AI Playbook | Transforming Supply ChAIn Into Your Competitive Advantage

Discover how AI is transforming global supply chains from reactive operations into predictive, data-driven ecosystems. This playbook explores practical applications of AI across logistics, finance, and planning — showing how smarter technology, better data, and human expertise combine to turn supply chains into a true competitive advantage.

Contents

1. Executive summary
2. Introduction: Is your supply chain falling behind?
3. AI explained: How AI solves real supply chain challenges
4. Five practical steps to using AI successfully
5. Checklist: AI partner questions that protect your data
6. Prove ROI and gain executive buy-in
7. How to choose the right AI ally
8. Real AI solutions delivering results today
9. Conclusion: Turning AI into advantage
10. FAQs

Executive summary

Why AI is your supply chain advantage

Supply chains have never been easy. Uncertainty, rising costs, and constant disruption are now the baseline, not the exception. Global shocks like the Suez Canal blockage and Red Sea reroutes add to everyday challenges: missed cargo ready dates, late trucks, and stretched teams. Leaders are asked to do more with less—maintain precision in a volatile market, cut costs without cutting corners, and keep teams motivated while balancing spreadsheets and inboxes.

Predictability is gone, but precision is more critical than ever. Companies clinging to old processes risk falling behind, while competitors leveraging technology can plan smarter, move faster, and execute more efficiently.

AI adoption is accelerating, yet only 9% of organisations use AI widely across their supply chains. This shows a clear opportunity for strategic, high-impact deployment, rather than shallow experimentation.

At Zencargo, we see a turning point: AI closes visibility gaps, converts siloed data into actionable insights, and transforms reactive firefighting into proactive planning. This playbook explains why AI matters, where it delivers measurable value today for every stakeholder within the supply chain, and how companies can integrate it effectively, aligning supply chain performance with wider business KPIs.

Introduction: Is your supply chain falling behind?

Today, businesses face unprecedented challenges: rising costs, market volatility, geopolitical disruption, scattered data, and outdated manual processes, to name a few. As a result, teams are often stuck in firefighting mode, struggling to maintain visibility and precision, two areas in which AI can drive significant improvement. However, while momentum in this space exists, progress is slow; only 23% of supply chain leaders report having a formal AI strategy, while McKinsey finds that 78% of organisations use AI in at least one function.

The reason for this is clear. Traditionally, supply chains are viewed as a back-office initiative. Front-of-house teams (such as Merchandising, Sales and Marketing) are integrating  AI-powered tools into workflows to ease repetitive, manual tasks. Logistics teams are missing out on a momentous opportunity; the ability to foresee challenges – to turn hindsight into foresight –  presents a chance to capitalise on supply chains as a competitive advantage.

There is no denying that product and brand are vital; however, getting the right product in front of customers at the right time gives businesses an edge. And we aren’t going to get there while we’re stuck in spreadsheets, buried beneath inconsistent documentation, and following up on countless emails.

Let’s explore how AI can transform reactive operations to predictive, precise, and efficient systems—and help leaders make smarter, faster decisions.

AI explained: How AI solves real supply chain challenges

However, the market remains highly fragmented, relying heavily on manual processes, phone calls, emails, and disparate systems. This creates inefficiencies, such as underutilised shipping capacity and administrative costs that can account for up to a fifth of the cost of moving goods.

AI moves beyond simple digitisation, enabling predictive, prescriptive, and autonomous capabilities that power data-driven decision-making and operational execution. The AI in logistics market is projected to grow from $26.35 billion in 2025 to $707.75 billion by 2034—a CAGR of 44.4%—illustrating massive opportunity for innovation. Yet, adoption is not yet universal: while 70% of logistics companies report using AI solutions, many struggle with integration, data quality, and workforce readiness. This underscores the need for strategic deployment rather than broad, shallow experimentation.

Traditional logistics faces persistent challenges across industries. AI helps address these by providing visibility, improving coordination, and delivering predictive insights. These capabilities allow teams to anticipate disruptions, optimise operations, and make smarter day-to-day decisions.

