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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.

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AI
Executive Update

AI & Machine Learning in the Enterprise, Part XII: The Most Viable Cases

by Curt Hall

/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.


Advisor

Making the Most of Key Risk Indicators

by Tom Teixeira, by George Simpson, by Immanuel Kemp

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.


Advisor

AI for Business Strategy Development? Insight from IBM Project Debater

by Curt Hall

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.


chart
Article

Accountability of Algorithmic Systems: How We Can Control What We Can’t Exactly Measure

by Yiannis Kanellopoulos

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.


road
Article

Decision Automation: Challenges and Opportunities

by Daniel Power, by Ciara Heavin

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.


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Article

Defining a Roadmap for RPA and Intelligent Automation

by Mohan Babu K

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.


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Article

Designing for Smart Automation

by Aravind Ajad Yarra

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.


cybersecurity
Article

AI-Powered Cybersecurity: The Need of the Hour

by Prerna Lal

In her article, Prerna Lal discusses the use of ML techniques to address cyberthreats and explores the benefits of AI-based cybersecurity solutions.