Towards environmental efficiency in the age of AI
ITU – The rapid adoption of artificial intelligence (AI) and emerging technologies has sparked the need for a sustainable approach able to safeguard the environment. A recent ITU workshop provided a platform to discuss environmental efficiency in the age of AI, increasing automation, and smart manufacturing.
The workshop discussed emerging technologies’ potential to contribute to climate action as part of global efforts to achieve the UN Sustainable Development Goals. It also highlighted practical tools to evaluate environmental aspects of emerging technologies and discussed the role to be played by international standardization in supporting the expansion of this toolkit.
The workshop’s discussions fed into a meeting of the ITU Focus Group on environmental efficiency for AI and emerging technologies (FG-AI4EE). The group is analyzing the relationship between emerging technologies and environmental efficiency to benchmark best practices and provide a basis for new ITU standards. “This focus group is among the first global platforms for the environmental aspects of emerging technologies,” noted Paolo Gemma, Huawei, Co-Chair of the Focus Group.
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Derisking digital and analytics transformations
While the benefits of digitization and advanced analytics are well documented, the risk challenges often remain hidden.
By Jim Boehm and Joy Smith – bank was in the midst of a digital transformation, and the early stages were going well. It had successfully transformed its development teams into agile squads, and leaders were thrilled with the resulting speed and productivity gains. But within weeks, leadership discovered that the software developers had been taking a process shortcut that left customer usernames and passwords vulnerable to being hacked. The transformation team fixed the issue, but then the bank experienced another kind of hack, which compromised the security of customer data. Some applications had been operating for weeks before errors were detected because no monitors were in place to identify security issues before deployment. This meant the bank did not know who might have had access to the sensitive customer data or how far and wide the data might have leaked. The problem was severe enough that it put the entire transformation at risk. The CEO threatened to end the initiative and return the teams to waterfall development if they couldn’t improve application development security.
This bank’s experience is not rare. Companies in all industries are launching digital and analytics transformations to digitize services and processes, increase efficiency via agile and automation, improve customer engagement, and capitalize on new analytical tools. Yet most of these transformations are undertaken without any formal way to capture and manage the associated risks. Many projects have minimal controls designed into the new processes, underdeveloped change plans (or none at all), and often scant design input from security, privacy, and risk and legal teams. As a result, companies are creating hidden nonfinancial risks in cybersecurity, technical debt, advanced analytics, and operational resilience, among other areas. The COVID-19 pandemic and the measures employed to control it have only exacerbated the problem, forcing organizations to innovate on the fly to meet work-from-home and other digital requirements.
McKinsey recently surveyed 100 digital and analytics transformation leaders from companies across industries and around the globe to better understand the scope of the issue. While the benefits of digitization and advanced analytics are well documented, the risk challenges often remain hidden. From our survey and subsequent interviews, several key findings emerged:
- Digital and analytics transformations are widely undertaken now by organizations in all sectors.
- Risk management has not kept pace with the proliferation of digital and analytics transformations—a gap is opening that can only be closed by risk innovation at scale.
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