How RSM’s AI Strategies Help Supply Chain Leaders Stay Ahead
RSM offers key insights for leaders as AI adoption grows in the middle market, but supply chain integration lags RSM offers key insights for leaders looking to enhance efficiency and resilience.
Artificial intelligence is becoming an essential tool for businesses looking to improve efficiency and build resilience - and the middle market is no exception.
Supply chain leaders, in particular, are under pressure to develop AI strategies that optimise operations, reduce costs and increase visibility across their networks.
However, many companies struggle to fully integrate AI into their supply chains, despite its potential to reshape processes and drive meaningful value.
RSM works with middle market firms worldwide to help them manage these changes. With a presence in more than 120 countries, it provides insights and solutions tailored to businesses seeking to navigate today’s complex global landscape.
The RSM Middle Market AI Survey 2024: US and Canada highlights AI’s growing role, with 78% of executives using AI in some capacity.
However, only 20% believe they have meaningfully integrated it and 67% of those using generative AI say they need external help to maximise its potential.
While AI is widely used in areas such as data analytics (54%), IT (54%) and customer service (46%), supply chain management lags behind, with just 15% reporting AI adoption in this function.
This gap presents an opportunity for supply chain officers to harness AI’s capabilities to improve forecasting, logistics, tracking and overall efficiency.
During RSM’s webinar, AI in the supply chain: Transforming efficiency, resilience and visibility, experts Steve Biskie, George Casey and Jake Winquist shared practical strategies for leveraging AI in supply chain management.
Building an effective AI strategy for supply chains
The role of AI in supply chains is shifting from an advantage to a necessity. Rising trade uncertainties, increasing costs and complex global networks make AI a powerful tool for improving decision-making and agility.
However, successful AI adoption requires a clear vision from leadership.
“Do you know what your employees and colleagues are using from an AI perspective?” asks Jake.
"What is really impactful and what do we want to provide across the board? We need to be talking about this to get to a formalised vision and plan for AI.”
- Margin improvement: Using AI for cost efficiencies, SKU rationalisation, and optimised logistics and distribution strategies.
- Working capital reduction: Aligning supply chain strategies with market trends and corporate objectives for better cash management.
- Business scaling: Enhancing inventory management with predictive forecasting and leveraging digital twins to simulate operations.
- Risk reduction: Identifying potential disruptions, from production defects to supplier vulnerabilities, to improve resilience.
- Workforce optimisation: Automating manual tasks to allow employees to focus on strategic initiatives, improving productivity and retention.
AI’s effectiveness depends on data quality. Supply chain leaders must prioritise data governance to ensure accuracy and reliability.
“The fundamental data structure is critical to set yourself up for success,” adds Jake.
“Don’t assume that AI can just come in and fix all of the problematic data that you may be used to. Instead, you need to focus on data cleanliness and the due diligence process up front.”
George adds: “In the last couple of years, we’ve seen a lot of invention and new models and capabilities being released.
“A lot of companies have been in that proof-of-concept, proof-of-value phase and kicking the tires by starting with a single use case. Over the next year to 18 months, we are going to see some of the results of those projects.”
Balancing AI opportunities with risks
AI adoption brings both opportunities and challenges, requiring a structured approach to implementation. Supply chain officers must consider struggles such as AI misinformation, risk management, governance models and wider readiness.
Beyond these challenges, AI poses broader risks, including security breaches, reputational damage, legal liability and ethical concerns. Without a robust governance model, companies may struggle with compliance and accountability.
“Now that we’ve identified the risks, our next thought is, what are we going to do about them and what are the strategies?” continues Steve.
“So when we talk about working with AI and helping protect organisations, it’s all about having a good governance model.”
How AI strengthens supply chain operations
Despite the challenges, AI has the power to transform supply chains by enhancing forecasting, automation and operational efficiency.
Key applications include predictive analysis, workflow automation and generative and autonomous AI. Whilst common supply chain examples of AI-driven improvements include precision demand forecasting, intelligent freight cost allocation, dynamic logistics planning and virtual customer assistants.
While integrating AI can seem overwhelming, its benefits for supply chain resilience, efficiency and innovation are undeniable.
Businesses that invest in AI today will be better positioned to navigate future challenges and stay competitive in an increasingly data-driven world.
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