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 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.
Daniel Flatt contends that an evaluation framework that promotes accuracy and objectivity is a commercial necessity. He points to B2B publishing as a sector of particular note: an industry built on credibility and accountability that AI could undermine, even as it offers opportunities to accelerate time to output. In response, new tools for detecting inaccuracy and bias in AI-generated copy are emerging, alongside collaborations across the publishing workflow and the wider industry.
This Advisor series explores the emergence of on-orbit data centers — space-based platforms that enable real-time, AI-powered data processing and analysis directly in orbit. Here in Part I, we examine the enabling technologies, key benefits, and transformative potential of these systems for autonomous space operations, Earth observation, defense, manufacturing, and beyond.
This Advisor explores the accelerating convergence of academic research and industry investment in quantum algorithms, marking a shift from theoretical promise to practical application. From simulating molecules in drug discovery to optimizing logistics and advancing financial modeling, quantum algorithms are beginning to deliver domain-specific value. As industries invest in identifying where quantum advantage matters most, the focus is turning to integrating these tools into real-world workflows and architectures.