Using Gen AI for Global Goals: Private Sector’s Guide to Sustainable Development with Technology

It seems difficult to achieve the 2030 Agenda for Sustainable Development and achieve the SDGs. With increased geopolitical tensions, inequalities and climate change are affecting things at large. This is why this report on using using Gen AI for global goals provides important insights about the challenges and solutions offered.

“The 2030 Agenda — our global blueprint for peace and prosperity on a healthy planet — is in deep trouble. AI could help to turn that around. It could supercharge climate action and efforts to achieve the 17 Sustainable Development Goals by 2030. But all this depends on AI technologies being harnessed responsibly and made accessible to all,” said António Guterres, United Nations Secretary-General.

Aim of the Report – To help achieve the UN objectives by providing actionable insights and recommendations. 

What is GEN AI?

It is a machine-based system which replicates human thinking and converts various inputs into outputs. These outputs can range from recommendations or predictions to content. [7] Generative AI (GEN AI) is a type of artificial intelligence that generates new content which is beyond what has already been exposed. [8]

All this is done by identifying and replicating the patterns already present in text and images along with other data to create realistic new data. Some common Gen AI products include GPT-4/4o, Claude, Midjourney, and Claude.

Presently, the attention is on Large Language Models (LLMs) that can mimic human language, and models also generate protein memes and structures. Foundation models for general purposes are trained on extensive datasets derived from the core of Gen AI ecosystem. Customization with specific data for various applications is possible and cloud providers can train the system.

Using Gen AI for Global Goals: Private Sector’s Leading Role in Sustainable Development

Accounting for more than 60% of the global GDP, the private sector plays an important role in the production of goods and services. Thus, there is a significant opportunity to lead in Generative AI for sustainable development by focusing on the Sustainable Development Goals (SDGs).

2 main objectives urged by the UN Global Compact for Gen AI companies are as follows:

1. Companies must cautiously proceed while adopting Gen AI and ensure human oversight to develop it safely for use.
2. Along with responsible implementation of Gen AI, the private sector should bridge the gap between actions and intentions on the SDGs.

Using Gen AI to Advance the Sustainable Development Goals

There are 3 key elements to support the system for successful and responsible Gen AI usage.

1. The companies should be sure that they clearly understand the problem they are solving. They should also agree that Gen AI is an ideal solution.
2. Companies must train the workforce to use Gen AI responsibly. This can be done by supporting them with appropriate data, AI literacy training, and digital.
3. The companies should set up the right government structures and maintain safety and accountability.

Gen AI and Sustainability

Gen AI has the potential to support sustainable development by acting as a Data Miner, Knowledge Amplifier, and Insight Navigator. With these, Gen AI can also enhance existing technologies and business operations, further promoting sustainability in 4 key areas, as discussed ahead.

Operational Efficiency

It is important for companies to efficiently manage limited resources to achieve sustainable returns. With Gen AI it is possible to enhance various operations as mentioned below.

• Resource Optimization: Cost reduction and environmental impact can be done by minimizing resource needs. Employees can optimize resources like logistics and computing power by applying Gen AI alongside current analytics. Like upgrading predictive analytics systems into prescriptive maintenance systems that offer actionable recommendations.
• Worker Effectiveness: It can be improved with adequate training and tools. With Gen AI, each employee’s training can be personalized by considering their role, local regulations, and language. Moreover, it can also design training programs aligning with the company’s goals, further enhancing employees’ decision-making and productivity.
• Efficient Code: To manage software’s environmental impact, effective coding is important. With Gen AI, coding tasks can be automated and existing code can be optimized, along with improving pinpoint. This will make the team more efficient. Effective coding is vital for managing software’s environmental impact. Gen AI can automate coding tasks, optimize existing code, and pinpoint improvements, helping teams become more efficient.

All in all, by streamlining development processes, businesses can possibly reduce resource use and lower their emissions.

Case Studies

SuperHumanRace – Aimed at improving maternal health in India’s poorest states, they developed an app. It provides personalized doctor recommendations. This app uses Gen AI and machine modelling to analyze maternal health data. Then it creates tailored questionnaires on the basis of patient’s pregnancy stage and risk factors.

