Bridging the gap between AI demand & talent proficiency in supply chain, manufacturing, and logistics

AI is quickly becoming the key to greater productivity, operational savings, and competitive differentiation within supply chain, manufacturing, and logistics.

It’s not surprising, given that companies effectively using AI in supply chain functions report a 15% improvement in logistics efficiency and a 35% reduction in inventory-related costs. The promise of such impressive results is driving organizations to pursue AI investments at an accelerated rate:

• 75% of logistics firms plan to significantly increase AI investment over the next 2 years.
• 50% of supply chain teams are piloting Generative AI (Gen AI).
• Talent development is the #1 initiative for supply chain leaders.

But lack of skilled AI talent is holding organizations back. 

• Demand for AI skills has grown 21% annually since 2019.
• 44% of supply chain execs cite lack of in-house AI expertise as a key barrier to Gen AI implementation.
• Talent development is the initiative supply chain leaders feel least confident they can achieve.

If investment in AI for manufacturing and logistics is one of your top priorities going into 2026 planning, ensuring you have the skilled talent to optimize it is crucial to success. 

The following tips and advice can help you get creative in sourcing talent, and ensure you’re looking for the right skills to support the AI tools most relevant to supply chain, manufacturing, and logistics.

5 Actionable Steps for Closing the AI Talent Gap

AI evolution is outpacing AI proficiency within most organizations. Yet, as market uncertainty and regulatory challenges continue to impact revenue, AI-driven logistics and operational gains are more crucial than ever.

To stay in lockstep with AI demands (and stay competitive in the industry), here are five ways you can begin to shrink the AI skills gap:

1. Upskill your existing workforce. Invest in AI-centered training programs, certifications, and courses to support AI upskilling for current employees. Upskilling or reskilling in-house talent is often a cost-effective means of backfilling open roles requiring machine learning and digital skills, particularly those where a combination of business prowess and digital proficiency is needed. Consider designing post-training projects for workers to gain practical experience applying newly learned AI skills.
    
2. Embrace outside partnerships. Local colleges and industry technical schools can be a great source of freshly skilled AI talent. Consider connecting with these institutions by creating internships or co-op programs that allow students to hit the ground running post-graduation. Aligning with government programs is another way to build a talent pipeline to support ongoing AI demands.  
    
3. Use AI to build AI skills. Leverage AI tools like chatbots and digital assistants to inject training and learning opportunities into existing workflows, giving workers a platform for real-time knowledge gains and a means to extend skills aptitude by putting theory into practice. Encourage employees to seek answers, solicit advice, and use the technology to refine their understanding as they progress.  
    
4. Swim in a bigger talent pool. When it comes to finding AI skills, especially those that are niche or emerging, it’s helpful to cast a wider candidate net. This often calls for rethinking your recruiting strategy. One way is to expand your search beyond local talent by working with a nearshore talent partner who can find top contract-based tech talent as skilled and flexible as U.S. counterparts. Another way is to consider candidates with transferrable or “adjacent” skills from other industries. 
    
5. Keep pace with AI evolution. To determine what AI skills you need, it helps to keep track of the AI tools you have in place and how effectively your current workforce is using them (if they’re even using them). Establishing training measures and KPIs around certifications and operational gains is a good place to start. To guide future AI investment, It also helps to stay on top of AI trends in the industry, how AI is being used by your competitors, and how your changing business needs impact AI adoption.

Pursue In-Demand AI Skills: Where to Focus Your Search

Within supply chain, manufacturing, and logistics, the most in-demand AI skills we’re seeing—based on both current trends and client requests—are related to machine learning, data, modeling, and agentic agents. Specifically, those around visualization and interpretation of datasets for forecasting, predictive modeling for supply chain optimization, and the creation and support of large language models (LLMs). The chart below highlights the five application “buckets” industry organizations are investing in now from a skills perspective, which are consistently delivering the biggest downstream return.

 

"Many organizations focus first on going wide in terms of onboarding AI talent across the enterprise. However, we’ve found that for companies in the supply chain space, it’s more effective to target a smaller range of core applications and then go deep within those functional areas. This caters better to the unique workflows and co-dependencies within the industry, and helps create the strongest foundational AI backbone from which all other technologies and applications can flourish."-Todd Workman, CodeRoad GM & Vice President, Global Services

It’s Time to Take Action on AI

Ready to permanently close your AI skills gap to propel operational excellence? We partner with organizations in this sector every day to deliver AI-ready talent that can hit the ground running, often in record time and under budget. Contact CodeRoad today to partner with a talent provider who can help you put AI ambitions into action.