1. Predict disruptions before they happen

Challenge: Traditional supply chains are reactive, managing crises as they occur. Delays are often discovered too late, disrupting planning.

Solution: AI platforms analyse vast datasets—geopolitics, weather, port congestion, carrier performance, customs rules, and demand signals—to predict disruptions, delays, or cost changes with unprecedented accuracy. This includes forecasting port delays weeks in advance, predicting demand shifts with high precision, and identifying optimal shipping windows.

• Inbound teams use predictive ETAs to secure earlier cargo readiness and adjust bookings before congestion hits.
  
• Finance teams can forecast landed costs more accurately by modelling tariff or fuel-related shifts.
  
• Supply chain directors gain early visibility of macro-level risks, enabling faster scenario planning and inventory reallocation.

2. Optimise routes and carriers instantly

Challenge: Optimising routes and carrier selection is complex, involving numerous factors that are difficult for people to analyse at scale.

Solution: AI can dynamically orchestrate entire networks, instantly analysing millions of permutations for routing, carrier selection, consolidation, and mode of transport. It considers factors beyond just cost or speed, such as load size, capacity, sustainability, risk, and reliability.

• Inbound teams benefit from smarter consolidations and load optimisation, reducing demurrage and detention.
  
• Finance teams gain from automated cost-to-serve analysis, improving budgeting and contract management.
  
• Supply chain directors can re-model transport networks instantly to evaluate the impact of geopolitical or capacity shifts.

3. See your entire supply chain clearly

Challenge: Fragmented data from multiple systems leads to siloed information and no holistic view, hindering accurate planning and real-time decision-making.

Solution: AI unifies fragmented data from ERP, TMS, carrier portals, IoT sensors, and customs declarations. It creates a single intelligent view of the supply chain, highlighting anomalies and insights beyond simple tracking. This includes SKU-level visibility across complex consolidations and insights into potential customs bottlenecks.

• Inbound teams see real-time supplier and shipment status, with automatic CRD updates and exception alerts.
  
• Finance teams access consistent landed-cost data linked to shipment milestones, improving accrual accuracy.
  
• Supply chain directors gain SKU-level, multimodal visibility to manage inventory globally and prioritise at-risk flows.

4. Cut costs and boost efficiency

Challenge: Manual processes, constant chasing for updates, and human error are time-consuming and costly.

Solution:  AI changes cost structures by automating decision-making and execution. It significantly reduces manual labour, minimises errors, and optimises resource utilisation (e.g., container space, truck mileage, warehouse capacity). AI can also automate repetitive tasks like documentation generation, data entry, and tracking updates, and free up teams to focus on complex edge cases.

• Inbound teams automate document parsing and supplier follow-ups, saving hours of manual admin.
  
• Finance teams automate invoice validation and classification, improving cash-flow forecasting and compliance accuracy.
  
• Supply chain directors use AI analytics to benchmark performance and track savings across carriers, modes, and regions.
  

AI is no longer a distant ambition for supply chains—it is already solving the industry’s most persistent challenges, from visibility gaps to rising costs. By shifting operations from reactive to predictive, it enables leaders to make smarter, faster, and more resilient decisions. The next step is not asking whether AI belongs in the supply chain, but how to deploy it at scale to turn disruption into competitive advantage.

Five practical steps to using AI successfully

An AI-ready supply chain is not built overnight. Research suggests that executing AI across your operations should start with experimentation. Although only 9% of companies use AI widely across their supply chains, leading organisations — the early adopters — are already realising disproportionate benefits, suggesting a staged test-and-scale approach is prudent. 

