Waking up to Reality, the SDGs Can't be Achieved Without AI

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Today, artificial intelligence punctuates the headlines of newspapers, podcasts, and social media posts around the world. Each morning, as it breaks through the horizon, the sun seemingly brings a new AI breakthrough. The morning announcement is routinely followed by midday and evening dispatches with equally astounding AI-driven triumphs. The promise of AI wakes us up in the morning, and - for some - the deep concerns about the technology keeps them up at night.

As an entrepreneur and investor, for me, following the AI headlines is as fascinating as it is functional. I co-founded an AI-powered enterprise market research platform - Gauge. We help brands
connect to consumers and make better decisions. I have also spent a decade doing casting for Shark Tank and other business shows on television. Each year, I meet thousands of entrepreneurs around the world. I have developed and led entrepreneurship programs in Ulaanbaatar, Kampala, Silicon Valley, and parts in between.

During my career, tech trends have come and gone. This is different. The potential for continued innovation and impact extend beyond a market cycle. AI is awakening.

The potential of the technology, in partnership with humanity, is being realized each day. However, as our brains normalize the headlines and we dream about AI's future, we risk missing the reality of the AI revolution today. The consistent rhythm of AI progress can become background noise, leaving us asleep to the true potential of the moment.

Awakening and understanding the moment is exactly what 193 member states did when the United Nations (UN) unanimously adopted the 17 Sustainable Development Goals (SDGs) of the 2030 Agenda for Sustainable Development in 2015. The SDGs push for solutions to pressing global challenges. These solutions are needed for sustainable progress in education, conservation, healthcare, business, and government. In 2015, the governments of the world agreed that achieving these goals was critical to building a world that our children deserve.

Now a decade later, many of the SDGs are off track. According to the latest reports from the United Nations, only 17% of the SDG targets are currently on pace to be achieved by the 2030 deadline. Many of the 169 targets have not only stalled, but have regressed. Some regression was driven by the COVID-19 Pandemic, but a range of variables impede progress. To their credit, UN leaders have announced the deficiencies and asked the world to increase their commitment. Many have even proclaimed the positive potential in applying AI to the SDGs.

UN Secretary-General António Guterres has said: "Artificial Intelligence (AI) is changing our world and our lives. And it can turbocharge sustainable development."

Taking it a step further, Doreen Bogdan-Martin, Secretary-General of the International Telecommunication Union said: It is her wish "to scale AI solutions to rescue the SDGs."

These statements from UN leaders are a step in the right direction, but they do not fully encapsulate the reality of the moment. As currently constructed, without AI the United Nations Sustainable Development Goals cannot be reached by the 2030 target date. Without direct application of artificial intelligence, many of the SDG targets may not be reached at all.

I began my career working in the United States Senate. I pursued a role as a Congressional Staffer on Capitol Hill in Washington, DC because I wanted to make a positive difference. I learned about the Millennium Development Goals (MDGs) that preceded the SDGs and saw their promise.

Since leaving government, I have worked with social entrepreneurs on every continent; many of whom align their mission-driven innovations with specific SDGs. I have worked with AI for Good

- the United Nations' AI platform - hosting pitch competitions with entrepreneurs using AI to support sustainable development. Recognizing the dynamism in these business owners, I created and host the 'Impact with AI' interview series. I engage entrepreneurs using AI and machine learning to move the SDGs forward.
 

The SDGs can drive meaningful progress, but we must fully recognize the moment and AI's place in achieving the goals.

In his sermon "Remaining Awake Through a Great Revolution," leader and activist Dr. Martin Luther King, Jr. said "There are all too many people who, in some great period of social change, fail to achieve the new mental outlooks that the new situation demands."

AI is awakening. Households, startups, enterprise organizations, and governments must remain awake through the AI revolution to achieve the SDGs and build a better world.

On the morning of June 13, 2022, I was sitting in a hotel room staring at a laptop screen. Late in the evening on the previous day, I had arrived in Osh, a beautiful 3,000 year old city in Kyrgyzstan's Fergana Valley. Osh, a hub for Silk Road, was now trading in the information economy. I had worked with local innovation hubs, universities, and the United States Embassy in the Capital - Bishkek

- to arrange a week of programming on entrepreneurship and AI.

