Competing in a Digital First World AI is unlocking tremendous value for businesses by solving many last-mile automation problems that previous waves of technology could not address. But just like any technology early in its evolution and application, there are a few things to watch out for – and unintended bias tops the list. Proper design and rigorous planning can mitigate the issue.
Competing in a Digital First World More companies are investing in AI. However, to succeed, they need to make practical considerations for explaining its reasoning, low-data density environments, and the need for richer knowledge graphs.
Competing in a Digital First World As more and more enterprises use artificial intelligence to make decisions on their behalf, governance is critical, and traceability into AI reasoning paths key to build trust from customers, employees, regulators, and other key stakeholders.
Competing in a Digital First World The value of data is getting increasing attention from boards and CXOs as digital technologies disrupt entire industries, so getting a handle on the enterprise value of data is becoming more important for most corporations.
Competing in a Digital First World Digital technologies will continue to offer companies the opportunity to radically alter their business models, transform operations and create better customer experiences.
Competing in a Digital First World While much of the discourse around AI and automation has been concerned with the looming threat to the workforce, this ignores the fact that history has shown disruptive technologies often lead to progressive evolution.
Competing in a Digital First World What is possible today is represented by the intersection of these three fields. Progressive CIOs and other executives must pick the business use cases that are at this intersection to get the most out of AI.
Competing in a Digital First World Industry experts expect the Internet of Things to generate about 44 trillion gigabytes of additional data worldwide by 2020. Which drives us to the central question: What is the best technology architecture to adopt to plan for this explosive data trend? Local, cloud, or hybrid architectures? The answer, as always, depends on the use case.
Competing in a Digital First World RPA hype is at an all-time high, with an alphabet soup of providers and offerings. Industry watchers are declaring RPA a must-evaluate technology, and some are heralding the start of the next industrial age. Software providers on both ends of the automation/AI spectrum scramble to incorporate new capabilities and meet in the middle, Given all that, one fact is often lost – many RPA implementations actually fail.