There are a few areas to consider when strategising how best to implement AI-powered tools to empower your workforce:

1. Data first, AI second: The effectiveness of AI hinges on high-quality data. Many forwarders struggle with AI integration due to a lack of full digitisation and fragmented data.
2. Ensure interoperability: AI capabilities must integrate well  with existing systems. While full interoperability can be elusive, designing trust-worthy systems capable of  relaying messages between different chains is critical.
3. Choose the right partner: Selecting a technology and freight forwarding partner who truly understands your data, operations, and industry context is vital. The right partner doesn’t just deliver tools—they co-design solutions aligned with your business goals, ensuring that AI deployment creates measurable value, not additional complexity. Look for proven results, transparent governance, and a partner capable of integrating human expertise with advanced automation.
4. Leverage human-in-the-loop intelligence: AI is a powerful co-pilot, not a replacement for human expertise – at least, not until it has been heavily tested and observed. While AI provides intelligent recommendations, human judgement remains central to complex problem-solving and strategic insights.
5. Ensure data privacy, security, and explainability in AI: For supply chain leaders, trust is the cornerstone of AI adoption. It is critical to understand how data is stored, who has access, and whether it is ever shared externally. Beyond security, explainability is essential. AI outputs must be transparent, with prompts and activities logged so decisions can be traced and validated.
6. Build an adaptive workforce: The advent of AI presents huge potential for how teams operate but requires upskilling and training on how to effectively interact with AI systems and interpret AI-generated insights. Accenture estimates generative AI could impact approximately 43% of working hours across the entire supply chain function (either automating work or materially augmenting employee activity) — underscoring the need for reskilling, role redesign and change management, not headcount cuts.

Checklist: AI partner questions that protect your data 

Data security and privacy

• How is my data encrypted both in transit and at rest?
• Who within your organisation has access to my data, and how is this access controlled?
• Is my data ever shared with third parties, including AI model providers, for training purposes?
• Do you comply with GDPR (or other relevant data protection regulations) across all regions you operate in?

Data ownership and control

• Do I retain full ownership of my supply chain data?
• Can I request complete data deletion if I terminate the service?
• How do you ensure my sensitive operational data remains confidential?

Explainability and transparency

• Are all AI outputs traceable and explainable?
• Do you log and document prompts, system flows, and decision points for auditability?
• How can I validate or challenge an AI-generated recommendation (e.g., ETAs, routing decisions)?

Reliability and accountability

• What governance framework is in place to handle AI errors?
• How do you ensure accountability for AI-driven mistakes (e.g., inaccurate ETAs, missed compliance steps)?
• Do you provide human oversight (“human-in-the-loop”) for critical supply chain decisions?

Future-proofing and trust

• How are AI models monitored and updated to remain accurate as market conditions change?
• What safeguards are in place to prevent “hallucinations” or unreliable AI outputs from impacting my operations?
• How do you communicate when the system makes an error, and what remediation process is in place?

Download the checklist.

Prove ROI and gain executive buy-in

Understanding the challenges and opportunities for supply chain leaders is the first step. The next is positioning AI as a driver of measurable return on investment — not as a technology experiment, but as a strategic enabler of resilience, efficiency, and profitability.

AI is no longer a buzzword. It’s a boardroom discussion point, with clear financial implications. In the supply chain, AI drives measurable value across three key areas: 

• Cost reduction
• Precision
• Efficiency

Gartner research shows that top-performing supply chain organisations use AI and machine learning at roughly twice the rate of lower performers for critical processes like supply planning, logistics, and S&OP,  illustrating that adoption directly correlates with operational outperformance. Below we explore how to identify tangible business benefits across the supply chain, and secure internal alignment for AI adoption.

Reducing operational costs

For Supply Chain Directors

Strategic benefit: Turning logistics into a competitive advantage.

AI empowers Supply Chain Directors to position their operations as a profit centre, not a cost base. Predictive insights allow for scenario planning that prevents costly disruption, while automated network modelling supports long-term decisions about sourcing, routing, and capacity.

• Business benefit: Improved on-time performance, reduced cost-to-serve, and enhanced customer experience through fewer disruptions.
• How to gain buy-in: Link AI’s predictive capabilities directly to resilience and business continuity goals, showing how earlier visibility prevents revenue loss from missed deliveries or stockouts. Demonstrating AI’s contribution to overall EBITDA improvement strengthens the case at board level.
  

For Inbound Managers/Directors

Strategic benefit: Enhancing executional visibility and efficiency.

