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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In this two-part Advisor series, we illustrate how healthcare providers — hospitals, in particular — are starting to reorient their investments in core technologies and how technology vendors are adapting their solutions and services to meet these needs. Here in Part I, we focus on the key features of this paradigm shift.
What are the ramifications of moving from a project orientation to a process or product orientation? First, an organization needs to establish an efficient means of production. It must create a funding model and a value stream equipped and staffed with people able to deliver its software requirements. Then the organization must manage the flow of work into that development value stream. The concept of flow is critical to value stream performance. Lean manufacturing principles have established that an overloaded value stream slows down the rate of delivery, increases costs, and decreases quality.
Data is not a resource. Data is the breadcrumb trail of human activities. Like a true breadcrumb trail, it indicates the activity but never fully describes it. Data doesn’t fall out of the sky like manna from heaven; it cannot be mined like cobalt, either. Data is a residue of activity, and when you think about data in this way, you can envision a data management practice where you start to fully focus on the activities that bring you value and, therefore, the data you need to collect.
Neural-symbolic artificial intelligence (AI) combines the pattern-recognition and pattern-matching capabilities of neural networks with the symbolic-reasoning functionality and transparency features of rule-based and knowledge-based systems (KBSs). One of the main goals of neural-symbolic AI is the development of hybrid AI systems that are more explainable and transparent in their reasoning.
Continuous disruption is going to be the new normal; you will either disrupt or be disrupted. For those that succeed in adapting to the new era and reaching beyond their current business models, digitalization brings great opportunities for business growth both within industries and across industries.
Yesha Sivan and Yonatan Rabinovitch discuss how to manage “black swans” crises with what they propose as the Three New Normals (3NN) framework. The authors explicitly state that the 3NN framework “was designed as a flexible descriptive framework, allowing for optimism, pessimism, or realism.” It focuses on how digital leaders can navigate sea changes.
Robotic process automation (RPA) and artificial intelligence (AI) are complementary – when implemented together they enlarge the scale of value that can be created. In this Advisor, we share some practical insight into how communication service providers (CSPs) and other organizations can embark on the AI-powered RPA opportunity.
Cui Zou, Wangchuchu Zhao, and Keng Siau respond directly to COVID-19 by framing the skills and training necessary to survive crises. The authors focus on the importance of helping organizations prepare beyond the current pandemic by teaching everyone how to use the technology tools — and exploit the processes — around remote working.