SVLcode at TNW Conference 2025: Logistics Innovation Powered by AI, Automation, and Cost Reduction

On June 19–20, 2025, our team attended TNW Conference at NDSM in Amsterdam to connect with founders, operators, and corporate innovators—and to focus specifically on what’s changing fastest in logistics and supply chain tech: AI-enabled automation, end-to-end digitization, and practical cost reduction at scale. TNW | The heart of tech

TNW framed this year around “Big Problems, Bold Solutions, and Business Impact,” with AI & Machine Learning featured as a core theme alongside domains that intersect directly with logistics—such as Mobility, Cybersecurity & Privacy, and Energy. TNW | The heart of tech

What stood out across conversations was not “AI hype,” but a clear shift toward operational-grade implementations: systems that reduce manual work, improve reliability, and hold up under compliance and procurement scrutiny.

Why logistics is having an “AI moment” (and why it is different this time)

Logistics teams have always been measured on execution: on-time delivery, warehouse throughput, inventory accuracy, and cost-to-serve. The difference now is that AI is moving beyond analytics dashboards and into workflow execution—handling the messy work that consumes time:

• documents and exceptions

• cross-system reconciliation

• customer and carrier communications

• scheduling and resource allocation

• compliance checks and audit trails

The theme that kept repeating at TNW: the winning teams are not “adding AI.” They are rewiring processes so AI can remove friction while humans retain control.

What we discussed most: automation that pays for itself

Our conversations at TNW consistently came back to one question:
Where is the fastest ROI inside a logistics operation?

Across warehousing, freight forwarding, and last-mile delivery models, the best ROI clusters looked like this:

1) Document automation (the hidden cost center)

Even modern logistics workflows still depend on documents: bills of lading, PODs, customs forms, invoices, packing lists, claims, and exception notes. The operational drag comes from manual extraction, verification, and follow-up.

The practical pattern we saw again and again:

• ingest documents from email, portals, APIs, scans

• classify + extract into structured fields

• validate against rules (tolerances, required fields, duplicates)

• route exceptions with ownership, SLA, and evidence attached

• integrate outputs into TMS/WMS/ERP automatically

This is often the fastest path to measurable savings because it reduces both labor hours and error-related rework.

2) Exception handling as a first-class product

Most teams underestimate how much cost sits in exceptions: missing data, mismatched weights, delayed PODs, damaged goods, and billing disputes. Exceptions become expensive because the work is fragmented—email chains, spreadsheets, and tribal knowledge.

The automation approach that resonated most:

• centralize exceptions into a queue

• standardize resolution playbooks

• capture audit trails automatically

• continuously measure root causes and recurring patterns

Exception automation is where AI becomes more than “assistive”—it becomes a throughput multiplier.

3) Cost-to-serve reduction through workflow orchestration

The strongest logistics operators treat cost-to-serve like a product metric. Automation helps when it is deployed as orchestration across systems—not as isolated scripts.

The operational stack we discussed repeatedly:

• event-driven integrations (orders, scans, statuses, payments)

• orchestration layer for decisions and routing

• human-in-the-loop approvals for high-risk steps

• performance monitoring and error budgets

This is the “enterprise” version of automation: reliable, observable, and hard to break.

The AI adoption pattern that is actually working

A consistent takeaway from TNW conversations: most companies do not win by trying to “AI everything.” They win by choosing two to three workflows where:

• the data is already available (even if messy)

• the volume is high enough to justify automation

• the failure modes are understood

• the output integrates into existing systems of record

From there, teams expand carefully.

This aligns with how TNW positions its core audience: builders and corporate innovators looking for business impact, not experiments. TNW | The heart of tech+1

Security and governance: logistics AI must be enterprise-ready

A major thread across enterprise discussions at TNW was governance. Logistics touches sensitive data: customer details, shipment content, financial documents, and sometimes regulated goods. That is why Cybersecurity & Privacy was a highlighted conference vertical. TNW | The heart of tech

The practical requirements we heard from larger operators and enterprise buyers:

• role-based access and least privilege

• encryption in transit and at rest

• audit trails and evidence retention

• vendor risk management and clear data boundaries

• monitoring and incident response readiness

In other words: if you want AI automation in logistics, you need enterprise delivery discipline, not prototype tooling.

What we believe founders and operators should do next

If you are building or buying AI-driven logistics automation, here is a pragmatic starting point:

Step 1: pick a single workflow with measurable economics

Good candidates:

• POD collection and matching

• invoice reconciliation and dispute workflows

• document extraction for customs/compliance

• carrier onboarding and data normalization

• exception queues for delayed or mismatched shipments

Define one measurable outcome: time-to-resolution, cost per transaction, error rate, or days sales outstanding.

Step 2: build the “control plane” first

The control plane is what makes automation safe:

• validation rules

• exception routing

• approvals

• audit logs

• observability (what happened, when, why)

This is where enterprise readiness comes from.

Step 3: integrate into systems of record

AI automation that does not integrate creates new work. The highest ROI comes when outputs land directly into:

• TMS / WMS

• ERP / finance systems

• customer portals and reporting layers

Step 4: scale only after the workflow is stable

The pattern is:
pilot → stabilize → scale
Not:
pilot → expand → firefighting

Closing: why TNW mattered for us

TNW Conference 2025 reinforced a simple reality: AI adoption in logistics is maturing. The companies winning are the ones treating AI as a production capability—embedded into workflows that drive cost reduction, operational reliability, and better customer outcomes. TNW | The heart of tech+1

For us, the event was valuable because it connected the macro trend (AI everywhere) with the practical truth (automation only matters when it survives operations, security reviews, and integration complexity).

If your team is exploring logistics automation—document processing, exception workflows, reconciliation, or end-to-end orchestration—and you want an engineering partner that builds for measurable ROI and enterprise standards, SVLcode is ready to help.