Starboard Bets AI Can Give Smaller Freight Forwarders a Fighting Chance Against Industry Giants

The Analysis

It keeps coming back to one image when I read these 'AI will save the small forwarder' stories. It is four in the afternoon on a Thursday at a quote desk with three people and an inbox that will not stop. PDFs stacking up, four carrier portals open, two agents who have not replied, and the rate you pulled at two o'clock is already stale. Sumeet Trehan, the founder of Starboard, looked at exactly that mess and argued this month that AI is what finally lets the smaller forwarder stand next to the giants. He is not wrong about the mess. He is only half right about the fix, and the half he misses is the part I want to talk about.

Before we touch any tool, I want to model that desk as a system. Treat the incoming requests for quotes as an arrival stream. Treat your ops people as the server. Treat the rate data as raw material that has to be processed before anything leaves the building. Once you see it that way, the whole 'level playing field' line stops being a slogan and becomes a question you can actually put a number on.

Start with the slice of fact we actually have, because everything else is argument. Starboard is a freight-tech player, and Trehan's point, reported by SeasNews and The Loadstar on 18 June 2026, is not that AI replaces forwarders. It is that the daily chaos of the quote desk, the emails, the PDFs, the spreadsheets, the carrier portals, the agent replies that arrive at the worst possible moment, and the rates that move before you finish typing, is the thing sinking small and mid-sized shops. His bet is that AI lets those shops compete on service and speed without building the giant back office that the big networks already have. That is the claim. Now take it apart as a process and see where it holds and where it does not.

Think of it as a queue. Everything about a quote desk is a queue. Work shows up at random times, it waits in a buffer, a server picks it up, and a finished quote leaves the other end. For a small forwarder the server is usually one or two people who also do the booking, the chasing, and the customer hand-holding. The arrival rate is the number of RFQs per day. The service time is how long each one takes to turn into a sendable rate. The bottleneck, the single variable that caps the whole system, is the server's available hours. When arrivals go up and service time stays fixed, the queue grows, quotes get late, and late quotes lose the shipment. That is the mechanism. Not 'competition is hard.' A specific, measurable bottleneck: server hours per day. Name the variable and the problem gets smaller.

Here is the part the press release skips. The giants are not winning because their people are smarter. They won because they bought a TMS, a rate engine, and a stack of carrier integrations that turn a chunk of that service time into near zero. Their server was partly replaced by software a decade ago. The small forwarder is still running the whole thing on a human who is also on the phone. So when Trehan says AI levels the field, what he really means is this: software can now do the intake, the reading of the email, the pulling of the PDF, the matching to a lane, the fetching of a rate, fast enough that a one-person shop behaves as if it has a small back office. I buy that. I also want to know which bottleneck actually moves, because a queue only speeds up at the step you automate, and the step you automate is rarely the only slow step.

Be precise about why this is happening now and not five years ago. Two things changed at once, and the crossing of the two is the real event. The arrival stream got nastier. More channels, more portals, more file formats, and rates that now wiggle intraday instead of once a week. The variance of the job content went up, so the human server's effective service time went up even when raw volume was flat. A quote that used to be a known shape became a different shape every time, and a server that has to re-decide from scratch on each job is a slow server.

On the other side, the tools to parse that mess got good enough to be worth wiring in. Five years ago an AI that read a PDF still needed a human to check every line; now the check is smaller and the exceptions are fewer. So the trigger is not 'AI got smart.' It is that the cost of processing one incoming quote finally dropped below the cost of leaving it to a person. That crossing point is the story, and it is a numbers question, not a hype question. Where is the bottleneck, and did the cost of removing it just flip?

Now who feels this, and by how much. The big networks already automated this corner, so their marginal gain from one more AI tool is small because they started early and the easy wins are spent. The mid-sized forwarder with a real but thin ops team gets the biggest lift, because today their server is saturated and any throughput gain shows directly in quotes answered before the customer walks. The tiny one-person shop gets a lift too, but it is capped by a different ceiling: they may not have the rate agreements or the carrier relationships to quote competitively in the first place, so speeding up the queue only helps if there is something worth sending at the end of it.

So the benefit is real and it is uneven. The shops drowning in RFQs win most. The shops that were never going to win the lane anyway win least. The field flattens on the quote desk and nowhere else yet, and pretending otherwise is how buyers of this software end up disappointed.

