How CSCOs can make gen AI honor its productivity promise in 2025

 A practical guide for supply chain leaders to bridge the gap between AI hype and operational reality through strategic implementation and team empowerment.

2024 marked the year of Generative AI in supply chains. From automated route planning to intelligent forecasting, from instant documentation to real-time decision support – supply chain conferences, webinars, and vendor presentations are buzzing with AI's transformative potential. 

Forward-thinking CSCOs are already deep into implementation. They're deploying AI-powered solutions across freight procurement, network planning, and operations. Teams are experimenting with language models to speed up documentation, testing AI insights for better capacity planning, and rolling out intelligent algorithms for optimization. But here's the catch: Behind the sleek demos and impressive algorithms, teams are grappling with unexpected challenges. AI outputs need extensive verification, automated processes require constant monitoring, and those promised efficiency gains remain frustratingly elusive.

Welcome to the great AI reality check of 2025. This isn't about potential anymore – it's about the growing chasm between promises and shop floor realities. That chasm is swallowing both budgets and careers, forcing CSCOs to face an uncomfortable truth: throwing AI at supply chain problems isn't the same as solving them. But here's the real question keeping CSCOs up at night: How do you turn this story around? How do you make generative AI deliver on its productivity promises without burning more resources? The answer lies in understanding where the hype came up short – and, more importantly, how to make it right.

The productivity gap: Understanding Gen AI’s current challenge

Recent studies paint a startling picture: 80% of workers using generative AI report increased workloads and decreased productivity. The situation in supply chain operations is no different. Teams are spending a significant portion of their time verifying and correcting AI outputs stemming from poor data quality, eating into crucial hours needed for strategic decision-making. In a function where every minute counts and multiple parts move simultaneously, this creates a cascading problem.
The problem goes beyond just time metrics. Generative AI is not a great problem solver when it approaches core business problems like supply chain, for example. Supply chains thrive on collaboration – between teams, partners, and systems. However, current AI implementations are creating isolated pockets of efficiency that disrupt this collaborative flow. It's like optimizing individual musicians without considering how the orchestra plays together.

When AI handles purchase orders without understanding supplier relationships, when it generates shipping documentation without considering regional compliance nuances, or when it optimizes routes without factoring in real-world constraints – that's when the productivity gap widens. Every "optimized" process becomes another silo, and every AI-driven efficiency creates a new integration challenge. The harsh truth? Individual task optimization isn't translating into team productivity. And in the interconnected world of supply chains, that's where the real value lives.

Course correction: A three-pronged approach to deliver Gen AI value

The gap between AI's promise and reality isn't insurmountable. For CSCOs looking to realize the true potential of their AI investments, a structured approach focused on people, processes, and performance can turn the tide. Here's the roadmap to make Gen AI deliver on its productivity promises.

Step 1: Culture transformation through experimentation

It should come as a no-brainer to leadership that many employees don’t trust AI or its output. Thus, structured experimentation forms the foundation for successful AI integration. It's about creating controlled environments where teams can safely test and refine AI applications without disrupting core operations. This approach helps organizations identify and resolve integration challenges early while building team confidence in AI capabilities.

Consider your freight procurement team . Every day, they analyze market rates across hundreds of lanes, negotiating with carriers to secure optimal pricing and making critical decisions about carrier allocation to maintain service levels during peak season. While implementing AI for rate prediction and carrier selection, the forecasts may look impeccable in testing, but the reality is far different: analysts with decades of market experience know that AI may not account for seasonal capacity crunches or the reliability patterns of specific carriers on key lanes. This is where structured experimentation becomes crucial. Instead of rolling out AI tools across your entire procurement operation, create pilot lanes for implementing AI where experienced analysts and AI work together to refine the system. Shippers can take this approach – Designate certain trade lanes as "AI learning corridors" where procurement analysts actively document when AI rate predictions misalign with their market knowledge. These aren't just corrections; they are learning opportunities that help tune the AI to understand market nuances.

