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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This Advisor presents a case study from a major London hospital, showing how a simulation-first approach can guide AI model development in complex clinical settings. By simulating blood bank operations, the team assessed the potential value of predictive models before investing in data acquisition and model training. The simulation revealed that even moderately accurate predictions could reduce waste, especially when combined with optimized ordering policies. Beyond technical insights, the process fostered early stakeholder collaboration, ensuring the final model addressed real-world needs.
This Advisor, Part II in a series that explores the current state of on-orbit data centers, spotlights the countries, companies, and initiatives leading their development. From national space agencies to emerging start-ups, it maps the key players building space-based computing infrastructure — transforming satellites into intelligent, autonomous systems that can process data in real time.
This Advisor argues that to unlock the full potential of private quantum hubs (PQHs), organizations must implement centralized governance systems tailored to the unique demands of quantum ecosystems. These systems must coordinate diverse technologies, manage hybrid architectures, and ensure secure, scalable access for a broad spectrum of users. Without such purpose-built governance, PQHs risk inefficiency and fragmentation — undermining their role as key enablers of enterprise quantum innovation.
In Part I of this two-part Amplify series on AI evaluation, we explore the impetus toward AI accountability that arises from tackling real problems in real-world settings. Understanding how AI can contribute, at what cost, and with what nth order effects in a given context requires rigorous socio-technical systems thinking.
As Marcus Evans, Rosie Nance, Lisa Fitzgerald, and Lily Hands explore, AI explainability is a legal requirement as well as a scientific challenge. Despite the EU and UK’s differing approaches in other aspects of AI regulation, both the EU and UK GDPR continue to uphold individuals’ right to an explanation of automated or semiautomated decisions that impact them significantly. The EU AI Act also provides a right to an explanation for individuals or organizations.
Kitty Yeung urges us to elevate our thinking and consider what we are trying to achieve — via AI or otherwise. She argues that the fashion industry has long failed to appreciate the imaginative journeys consumers are taking, journeys that weave together self, situations, social circles, and eclectic wearables. Destructive practices like fast fashion represent flawed attempts to address human complexity with incomplete information, cumbersome supply chains, and a narrow anthropology that undervalues consumers’ creative agency.
Joseph Farrington emphasizes the importance of evaluating AI systems against their end goals. In healthcare, where developing and deploying AI models is especially challenging, he argues for first modeling the business context and processes the AI will interact with — before moving ahead with development or deployment. This approach can be used to assess, in advance, whether a plausible AI model will provide the intended benefit. It can also be used to run alternative scenarios to identify what else might need to change for the AI to really work or what else might work better if the AI were in place.
Joseph Byrum introduces a framework to help organizations plan rationally and prudently for AI adoption. One element is defining performance thresholds beyond which emerging technologies become economically viable. Another is assessing how AI and humans should interact across different business functions: some tasks can be commoditized and handled by AI, while others remain critical differentiators under human responsibility, with hybrid possibilities in between.