After a morning lecture at Osh Technical University, I was slated to lead a session on AI and marketing at the OloloHaus coworking space. I was excited. Earlier that week, Blake Lemoine - a member of Google's Responsible AI team - had leaked private transcripts of his chats with Google's LaMDA AI model. Lemoine claimed the chats proved the neural network (a computer system modeled after the human brain) was sentient.

Was this the AI awakening? No. The claims were publicly refuted by Google. Moreover, the consensus of many AI leaders outside the company was that LaMDA didn't reach the bar for sentience. While statements refuting Lemoine's assertion were trickling out that week, they were largely drowned out by the deluge of public discourse on the subject. I couldn't wait to engage a group of entrepreneurs, technologists, and students about Lemoine's claim. I was even more excited because I would be discussing the headline with people from another country and culture.

Our world is filled with different countries, languages, cultures, and interests, but the human condition remains constant. Moments like this proved it. The prospect of the AI awakening sparks a similar curiosity in all of us. I knew the audience would be ready to dive in and they did not disappoint.

After asking if attendees were familiar with the news (many were) we began by unpacking artificial intelligence. We talked about the history of computers and machine learning. We then reviewed Google's development of the Transformer model used to generate text. We discussed how tracking relationships in sequential data could lead to understanding context and generating human-like responses.

Google's Transformer would become the basis for training OpenAI's ChatGPT and many of the Large Language Models (LLMs) developed by frontier AI companies in use today.

Through distillation of LLMs from OpenAI, Anthropic, and others, developers are now building large and smaller models with similar performance. This democratization of access to AI at lower cost is a key part of the awakening.

As AI becomes more available and even essential to daily life, how do we adapt? We discussed strategies that day in Osh; with the audience providing suggestions. Some attendees recommended following the headlines and studying to understand what AI is and how it works. Others implored us to upskill and embrace AI in our day-to-day lives. 

Suggestions also included preparing for future careers that require AI competency and planning to stay ahead of the technology curve.

All of these suggestions have some merit and are necessary for humanity to stay awake through the AI revolution. The topic that drove the most intense conversation was AI and ethics. what did it mean to use AI responsibly? What is our responsibility as humans when engaging with AI and machine learning technology?

During the session at OloloHaus we explored the ethics of Lemoine's unauthorized release of the LaMDA transcripts. If he believed LaMDA was sentient, did he have a moral obligation to make the information public? We discussed the many ways neural networks may be used in the future. Would the impact be negative or good? While we didn't solve the moral question, the discussion sparked questions about AI's potential and its use for good.

AI for Good has emerged as a popular tagline for AI use-cases that drive positive impact. Companies on every continent use #AIforGood to show the positive potential and real world results of their pioneering work in the space. 

The United Nations - for its part- began hosting the AI for Good Global Summit in 2017. Developed by the International Telecommunication Union (ITU), AI for Good has grown into the United Nations' leading platform on Artificial Intelligence for sustainable development.

I have attended the AI for Good Global Summit held annually in Geneva, Switzerland. I also partnered with the ITU to host 'AI for Good: Innovate for Impact' at the 2024 World Artificial Intelligence Conference (WAIC) in Shanghai, China. During the event, the United Nations IndustrialDevelopment Organization (UNIDO) and the Chinese Ministry of Industry and Information Technology (MIIT) announced a new Global Alliance on AI for Industry and Manufacturing Centre of Excellence (AIM Global CoE) in China. A number of entrepreneurs and enterprise organizations also presented examples of how they are using AI for positive impact.

Huaweii, Microsoft, vivo, Alibaba, and other companies shared business cases that not only benefit from AI, but require AI to succeed. After explaining how AI-powered sensors are helping aging populations remain safe and independent at home, Lili Qiu - Assistant Managing Director, Microsoft Research Asia- proclaimed "AI is a new general purpose technology that can change the world."

The conversation around AI has evolved from novel to necessary. There is, perhaps, no other initiative that needs AI'spotential to become reality more than the United Nations Sustainable Development Goals.

In 2015, all 193 member states of the United Nations came to a consensus. The world would benefit from a broad and interconnected set of goals to ensure sustainable progress. 