Inbound teams can use AI to eliminate the blind spots that cause delays, demurrage, and manual admin. Automated ETA prediction, supplier updates, and CRD monitoring create a continuous flow of real-time insight, enabling proactive action before delays cascade.

• Business benefit: Time saved from manual tracking and follow-up; reduced detention and demurrage fees; improved utilisation of container space and carrier performance.
• How to gain buy-in: Quantify time savings and operational efficiencies, e.g., “We can reallocate 20% of our team’s time from chasing updates to strategic supplier engagement.” Connect AI adoption to measurable productivity gains and improved service levels.
  

For Buying and Merchandising leaders

Strategic benefit: Protecting product availability and speed-to-market.

AI ensures product readiness aligns with promotional calendars and demand peaks. By providing SKU-level visibility, it supports smarter decision-making around order allocation, supplier diversification, and inventory prioritisation.

• Business benefit: Fewer missed promotions, reduced overstocking, and improved supplier collaboration through automated communication and exception handling.
• How to gain buy-in: Frame AI as a tool for protecting revenue, not just efficiency. Show how predictive visibility helps guarantee key product launches and sales events, linking logistics precision directly to commercial performance.
  

For Finance Directors/CFOs

Strategic benefit: Driving cost accuracy, cash flow control, and risk mitigation.

Finance teams gain full visibility into landed costs, in-transit inventory, and supplier performance data, improving forecasting and reducing accrual inaccuracies. Predictive cost modelling supports better budgeting and cash flow planning.

• Business benefit: Lower cost-to-serve, reduced working capital tied up in inventory, and improved margin predictability.
• How to gain buy-in: Position AI as a margin protection mechanism. Present quantified savings from avoided delays, reduced administrative burden, and optimised freight allocation. Use dashboards showing improved cost accuracy and working capital efficiency to communicate value at executive level.
  

Across every role, the common thread is visibility. AI replaces assumptions with precision, enabling teams to anticipate disruption, reallocate resources dynamically, and drive continuous improvement. When positioned as a revenue enabler and cost stabiliser, AI earns its place as a strategic investment, not a technology expense.

Enhancing precision and accuracy

Reliability is critical for supply chains operating at a global scale. AI strengthens precision by transforming raw data into proactive, validated intelligence.
Predictive ETAs become more accurate than carrier benchmarks, automated document processing eliminates human error, and unified SKU-level visibility empowers leaders to make confident, data-driven decisions.

Below, we explore how different teams in the supply chain can leverage AI to enhance precision and accuracy and articulate the business case internally.

For Supply Chain Directors

Strategic benefit: Turning uncertainty into confidence through real-time accuracy.

AI enables Supply Chain Directors to forecast disruptions, anticipate risk, and allocate resources with precision. Predictive ETAs calibrated against live network data, as opposed to static carrier estimates, allow for proactive rerouting and scenario planning before delays materialise.

• Business benefit: Improved reliability of delivery commitments, enhanced customer trust, and fewer downstream inefficiencies caused by inaccurate data.
• How to gain buy-in: Quantify the cost of uncertainty, e.g., “Each missed ETA causes an average of X% in lost productivity or downstream delay.” Demonstrate how predictive accuracy translates into stronger customer SLAs and measurable service-level improvement, reinforcing executive confidence.
  

For Inbound Managers/Directors

Strategic benefit: Creating operational precision through data-led execution.

Inbound teams rely on precision to synchronise bookings, supplier readiness, and transportation schedules. AI enhances this by automatically flagging deviations between supplier CRDs, carrier milestones, and expected delivery windows.

• Business benefit: Fewer missed cut-offs, reduced dwell times, and lower detention and demurrage costs through earlier risk detection.
• How to gain buy-in: Present before and after data on ETA variance, e.g., “Our predictive model reduced variance from ±48 hours to ±12 hours.” Show how data accuracy strengthens supplier accountability and operational reliability.
  

For Buying and Merchandising leaders

Strategic benefit: Safeguarding product availability with accurate visibility.

