Q&A: Mark Albrecht, VP–Artificial Intelligence and Enterprise Strategy, at C.H. Robinson
Summary
Mark Albrecht — C.H. Robinson’s VP for Artificial Intelligence and Enterprise Strategy — explains how logistics is shifting from “computer-aided work” (software that people operate) to “agentic AI” (models that plan, reason and decide workflows). He outlines why this is a paradigm change for the industry, how agentic systems are being applied practically (email quoting, document ingestion, LTL missed-pickup recovery), and why leaders must rethink data, organisation shape and investment priorities to capture value.
Key Points
- AI is moving from reactive pattern-matching to agentic systems that can reason, plan and take proactive action.
- Agentic models enable simpler integrations (for example, quoting via email) and handle edge cases that used to break traditional automation.
- C.H. Robinson reports automation rates rising from ~50–60% (traditional AI) to routinely above 90% with agentic approaches.
- A multi-agent LTL missed-pickup solution automated 95% of checks and cut unnecessary return trips by 42%.
- Visionary leaders must capture new “why” data (reasoning traces) and prepare organisational change — the structure shifts from a pyramid of tactical roles to a diamond with people managing fleets of agents and doing higher-value work.
- Prioritise stabilising and standardising the digital operating model (quote-to-cash) before scaling growth-focused AI initiatives.
Content summary
Albrecht frames AI as a general-purpose technology that, when it changes, remakes industries. A decade of software-led digitisation produced computer-aided workflows where humans remained the logical engine. Recent advances in generative models gave reactive capabilities, but the latest agentic systems add reasoning and agency — they can plan and execute workflows autonomously.
He gives concrete examples: agentic models parse emailed orders and PDFs previously unseen, map that data into systems, and produce quotes quickly (critical during peak periods). These agents tolerate variation, reducing brittle engineering maintenance. The firm has pushed agentic automation through quote-to-cash and paperwork (bills of lading, proofs of delivery) and built a multi-agent system to identify and resolve missed LTL pickups early, dramatically reducing unnecessary truck returns.
Context and relevance
This interview is timely for logistics and supply-chain leaders weighing AI investments. It highlights a practical shift from incremental ML use-cases (forecasting, pattern recognition) to agentic automation that redesigns workflows and headcount mix. The piece is relevant to anyone responsible for digital transformation, operations efficiency, integration strategy or customer experience in freight, 3PL and warehousing.
Author style
Punchy: Albrecht doesn’t linger on theory — he connects breakthrough AI capability to measurable operational outcomes (automation %, reduced returns) and gives an operational playbook: stabilise the core operating model, collect the right “why” data, then scale agentic automation.
Why should I read this?
Look, if you care about cutting manual work, stopping trucks turning back, or making quoting actually work over email at peak times — this is worth five minutes. Albrecht explains the difference between clever models and models that actually change how you run the business, and he shares konkrete wins (90%+ automation, 42% fewer returns). Saves you time — and a few headaches — by pointing at where to start.