The Sustainable Development Goals or SDGs would build upon the previously adopted Millennium Development Goals (MDGs); expanding their scope and requiring every member state to participate in their achievement.

Adopted at the turn of the century in the year 2000, the eight Millennium Development Goals sought to coalesce global resources around critical challenges.

1) Eradicate extreme poverty and hunger

2) Achieve universal primary education

3) Promote gender equality and empower women

4) Reduce child mortality

5) Improve maternal health

6) Combat HIV/AIDS, malaria and other diseases

7) Ensure environmental sustainability

8) Global partnership for development

Achieving an MDG would require progress towards targets measured using data from 1990 to 2015 - the end of the MDG goal period. The eight sections of the United Nations Millennium Declaration provided basic principles used to craft the goals.

Some MDGs were reached. MDG 1 - Eradicate extreme poverty and hunger and MDG 6 - Combat HIV/AIDS, malaria and other diseases saw targets achieved by the 2015 deadline. The proportion of people around the world living in extreme poverty - less than $1.25 a day - was reduced by more than half from 1990 levels. The advance of diseases like HIV/AIDS, tuberculosis, and others was halted with a dramatic reduction in deaths from 1990.

While not all the MDGs were reached, targets supporting education, maternal health, safe drinking water and other goals saw significant progress. Technology was instrumental in the MDG success. Mobile banking, digital learning platforms, digital disease tracking data, and partnerships with enterprise technology companies are a few of the many ways technology contributed to achieving the MDGs.

In 2015, UN leaders celebrated the progress and recognized the need for the world to recommit to sustainable development. High- level meetings and a global call for input led to the adoption of the Sustainable Development Goals.

1. No poverty

2. Zero hunger

3. Good health and well-being

4. Quality education

5. Gender equality

6. Clean water and sanitation

7. Affordable and clean energy

8. Decent work and economic growth

9. Industry, innovation and infrastructure

10. Reduced inequalities   

11. Sustainable cities and economies

12. Responsible consumption and production

13. Climate action

14. Life below water

15. Life on land

16. Peace, justice and strong institutions

17. Partnership for the goals

Tough problems require targeted action. The SDGs focus effort, leading to the intentional application of resources and measurement to poverty, education, digital access, climate and conservation, good governance, and other areas.

The 17 SDGs cover economic, social, and environmental concerns. From SDG1 - No Poverty to SDG 17 - Partnerships for the Goals, the SDGs were crafted to address every area needed to build a better future. Each goal also has a number of associated targets. With a deadline of 2030, these metrics were developed to measure the progress towards achieving the broader goals. Reporting substantial progress on key targets for a goal by 2030 is a marker of achievement.

Since their adoption, significant commitments - financial and otherwise - have been made to achieving the goals. Each year, UN employees, the governments of UN member states, non governmental organizations (NGOs), corporations, and volunteers work to make progress on goal targets and achieve the SDGs by 2030.

The collective effort has led to some progress. According to the UN's 2024 Sustainable Development Report, SDG Targets such as Equal Access to Education, Health Impact of Pollution, Access to ICT and the Internet, International Cooperation on Energy, and Small-scale Artisanal Fishing have either been achieved or are on track to be achieved by 2030.

Despite this progress, many more SDG Targets are off track. Moreover, some 20% of SDG Targets cannot be tracked due to lack of data. As of 2024, of the Targets that can be tracked, 17 percent show progress sufficient for achievement by 2030. Alarmingly, 17 percent also show regression from the 2015 baseline.

Almost half (48%) of SDG Targets have moderately or severely deviated from the path towards achieving the goal. In short, the good work being done on the SDGs is not keeping pace with real- world challenges holding back progress.

A number of obstacles have limited positive movement on the SDGs. The COVID-19 Pandemic required significant commitment of resources to both fight the outbreak and keep economies stable. These economic setbacks limited the ability of UN member states to make SDG commitments. In some cases, the impacts on healthcare, education, economic opportunity and other areas led to stagnation and regression on SDG Targets.

