When I was CIO at a construction industry data, analytics, and media
company, I was deeply interested in how technology and sustainability would
drive the industry’s future. Could a new smart building technology or a
green innovation lead to funding new construction that improves people’s
experiences and enables a more sustainable future?
One shining example back then was The Edge in Amsterdam, named
the world’s smartest building
that received a 98.4
BREEAM sustainability score. The Edge has a 14-story atrium,
capabilities, and over 28,000 sensors to capture motion, light, temperature,
humidity, and infrared. The Edge is still one of the
top sustainable buildings in the world.
Let’s focus on our responsibilities as
– the world of technology, cloud, data centers AI, and IoT – its power
utilization, water consumption, and carbon footprint.
The staggering ecological impacts of computation and the cloud
reports, “The cloud now has a greater carbon footprint than the airline
industry, and a single data center can consume the equivalent electricity of
50,000 homes.” Large data centers can
consume between 1 and 5 million gallons of water daily, and
2% of US energy utilization is in data centers.
The problem: AI and LLMs require significant power
These are power, water, and carbon metrics captured at the dawn of the AI
age, and the opportunity and hype around generative AI fuels intelligent
experimentation while also serving as an invitation to indulge in a
“Advances in AI, particularly in the realm of Large Language Models (LLMs),
have caused a surge in carbon emissions from the tech industry,” says David
Talby, CTO of
John Snow Labs. “In fact,
studies estimate that training a single AI model can emit as much carbon as
five cars in its lifetime.
This recent article asking
will responsible AI neutralize tech carbon footprint
includes an even more alarming data point: “A more recent study by Google
and the University of California, Berkeley, reported that training GPT-3
resulted in 552 metric tons of carbon emissions, equivalent to driving a
passenger vehicle 1.24 million miles.”
Talby adds, “Sustainability is a key aspect of responsible AI, and aligning
with standards like the CarbonNeutral Protocol helps companies offset their
emissions and advance their tech without burdening the environment.”
Read more about the
and the World Economic Forum’s view on
why we must care about responsible AI. You can also review declarations from
and Meta on their
approaches to responsible AI.
Bottom line: Organizations should experiment with AI, but recognize
both the economic costs and sustainability factors. When embarking on an AI
experiment, are you considering the potential value, cost, and carbon
The goal: Target carbon neutrality
As digital transformation leaders, our discipline requires stating upfront
goals and then reviewing how initiatives can address them.
“Many organizations are developing sustainability goals around environmental
commitments like carbon neutrality, net zero, or hitting other greenhouse
gas emission targets, according to Infosys’ ESG Radar,” says Sunil Senan,
global head of data, analytics, and AI at
Infosys. “To support sustainability
through digital transformation, organizations should focus on artificial
intelligence (AI), actionable data, and analytics to identify areas to
measure, improve, and streamline processes and decisions that have a
needle-moving impact on ESG achievements.”
Infosys’ ESG Radar
highlights several data points:
90% say ESG initiatives show positive financial returns, and 66% within
- 70% of companies are tracking carbon footprint
- 45% create ESG leaders as part of their organizational change strategy
That last data point suggests an opportunity for “Sustainability Digital
Senan continues, “Considering the recent AI boom, organizations can
capitalize on its power to deliver time and cost saving insights that
improve ESG outcomes and drive the organization’s bottom line.”
Bottom line: FinOps tools like
Hybrid Cloud Management X, Persefoni, and Microsoft Cloud for Sustainability
can help companies measure carbon emissions, and many companies, such as
Infosys, offer carbon management solutions.
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One solution: AI to improve resilience and sustainable energy solutions
One promising area is the application of AI and machine learning in energy,
resiliency, and sustainability.
“Leveraging artificial intelligence and automation is a key component to
ensure resilience and reliability across sustainable energy solutions,” says
Vikhyat Chaudhry, CTO, COO and co-founder of
Buzz Solutions. “Bolstering the
power grid is essential for a grid that can support ongoing clean energy
solutions necessary for the continued electrification of industries.”
Chaudhry continues, “AI can and should be used to spot risks and anomalies
early on in the production, installation, and ongoing maintenance of clean
energy and sustainable solutions. By training AI on data across a wide set
of utilities or energy management systems, risks can be automatically
spotted and addressed, preventing faults in the grid and potential
disruptions to clean energy production.”
Other sustainability initiatives focus on consumption and emissions, and
some of the biggest energy consumers state their commitments:
90% of the electricity consumed was attributable to renewable energy
sources, and “we remain on a path to reach 100% by 2025—five years ahead
of our original 2030 target.”
to achieve zero carbon emissions and waste by 2030.
to replace 120% of the water it consumes.
Bottom line: Nearly all global companies disclose ESG information, including sustainability goals. They are top-down strategic objectives
slowly getting on par with profitability and maximizing shareholder value.
Executing these goals requires bottom-up execution, and
have the opportunity to direct their digital transformation, data science,
and DevOps programs toward these objectives.
Some of my other articles on sustainability include
five ways devops can reduce energy consumption
three ways CIOs can target sustainability goals in digital
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