Strategic advice to leverage new technologies
Technology is at the heart of nearly every enterprise, enabling new business models and strategies, and serving as the catalyst to industry convergence. Leveraging the right technology can improve business outcomes, providing intelligence and insights that help you make more informed and accurate decisions. From finding patterns in data through data science, to curating relevant insights with data analytics, to the predictive abilities and innumerable applications of AI, to solving challenging business problems with ML, NLP, and knowledge graphs, technology has brought decision-making to a more intelligent level. Keep pace with the technology trends, opportunities, applications, and real-world use cases that will move your organization closer to its transformation and business goals.
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Here 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.
Antifragile Systems Design requires an organization to move as one toward solving the problem of complexity, which means changing the perspective from “us versus them” (IT versus business) to simply “us” (business). The steps outlined in this Advisor require a mix of skills within business, business architecture, and software engineering. However, this is not simply a business activity or a software design activity and cannot be divided into different tasks for different silos; each step in the process creates feedback loops to ensure that answers arrived at are coherent. Business leaders, business/enterprise architects, and software architects all need to engage with the process to make it work.
In Part VI of this Executive Update series, we take a look at “the procrastination metrics" of V5 and V8.
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.