Beyond COVID-19, climate change is accelerating faster than mitigation efforts. Inefficiencies in governance and infrastructure forestall progress. Global conflict adds to the drain on resources. Importantly, lack of data-driven decision-making and access to technology limit the ability of the UN to collect data for and achieve some SDG Targets.

It is clear that traditional methods - however sincere - are not enough to achieve the SDGs by the 2030 deadline.

"Instead of leaving no one behind, we risk leaving the SDGs behind…the SDGs need a global rescue plan" - UN Secretary- General António Guterres

Is AI the rescue plan for the SDGs? Predictions for the capabilities of fully "awake" AI range from fanciful to fatal. Some futurists claim AI can solve global energy demands and dramatically increase access to healthcare. Others warn of AI's negative impact on jobs, power consumption, and the potential for humanity to lose control of the technology.

I engage with thousands of entrepreneurs each year. Many of them are using AI and machine learning technology to build their innovative solutions. After hosting the 'Innovation Factory' pitch competition at AI for Good 2024, I created and launched a podcast series - Impact with AI.

I interview global entrepreneurs using AI to make progress on the SDGs. Let's explore how entrepreneurs are using AI to touch four communities and support the SDGs.

I met Himanshi Singh while serving as an expert mentor for her team as part of the Unleash+ Accelerator in Mysore, India. Himanshi's business, Bare Craft Consulting uses AI to connect rural artisans to small and medium enterprises (SMEs) that sell their hand-crafted products locally and globally.

In India, artisans travel from small villages to big cities to sell their products. During the COVID-19 pandemic, many artisans were forced to go back home - eliminating their source of income. The Bare Craft platform introduced a transformative model empowering artisans to access global markets directly from their homes.

"We realized that by empowering artisans to work from their villages, we could improve their incomes fivefold while preserving their cultural crafts. With AI, we're bridging the gap between rural talent and global markets." - Himanshi Singh.

What began as a platform to match artisans with SMEs has grown into a logistics platform allowing the more efficient procurement of raw materials, research-based product development, and access to markets around the world.

Local artisans on the Bare Craft platform have transitioned from earning less than $2 per day to $100-$200 per month, significantly improving their quality of life. This outcome directly supports SDG 1 - No Poverty. Himanshi's business also touches SDG 8 - Decent Work and Economic Growth, SDG 9 - Industry, Innovation, and Infrastructure, and SDG 12 - Responsible Consumption and Production.

Bare Craft Consulting is creating opportunities for artisans and SMEs in India, with the potential for global scale. In the United

States, Christopher Gray is building an education solution with similar potential for global reach.

As a teenager in Birmingham, Alabama - a city in the southern United States - Christopher Gray realized he would be responsible for funding his own education. Christopher would work to earn more than $1M in scholarship funding for his college education. While in college, Christopher co-founded Scholly - an app to connect college students with scholarships. He pitched Scholly on Shark Tank - a television show featuring entrepreneurs pitching their businesses to investors. 

His pitch earned him a deal with two Sharks and Scholly flourished. Ever the enterprising entrepreneur, immediately after Scholly was aquired, Christopher created Path: an AI-powered education and workforce development platform.

Path leverages generative AI to make test preparation and certification exams more affordable and effective. The platform provides unlimited practice opportunities, predictive scoring, and AI-driven tutoring to help students excel in standardized tests and workforce certification exams.

"AI allows us to democratize education by making test preparation accessible to everyone, regardless of their financial background. With AI-powered tutoring, students receive personalized guidance that levels the playing field." - Christopher Gray

By democratizing access to test prep and personalized education resources, Path is directly supporting SDG 4 - Quality Education. Path also touches SDG 8 - Decent Work and Economic Growth, and SDG 9 - Industry, Innovation, and Infrastructure

In Latin America, Camilo Huneeus founded Ainwater to transform water management through the integration of AI and water technology. The goal is to enhance efficiency, sustainability, and operational continuity for industries and water treatment plants.

Camilo grew up in the Patagonia region of Southern Chile'. As a child, he experienced a drought. He remembers community members questioning if they would have enough water for their farm and family. Living through a drought raised Camilo's awareness of water availability. Even as a child, he knew the power of data and technology. Ten-year-old Camilo wondered why data couldn't mitigate water scarcity.

