InPost Automation: How Phone Calls Dropped 25% Overnight

In the fast-paced world of logistics support, efficiency isn't just a goal—it's a necessity. InPost, a leading logistics provider, recently achieved a remarkable 25% reduction in phone call volume virtually overnight. This significant drop translates directly into faster response times, cost savings, and improved customer satisfaction.

So, how did InPost pull this off? The secret sauce lies in the strategic deployment of advanced AI-powered automation tools, blending conversational AI, deterministic workflows, and robust observability frameworks. In this article, we’ll unpack the key elements of InPost’s automation success, including the critical difference between AI that assists versus AI that resolves, the three crucial levels of automation, and the importance of compliance in regulated environments.

The Challenge: High-Volume, Regulated Logistics Support

Logistics companies like InPost manage enormous volumes of customer interactions daily, from parcel tracking to delivery scheduling, and problem resolution. Traditionally, a large percentage of these interactions culminate in phone calls—time-consuming and expensive touchpoints.

Reducing this call volume by any significant margin requires more than just implementing a standard FAQ bot. It requires a thoughtfully architected AI and automation ecosystem capable of handling complex scenarios while maintaining compliance and traceability.

AI That Assists vs AI That Resolves

When evaluating AI for support automation, the distinction between AI that assists and AI that resolves is a game changer.

• AI That Assists: Often takes the form of chatbots or virtual assistants providing information or guiding the user through self-service steps. These systems aim to reduce agent load but typically escalate complex cases to human operators.
• AI That Resolves: Goes a step further to autonomously solve customer issues end-to-end, including updating databases, triggering workflows, and providing effective outcomes with minimal human intervention.

InPost’s success stems from embracing AI that resolves. forethought ai support review This means fewer calls escalated to agents and more issues solved directly by AI—resulting in a noticeable dip in call volume.

Three Levels of Automation: Knowledge, Process, and Orchestration

Effective automation in support environments operates at three fundamental levels:

1. Knowledge Automation

   At the base layer is knowledge automation, powered by tools like large language models (LLMs) enhanced with Retrieval-Augmented Generation (RAG). These systems access company-specific knowledge bases to provide accurate, conversational answers instantly. For InPost, integrating knowledge automation enabled customers to get reliable parcel tracking updates and policy explanations without needing to call support.

2. Process Automation

   This level involves automating repetitive tasks such as updating delivery statuses or rescheduling pickups. Combining AI with deterministic workflows allows for repeatable, error-free execution of these processes. For example, an InPost customer could reschedule delivery through AI-driven steps that automatically updated backend systems, reducing the need for a human agent.

3. Orchestration Automation

   The pinnacle is orchestration, which coordinates multiple systems, teams, and processes seamlessly. Orchestration ensures that AI tools, CRM platforms, and human agents work together efficiently for complex scenarios where full automation isn’t possible. InPost leveraged orchestration frameworks to route unresolved cases efficiently while keeping customers informed—all with audit trails intact for compliance.

Deterministic Workflow Execution: Combatting AI Hallucinations

One common challenge with AI, especially LLMs, is the risk of “hallucinations”—AI generating plausible but incorrect or irrelevant information. This risk is unacceptable in regulated logistics environments where accuracy is paramount.

To mitigate this, InPost implemented deterministic workflow execution. This means AI outputs trigger predefined, rule-based processes that are fully auditable and predictable. Instead of letting AI freely generate actions, it provides controlled outputs that tie directly into trustworthy backend workflows. This approach drastically reduces errors and builds stakeholder confidence in AI automation.

Enterprise Observability, Audit Trails, and Compliance

For a high-volume support operation like InPost’s, observability and compliance are not optional—they’re essential. Enterprise-grade automation requires:

• Audit Trails: Every automated action, AI inference, and workflow step is logged with timestamps and user/agent attributions. This provides traceability for investigations and regulatory audits.
• Observability: Real-time dashboards monitor key metrics such as AI resolution rates, call deflection, and process exceptions. Observability ensures issues can be identified and resolved before impacting customers.
• Compliance: Automation tools must adhere to data protection laws (e.g., GDPR) and industry-specific regulations. Maintaining encrypted data storage and controlled access is a must.

InPost’s implementation layered these observability and compliance capabilities onto their AI stack to maintain trust at scale.

Technology Vendors Powering InPost’s Automation

InPost’s transformational journey leveraged a portfolio of industry-leading technologies:

• Zowie: Utilized for its AI-driven automation platform that operates on a per-conversation pricing model, which directly aligns cost with value generated. Zowie’s platform integrates LLM and RAG to power knowledge automation while enabling deterministic process execution.
• Intercom (Fin): Deployed for conversational front-end engagement, Intercom enabled smooth handoffs between AI and human agents, enhancing orchestration capabilities.
• Zendesk (Advanced AI): Provided enterprise-grade observability and compliance features critical for monitoring and auditing the entire AI-assisted support operation.

Pricing Considerations: Why Per-Conversation Pricing Makes Sense

Unlike traditional pricing models charging per agent seat, which often scale costs as teams grow, Zowie’s per-conversation pricing ensures that InPost pays strictly based on AI usage. This aligns incentives perfectly: the more calls AI deflects or resolves, the better the ROI.

Pricing Model Description Impact on ROI Per-Agent Seat Fixed monthly fee per human agent seat Costs rise with headcount, even if automation reduces workload Per-Conversation (Zowie) Charges based on AI-handled conversations Costs scale with value delivered; encourages automation efficiency

Conclusion: Lessons from InPost’s 25% Call Reduction

InPost’s overnight 25% phone call volume reduction is an exemplar of how logistics providers can harness AI-powered automation responsibly and effectively. The key takeaways include:

• Distinguishing AI that assists from AI that truly resolves customer issues.
• Building automation on the three-tier model: knowledge, process, and orchestration.
• Implementing deterministic workflows to eliminate hallucination risks inherent in AI.
• Embedding enterprise observability and audit trails to ensure compliance and trust.
• Choosing pricing models, like per-conversation, that reflect value rather than seat count.

For logistics and other regulated high-volume support environments, this approach offers a blueprint for scalable, compliant automation that delivers real and measurable reduction in operational costs and customer friction.

Stay tuned as the field evolves—future blog posts will dive deeper into effective orchestration and how to avoid automation failures (yes, including those infamous 2am breakdowns!).