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 Advisor, we explore a key trend uncovered by ADL’s 2024 CEO Insights study: AI use, particularly for efficiency and effectiveness, is on the rise; however, many leaders are still on a transformation path to fully understand its impact and integrate it across their organizations.
Pamela McCloskey and Bryon Graham explain “why” and “how” organizations in the banking industry are developing and using big data analytics capabilities (BDAC) to derive business value. Their article highlights the importance of a socio-technical approach to analytics: technical resources (e.g., data, technical infrastructure, software tools) and human capital with the skills to use these resources combined with the right social elements to nurture a data-driven culture that ensures the correct use of data for insight and decision-making. McCloskey and Graham present examples of how organizations have drawn on BDACs to successfully respond to recent shocks in the external environment, including the pandemic and regulatory changes in the financial services industry. The authors’ proposed framework for assessing and developing the resource base for BDAC helps organizations understand their level of analytics maturity to maximize operational efficiencies and performance. The framework outlines how (1) process integration (creating a “single source of truth”), (2) process assimilation (restructuring to embed data skills,) and (3) diffusion (creating a data-driven culture) can enable organizations to develop BDACs to increase business value and gain strategic benefit.
Oteng Ntsweng, Wallace Chipidza, and Keith Barrett Carter pose a thought-provoking question: how can organizations harmonize humanistic and financial values using generative AI (GenAI) analytics? The article includes an examination of three real-world cases (PwC, Morgan Stanley, and Ørsted), exploring how GenAI is changing the analytics value chain, how data experts are preparing for the future, and what is being done to re-skill employees to enable effective collaboration between GenAI and employees. Building on insights from these cases, the authors provide frank conversations on ethical digital transformation and AI for social good. Their recommendations include alignment between GenAI tools and need, adopting an employee-inclusive GenAI adoption approach, and promoting leadership teams that are knowledgeable in AI and analytics. The authors conclude that the incorporation of GenAI in business analytics offers the potential for significant advancements in business value, but there is a need to foster collaboration between GenAI and data experts to enhance value without losing humanistic outcomes.
Maria P. Diaz Campo, Arman Ghafoori, and Manjul Gupta explain the growing trend of AI hallucinations (when GenAI generates unreasonable or inaccurate output) and how it can have detrimental consequences for organizations and individuals. The authors suggest that the degree to which such hallucinations are tolerated depends heavily on context and argue that individuals have lower tolerance levels for AI hallucination when the stakes are high (and vice versa when the stakes are low). They point out the importance of considering the contextual nuances surrounding the use of GenAI to help developers, decision makers, and academics establish best practices and manage potential sources of error. They also highlight the significance of understanding culture at the national level, as it can be instrumental in assessing societies’ tolerance levels to AI hallucinations. The authors’ suggested model provides unique insight into levels of analysis (i.e., personality, organizational culture, national culture) and how they pertain to AI hallucination. They make a compelling case that understanding context is critical for making informed decisions about strategically adopting and implementing GenAI (and emerging technologies in general). The article concludes with several key takeaways that can help balance potential value opportunities and risk tolerance.
Conn Smyth, Samuel Fosso Wamba, Murray Scott, Sean Coffey, and Denis Dennehy take a look at the important topic of resilient agri-food supply chains, an issue that affects us all. They report on the challenges facing the industry and explain how agri-food supply chain organizations are leveraging AI-based systems to plan for, respond to, and recover from supply chain disruptions efficiently and cost-effectively. The results show that AI-enabled information processing can build resilience in agri-food supply chains while reducing food waste and improving supply chain performance. The article is part of a wider doctoral research project and is informed by 147 survey responses from practitioners in the global agri-food industry. The authors propose a framework that maps six benefits of AI-based systems to three benefits of the agri-food supply chain and end with a call to action for a concerted effort between industry and academia to design, develop, and deploy AI solutions to make the world a better place.
In this issue of Amplify, we explore several ways organizations leverage business analytics to create business value. As we delve into this dynamic business environment, it becomes evident that leaders who understand their business and data and can strategically align their analytical capabilities are best positioned to derive business value.
Investing in the circular economy involves financial support of businesses and projects that embrace sustainable practices. Unfortunately, a lack of data availability and poor data quality make it difficult for investors to choose where to invest and evaluate the performance of their investments. This Advisor explores new sources of data and emerging technology that can be deployed to offer a more comprehensive understanding of circular investments and performance.
Effective adoption of next-generation banking technology is the road to greater customer engagement; faster product development; better operational management; and improved compliance, efficiency, and growth. It will also enrich the customer experience through stellar, hyper-personalized service.