As adults, Camilo and the founding team at Ainwater would discover a gap in water management. They would use AI to build a bridge connecting communities to an untapped reservoir of resources. Water treatment and resource management facilities have existed for generations. These critical nodes rely on a network of chemical engineers, hydraulic engineers, environmental engineers, and others to function. Too often, these experts do not talk to each other and a critical resource - data - goes unused.

Ainwater's primary solution POSEIDÓN integrates data from sensors and sources to train models and power prediction. Camilo's intelligent platform designed for water treatment plants empowers municipalities and other water managers with real- time monitoring and visualizations, predictive alerts, regulatory compliance, and optimization of resources.

"AI can optimize how we use water, making treatment plants operate smoothly, reducing risks, and saving energy. By improving water quality before it even reaches the ocean, we are actively protecting marine life and ensuring sustainable water management," - Camilo Huneeus

Reducing costs and more efficiently managing water resources contributes to more efficient and sustainable water management practices.

Enterprise companies like Nestle and Coca-Cola use Ainwater along with water managers throughout Latin America. Ainwater directly supports SDG 14 - Life Below Water. It also touches SDG 6

- Clean Water and Sanitation, SDG 8 - Decent Work and Economic Growth, SDG 9 - Industry, Innovation, and Infrastructure, and SDG 12 - Responsible Consumption and Production.

In China, Jason Tu and MioTech use artificial intelligence to address sustainability challenges, focusing on environmental, social, and governance (ESG) data and analytics. Some SDGs are unable to be tracked because the data is not being collected, and/or shared.

If practices are not tracked, measured, and shared, they can't be properly quantified or - if necessary - improved. Empowering financial institutions, corporations, and governments with actionable insights to drive sustainable practices directly supports the SDGs.

The company aims to enhance transparency and efficiency in ESG reporting and analysis, thereby facilitating informed decision- making for stakeholders across various sectors.

MioTech provides a range of solutions designed to integrate sustainability considerations into business operations. These offerings include ESG data services, carbon emission tracking, and energy management.

MioTech directly supports SDG 9 - Industry, Innovation, and Infrastructure, SDG 12 - Responsible Consumption and Production, and SDG 13 - Climate Action. Ainwater, Path, and Bare Craft Consulting directly support the SDGs by providing AI-enabled products and services. MioTech delivers products to market and helps address the data deficiency currently limiting the full understanding of where we stand on many Sustainable Development Goals. These businesses show how AI- powered solutions can drive impact and progress on the SDGs.

AI's current impact on sustainable development and future potential are evident. However, concerns about power consumption, workforce impact, and potentially losing control of the technology cause apprehension and may - indeed - slow down the forward progress.

In 'The role of artificial intelligence in achieving the Sustainable Development Goals' published in Nature, Ricardo Vinuesa and others explored how AI would impact the SDGs. The group analyzed each SDG and quantified which percentage of SDG targets mapped to the specific goal that AI could impact - positively and negatively. For example: Vinuesa and others said that for SDG 5 AI could act as an enabler for 56% of the targets and act as an inhibitor for 33% of the targets. The group found that AI could act as an enabler for 58% of SDG 14 targets and act as an inhibitor for 25% of the targets.

This assessment strikes at the heart of concerns about AI's potential for negative impact tempering positive gains. These concerns should be actively monitored and managed as AI is applied to global challenges.

Power consumption is, perhaps, the most discussed concern about scaling AI solutions. There is some debate about the power demands of generative AI prompts vs. traditional web searches. A Reuters interview with Alphabet Chairman John Hennessy is often quoted:

"Alphabet's Chairman John Hennessy told Reuters that having an exchange with AI known as a large language model likely cost 10 times more than a standard keyword search, though fine-tuning will help reduce the expense quickly."

This claim that conversations with models like ChatGPT consume 10x more power than a traditional Google search has spread widely. Some researchers have pushed back on the generally accepted estimate, others claim it is too generous. Occasionally, OpenAI CEO Sam Altman posts about increases in efficiency and compares the resource consumption of ChatGPT conversations to everyday tasks. 

One chart reposted by Altman on X showed ~300 ChatGPT queries consuming about 1 gallon of water, compared to one hamburger requiring about 660 gallons of water.

