- Strong Tower Consulting now advises carriers, brokers and enterprise clients on applied AI inside the systems they already run.
- Data readiness comes first. If the keys do not line up across TMS, EDI, settlement and accounting, the first project is the data.
- AI may flag, sort and draft. A named person approves anything that moves money, changes a rate, or touches safety.
- Every AI output lands in an exception queue with one meaning and one next step, not a score someone ignores.
- Every decision leaves an audit trail: the input, the model version, the suggestion, the person, and the final action.
Why Strong Tower Consulting is opening an applied AI practice
Strong Tower Consulting, LLC was founded in Minnesota in 2008 by Scott Elliott and has been in continuous commercial operation for more than 18 years. The firm's work has always lived in the plumbing of freight: transportation management systems, EDI 204, 990, 214 and 210 transactions, driver settlement, rate and tariff engines, accounting integration, SQL data, and ELD and telematics feeds. That is exactly where most AI projects in trucking either pay off or quietly fail.
Carriers and brokers are being offered AI features in almost every system they buy. Some of those features are useful. Many assume clean data the operation does not have, or make decisions nobody approved. Strong Tower Consulting is formalizing its applied AI practice so clients have an independent advisor who understands both the model and the freight system it has to sit inside. The goal is plain: decide where AI belongs in your operation, prove the data can support it, and wire it in so a person stays accountable for the outcome.
The practice serves motor carriers, freight brokers, private fleets and enterprise clients with shipping operations. It sits beside the firm's existing work in TMS implementation, ELD and EDI integration, and SQL data mining. A broader overview of the service is on the AI consulting page.
Data readiness comes before any model
A model can only be as reliable as the records it reads. In a carrier, those records are spread across the TMS, the EDI translator, the settlement system, the accounting package, and one or more telematics providers. Each one has its own idea of a customer, a lane, a unit and a driver. Before Strong Tower Consulting recommends any AI step, the practice checks whether those keys actually line up.
Readiness is not a feeling. It is a short list of tests run against the client's own tables. Are the fields the AI step needs populated? Does every 214 status message tie back to a 204 tender and a load in the TMS? Does every settlement line point at a trip? Does the unit number on a fuel or telematics record match a unit in master data for that date? Are there duplicates for the same customer under different spellings? Where the answer is no, that gap is the first deliverable, because a model trained on broken keys will learn the breakage.
Field completeness for a column the AI step depends on:
completeness = rows with a usable value / total rows in scope
Key match rate between two systems, for example 214 status messages to TMS loads:
match rate = 214 messages that resolve to one TMS load / total 214 messages received
The client sets the threshold before the test runs. A result below that threshold means fix the data first.
Carriers who want a first look can use the carrier AI readiness calculator and the carrier AI readiness evaluation guide. Neither replaces running the tests against real tables, but both help frame the conversation.
Where AI fits and where it does not
The most useful thing an advisor can do is say no to the wrong project. In freight, AI is strong at reading messy documents, sorting large volumes of exceptions, spotting records that look different from the rest, and drafting a first answer for a person to check. It is weak where the rule is already exact and written down, where a mistake moves money that is hard to recover, and where a law or a contract already says who decides.
Good fits for AI
- Reading rate confirmations, bills of lading and proof of delivery documents into structured fields for review
- Sorting EDI 214 and 990 exceptions so a person sees the urgent ones first
- Flagging settlement lines, invoices or fuel charges that look unlike the rest of the week
- Drafting a customer or carrier reply that a person edits and sends
- Spotting duplicate customers, locations or units in master data
Keep with people and exact rules
- Releasing driver pay or posting a settlement
- Posting an invoice or a 210 to a customer
- Changing a contract rate, tariff or fuel surcharge table
- Overriding hours of service or a safety hold
- Any calculation the contract already defines exactly, such as a written accessorial rule
The second column is not a statement that AI is useless there. A model can still flag a settlement line that looks wrong or a rate that looks out of pattern. What it does not do is act. A rate engine that already computes a tariff correctly does not need a model in the middle. It needs good tables and good tests, which is the work described in carrier rate matrix optimization.
Human-in-the-loop controls and exception queues
Strong Tower Consulting designs AI steps so their output goes to a queue, not straight into the ledger or the dispatch board. Each item in the queue has a code with one meaning and one next step. A document the model could not read is a different code from a document it read with low confidence, and both are different from a charge that matched nothing. A person works the queue, and the system records who cleared each item and how.
