Roundtable insights: AI in supply chain and logistics: From hype to readiness

For the third time this year, NashTech, in partnership with CILT UK, hosted a Supply Chain and Logistics Roundtable in Birmingham. Led by NashTech’s Chris Weston, Thomas Pointer, Robert Stenzel, and Stuart Simpson, and CILT’s Richard Atkinson CBE, the event fostered an open discussion on the realities of AI adoption in logistics, as well as to identify practical AI use cases, understand readiness requirements, and take actionable ideas back to their businesses. 

The elephant in the room, AI hype versus practical implementation 

Chris highlighted how industry predictions, such as Gartner’s claim that over 50% of supply chain organisations would use machine learning for decision-making by 2026, have not materialised. Despite bold forecasts, many businesses remain in the early stages of automation, constrained by legacy systems, data silos, and cultural resistance. 

During his presentation, Chris offered a refreshingly honest perspective on the current state of AI in logistics. He tackled the AI ‘hype versus reality’ conundrum head-on, noting that while ambitious promises about AI are commonplace and often fuel pressure at board level, the pace of practical adoption remains slower than many anticipate. Chris highlighted a number of persistent challenges facing the sector, such as the complexities of integrating legacy systems, the ongoing struggle with poor data quality and fragmented IT landscapes, and the frequently underestimated effort involved in effective change management. 

He went on to outline some of the key readiness criteria necessary for meaningful progress. These include:  

• the need for technology modernisation and data consolidation,  
• the need to ensure organisational alignment and robust governance frameworks, and  
• the crucial question: are the risks associated with new technologies and processes being properly understood and managed? 

Importantly, Chris reminded everyone that successful transformation extends far beyond installing new technology. It requires a holistic approach that brings together people, processes, and governance. Without genuine cultural buy-in and clear strategic alignment, even the most sophisticated AI solutions are unlikely to deliver lasting value. 

Moving from theory to practice 

The second half of the event was an interactive workshop designed to move from theory to practice. Participants were split into two groups and guided through three stages: 

1. Problem definition: 
   Identify real operational bottlenecks or inefficiency issues that impact revenue, cost, or customer satisfaction. 
2. Ideation: 
   Generate potential solutions, considering AI alongside other technologies like RPA and IoT. The focus was on pragmatic ideas rather than chasing trends. 
3. Readiness reflection: 
   Assess organisational blockers such as legacy systems, inconsistent processes, cultural resistance, and data quality. Groups then outlined action plans to overcome these challenges within 12–18 months. 

The outcome 

Group 1: A digital finance agent for risk and debt management 

Core problem: 

Businesses face bad debt, lack of real-time visibility, and manual manipulation of financial data. Often, operational data lags finance data quality. 

Proposed solution: 

• An AI-powered dashboard showing aged debt and customer risk status. 

Agentic AI actions: 

• Block bookings or deliveries for high-risk customers 
• Apply liens on goods when risk thresholds are met 

Data sources: 

• Company accounts, social media, news feeds, credit agencies, director screening, internal finance data, payment history 

Benefits and extensions: 

• Unified data platform across group companies for holistic risk view 
• Predictive analytics for future planning (e.g., capacity needs after major contracts) 
• Empower decision-makers with real-time insights while retaining human judgment 

Challenges: 

• Data readiness and integration 
• Stakeholder buy-in and cultural acceptance 
• Cost justification: calculate the cost of bad debt vs. investment in solution 

Group 2: Proof of delivery accuracy and real-time visibility 

Core problem: 

• Inconsistent, inaccurate proof of delivery data due to manual entry and subcontractor processes 
• Blurry photos, unclear geolocation, and delayed updates impact KPIs and customer contracts. 

Proposed solution: 

• AI-enhanced photo clarity and geolocation tagging 
• Real-time POD data flow accessible to customer service and clients 
• Reduce manual intervention and improve accountability 

Additional ideas: 

• Predictive vs. Preventative logistics:
  - Predictive - anticipate issues before they occur
  - Preventative - intervene when known issues arise 

• AI assistants for efficiency and backlog reduction 
• Cloud connectivity to ensure data transmission reliability 

Strategic considerations: 

• Align solutions with business plans 
• Balance investment vs. efficiency gains 
• Cultural shift toward trust and governance 

The roundtable discussions made one thing clear, successful AI adoption in supply chain and logistics goes beyond technology. Organisational culture and governance are critical to ensure buy-in and sustain change.  

Both groups stressed the importance of real-time data and automation, as timely insights drive smarter decisions. AI enablement, whether through predictive analytics or operational assistance, offers transformative potential—but only if readiness challenges are addressed.  

Finally, the risk of doing nothing emerged as a powerful motivator; the cost of inaction can far outweigh the investment required to modernise systems and processes. 

In short, AI is not just a tool, it’s a strategic capability that demands alignment across people, processes, and technology. 

Interested in joining the discussion? 

Register your interest here to be informed about the next Supply Chain and Logistics event.