AI provides SKU-level precision, ensuring teams know exactly when and where products are in transit, and when they’ll be available for allocation, promotion, or launch. Automated data validation across supplier and carrier inputs reduces dependency on manual updates and fragmented spreadsheets.

• Business benefit: More accurate replenishment timelines, fewer stockouts, and higher on-shelf availability for campaigns and seasonal launches.
• How to gain buy-in: Link precision to revenue protection. Demonstrate how improved visibility reduces lost sales from late arrivals and helps marketing and sales teams plan with confidence.
  

For Finance Directors/CFOs

Strategic benefit: Achieving financial accuracy through verified supply data.

AI validates cost and compliance data automatically, cross-referencing invoices, carrier contracts, and customs documentation. This reduces financial exposure caused by errors in landed-cost calculation, misclassification, or missing paperwork.

• Business benefit: Greater audit readiness, fewer charge disputes, and higher accuracy in accruals and cost forecasting.
• How to gain buy-in: Emphasise that data integrity equals financial integrity. Demonstrate how AI’s validation processes reduce compliance risk and variance, allowing Finance to forecast with higher confidence and fewer adjustments at quarter-end.
  

Precision is the foundation of profitability. By increasing the accuracy of ETAs, documentation, and inventory data, AI reduces waste, protects revenue, and enhances decision-making across all levels of the supply chain.

Optimising efficiency

AI doesn’t just change what supply chains achieve. It transforms how they operate. By connecting fragmented data and automating routine work, AI creates more efficient teams, faster decisions, and smoother execution. It shifts organisations from reactive problem-solving to proactive, insight-led management, where exceptions are managed automatically, and focus moves to strategy, not chasing updates.

Below, we explore how each team realises tangible efficiency gains, and how to articulate the value to executive stakeholders.

For Supply Chain Directors

Strategic benefit: Orchestrating end-to-end performance.

AI allows Supply Chain Directors to run complex, global networks. Teams are able to focus on high-risk and outlying scenarios, rather than being stuck in the weeds of manual, time-consuming tasks, by centralising data and automating decision-making. Predictive workflows help identify which shipments, routes, or suppliers require intervention — turning vast complexity into prioritised clarity.

• Business benefit: Streamlined execution, reduced overhead costs, and faster responsiveness across multiple modes and markets.
• How to gain buy-in: Present efficiency as a scalability advantage — e.g., “With AI-driven automation, we can manage 20% more shipments with the same headcount.” Link these gains to improved resilience and higher ROI per operational resource.
  

For Inbound Managers/Directors

Strategic benefit: Automating execution to eliminate wasted effort.

Inbound teams spend countless hours tracking updates, reconciling data, and chasing suppliers. AI automates these repetitive workflows, handling supplier follow-ups, documentation parsing, and milestone validation in real time.

• Business benefit: Up to 40% reduction in manual admin and faster response times to exceptions or schedule shifts.
• How to gain buy-in: Quantify the opportunity cost — “Each automated update saves 5–10 minutes per PO; across 2,000 POs per quarter, that’s over 150 hours reclaimed for higher-value work.” Demonstrate how automation improves both accuracy and morale.
  

For Buying and Merchandising leaders

Strategic benefit: Aligning logistics agility with commercial priorities.

AI ensures operational decisions support sales and merchandising goals by providing real-time insight into availability, delivery timelines, and supplier performance. Teams can reprioritise high-value SKUs instantly, reroute freight dynamically, and ensure campaign-critical stock is on time.

• Business benefit: Faster reaction to demand changes, improved product launches, and reduced lost revenue due to delays.
• How to gain buy-in: Frame efficiency as commercial enablement — e.g., “AI reduces the lag between supply data and sales decisions, allowing faster go-to-market response.” Highlight how efficiency directly impacts topline agility.
  

For Finance Directors/CFOs

Strategic benefit: Streamlining financial operations and decision cycles.

Finance teams gain from the automation of documentation, invoice validation, and data reconciliation. AI’s ability to streamline and analyse complex datasets means fewer manual checks, faster reporting cycles, and greater accuracy in forecasting and accruals.

