AI is changing how freight, logistics and supply chain companies are found, researched and sold to. This is what LeadCoverage believes about it, what we are building, and where we keep people in charge.
Kara Smith BrownCEO of LeadCoverage and author of The Revenue Engine, in her own words.
We rent the same AI everyone else does. Our edge is the data, context and discipline we wrap around it.
“We built our own agents: agent architecture, agent harnesses, how MCPs and RAG all go together. It demystified everything for me. I can do this myself. This is not that hard.”
For you: we build agents around your pipeline and your data, not a generic tool bolted onto your CRM.
“If you are building an agent harness in Claude or in OpenAI, there is a bunch of risk in just having one model.”
For you: the model is the part that changes every quarter. Your data, your process and your rules are the part worth owning.
“They crawl your website before a human sees it.”
For you: buyers now research carriers, forwarders and 3PLs through AI assistants. Your site has to be readable by a machine before it persuades a person.
“They are looking for press releases or third party content.”
For you: LeadCoverage sent one press release a week for a quarter, and our LLM citations went up. Third party proof beats your own blog for being found in AI answers.
“There is no clear SEO type advantage to this stuff. The real answer is there is no answer. But I will give you everything that we know and all the math that we have.”
For you: no guarantees about AI visibility from us. You get the test, the result and the method.
“…the places where I think you should be using a human being.”
For you: agents take the repeatable work. A person makes the call on who to contact, what to say and what to promise.
All quotations: Kara Smith Brown, CEO, LeadCoverage.
Our AI approach rests on three commitments, each one tied to revenue for supply chain companies.
AI makes our own team faster at research, drafting and reporting, so more of every hour goes to client strategy.
“I spent two weeks at MIT and I promised the team that I was going to make a bunch of stuff.”In use
Agents that support prospecting, nurture and marketing for freight and logistics GTM teams.
“We are going to start selling agents.”Being built
A place to run hypothesis tests on GTM strategy and publish what works before we recommend it.
“We are going to start an agent laboratory where we test things, like we tested press releases to get LLM citations, and that worked.”Launching
An illustrative outbound workflow for a freight or logistics sales team. Filter by who does the work.
Watches for buying signals: a new lane, a leadership change, a funding event, a spike in intent.
Checks each account against the ideal customer profile before anyone spends time on it.
Fills in the account and contact record with the data a rep needs to start a real conversation.
Writes a first message grounded in the account’s own situation, in the client’s voice.
A human reads it, edits it and decides whether it goes out. Judgment stays with people.
A human takes the reply, builds the relationship and moves the deal. Share good news, track interest, follow up.
Illustrative workflow. Steps vary by client, stack and funnel.
The model is rented. These three are what we add, and they are why the same AI gives different results in different hands.
Clean, connected records in HubSpot and the systems around it. An agent is only as good as the data it can read, so the architecture comes first.
Freight, logistics and supply chain, and nothing else. We know what a spot quote, a contracted lane and a TMS handoff mean for the way your buyers decide.
We are a consulting practice, so we keep isolation between client work. Process, review and measurement sit around every agent we run.
Kara’s view after two weeks of study: “Nobody has prolonged practice in AI.” Our answer is to run small, honest experiments and share what we learn.
Kara Smith Brown, CEO of LeadCoverage and author of The Revenue Engine, completed MIT Sloan’s AI Executive Academy, a two-week, in-person program at MIT in Cambridge, and brought the lab approach back to the team.
MIT Sloan Executive Education is an independent program. This page does not reproduce its materials, and the program has not endorsed LeadCoverage.
Stay close. Results from the lab get shared as they come in.
Nine views on where AI changes the work for freight and logistics companies. These are opinions to test against your own operation, not predictions.
Capability arrives in jumps, so a freight task AI could not do last quarter may be routine next quarter. The operators who keep retesting will pull ahead of the ones who tried once and moved on.
In freight, the model is not the moat. The data is. Lane history, tender acceptance, dwell, claims and carrier performance are what competitors cannot copy, and any model built on them has to prove itself on data it has not seen.
Tokens are becoming a cost line, and verification eats much of the time AI saves. Freight companies that route work to the right model and design for review will see the gains. The rest will mostly see a bigger bill.
If an agent is a series of prompts in a loop, the value in freight software shifts from features to the data, context and workflow design around the model.
In an AI fog, spreadsheet ROI is fragile. Freight leaders should judge AI by cost per successful task and pick models on purpose, not by headline token price.
Geopolitics belongs on the income statement, and in freight it shows up first. The next constraint is trust: shippers will delegate to an agent when they can see how it reasons, and when it reasons the way they do.
The order holds for every shipper and LSP: transform the process, then automate, then let AI decide. One warning holds for every agent: it cannot tell a command from data.
AI lets a junior rep sound like a top performer, and that is the risk as much as the gain. Freight expertise is tacit and built through the struggle, so protect it while you automate.
The technology is the easy part. Freight’s AI projects will stall on silos, decision rights and habits, the same way its digital transformations did.
The books, papers and tools Kara flagged while studying AI. Start with the first three if you only have an hour.
Dario Amodei, September 2026
A case for slowing frontier AI, with outside evaluators inside the labs and coordination among democracies.
Ethan Mollick
A practical frame for working alongside AI. The four principles are the useful core.
Toby Stuart, Harvard Business Review, April 2026
Why planning around AI is hard when capability moves in steps, not a straight line.
Carol Long, David Simchi-Levi, Andre Calmon and Flavio Calmon, Harvard Business Review, December 2025
What happens when agents run supply chain decisions, and where people still need to be in the loop.
Vaswani et al., Google, 2017
The transformer paper behind today’s models. The abstract and the first figure are enough for non-engineers.
Kaplan et al., OpenAI, 2020
The research behind the idea that more data and compute keep making models better.
Daniel Kahneman
The System 1 and System 2 idea, and a useful way to think about reasoning models.
Andrej Karpathy
A small open-source chat model you can read end to end. The fastest way to see what is inside one.
MIT
Brings MIT researchers and industry together on open, problem-first generative AI.
LMArena
Blind, side-by-side model comparisons by task type. Useful when choosing a model for the job.
OpenRouter
One API across hundreds of models, which makes it easier to avoid betting on one.
Links lead to third party sites. Inclusion is Kara’s reading list, not an endorsement of LeadCoverage by any author, company or institution.
Yes. Agents handle research, enrichment and first drafts. People review the work and own the decisions. The split is shown above.
Our position is that AI replaces repeatable tasks, not judgment. We help you decide which work goes to an agent and which stays with a person.
We are a consulting practice, so we keep isolation between client work. Ask your strategist for the specifics that apply to your account.
We do not build on a single model. One model is a single point of risk, so we design agents that can move between models as the market changes.
No, and no one honestly can. There is no proven playbook yet. We test, share the method and the results, and build on what works.
Bring your stack and your questions about AI. We will show you where agents help and where people should stay in charge.