When does any of this reach your cost or your customer's clock. If a forwarder signs a quote-automation tool this quarter, integration is rarely a weekend job. Count four to eight weeks to connect email, portals, and your rate sources, then another quarter or two before the team trusts the output and stops re-checking everything by hand. The first visible change is turnaround time: a quote that took twenty minutes of hunting now assembles in under a minute for the intake. The money effect lags, because answering faster only pays if it lifts your win rate, and that depends on the rate you are allowed to quote, not just how fast you quote it. So the clock on the cost side starts ticking in weeks, the clock on the revenue side in quarters, and neither hand you a result on day one. Anyone who promises instant savings is selling the demo, not the deployment.

Run a number so we are not waving hands. Assume a mid-sized forwarder gets fifty RFQs a day, and the average one takes eight minutes of ops time to process by hand: read the request, find the lane, pull three carrier rates, build the quote. That is four hundred minutes a day, almost seven hours, just on quoting, before anyone does the actual booking. Now assume the AI intake handles the reading and the rate pull in about one minute per RFQ, leaving two minutes of human review on the tricky ones.

That drops the load to roughly one hundred fifty minutes, under three hours. You freed about four and a half hours a day. Quantify before you optimize: the win is real, but notice what it did not change. The freed time lets you answer more quotes. It does not change the rate you are allowed to offer. If your contracts are thin, you now answer fast and still lose. The server got faster; the raw material did not get better.

Run the same math on a one-person shop to see the ceiling. Say that shop gets fifteen RFQs a day at six minutes each, ninety minutes of quoting, and the rest of the day is booking and chasing. Automation cuts the quoting to maybe twenty minutes. They gained seventy minutes, which is nice, but the constraint was never the seventy minutes. The constraint was that this shop quotes one carrier they have a deal with and loses the lanes where the customer wanted three options. Speeding the queue from six minutes to one does nothing for the fact that only one rate exists to send. So the same tool, same vendor, same hype, produces a transformed shop in case one and a slightly less tired person in case two. The difference is not the AI. It is what sat at the end of the queue.

This is the point the headline misses, and it is the one I actually care about. 'AI levels the playing field' sells the idea that software closes the gap between small and giant. It does, on exactly one dimension: quote throughput. It does not close the gap on the rate desk, which is a relationship and a contract, not a process you can automate. When you speed up the queue, you expose the next constraint. In queueing terms the bottleneck migrates. The small forwarder's real ceiling was never how fast they typed the quote. It was whether they had a rate worth sending. So the field gets a little flatter where it was bumpiest, and the next hill, the one you cannot code away, shows up clearer. Anyone telling you AI fixes the whole competition is selling the intake and ignoring the contract. The intake was the easy part.

There is a second thing the coverage leaves out, and it is the more useful one for an operator who has to spend money. The quote desk is not just a queue with too much volume. It is a queue with unstable content. Rates shift before the data is processed, which means each job's correct answer is moving while it sits in line. A tool that only reads faster does not solve that. The tool that matters detects that the rate you pulled is stale and flags it before you send. So the real value is not parsing speed. It is freshness detection. If your 'AI' just OCRs PDFs faster, you have sped up a queue that was already feeding bad numbers to the customer. The bottleneck was never the reading. It was the validity of what you read. Name that variable and the buying decision changes completely, because most of what is being sold today is the reader, not the validator.

Flip the premise and see where it breaks, because a claim with no boundary condition is not analysis. If rates were stable and channels were few, the way it was before 2020, that small forwarder's quote desk was never the bottleneck, and none of this tooling would matter. The problem Trehan describes only exists because volatility and fragmentation rose together. Now the other side of the flip: if AI quoting becomes table stakes and every shop has it, then quoting speed stops being a differentiator at all, and the bottleneck migrates back to capacity and relationship, the part software cannot buy. So the leveling he predicts is real but temporary, and it is temporary precisely because the thing it fixes is the easiest part to copy. The durable advantage was never going to be the parser. It was going to be the rate and the relationship behind it, and those are unchanged by a faster inbox.

So what do you actually do with this if you are the importer or exporter standing on the other side of that quote desk, or a forwarder trying to survive it. Stop measuring the tool by how fast it reads. Measure it by quote-to-win rate and by stale-rate incidents caught, because those are the variables that move money, not the demo's typing speed. Before you spend a dollar on AI, map your own RFQ queue: arrival rate, service time, where jobs pile up. You cannot fix a bottleneck you have not measured, and most shops have never timed their own quote desk, so they buy software for a problem they described from memory.

