Integrating AI and machine learning into your analytics strategy

Machine learning and AI mean we can now automate repetitive tasks, leading some people to speculate that we’re on the verge of a shift in working practices that could lead to mass unemployment and chances are, your business is storing loads of dark data-unused information from transactions, connected devices and other sources. And if you're already using automation and AI technologies, you're likely creating more "data exhaust" than ever.

Secondly, Artificial Intelligence (AI) in business-enabling the customers to do things differently and do different things. With the embedding AI, automation and analytics at the core of the enterprise to break down silos and create more agile and adaptive processes, enable better decision making, and empower businesses to identify and capture completely new opportunities. And, also can do it at speed and scale thanks to an extensive suite of industrialized solutions for industries and functions.

But it’s also true that machine learning and AI can be used to make human jobs more meaningful. The machine learning and AI can be used to improve human performance and help redesign operations in a contact centre by various services including :

Amplifying the ability to solve complex problems

Providing agents with real-time information

Making things easier for customers

Offering customers a more personalised service

Chief data officers looking to modernize their strategy approach, as per Gartner

* Increase the power of AI techniques by engaging data scientists and AI specialists in every innovation project throughout the organization.

* Ensure sustainable outcomes from AI techniques by using your AI team to support production teams for continuously improving enterprise wide model management and performance monitoring.

* Draft a portfolio of analytical scenarios and use cases, including those that your organization is already executing or planning. Present it to C-level executives. Document their preferences to better rationalize funding decisions for AI projects.

* Maximize the business impact of AI implementations by maintaining an intense dialogue between the AI team, the business functions (including lines of businesses) and the executives charting the corporate strategy