Three rules keep the queue honest. First, the model cannot clear its own exceptions. Second, a person clears an item only by fixing the source record or by approving the suggested action, never by marking it accepted with no change. Third, anything that moves money above an amount the client sets needs a second approver. These are the same habits described in the settlement work in automated driver settlement auditing, applied to every AI step rather than one.
If an input file or feed did not arrive, the AI step does not fill the gap with an estimate. The batch holds until a person confirms the real data, or documents the outage and approves a replay from the source.
Queue reporting stays factual: how many items, which codes, how long they have been open, and who cleared them. A queue nobody works is not a control. Part of each engagement is making sure the queue has an owner whose name is on it, and that the owner has the time to work it. That is often a business process optimization question as much as a technical one.
Audit trails a controller can read
When a customer, a driver, an auditor or an insurer asks why something happened, the answer should come from a table, not from memory. Every AI step the practice designs writes a record of what it saw, what it suggested, and what a person did with the suggestion. If the model is updated, the version changes, and old decisions still point at the version that made them.
A minimal table for AI suggestions and the human decision on each one:
CREATE TABLE ai_decision_log (log_id BIGINT PRIMARY KEY, source_system VARCHAR(40), source_key VARCHAR(64), input_hash CHAR(64), model_version VARCHAR(40), exception_code VARCHAR(40), suggestion TEXT, reviewed_by VARCHAR(80), reviewed_at DATETIME, final_action VARCHAR(40), second_approver VARCHAR(80));
The source key points back to the load, invoice, settlement line or document. The input hash proves which version of the input the model saw.
The log lives in the client's own database, next to the TMS and accounting data it describes, under the same backups and access controls. It is not a vendor dashboard that disappears when a subscription ends. Access to the log, and to any personal or card data the AI step reads, is handled as data protection work. Tying the log to the general ledger is part of transportation accounting integration.
How a Strong Tower Consulting applied AI engagement runs
Engagements follow the same order every time. The order matters more than the model.
- Pick one decision. Name the specific decision or task, the person who owns it today, and what a mistake costs.
- Test the data. Run readiness checks on the client's own tables. Fix keys and gaps before anything else.
- Decide if AI fits. If an exact rule solves it, write the rule. If not, define what the model may suggest and what it may never do.
- Design the queue and the log. Exception codes, owners, approval amounts, and the audit table, all inside the client's systems.
- Run beside the current process. Compare suggestions to what people actually decided on later weeks before anyone relies on them.
- Hand over ownership. Document who works the queue, who reviews model changes, and how to turn the step off.
Strong Tower Consulting does not report results as vanity percentages. The measure of an engagement is whether the client's own team can show, from its own tables, what the AI step suggested, what people did, and whether the later weeks bore it out. For carriers planning several projects, a fractional freight CIO or IT strategy planning engagement can sequence them so the data work is done once.
Frequently asked questions
What is the Strong Tower Consulting applied AI practice?
It is independent advisory work for motor carriers, freight brokers, private fleets and enterprise clients who want to use AI inside their existing TMS, EDI, settlement, accounting and telematics systems. The work covers data readiness, where AI fits and where it does not, human review controls, exception queues and audit trails.
Does Strong Tower Consulting require us to replace our TMS to use AI?
No. The practice designs AI steps that read from and write back to the systems a carrier already runs, through SQL views, EDI 204, 990, 214 and 210 transactions, and the integration points the TMS already supports. Replacing a TMS is a separate decision with its own business case.
What does data readiness mean for a carrier or broker?
It means the records an AI step would depend on are complete, consistent, and traceable to a source. Customer, lane, unit and driver keys must line up across the TMS, EDI files, settlement and accounting. If the data cannot pass that check, the first project is cleaning the data, not training a model.
Where should AI not make decisions in a trucking operation?
AI should not release driver pay, post invoices, change a customer rate, override hours of service, or approve a safety decision on its own. In those areas a model can flag, sort and draft, but a named person approves. Anything that moves money or affects safety stays with people.
How is Strong Tower Consulting related to Exspeedite LLC?
Strong Tower Consulting, LLC and Exspeedite LLC are separately organized sister companies under common ownership.
Put AI where it helps, and keep people in charge.
Carriers, brokers and enterprise clients can call Strong Tower Consulting at 763-244-1373 to talk through one decision, test the data behind it, and decide whether AI belongs there at all.
Published October 23, 2026 by Strong Tower Consulting, LLC. Founded 2008 in Minnesota by Scott Elliott.