Why it’s time to accept artificial intelligence reality — and realize its benefits

Supply chain leaders perhaps can be excused for approaching artificial intelligence (AI) with a dose of skepticism. After all of the disproportionate buzz and attention around other new technologies like non-fungible tokens and blockchain over the past few years, it seems reasonable. But generative AI (GenAI) is different.

Off-the-charts early adoption by consumers has led to a tidal wave of business integrations and it’s happening across almost every industry. Now AI is at the doorstep of supply chain operations, an industry traditionally slower to adopt and adapt to change, promising efficiencies, insights and optimization.

As your competition and suppliers rush to embrace GenAI tools, and leadership adds pressure to adopt, it is worth pausing for a moment to create a thoughtful strategy that will help you better understand AI’s potential and realize real benefits beyond the buzz. There are four key issues to know.

Adoption is going to happen much more quickly — and you must be ready

Typically, supply chain organizations measure transformation in years not months. Managing issues like adapting legacy infrastructures and tech stacks, working with inconsistent data collection both internally and externally, and the labor intensive and vast cost of transformation programs can be a lengthy process.

But GenAI promises neither this enormous lift or capital expenditure, and depending on an organization’s level of maturity, it can be designed as a custom integration or quickly and inexpensively adopted with ready-to-embed tools that can help with quick wins.

Prepare for the scale of opportunity

Analysts predict the global AI market will reach $190.61 billion by 2025. And with more than $1 trillion in spend, the logistics industry has massive potential to realize huge savings and efficiencies if integration is managed well. In an EY survey of executives, 65% of CEOs said they recognize the potential of AI to drive efficiency and better outcomes, but even more — 67% — said more work is needed in regulations to guarantee security, privacy and ethical use.

With the rate of growth far outstripping current oversights and both Europe and the US introducing legislation, it’s incumbent on any supply chain leader to put into place the policies and governance that will soon be mandatory. This starts with understanding and managing AI-related risk that comes in many forms: from third-party suppliers and carriers using AI in their businesses to issues over privacy and exposure of sensitive data, or inherent bias in AI algorithms and learning models.

Prioritize leading supply chain use cases

GenAI can promise a lot of things, but it’s important to understand and prioritize the actual use cases that can benefit your business. In planning, AI-powered tools can optimize inventory by analyzing historical demand, supply data and predict future trends. Region-specific demand forecasting can align production with demand spikes caused by events, festivals and holidays. AI helps reduce the bullwhip effect — a pain point in supply chains — by leveraging demand and supply data collected from customers, distributors, manufacturers and suppliers to reduce both stockouts and overstock situations.

In procurement, AI-enabled supplier relationship management software can track and analyze pricing, purchase history, sustainability and overall performance to improve supplier evaluation, selection and reliability; or use algorithms and marketplace data to rapidly accelerate and streamline the purchase process.

AI algorithms can rapidly create manufacturing protypes and iterations to test product variables and accelerate the time to market. And in logistics, AI-controlled GPS and radio-frequency identification technologies can analyze route disruption, traffic and road closures to reduce the miles traveled, lower emissions, and automate both dispatching and create end-to-end real-time visibility over goods in transit.

Start with a responsible AI framework

Supply chain executives looking to integrate GenAI in an effective, ethical and efficient manner should follow a responsible AI framework based on these six principles:

1. Unbiased design – Identify and address inherent biases that may arise from the composition of your development team, data practices and training methods.
2. Resilience – Guarantee the security of data used by AI system components and the algorithm to safeguard against attacks and breaches.
3. Explainable – Provide complete clarity on the AI’s learning methods and decision criteria. Aspects should be thoroughly understood and documented.
4. Transparency – Provide appropriate notification to users, clearly identifying their interaction with an AI system and allowing them to select their level of engagement.
5. Consistent performance – Ensure that the AI’s outcomes align with company stakeholder expectations.
6. Training and education – Employ proper training and communication for your workforce for easy deployment and operations.

Takeaways for supply chain leaders

The pace, scale and adoption of GenAI tools into supply chain operations aren’t like anything the industry has seen to date. Supply chain executives must accelerate adoption within their organizations and networks to remain competitive — but take caution and put the right governance, due diligence and policies into place for success over the long term. That journey must start today.

The Establishing an AI framework content was featured in the January 2024 issue of "Inbound Logistics". The views reflected in this article are the views of the author and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.