Cleo and CloneOps Team to Bring Voice Automation to Supply Chains – PYMNTS.com

Strategic Partnership to Advance Sustainable Supply Chain Operations

A strategic partnership has been formed between supply chain orchestration firm Cleo and CloneOps.ai, a provider of artificial intelligence (AI) virtual agents. The collaboration aims to integrate intelligent voice automation into logistics and transportation sectors, enhancing communication and operational resilience in alignment with global sustainability targets.

Aligning Technological Innovation with Sustainable Development Goals

The integration of CloneOps.ai’s voice automation with Cleo’s supply chain orchestration platform is designed to empower businesses to respond to disruptions in real-time. This initiative directly supports several key United Nations Sustainable Development Goals (SDGs) by creating more efficient, resilient, and sustainable supply chain ecosystems.

Fostering Resilient Infrastructure and Innovation (SDG 9)

• Enhanced Resilience: The technology enables a faster, more intelligent response to unforeseen supply chain events, reducing downtime and building the resilient infrastructure essential for sustainable industrialization.
• Technological Upgrading: By introducing AI-powered voice agents, the partnership promotes the upgrading of technological capabilities within the logistics industry, a core target of SDG 9.
• Improved Communication: The automation of communication channels strengthens the entire supply chain network, making it more robust and less prone to failure.

Promoting Decent Work and Economic Growth (SDG 8)

• Increased Productivity: AI voice agents automate repetitive tasks and optimize call flows, which improves operational efficiency and contributes to higher levels of economic productivity.
• Value-Added Work: Automating routine work allows employees to focus on more complex and strategic tasks, fostering a more skilled and engaged workforce.
• Economic Resilience: By improving service levels and creating value through stronger business relationships, the partnership helps companies achieve sustained and inclusive economic growth.

Enhancing Responsible Production and Consumption Patterns (SDG 12)

• Resource Optimization: Greater efficiency in logistics and transportation can lead to reduced fuel consumption and lower emissions, contributing to more sustainable production patterns.
• Waste Reduction: A more responsive supply chain can minimize waste, particularly in the transport of perishable goods, by reducing delays and improving coordination.
• Operational Efficiency: The ability to shorten lead times and lower transport costs directly supports the efficient use of natural resources as outlined in SDG 12.

Broader Implications for Sustainable Economic Systems

The application of AI extends beyond this partnership, signaling a wider industry trend toward integrating intelligent technologies to build more sustainable and financially sound economic systems.

The Role of Artificial Intelligence in Sustainable Procurement and Logistics

1. AI models are increasingly used to identify and quantify supply chain risks, including environmental factors and supplier compliance failures, allowing for proactive management.
2. Generative AI in procurement functions has been shown to reduce costs by 15% to 45% while automating up to 30% of routine work, freeing resources for strategic sustainability initiatives.
3. Early adopters of AI in production and logistics are reporting tangible benefits such as lower transportation costs and shorter lead times, which are key indicators of operational sustainability.

Strengthening Financial Sustainability and Corporate Responsibility

• CFOs are increasingly viewing supply-chain finance as a strategic function, with AI and automation transforming working-capital management.
• The integration of payments, procurement, and logistics systems through AI strengthens corporate liquidity and improves financial visibility across global operations.
• Enhanced financial systems allow companies to extend early-payment programs to suppliers, fostering stronger, more equitable relationships throughout the value chain.

Conclusion: A Collaborative Approach to Sustainable Development (SDG 17)

The alliance between Cleo and CloneOps.ai serves as a model for SDG 17: Partnerships for the Goals. It demonstrates how collaboration between technology providers can accelerate the adoption of innovative solutions that not only drive business value but also contribute significantly to building more sustainable, resilient, and responsible global supply chains.

Analysis of Sustainable Development Goals in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

• SDG 8: Decent Work and Economic Growth

  The article connects to SDG 8 by focusing on enhancing economic productivity through technological innovation. The partnership between Cleo and CloneOps.ai aims to improve operational efficiency, reduce costs, and automate routine work, which are key drivers of economic growth for the companies involved and the broader logistics industry.

• SDG 9: Industry, Innovation and Infrastructure

  This is the most prominent SDG in the article. The entire text revolves around the application of innovation (Artificial Intelligence, voice automation) to upgrade critical infrastructure (supply chains, logistics, and transportation). The goal is to create more intelligent, resilient, and efficient industrial processes.

• SDG 12: Responsible Consumption and Production

  While not explicitly mentioned, the article’s themes of efficiency and optimization are directly linked to SDG 12. By improving supply chain orchestration, reducing downtime, and lowering transport costs, the technologies discussed contribute to more sustainable production patterns by minimizing the waste of resources such as fuel, time, and materials.

2. What specific targets under those SDGs can be identified based on the article’s content?

• SDG 8: Decent Work and Economic Growth

  • Target 8.2: Achieve higher levels of economic productivity through diversification, technological upgrading and innovation, including through a focus on high-value added and labour-intensive sectors.
    
    Explanation: The article directly addresses this target by describing how the adoption of AI and automation technology is used to achieve significant efficiency gains. The text states that these technologies can “automate up to 30% of routine work” and “reduce costs by 15% to 45%,” which are clear examples of using technological upgrading to boost economic productivity in the logistics sector.

• SDG 9: Industry, Innovation and Infrastructure

  • Target 9.4: By 2030, upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency and greater adoption of clean and environmentally sound technologies and industrial processes, in accordance with their respective capabilities.
    
    Explanation: The partnership’s aim to “bring intelligent voice automation to logistics and transportation companies” is a direct example of upgrading infrastructure and retrofitting industries with new technology. The article highlights outcomes like “reducing downtime,” achieving “lower transport costs,” and “shorter lead times,” which all point towards increased resource-use efficiency as stipulated in this target.

• SDG 12: Responsible Consumption and Production

  • Target 12.2: By 2030, achieve the sustainable management and efficient use of natural resources.
    
    Explanation: The article implies progress towards this target through the efficiency gains it describes. By creating systems that “respond in real-time to unforeseen or disruptive supply chain events faster and more intelligently,” the technology helps prevent the inefficient use of resources. Lower transport costs and reduced downtime inherently lead to a more efficient use of natural resources like fuel.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

• For Target 8.2 (Economic Productivity):

  • Percentage of cost reduction: The article explicitly mentions a study showing that procurement functions using generative AI can “reduce costs by 15% to 45%.” This is a direct quantitative indicator of increased economic productivity.
  • Percentage of work automated: The article states that these technologies can “automate up to 30% of routine work,” serving as a clear metric for measuring the impact of technological upgrading on labor productivity.

• For Target 9.4 (Infrastructure Upgrade & Efficiency):

  • Reduction in transport costs: The article notes that early adopters of these technologies are “seeing lower transport costs,” which is an implied indicator of improved infrastructure efficiency.
  • Reduction in lead times: The mention of “shorter lead times” is another implied indicator that measures the efficiency and effectiveness of the upgraded logistics infrastructure.
  • Reduction in operational downtime: The partnership aims to empower customers by “reducing downtime,” which can be measured as an indicator of supply chain resilience and efficiency.

• For Target 12.2 (Resource Efficiency):

  • Improvement in service levels: The goal of “improving service levels” implies a reduction in errors, delays, and waste within the supply chain, which serves as a proxy indicator for more efficient resource management.
  • Real-time response to disruptions: The capability to “respond in real-time to unforeseen or disruptive supply chain events” can be measured by tracking the time and resources saved when disruptions occur, indicating a more efficient use of resources that would otherwise be wasted.

4. Table of SDGs, Targets, and Indicators

Source: pymnts.com