Strategic advice & alerts to leverage data analytics & new technologies
Leverage data and the technologies that generate it, from IoT to AI/machine learning, wearables, blockchain, and more, to improve decision-making, enrich collaboration and enable new services.
/sites/default/files/DA_DT/dadtu1904.pdfHere in Part XII of this ongoing series on artificial intelligence (AI) in the enterprise, we examine findings pertaining to the use cases that organizations in our study viewed as most viable for applying AI.
Shortfalls in the risk management approaches many companies currently take can leave them dangerously exposed. These companies either have no corporate-level mechanisms for monitoring and acting on risk exposure or gather potentially relevant data but fail to develop appropriate metrics to support effective monitoring, control, and timely remediation. These metrics can take the form of key risk indicators (KRIs), which all levels of management can use to provide evidence of the effectiveness of the implemented risk management strategies. In this Advisor, we share some features of effective KRI implementation.
In the future, and as the technology advances, corporate, government, and military leaders will increasingly turn to advanced AI advisory systems to assist them with business strategy development. Such advanced AI advisory systems will likely function in the form of some kind of assistant. In this Advisor, IBM’s Project Debater application offers some insight into how such an advanced AI advisory system might function.
Yiannis Kanellopoulos addresses a key issue that we need to satisfactorily tackle: the accountability of algorithmic systems. Automated decision making can go seriously wrong, and hence, evaluating an algorithmic system and the organization that utilizes it in terms of their accountability and transparency assumes ever greater importance.
Daniel Power and Ciara Heavin discuss the need for — and the benefits of — automating decisions and decision processes and explore major areas of decision automation. They examine emerging, innovative sensing technologies — such as ambient intelligence and the IoT — that support decision automation and identify five major challenges and opportunities associated with deploying decision automation and sensors.
Mohan Babu K presents a roadmap for rolling out RPA and examines RPA solutions from key vendors. He then presents a snapshot of real-world stories of RPA adoption across industry domains and, based on his personal experience, recommends five key design topics to consider in rolling out scalable RPA solutions.
Aravind Ajad Yarra emphasizes that automation is most effective when humans and machines work together to deliver business outcomes and recommends that automation be designed in harmony with human experiences and business processes. He outlines three types of automation — experience automation, process automation, and platform automation — on which smart automation manifests, considers some smart automation fallacies, and examines how a design thinking approach can successfully be applied to smart automation.
In her article, Prerna Lal discusses the use of ML techniques to address cyberthreats and explores the benefits of AI-based cybersecurity solutions.