithms identify patterns that indicate which leads are most likely to convert.
An AI-based tutor enhances learning by tracking the mistakes made by learners and adjusting the course material to emphasize areas where the learner is struggling. The AI-based tutor will also detect the learner’s style of learning (e.g., visual, auditory, kinesthetic) and adapt course material to the style most effective for the learner.
Content personalization allows media companies to ensure each customer gets content suggestions based on their preferences which increases usage, improves customer retention, and enhances the customer experience.
AI in the media and entertainment industry allows companies to display individually targeted advertisements to each customer — based on their individual preferences.
AI-enabled solutions can use natural language processing to read through documents, interpret the information, and communicate their findings to human decision makers. They can also crosscheck documents with external sources.
AI-based systems specifically designed to provide automated financial advice can offer a bank’s customers the investment products best suited to their needs based on their profile and an analysis of the customer’s historical banking data.
Advanced analytics, AI, and ML are at the leading edge of information technology. Architectures, tools, and techniques for designing, developing, testing, and managing these capabilities are embryonic. However, the benefits are potentially huge, and many companies are fighting through these challenges.
Data: AI systems function by being trained on a set of data relevant to the topic they are tackling. This training requires a large quantity of high quality (e.g., accurate, timely, deduplicated) data. Companies seeking the advantages of AI must first ensure that their corporate data is pristine.
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Infrastructure: Artificial Intelligence systems generate the expected results by processing large amounts of information in fractions of a second. Few companies have AI-capable infrastructure in-house. Those companies must either invest in new infrastructure or, more likely, move to the cloud.
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Integration into existing systems and business processes: AI-based analysis systems cannot provide business benefits if they exist in a vacuum. The AI-based analysis must integrate with existing systems and business processes. In many cases, the speed of AI-based analysis will require significant redesign of existing business processes.
Lack of AI talent: Finding people with the necessary knowledge and skills is a considerable challenge. In fact, lack of internal knowledge keeps many businesses from even experimenting with AI.
Validating AI models: The dynamic nature of AI models and the amount of data they can process present an enormous potential benefit. However, these same strengths make AI models opaque and hard to validate. Validation of AI models is not only important for accuracy but sometimes a regulatory requirement. In the banking industry, for example, each statistical model needs to be reviewed and validated by an independent assurance unit such as the second line of defense function or a model validation team. These teams review and potentially accept a model for its use by a bank by following the U.S. Federal Reserve’s standard on model review and definition – SR 11-7: Guidance on Model Risk Management.
Infinitive helps companies and organizations at any stage of their data transformation journey. We partner with you to understand your business and develop data strategies in support of your goals. We drive successful outcomes though the following: