The future is not merely about incorporating AI into businesses but embedding sustainability into this technological revolution. "Sustainable AI" is more than just a catchphrase; it is a movement aimed at intertwining ecological integrity and social justice with the entire lifecycle of AI products.

Sustainable AI is a progressive approach that encourages the development of AI compatible with environmental sustainability, economic models, and societal values. It has two core branches: AI for sustainability and sustainability of AI. The former focuses on how AI can promote sustainable practices, whereas the latter revolves around reducing the environmental impact of AI systems, including their carbon emissions and computing power.

The environmental impact of AI is often overlooked, but studies show significant carbon emissions associated with training AI models. Addressing these concerns is fundamental to the sustainability of AI for the environment, emphasizing the need for both AI for sustainability and sustainability of AI.

This section will delve into how to measure and reduce the carbon footprint from training and tuning algorithms. It will highlight the importance of using sustainable data sources and power supplies in AI systems. Knowing the environmental cost of AI-oriented tasks will be a significant step toward reducing their negative impact.

A variety of tools, such as the 'machine learning emissions calculator,' 'experiment-impact-tracker,' and 'carbontracker,' offer developers an opportunity to track and predict the energy consumption and carbon emissions of their deep learning models. These tools form an integral part of sustainable AI development, aiding in creating more eco-friendly AI solutions.

Governments and business leaders worldwide are recognizing the need for sustainable AI practices. The European Green Deal and the United States re-joining the Paris Climate Agreement are prime examples of global initiatives to tackle climate change. The integration of these efforts in the AI industry is a testament to the growing importance of sustainable AI.

AI's influence is expanding across various sectors like healthcare, legal, and human resources. Given this, the environmental impact of AI deployment in these industries cannot be ignored. AI solutions need to be developed with a sustainability-first approach.

Safeguarding ethical practices in the development and deployment of AI is pivotal to achieve sustainable AI. Proposals have emerged for sustainable AI task forces in governments and governance schemes for sustainable AI development. There's also been a call for reporting carbon emissions in AI systems as a step towards transparency.

Achieving sustainable AI is not without challenges. The tensions between AI innovation, equitable resource distribution, inter and intra-generational justice, and the trade-offs between the environment, society, and economy need addressing. Balancing these conflicting aspects is key to the maturation of sustainable AI.

For sustainable AI to become a reality, there needs to be a shift in the way AI systems are developed. Implementing frameworks and policies to reduce emissions and energy use in AI development is an important step, but it also requires a collective effort from policymakers, industry developers, and researchers.

The incorporation of AI into businesses is inevitable, and so is the need for these systems to be sustainable. With the environmental impact of AI gaining attention, the focus must shift towards embedding sustainability within AI innovations. After all, the goal isn't just to build a technologically advanced future but a future that's technologically advanced, sustainable, and fair for all.

The time for Sustainable AI is now. Let's innovate responsibly.