For the past few years Artificial Intelligence (AI) has been a game changer across a broad spectrum of industries right from Information Technology (IT) to manufacturing, automotive, healthcare, energy, and more.
Since the introduction of Generative AI (GenAI), the booming technology has reshaped industries [1] and fostered innovation within a short span driving rapid transformations and advancements in fields like healthcare, pharmaceuticals, and manufacturing. Models, such as Generative Adversarial Networks (GANs) and Recurrent Neural Networks (RNNs), have impacted these industries the most besides others. Additionally, GenAI leverages data-driven learning leading to creativity and adaptation that works as a catalyst for growth among Independent Small Vendors (ISVs). [SL1] GenAI frees up manpower especially in routine tasks such as data entry, customer support questionnaires, and social media engagement, ensuring these tasks are performed efficiently with minimal errors. There are many use cases how GenAI has been optimized across Industries [2].
AI has the potential to revolutionize software by optimizing energy usage. We could see AI driven tools in the future refactoring code for better energy efficiency. When we leverage AI and edge computing we can further enhance the sustainability of software products. In our previous articles we have covered how sustainability is imperative for Product engineering and has great significance for software development.
The convergence of AI and Sustainability promises a paradigm shift that could help solve environmental and societal challenges. The emergence has been a pivotal nexus bringing change that so far we could only imagine as science fiction!
According to a study conducted by PricewaterhouseCoopers AI is expected to play a pivotal role in curbing greenhouse gas emissions by 4% by 2030 and promote sustainable practices [3]. [SL2] A prime example is Shell, the energy giant, which uses AI and Machine Learning (ML) solutions integrated with IoT technology to automatically identify safety hazards, bringing down accidents and reducing operating costs leading to a positive environmental impact [4].
Before we discuss about how the merging of AI and sustainability could revolutionize industries and bring down carbon emissions which could only spell good news for our Universe, we need to understand the impact of AI on the environment, good or bad!
The AI compute infrastructure encompasses data centers, processors, and other specialized computing hardware. All this is stated to have a negative impact on the environment. A small example, the data centers account for the highest percentage of energy use, water use and greenhouse gas emissions. According to Techspot, data centers consume more than 205 terawatt hours of electricity per year [5]. And with the number of IoT devices growing rapidly, technology industries could account for high global energy demands anywhere from 7% to 20% by 2030. According to a recent report by Statista, data center carbon footprints keep growing at an alarming rate as they are known to keep replacing their servers every four years! [6]
The AI compute lifecycle has been split into four stages: i) production, ii) transport, iii) operations and iv) end-life stages. The most significant emissions (around 70-80%) come from the operational stage. An understanding of the environmental impact AI and its applications can have on sustainability of the environment, needs to be understood before extolling its virtues [7].
On the other hand, AI has also the capacity to analyze vast datasets at our disposal which can help us figure patterns, help render real-time decision making and strategize for a better tomorrow. AI can also optimize complex systems thereby enabling informed decision-making. A prominent example of this is where Taichung and Vienna have harnessed AI algorithms to analyze real-time traffic data, achieving remarkable reductions in congestion and improvements in travel times, showcasing practical benefits in urban settings.
The World Economic Forum highlights the significant role of AI in the energy transition, estimating that every 1% increase in efficiency can create $1.3 trillion in value between 2020 and 2050 due to reduced investment needs. AI contributes notably to optimizing supply chains, balancing power grids, and forecasting energy demand, thereby enhancing the sector’s overall efficiency and responsiveness to changing energy needs.
The use of AI for a sustainable future has been well summarized [8:replicating it here]::
And many more:
The introduction and adoption of Green algorithms or use of AI-driven tools ensures not just execution of the tasks efficiently but also in a sustainable manner [9].
Although AI can bring in beneficial advancements, it has its challenges too that need to be ethically managed. Data and cyber security are at the top of the priority list, besides maintaining accuracy, ensuring regulatory compliance, alongside the emergence of adversarial machine learning and biases in AI algorithms – all have raised new ethical and operational concerns. For example, if care is not taken when designing, AI algorithms can perpetuate existing societal biases especially in the field of recruitment, more so if historical hiring data contains biases affecting future hiring decisions.
