Strategic advice to leverage new technologies

AI and other advanced, innovative technologies are being applied to precision agriculture to help reduce the environmental impact of such operations. This Advisor explores how one cutting edge company, Iron Ox, is using ML, machine visioning, and autonomous robots to transform the agricultural industry.

This Executive Update presents best practices to help you succeed on your journey of establishing business architecture within your organization, whether you are just starting out or seeking ways to fine-tune engagement with your existing practice.

The hype and reality of artificial intelligence (AI) bombards today’s enterprises on an almost daily basis, but what does it take to actually implement it? Simply put, organizations need to address issues across several critical steps in making AI work to their best advantage, including acquiring AI and machine learning (ML) tools. This Advisor explores six main components of AI and ML tools.

Simon Schillebeeckx proposes a focus on regeneration as a way for small carbon footprint firms (e.g., consulting, financial services firms) to make a positive sustainability impact. He highlights that service industry firms can proactively contribute to the regeneration of common pool resources, such as forests and lakes, which often become neglected or overused. What makes regeneration different compared to more traditional donations to a conservation nonprofit is the use of digital technology that enables an organization to lay claim to the eco­system benefits it generates through its support. The digitization of benefits claims provides a transparent accounting system for environmental benefits. Schillebeeckx explains how transparency and accountability can lay the foundation for firms to work together to preserve and restore common pool resources.

Rohit Nishant and Thompson S.H. Teo explore the environmental impact of AI and ML. Specifically, the applications where these technologies add the most value are those that require heterogenous data in complex settings (e.g., optimizing smart cities, modeling climate change). In the process of creating value, these large AI and ML models require energy-intensive computing, leaving a huge carbon foot­print. To counteract these concerns, Nishant and Teo offer the “Align, Reduce, Measure” (ARM) framework for mitigating the environmental impact of AI and ML algorithms. The framework encompasses the organiz­ational structure, addresses data heterogeneity, and measures results to create accountability.

Jacek Chmiel draws attention to the increasing energy consumption by the electronic devices integrated into our daily lives. In addition to the ubiquitous mobile devices we all carry around, there are billions of devices, not to mention back-end servers, all of which consume energy. Exacerbating this problem are ML and distributed ledger technol­ogies, such as blockchain, that require intensive computing cycles, and use even more energy. The good news is that a greener software development strategy can have significant impact on energy usage of electronic devices. Chmiel explains the challenges to implementing this strategy and how organizations can overcome them.

Cutter Consortium Senior Consultant Curt Hall presents intriguing examples of how corporations and governments use AI and compatible technologies to move us toward a more sustainable world. He explains how many companies are using AI to reduce the energy consumption of … AI(!) and other digital technologies. He illustrates innovative technology-based solutions being developed for tracking carbon emissions and presents a selection of companies targeting key infra­structure areas for carbon reduction initiatives (e.g., data centers, transportation, waste management).

This Advisor explores the main function of the quantum bit (qubit), how qubits enable exponential scaling, and how quantum computing is being utilized in the real world, from hybrid approaches to experimenting with development kits.