• Business benefit: Shorter month-end close times, reduced reconciliation errors, and clearer financial visibility across all shipments.
• How to gain buy-in: Connect efficiency to financial velocity — “Automating validation across shipments shortens reconciliation cycles and reduces finance team hours spent on manual data verification.” Emphasise governance improvement alongside cost efficiency.

Efficiency is where AI delivers compounding value. By reducing friction, automating low-value tasks, and unifying data flows, AI gives every team more bandwidth to focus on strategy, growth, and collaboration.

From ROI to strategic value

The ROI of AI is not solely financial—it strengthens trust and long-term positioning. In the boardroom, AI should be considered not just as a cost-saver, but as a resilience tool, a way of delighting customers and strengthening relationships, and a method of bringing digital maturity across the organisation. 

In fact, companies that have AI-led supply chain capabilities materially outperform peers; Accenture found these organisations deliver approximately 23% greater profitability versus peers. For business leaders, the message is clear: embedding AI is not a technology initiative, but a  competitive advantage.

How to choose the right AI ally

Integrating AI into your supply chain is as much about people and partnerships as it is about technology. Selecting the right partner can make the difference between incremental improvements and transformative change. For supply chain leaders, this means looking beyond feature sets to the broader capabilities, trustworthiness, and long-term alignment of your partners. 

Here are a few examples of the areas you should consider when approaching an AI partnership:

1. Proven track record matters

A capable partner should demonstrate a track record of embedding AI into real-world supply chain operations. Look for evidence of measurable outcomes, such as customer testimonials and use cases, improved forecasting accuracy, reduced operational costs, or enhanced on-time performance. Ask how their AI models have been applied across multimodal logistics, demand planning, and exception management.

Zencargo AI is built on a digitally native, cloud-born infrastructure. This ensures a clean, structured, and instantly AI-ready data foundation, free from the constraints of legacy systems. This extensive, high-quality operational data, including years of SKU-level, multimodal shipment data, is crucial for powering accurate AI decisions. We work closely with customers to develop tried and tested AI-powered solutions and our platform is delivering measurable value to our customers every day (explored later on in this ebook).

“With visibility in the Zencargo platform, SLG has more reliability and confidence. This frees our team to predict outcomes and drive operational savings.” Devinder Chana, Director of Supply Chain, SLG

2. Protect your data and ensure compliance

Your partner should treat data as a strategic asset. This includes rigorous standards for encryption, access control, and compliance with the  GDPR and other relevant regulations. Equally important is transparency: AI outputs should be explainable, auditable, and traceable, allowing your teams to validate recommendations and understand the reasoning behind decisions.

At Zencargo, data confidentiality is ensured through encryption both in transit and at rest, with access tightly restricted to authorised personnel. Importantly, customer data is never used to train third-party Large Language Models such as those from OpenAI or Google, protecting privacy and control. Zencargo also operates in full compliance with GDPR, safeguarding sensitive supply chain data across jurisdictions.

Zencargo documents all prompts and system flows to provide full auditability and understanding and enforces a rigorous data governance framework and takes accountability for AI-driven errors to the same standard as human errors.

3. Systems that play well together

Supply chains rely on a variety of systems — ERP, TMS, WMS, and IoT sensors. The right partner will enable seamless integration across your existing infrastructure, supporting both current workflows and future automation. Flexibility also extends to scale: a partner should support your experimentation efforts and allow AI initiatives to grow without being locked into a rigid platform.

Zencargo endeavours to make all AI integrations compatible with existing systems. We work closely with our customers to understand their specific challenges and business objectives, and tailor our AI solutions to their needs. Workflows are also built to change as our AI initiatives scale, meaning processes evolve to be automated, requiring less human input. 

4. Keep human oversight where it counts

AI is most effective when paired with human expertise. Your partner should design systems that allow for human oversight at critical decision points. This ensures that AI recommendations enhance strategic decisions rather than replace human judgment, and helps build trust with internal stakeholders and external partners alike.