Altman has also noted that "It's totally fair to say that AI is going to need a lot more energy," and "There's no way to get there without a breakthrough,"

The OpenAI CEO has often mentioned nuclear fusion and other clean technology as opportunties to meet the AI power demand. Recent investments from Bill Gates in nuclear energy and commitments from Softbank and others to build AI datacenters underscore the urgency of the need to develop the energy infrastructure to power AI's promise.

Another recent headline sparked debate and interest in AI's future potential; DeepSeek. In December 2024, reporting began to surface that DeepSeek's open source DeepSeek-R1 model was not only performing on par with OpenAI's o1 model, but also doing it at a fraction of the cost.

Through distillation of LLMs created by frontier AI companies, researchers and technologists around the world are building models cheaper, faster, and more efficiently. These models achieve similar performance results as LLMs that took years and billions of dollars to train. The opportunity to build smaller models - bringing innovation closer to the edge - while using less resources is significant.

Salesforce partnered with open source AI platform Hugging Face to create the AI Energy Score to measure and compare the resource usage of AI models. Public accountability for resource usage and continued innovation in model efficiency and power creation are critical to maximize the positive impact of AI.

As innovation in power generation and model efficiency move forward, AI can help achieve the SDG targets by 2030 in a number of ways. AI can accelerate progress, enhance data-driven decisions, automate processes, inform policymaking, better connect people to information, and make a range of resources more accessible. Each of these opportunities support progress towards reaching SDG targets, and most targets cannot be reached without them.

SDG 1 (No Poverty) Target 1.5 is "Resilence to Disasters." This target aims to reduce "exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disasters."

AI-powered climate models help predict and mitigate the effects of climate change. These solutions support SDG 13 - Climate Action - and SDG 1 Target 1.5.

Actionable intelligence of weather patterns and informed planning for natural disasters means that the most vulnerable populations can reduce risk during these events. Solutions like Planette help the agricultural sector better understand weather to mitigate climate shocks to crop production. More consistent production means better availability for communities targeted by SDG1. Robotics88 uses AI-driven unmanned aerial vehicles to monitor forests and set controlled fires. These actions lower the risk of wildfires. Solutions built on NVIDIA's StormCast model deliver predictive weather insights helping communities better resist natural disasters.

SDG 1 Target 1.5 directly benefits from AI solutions. Furthermore, it is difficult to imagine how the reduction in exposure and vulnerability at the scale required to achieve the target could be achieved without artificial intelligence.

These advancements also accelerate SDG 11 (Sustainable Cities and Communities) Target 11.5. This target seeks to "significantly reduce the number of deaths and the number of people affected and substantially decrease the direct economic losses relative to global gross domestic product caused by disasters."

Again, Target 11.5 would be difficult - if not impossible - to achieve without the advanced climate modeling and data-driven decision- making powered by artificial intelligence solutions.

Of course, SDG 13 (Climate Action) should be included here as well. SDG 13 Target 13.1 will be achieved by "Strengthen[ing] resilience and adaptive capacity to climate-related hazards and natural disasters in all countries." Achieving Target 13.1 requires solutions like those built on Huawei's Pangu-Weather model and others. Mitigating climate risks also has a financial benefit. Individual households, businesses, and governments will save money by being better equipped to resist climate disasters.

Similarly, AI is enhancing financial inclusion. Returning to SDG 1 (No Poverty), Target 1.4 calls for equal access to financial services. AI-driven fintech solutions, such as mobile banking algorithms, enable underserved populations to access credit and banking services without traditional financial infrastructure. AI-powered microfinance platforms like Tala assess creditworthiness using non- traditional data sources, allowing more people to participate in the global economy. Scaling access to financial services would not be possible without artificial intelligence.

Another area in which AI is required is data availability. 34 of the 169 SDG Targets cannot currently be tracked because the data is not available. UN Member State participation is required to access some of this data. However, there are many data points that simply require better coordination, collection, and sharing. Automating data collection while respecting privacy rights is necessary to achieve a range of SDG Targets.