To help curtail this and bring some standards and ethical modes, the European Union is passing its first broad AI Act after more than two years of debate. The proposed legislation focuses primarily on strengthening rules around data quality, transparency, human oversight, and accountability.
The introduction of AI across all spheres of work to integrate and align with the 17 Sustainable Development Goals (SDGs), is leading to a seismic shift in operational efficiency [10].
AI can be a catalyst in achieving the Economic, Social and Governance (ESG) objectives, from environmental sustainability to social responsibility and governance. With growing pressure from different stakeholders and investors for greater compliance in ESG practices, organizations are increasingly turning towards AI to expedite their sustainability initiatives.
According to a study published in Nature Communications, AI could help achieve 79 % of the SDGs [11].
As per research by PwC and Microsoft, by 2030, the use of AI for environmental technologies could add up to USD $5.2 trillion to the world economy, an improvement of 4.4% compared to business as usual [12].
Further, AI in aligning with the SDGs goals has pushed for a Circular Economy of Reduce, Reuse and Recycle rather than the Linear Economy of Manufacture, Use and Dispose.
| Industry | AI Impacts | Sustainability Transformation Results |
| Manufacturing | • Detects production defects • Validates quality insurance • Intervene and modify changes in real-time • Improves automation and safety controls • Equip for enhanced green capacity reduces downtimes | • Proposes alternative generative design, driving sustainability in product creation • Reduces fuel consumption and emissions, promoting sustainable supply chain operations • Predictive maintenance, process optimization, and waste reduction AI minimizes carbon emissions and water consumption • Engineers with real time access, AI powered insights, and operations advice via digital twins • With AI powered customer segmentation, sales forecasting, helps reduce overproduction and waste • AI can aid in skills gap analysis ensuring effective reskilling and upskilling, promoting workplace sustainability |
| Automotive | • Reduces accidents • Streamlines traffic flow • Advance supply chain management • Maintenance of truck fleet | • Cost cutting • Enhance operational visibility • Optimize transportation routes and schedules to reduce costs • Improve delivery times • Minimize carbon footprint |
| Healthcare | • Process extensive datasets • Enhance diagnostics • Improve disease detection • Provide for speedy personalized treatment | • Reduce trial-and-error approaches • Reduce the need for treatment • Reduce the need for resource-intensive • Avoid later-stage interventions |
| Retail | • Draw predictive insights • forecast & anticipate demand • retailers streamline their supply chains | • Efficient & eco-friendly delivery routes • Maximize truck loading & delivery capacity • Reduce amount of excess products • minimize unnecessary wastage |
| IT & Telecommunication | • Green coding • Refactoring of coding • Specialized hardware • Optimize AI algorithms & models • Improving data collection | • Less power-consuming ML models • Reproducible codes • Low-power hardware & minimal green sustainable infrastructure |
For organizations that wish to revamp their legacy systems and infrastructure and enable a smooth AI digital transformation, this can prove to be expensive. But this is where partnering with an ISV (Independent Small Vendor) helps you build the Industrial Internet of Things (IIoT) and data layer while maintaining or migrating away from legacy infrastructure. It enables the use of other niche systems to deliver targeted solutions [13].
ISVs are drivers of innovation, have competitive edge over larger partners, no complex organizational structures, and play a crucial role in shaping the intersection of digitalization and sustainability. ISVs are being called on to support and enable the AI digital transformation and application modernization across the industry spectrum [14].
The fusion of AI and sustainability marks a revolutionary era, reshaping industries and contributing to global goals. AI’s role in manufacturing, automotive, healthcare, and more brings about operational efficiency and reduces environmental impact. Despite challenges, ethical considerations, and costs, the alignment of AI with Sustainable Development Goals foresees a substantial economic boost and a transition towards a circular economy. As businesses navigate this transformative journey, collaboration with Independent Small Vendors emerges as a strategic move, ensuring a smoother AI-led digital transformation and fostering innovation for a sustainable future.
Calsoft, being a Technology-First company and a pioneer in software product engineering services, takes on the responsibility and commitment as a business differentiator to be the change to contribute towards building a safer future in the IT products and services for a sustainable tomorrow.
References:
[1] https://www.datacamp.com/blog/examples-of-ai
[2] https://www.eweek.com/artificial-intelligence/generative-ai-enterprise-use-cases/
[3] How can artificial intelligence help the environment? | World Economic Forum (weforum.org)
