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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Although most individuals associate non-fungible tokens (NFTs) as a form of “digital art,” as we explore in this Executive Update, they more broadly open up models for anything to become tokenized.
In this final installment of our series on intelligent process automation (IPA) in the enterprise, we cover the remaining key industries where surveyed organizations see IPA having its greatest impact.
How are today's organizations adopting and deploying emerging technologies? Research suggests that companies have abandoned their obsession with “requirements” and — however quietly — appear to instead endorse a “technology-first/requirements-second” approach to technology adoption.
Organizations can utilize business architecture to inform and shape software designs to achieve more stable, maintainable, and scalable software systems. This Advisor examines the root cause of ineffective software design, specifically, the lack of consistency and clarity of the business perspectives being used as input to software design efforts.
Neural networks and other ML model development typically use large amounts of data for training and testing purposes. Because much of this data is historical, there is the risk that the AI models could learn existing prejudices pertaining to gender, race, age, sexual orientation, and other biases. This Advisor explores how the Data & Trust Alliance consortium created an initiative to help end-user organizations evaluate vendors offering AI-based solutions according to their ability to detect, mitigate, and monitor algorithmic bias over the lifecycle of their products.
In Part XI of this Executive Update series on intelligent process automation (IPA) in the enterprise, we cover another five key industries in which organizations see IPA having its greatest impact.
Project success is evaluated by the degree to which an end goal is achieved. Having a vivid understanding of the result, its impact, and the potential impediments to success can help improve a project. This Advisor explores the three beginning stages of a technology proj­ect and their keys to success: define, assess, and plan. Each stage has its own characteristics that need examination.
In this webinar on demand, William Ulrich shows you how you can define and validate your organization’s data architecture to inform data transformation requirements, and provide a foundation for IT investments.