SDG 9 (Industry, Innovation, and Infrastructure) Target 9.3 seeks to "increase the access of small-scale industrial and other enterprises, in particular in developing countries, to financial services, including affordable credit, and their integration into value chains and markets."

A better understanding of credit availability and transactions will help to track the progress of this goal. AI-powered bookkeeping and small business fundraising solutions like Bags can help entrepreneurs access the financial services they need. Small business owners often need education to acquire the new skills needed to grow their businesses. SDG 4 (Quality Education) Target

4.4 aims to "increase the number of people with the skills needed for jobs, entrepreneurship, and decent living."

Target 4.4 cannot currently be tracked due to the lack of relevant data. Artificial intelligence solutions like Path can create custom learning plans for career education and certifications. Scaling and tracking the use of tools like Path will generate the data needed to track Target 4.4 and the outcomes needed to achieve the Target by 2030.

AI's ability to process vast amounts of data in real-time provides actionable insights that drive evidence-based decision-making, helping governments, businesses, and non-profits optimize resource allocation and policy implementation.

Moving through the list of 17 Sustainable Development Goals and 169 SDG Targets yields the same result. Target after target cannot be reached without the use of artificial intelligence and machine learning technology. Many of these goals cannot be achieved at all without artificial intelligence. Very few could be achieved by 2030 without AI.

If AI is required to reach the SDGs by 2030, how do we implement these solutions at scale? The ITU's AI for Good Summit includes a Global Governance Day. This key inflection point with diplomats and leaders from UN member states and key industry experts is an opportunity to set policy governing AI.

During my Impact with AI interviews, I ask each guest a big question: "Do you think AI can rescue the SDGs?" Ainwater CEO Camilo Huneeus gave a striking response:

"AI is a tool. As a tool, it will help, as a tool. But this is politics. It's politics. Our political leaders have to have the political will to drive those SDGs. And yes, if they have the political will, AI will make things faster, quicker, it's useful. But if they don't want to…"

Shifting focus from an AI "rescue" to an AI "requirement" in global forums will orient leaders towards the need to scale solutions and allocate resources. Beyond AI for Good, other UN organizations including the United Nations Educational, Scientific and Cultural Organization (UNESCO), UNIDO, and United Nations University (UNU) hold regular AI summits. These too are prime opportunities to articulate the need for AI in sustainable development. Begin with communicating the need and continue with communicating what's working.

To truly reach the full potential of artificial intelligence in sustainable development, Responsible AI must be implemented as well. As neural networks become more integrated into global systems, it is crucial to develop AI responsibly to ensure it aligns with ethics and sustainability goals.

As covered earlier, AI requires significant energy resources. SDG 7 (Affordable and Clean Energy) Target 7.2 looks to increase the share of renewable energy in the global energy mix. Researchers, technologists, investors, and development practitioners are working to build the energy infrastructure to support AI. Many of these efforts focus on nuclear and other renewable energy sources that directly support Target 7.2 and move the world closer to broader use of renewables.

Another challenge is AI bias and fairness. SDG 10 (Reduced Inequalities) Target 10.3 will be achieved by ensuring equal opportunities and reducing inequalities of outcome. Bias in AI developers and datasets can create and/or reinforce disparities. The Frontier AI companies have committed to a range of ethics standards. These guidelines must be monitored and continually updated to ensure harms are limited. Transparency in AI decision- making and inclusive dataset training are essential for ensuring AI benefits all communities equitably.

Ultimately, for AI to reach its full potential and rescue the SDGs, we need technology, political will, resources, and a commitment to building responsibly.

Returning to Dr. Martin Luther King, Jr.'s sermon. He says: "What we are facing today is the fact that through our scientific and technological genius we've made of this world a neighborhood. And now through our moral and ethical commitment we must make of it a brotherhood."

Artificial intelligence and machine learning solutions have further connected an already interconnected world. Staying awake during the AI revolution means understanding that AI is required to reach the SDGs that 193 UN member states agreed were critically important. Without this new understanding, 2030 will come and go without the desired progress. 

Will every UN SDG Target be met if AI is fully implemented? Probably not. AI isn't a cure-all and unexpected shocks to the system will continue. However, it is clear that without a global commitment to applying artificial intelligence to the SDGs, we will have less to celebrate.