How Supply Managers are Maximizing the Potential of Generative AI with Automation
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Until now, artificial intelligence (AI) initiatives in supply management mostly leveraged analytical AI. The latest development in the field of Generative AI has taken the world by storm.
Leaders view Generative AI as a powerful tool for innovation and problem solving. In fact, Generative AI combined with automation has proven to eliminate hours of otherwise tedious, manual, repetitive tasks in sourcing and finance – Canon processes 90% of invoices without human intervention. Another giant, British American Tobacco (BAT) boasts 230,000 labor hours are saved in recording leaf stock acquisitions and cutting costs of materials used. In both cases, supply management professionals are freed up to focus on higher value activities - negotiation strategies, costing accuracy, reducing energy usage, improving supplier relations and preventing possible delays.
The vast majority of AI will positively impact supply chain. However, we should be aware of the potential pitfalls of Generative AI and ensure proper measures are taken to mitigate them.
- Hallucinations -Generative AI may produce inaccurate or misleading results, especially when dealing with complex data or images. These are known as "hallucinations." They can be a significant drawback in supply management, where consistency and accuracy are crucial.
- Deepfakes -Generative AI algorithms can produce media based on patterns learned from existing data. However, their misuse can lead to the creation of deepfakes—such as manipulated videos or images—that spread misinformation.
- Transparency -Generative AI can be opaque, making it difficult to understand how it arrives at decisions or outputs. This lack of transparency can lead to mistrust and may make it difficult to explain its results to stakeholders.
- Legal and ethical issues -As with any AI technology, Generative AI raises legal and ethical issues related to data privacy, intellectual property, and bias. Leaders must comply with relevant laws and regulations, and address ethical concerns related to using generative AI.
- Security and privacy concerns -Generative AI relies on users sharing large amounts of data to generate new content. While this gives Generative AI models more data to train and improve on, it also makes the data vulnerable to security breaches and data privacy issues. Establishing appropriate security measures to protect data is paramount.
While businesses are addressing these challenges, remember that Generative AI is only one enabler of end-to-end digital transformation. Siloed business initiatives can only provide one piece of the larger puzzle. Fortunately, with integrated platforms like the UiPath Business Automation Platform, the business value of Generative AI is elevated through automations that result in efficient, accurate outcomes and high-value use cases across the supply chain.
Why? Generative AI functions as the ‘brain’ behind the digital transformation ecosystem; AI-powered automation serves as the requisite ‘muscle’ to act on the generated insights. Realizing the power, manufacturing and supply chain leaders are adopting AI-powered automations into their digital transformation roadmaps. The following should be considered:
Value proposition
It’s easy to be distracted by the buzz, focus on driving business outcomes is key.
While performing demand forecasting, Generative AI can be used to analyze historical sales data and other relevant factors, such as weather patterns, to generate more accurate demand forecasts. This can help businesses optimize their inventory levels and reduce waste, while automation workflows can be used to automatically adjust inventory levels and reorder products when necessary.
Leaders in supply management should define success criteria early.
Operational readiness
Supply management practitioners need to ensure they have the maturity level required to adopt Generative AI. This includes assessing if underlying systems are scalable, secure, and can integrate with Generative AI and automation capabilities. In addition, explore if you have the necessary data management processes in place.
There’s a clear need to establish governance and change management processes to ensure that automation and Generative AI initiatives align with business goals and that stakeholders are prepared for the changes. This includes establishing roles and responsibilities, creating communication plans, and providing training and support to employees.
Governance, risk, and compliance
This pillar focuses on identifying relevant regulations and standards and managing compliance effectively. A best practice is to establish clear policies and procedures for managing data, ensuring that the technology is compliant with relevant regulations, and creating contingency plans in case of unexpected events.
Summary
Professionals in supply management don't have the option to watch and wait for their competitors to outperform them. The pillars outlined here are important to understand as you begin to accelerate business outcomes with AI.
UiPath has long been at the forefront of AI-powered automation. Join us on October 5, right here on ISM® as we sit down with ISM member and supply management trail blazer, Max Ioffe, Global Intelligent Automation Leader of WESCO and Siddharth Shah, Automation Leader – International Trade Group of Intel to discuss practical use cases to Streamline supply management with AI and Intelligent automation.