Siemens – They implemented the Industrial Copilot (Microsoft’s Gen AI solution) on a Schaeffler manufacturing line to enhance industrial efficiency. The tool helps automation engineers in creating code for programmable logic controllers (PLCs). This further controls factory machines, that is 1/3 running on Siemens devices. With natural coding language, it reduces time, effort, and errors. Thus, allowing engineers to focus more on important tasks. With this, less-experienced employees can also move into engineering roles.

Sustainable Value Chain

For effective transition, it is important to engage the entire supply chain in sustainable development. Gen AI can streamline a lengthy data collection process by analyzing unstructured data. It further enables efficient Lifecycle Assessments (LCAs), improved supplier engagement, and responsible sourcing. LCAs are important for clear sustainability data but creating them is resource intensive. Gen AI enhances the efficiency of maintaining accurate LCAs.

As per the 2030 SDGs, responsible sourcing is important as it affects the social and environmental footprints. Another important element is supplier engagement as not all risks can be mitigated through sourcing alone. Gen AI can help in identifying risks and the right opportunities to improve them. All this along with offering tailored training for suppliers and thus promoting transparency and sustainable practices.

Case Studies

Accenture – Its N-Tier Supply Chain Navigator uses Gen AI to enhance supply chain operations. This is done with real-time insights provided by Gen AI for sustainability and procurement managers. It identifies human rights risks and sustainability by analyzing supply chain data against key indicators. Recently, Accenture found that Tier 2 and 3 suppliers are accountable for 50%-60% of CO2 hotspots. This info was derived after assessing more than 122,000 suppliers. Thus, highlighting the capacity of the tool to inform sustainable procurement decisions.

Unilever – Partnered with Google Earth’s Engine since 2020, Unilever developed geospatial analytics to monitor deforestation and manage forest commodity risks. To cross-reference geospatial and supply chain data, they integrated Gen AI. It thus helps in making better commercial decisions by incorporating geospatial insights into buying and supplier management.

SAP – The Sustainability Footprint Management of SAP uses its Business AI to reduce carbon footprint by mapping factors related to emission for purchase factors. OpenAI Embedding models are employed in the tool to find matching products and validate them. This is done by analyzing LCA and ERP databases. Around 10 close emission factor mappings are identified, and data fields are provided, which are product name, description, and Similarity Score. The company then assesses match quality and enhance emissions data visibility and documentation for audits.

Innovation

With limited time to achieve the SDGs, there is an urgent need for innovative solutions to cover the gap between intentions and results. Gen AI can generate ideas for the following.

• Green Finance: Gen AI can help small and medium enterprises struggling with financial sustainable development by assisting them in securing resources. Financial institutions can create context-appropriate solutions like green loans and bonds with Gen AI.
• Sustainable Product and Service Design: Gen AI can help in integrating sustainability concepts throughout the design process. This way, it can embed sustainability requirements in the early stages of design and development. Designers can manage competition throughout the process and satisfy functional requirements without neglecting sustainable factors.
• Cutting Edge Research: Sustainability can be accelerated with Gen AI as it helps in identifying trends, correlations, and emerging sustainability solutions.  It can not only quickly analyze vast amounts of data but can also create synthetic datasets to address under-representation in data. It can enhance research and technology further helping the private sector to achieve 2030 SDGs.

Case Studies

Yamaha & Final Aim – The Concept 451 by Yamaha and Final Aim is a compact EV for agriculture in the Japanese mountains which addresses demographic changes. With Gen AI, they accelerate design by researching sector challenges. It helps them generate 2,000 design variants, leading to communication during 3D modeling. This shows that it is possible for businesses to tackle social issues with faster R&D cycles.

Crayon – To enhance research, the company co-developed an LLM-powered chatbot for an international energy company. It makes searches and summaries easier for various sources. The market research was improved with chatthe bot and it provided 15% more relevant answers. This helped in better strategic and operational decisions along with customer interactions.