Keep the two problems separate on purpose. Buying intake automation is a throughput play. Renegotiating carrier deals is a capacity play. Do both, but do not let the shiny one excuse you from the boring one, because the boring one is the one that decides whether the faster queue meant anything. If you are a shipper choosing a forwarder, ask what their quote turnaround actually is and whether a human or a system produced your rate; a fast automated quote on a thin rate is still a thin rate, and you will find out at the booking.

Watch the data feed, not the demo. The differentiator Trehan himself named was operational deployment, not the presentation. A tool wired into your real portals beats a beautiful slide every time. Set a threshold before you commit: pilot the intake on your highest-volume lane for one quarter, track quotes answered per ops hour, and only expand when that number climbs by at least a third without a rise in wrong quotes sent.

One more modeling move makes the bottleneck impossible to hide. In any queue, the work sitting in the buffer equals the arrival rate times the time each job waits. Push arrivals up, or let service time creep, and the backlog grows even if you never miss a single quote. A small forwarder rarely watches this number; they watch the inbox and feel busy. But the backlog is the honest signal. If RFQs rose twenty percent and your service time rose ten percent, your queue did not rise thirty percent, it rose more, because the two multiply inside the wait.

An AI that cuts service time attacks exactly this. But if your arrival rate keeps climbing because you won five new customers, the queue returns unless the tool also changed your capacity, not just your speed. So the question is never 'did the tool make us faster.' It is 'did the tool change the steady-state backlog,' and almost nobody measures that, which is why most automation projects get declared a success on a demo and quietly disappoint in the quarter.

Add the cost of a bad quote to the model, because speed without accuracy is just a faster way to lose money. Suppose one in twenty manual quotes goes out with a stale or mistyped rate, and fixing it after the customer notices costs an average of two hours of firefighting plus a dent in trust. At fifty RFQs a day that is two or three bad quotes a week, maybe six hours of cleanup and a relationship you have to rebuild. An intake tool that catches stale rates before send does not just save time; it removes a recurring leak. The value is not the minutes saved on the quote. It is the two hours you did not spend apologizing and the customer you did not have to win back. Name that variable and the return looks different from the brochure, which only ever counts the fast part.

Step back to why the giants stay ahead even after the small shop buys the same tool. Their advantage was never only speed. It was the rate desk behind the speed: the volume commitments that buy them better pricing, the multi-year carrier deals, the credit lines that let them float a shipment, the bonded warehouses, the dedicated support when a booking breaks at midnight. A faster quote on a thinner rate still loses to a slower quote on a better one. So the tool closes a gap that was never the whole gap. The small forwarder who treats AI as the finish line stays a step behind. The one who treats it as the start of fixing the rate desk closes the part that actually decides the win. Same software, opposite outcome, and the difference is entirely in what they did next, not in what they bought.

From the importer's chair the practical read is narrower and more useful than the headline. You do not need to build any of this. You need to know which of your forwarders actually runs it, because a forwarder on manual quotes will be slowest exactly when the market is loudest and rates move fastest, which is when you need speed most. Ask two questions at the next rate review: how long between your RFQ and their firm quote, and who or what produced the number. A system-produced quote on a weak rate is still weak. A human-produced quote on a strong rate, answered fast because the house is disciplined, is the one you want. The tool is a signal about how the shop runs, not a guarantee of a good rate, and learning to read that signal beats chasing the cheapest quote on a bad day.

Consider what the tool does to your own hiring math, because that is where the money actually lands. A saturated ops person is a hiring trigger: you add headcount the moment the queue spills. An intake tool that removes two thirds of the per-quote time pushes that trigger further out, so the same team carries more volume before you post the job. The saving is not only the hours freed this quarter. It is the hire you did not make next quarter, and the ramp you skipped. But run the same litmus test: if the constraint was never the hours but the rate you could offer, the hire you avoided was never going to win the freight either, and the tool just let a thin operation look busy instead of letting it get better. Count the avoided hire, then check whether the underlying rate desk got any stronger. If not, you bought breathing room, not a moat.

The honest version is this. AI can make the small forwarder's quote desk look like a bigger company's, and that is worth real money on the lanes where speed wins. It cannot manufacture the rates, the contracts, or the trust that actually win the freight. Treat it as a server you added to a queue, not a magic equalizer. The field gets flatter in one spot, you can see the next constraint from there, and that is exactly where you should be looking next, because the bottleneck always moves to wherever you stopped automating.

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— By Ravi Chandran