Zencargo’s approach integrates human oversight at critical points. For instance, in our Document Parser solution, Luca Scan, every document undergoes meticulous human-in-the-loop verification, which not only ensures accuracy but also continuously trains and refines AI models.

5. Align strategy, culture and technology

Finally, your AI partner should share your vision for the supply chain — not just as an operational function but as a strategic lever. They should support capability-building, change management, and upskilling, enabling your teams to thrive alongside AI, rather than feeling displaced or overwhelmed by technology.

The right supply chain partner acts as an enabler, guiding you through the complexities of AI adoption while ensuring that technology, people, and processes are aligned to deliver measurable, sustainable value.

Zencargo empowers supply chain professionals to focus on complex problem-solving and strategic thinking. The learning curve is designed to be gentle, with intuitive, user-friendly features integrated into the familiar Zencargo platform, supported by dedicated customer success teams.

Real AI solutions delivering results today

Zencargo is actively spearheading the AI-driven digitisation of global trade, with AI deeply embedded across our platform to deliver tangible value. Here’s how we’re applying AI to solve four key supply chain challenges:

Luca Scout – Purchase Order (PO) Confirmed Ready Date (CRD) Management

Luca Scout automates the critical, often manual process of monitoring PO conditions and following up with suppliers. Luca Scout identifies which PO’s have upcoming CRDs, automatically sends reminder emails to suppliers in their native language, processes responses using Natural Language Processing (NLP), and triggers platform updates or escalations. By providing CRD updates days in advance (depending on a customer’s specific CRD ranges), Luca Scout not only delivers earlier visibility but also reduces manual errors, streamlines supplier engagement, avoids costly delays, enables real-time supplier performance tracking, and supports more accurate inbound planning and inventory management.

Luca Flow – Drayage Booking

Luca Flow automates complex steps in the drayage process, including booking and communications. Acting as an extra colleague on your operations team, Luca Flow handles routine tasks so your people can focus on exceptions and higher impact work.

By dynamically orchestrating routes and carrier selection — including factors like load size, capacity, sustainability, risk, and reliability — Luca Flow not only optimises cost allocation but boosts efficiency, reduces manual effort and errors, and enhances operational control with greater predictability. 

Ask Luca – Analytics and strategic insights

You have questions – Luca has answers. Ask Luca  transforms fragmented freight data into actionable intelligence, providing real-time visibility into shipments. Ask any supply chain question in plain English and instantly receive data-backed responses, such as ETAs, SKU-level visibility, and recommendations.

Ask Luca aggregates and unifies data from multiple systems, acting as a single source of truth. The solution identifies trends, anticipates disruptions, and proactively flags shipments at risk of delay. No more hunting for answers to last minute, urgent questions – this is supply chain insight at your finger tips.

Luca Scan – AI-powered document parsing

Luca Scan transforms fragmented freight data into actionable insights by automating complex document processing. It applies AI and Large Language Models (LLMs) alongside Optical Character Recognition (OCR) to extract key information from documents and emails, automatically updating the Zencargo platform. A human-in-the-loop verification process ensures accuracy and continuously trains the models.

Conclusion: Turning AI into advantage

The supply chain has always been a story of adaptation. Each disruption—whether a closure, new tariff, or missed cargo date—forces leaders to reassess resilience, efficiency, and foresight. AI represents the next chapter: a set of capabilities that can transform logistics from a cost centre into a competitive advantage.

Its real power is helping organisations operate with precision in uncertainty, build resilience without adding cost, and free teams to focus on strategy instead of constant firefighting. For leaders, the challenge is twofold: position AI as a driver of measurable ROI in the boardroom, and champion it across the organisation as a catalyst for cultural change.

The call to action is clear: experiment quickly, build on high-quality data, embed explainability and governance, and empower your people to thrive alongside AI. Companies that act decisively today won’t just survive disruption—they’ll use it to fuel growth and set the pace for tomorrow.

To learn more about Zencargo’s AI powered platform, click here.

Frequently asked questions