Alive Verified Standards

Commitment to Sustainable Business Practices

We ensure companies meet 7 alive requirements for verification, focusing on sustainability, ethical AI, and circular economy practices to promote responsible business operations.

Transforming businesses for a sustainable future.

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Alive Verification

These are the Alive Verified Standards.

GRI Standards

Integrating GRI standards into core business practices effectively.

1.1 Establish Sustainability Reporting Framework: Develop framework that aligns to GRI standards. Train Business units on these standards. Regularly report sustainability KPIs: carbon footprint, water usage, and waste generation. Select the GRI Standards that apply best to your business goals.

1.2 Run Regular Materiality Analysis: Stakeholder materiality assessments should be run regularly with customers, suppliers, investors, and employees to identify, prioritize, and adapt the most material environmental, social, and governance issues into its business strategy. The idea is to find overspending on materials and gaps during procurement of these materials.

Circular Economy

Implementing circular economy practices across the organization.

2.1 Redesign for Long Life/Recyclability: Design shall focus on strategies of durability, modularity, reparation, and sustainable or post-consumer recycled content. Products shall be designed for easy disassembly and recyclability at the end of the product life for further use. The usability of products when disassembled shall be ranked.

2.2 Switch to Product-as-a-Service: Shifting traditional ownership models into offering products as services-that is, leasing, subscription, or rental models to ensure products are not stored away. This will provide a situation the selected product to remain in one's control for reuse and refurbishment, hence extending the product life cycle.

AI Technologies

Investing in energy-efficient AI technologies for sustainability.

3.1 Low-Power AI Model Development: The company should invest in a wide array of R&D projects focusing on the development of low-power-consuming AI models through pruning, quantization, and knowledge distillation. All these methods will reduce model size and complexity without sacrificing model accuracy, hence reducing energy consumption associated with training and inference.

3.2 Leverage Renewable-Powered AI Infrastructure: The first area of concentration is renewable-powered AI infrastructure, including data centres or servers powered by wind or solar as a source of energy. Work with cloud providers committed to carbon neutrality and leveraging green energy for operations.

Green Data

Utilizing green data centers for efficient data management.

4.1 Migrate to Green Data Centers: Shift the operation of data storage and processing to data centers with high ratings in energy efficiency, using renewable energy as the basis of their generation. Choose vendors and partners using green certifications, such as LEED or ISO 50001.

4.2 Implement Data Minimization Practices: Institute strict data management policies to minimize the data collected and stored. Collect only indispensable business data or that required by AI model developments, which saves energy consumption and resultant emissions on that front.

AI Sustainable Innovation

AI for Circular Economy

Utilizing AI technologies for sustainable innovation.

5.1 AI for Design-for-Sustainability Product: Provide a bundle of design simulation and optimization tools empowered by AI that will minimize material use, ensure full recyclability, and optimize energy use along the entire product life cycle.

5.2 AI for Supply Chain Optimization: AI-enabled analytics and machine learning models deployed will support the optimization of supply chain logistics-emission reduction in transportation, improvement in inventory management, and ethical sourcing through environmental and social performance monitoring of suppliers.

Extending circular economy practices with AI.

6.1 Automation of Recycling and Waste Management: Develop and employ AI technologies in improving sorting and recycling processes, such as the use of computer vision together with machine learning techniques to offer more accurate and efficient means of material separation and sorting.

6.2 Extend Asset Life using Predictive Maintenance: Put into place predictive maintenance tools using AI that monitor operating equipment and machinery to identify imminent failures before they occur and extend asset life by optimally scheduling their repair, reducing downtime.

Ethical AI Governance

Thinking ahead and investing time to structure AI into your organization.

7.1 AI Ethics Committee: Create a concentrated AI ethics committee that will be able to guide the development and implementation of AI technologies. It shall guide all AI initiatives to meet all ethical considerations, sustainability objectives, and regulatory imperatives.

7.2 Institutionalize Explainable AI Practices: Design AI models that are more open, transparent, and explainable; provide clear insights to stakeholders on how decisions are reached. Audit the AI systems periodically to discover and overcome any predisposed biases or unintentional outcomes that might weaken efforts toward sustainability.