Communication and Reporting 

With increased scrutiny from investors, regulators, and consumers, corporate sustainability faces various policies and frameworks. Gen AI is helping ESG reporting and sustainability marketing while fostering collaborations within companies. 

• Sustainability Reporting: ESG reports are important for showing results, accountability, and compliance. Gen AI analyzes data to identify metrics, highlight initiatives, and make reports, thus enhancing reporting efficiency.
• Marketing Sustainability: To gain consumer and investor support, honest messaging about sustainable development is important. Marketing teams can use Gen AI for creating tailored content, thus ensuring alignment with brand strategy. Gen AI tools are also helpful in preventing greenwashing by clarifying complex sustainable concepts for marketers.
• Boosting Collaboration: Gen AI also guides companies to integrate sustainability by enhancing collaboration and communication between teams. It can easily simplify jargon and provide a better knowledge base for decision-makers.

Case Studies

Salesforce – They integrated Einstein (AI system) into Net Zero Cloud thus improving their ESG management. By using historical ESG data and other documents, it populates report responses. This allows companies to focus on sustainability initiatives rather than reporting.

Microsoft – The Copilot template helps companies in sourcing and sharing sustainability data. By using Gen AI, it analyzes data to support employees, thus allowing results to be shared as documents and reports. Companies compare their sustainability progress against others, which enhances accuracy and confidence. This tool promotes sustainable knowledge, reduces errors, and improves decision-making.

Leeward Renewable Energy secures 400 MW solar projects from Microsoft in Texas

User Risks of Private Sector Using Gen AI for Global Goals

• Gen AI Processes Are Often Opaque – Companies depend on a 3rd party system for infrastructure, models, and data which creates an accountability gap. External data may be mislabeled or violate copyright, which developers cannot understand while making conclusions. It is also possible that users may misrepresent their validation of Gen AI outputs, which increases risks.
• Gen AI Can Produce Uncertain and Problematic Results – Humans are models for Gen AI and can reflect bias and uncertainty which can introduce gender, socioeconomic, and racial biases. This issue can increase with faulty model design, existing user biases, and incomplete training data. There is also a possibility of false content presentation and the generation of toxic outputs. However, with proper transparency and governance, these errors, toxicity, and bias can be prevented.
• Gen AI Can Breach Data Privacy and Security – Gen AI apps can sometimes unintentionally expose sensitive corporate or personal information. This leads to violations of data security protocols like GDPR. If large datasets are improperly audited even with screening, there is a risk of revealing confidential data with Gen AI’s ability to index public information and make associations.
• Gen AI’s Power Can Be Misused – People and the planet are impacted by users’ interaction with Gen AI. Without safeguards, it can undermine SDGs by spreading misinformation through deepfakes. This way it offers guidance for weapons or support deception. There is a possibility of users manipulating Gen AI to produce toxic outputs or breach data privacy, thus increasing these risks.

Cross-Reference: Does automation bias decision-making?

External Risks of Private Sector Gen AI Use

• Gen AI Has Significant Resource Requirements – Gen AI requires resource-intensive data centers, and increased electricity consumption by 2026, which is already comprising more than 1.5% of global energy. It also affects water security through cooling needs and hardware production has environmental impact.
• Gen AI Will Redefine and Could Displace Jobs – With new opportunities, Gen AI also offers significant automation threats to workers. Companies must upskill themselves to reduce layoffs and collaborate with governments for a people-centric transition in the evolving workforce.
• Gen AI Will Disrupt Society, Which Could Widen Existing Divides – Societies and economies will transform with Gen AI. However, this will require digital literacy, quality training data, and computational power. Inequalities in internet and electricity distribution will only hinder progress, worsening the divides and generating human rights issues for vulnerable groups.

In conclusion, Gen AI is not a universal solution to challenges in the private sector. But it holds great potential to revolutionize business and sustainable development. The report shows how early applications of Gen AI helped in addressing global issues of poverty and gender inequality. The private sector should align its decisions with the Ten Principles of the UN Global Compact. There is hope that Gen AI can be helpful in achieving the 2030 goals.

Source: Private Sector’s Guide to Accelerating Sustainable Development with Technology