The same approach works in network planning. The planning analysts balance multiple variables daily – from warehouse capacity constraints and transportation costs to delivery windows and inventory levels across different nodes. Rather than immediately implementing AI-driven network optimization across all regions, strategic organizations must create experimental zones. Here, logistics managers can test how AI suggestions for lane assignments and consolidation opportunities align with existing carrier relationships and service requirements. When friction points emerge – like AI suggesting carrier changes that conflict with established service level agreements – planning teams can resolve these issues before full-scale deployment.

Step 2: Strategic talent evolution

As AI reshapes logistics operations, organizations need a deliberate approach to evolving their talent including upskilling and reskilling existing workforce. This means creating new roles, developing hybrid skill sets, and building career paths that value both technological proficiency and operational expertise. Success requires careful orchestration of upskilling programs that maintain core competencies while building new capabilities.

For example, your veteran logistics manager understands the subtle patterns of peak season capacity fluctuations. Your data-savvy demand planner may excel at AI tools but miss the nuances of how certain customers' ordering patterns affect network planning. This is where strategic talent evolution becomes vital. Start by restructuring your transportation planning teams to create "AI-Enhanced Planning Units," where experienced logistics managers work alongside analysts to refine AI forecasting models. Together, they can instruct the system about market dynamics while learning to leverage AI's analytical capabilities for better network optimization.

Forward-thinking organizations are creating roles that understand both logistics planning complexities and AI capabilities. These aren't just analysts; they're integration specialists who can translate between AI recommendations and operational realities. They help refine how AI tools interact with procurement strategies, capacity planning, and customer service requirements.

Step 3: Performance management reimagined

Traditional logistics metrics fail to capture AI's true impact on operations and require smart KPIs that balance quantitative efficiency gains with qualitative improvement indicators, focusing on how effectively AI tools integrate with existing workflows and decision-making processes.

If one implements AI in their freight procurement process, the instinct is to track basic metrics: the number of quotes processed and the response time. But, these metrics miss crucial aspects of team performance. Instead, measure how accurately AI-generated quotes need revision before becoming confirmed bookings. The same thinking applies to booking operations. Rather than just measuring quote turnaround times, track "Quote Accuracy Alignment" – how often AI-generated quotes match final booked rates. Monitor "Exception Resolution Time" – how quickly teams can resolve discrepancies between AI predictions and actual market rates. These metrics help identify where AI tools need refinement and where human expertise adds the most value.

Success tracking must focus on collaborative outcomes rather than individual optimization. For instance, when measuring AI's impact on capacity planning, leading organizations don't just track forecast accuracy. They measure how effectively their planning teams use AI insights to make better decisions about space allocation and carrier selection. Measuring AI's impact requires looking beyond traditional efficiency metrics to understand how well the technology enhances your teams' decision-making capabilities. For logistics operations, this means developing metrics that capture both the speed and quality of AI-augmented processes while ensuring nothing falls through the cracks in compliance and accuracy.

Making AI work: From hype to reality

Let's cut to the chase: Your teams are drowning in AI-generated busy work. Every day, the gap between promises and operational reality grows wider. But here's the truth that vendors won't tell you: The gap between AI's promise and reality isn't a technology problem – it's an implementation problem. And you, as a CSCO, hold the key to fixing it.

Generative AI can rethink the supply chain for CSCOs. But the solution isn't buying more AI tools or rolling out more pilots that go nowhere. It's about fundamentally rethinking how your organization approaches AI adoption. Start small but start smart. Create those experimental zones where your veteran logistics managers can teach AI about real-world constraints. Build those hybrid teams where market expertise meets technological capability. Most importantly, measure what matters – not just how fast AI works but how well it works within your operations. 

The vendor pitches won't stop. The demos will keep getting slicker. The ROI calculations will look even more impressive. But you now have a choice: Continue chasing the AI hype or build something that works. The path forward is clear. It's not about having the most sophisticated AI – it's about having the most effectively integrated AI. Your competitors are already making their choice. What's yours?