How AI Deep Research Can Transform The Freight Sector

Forbes

a day ago

Albert Lie is the cofounder and CTO at Forward Labs—next-gen AI-driven freight intelligence for sales and operations.
For years, freight professionals have struggled with the logistics intelligence gap, relying on siloed systems, gut feelings and outdated tools to inform high-stakes decisions.
All of those hours of manual cross-platform searches and spreadsheet wrangling have kept prospecting slow and turned big business decisions into analytic leaps of faith, which were, at best, latched to third-party intel.
But a new category of AI-powered tools with 'deep research' capabilities is beginning to replace guesswork with contextual intelligence. As PYMNTS explains, these reasoning-capable, vertical-specific agents—offered by companies like OpenAI, Google, Anthropic and Amazon—could transform decision making by synthesizing fragmented, cross-platform data.
By understanding these systems, freight professionals can make more informed decisions about where and how such technologies may provide the most value. This article explores how deep research AI works, how it differs from horizontal tools and what it takes to successfully adopt it.
Closing The Logistics Intelligence Gap
Freight professionals know the traditional research model all too well: digging through customer relationship management (CRM) tools, manually building prospect lists, chasing down historical shipment data.
These workflows are unsustainable with the high-velocity flows of data unique to this field, and the generic tools of the trade, like search engines and lead tools, have long shown their age, returning keyword-matched noise or outdated contact info lacking context.
AI deep research platforms could change that. They don't just scrape websites, but they crawl, synthesize and analyze large-scale data across internal and external sources.
Also often referred to as enterprise research agents, vertical copilots or reasoning AI, these systems are deployed by connecting to internal platforms like CRM and transportation management system (TMS) tools, then tuned to specific use cases like request for quote prep or carrier research. Under the hood, they typically combine retrieval-augmented generation (RAG), web search, domain-specific embeddings and real-time indexing.
While tools from the big players mentioned above are the most well-known and offer the broadest use cases, sector-specific agents, such as those in the fintech space highlighted in a recent Forbes article, are also emerging across verticals to synthesize high-volume, fragmented data into usable insights.
This has significant implications for freight teams. It reveals the hidden cost of time spent sourcing, vetting and interpreting scattered data. Instead, teams can surface unified, dynamic and contextualized intelligence that cuts through noise.
For the freight industry, this trend could mean having tools capable of returning contextual answers to natural language questions like, 'Which shippers increased volume in Austin last quarter?' or 'Which routes are most likely to face drayage delays next month?'
Freight Workflows Already Being Transformed
While existing consumer-facing tools like Perplexity and Liner aren't built for logistics-native data, the infrastructure exists. Of course, AI isn't immune to the classic 'garbage-in, garbage-out' problem. Without proper data cleansing and enrichment, the insights will remain unreliable.
But deep research remains highly promising, and the tools are already making a dent in a few key areas:
AI-powered visibility platforms can take in real-time carrier and traffic data to detect deviations, flag delays and recommend reroutes, giving operators a faster response window.
A couple of years ago, a Forbes Technology Council article highlighted how this increased visibility translated into reduced disruption costs and stronger service-level performance.
In drayage and last-mile contexts, AI tools are improving routing decisions by dynamically adjusting for traffic, weather and load constraints.
A 2025 Fast Company piece on AI in drayage highlighted how optimized dispatching through AI led to double-digit improvements in fleet utilization and reductions in idle time. Dirox's supply chain and logistics report similarly points to a growing role for AI in minimizing fuel waste and coordinating just-in-time delivery windows.
Forecasting has traditionally been a mix of guesswork and lagging indicators. But with access to real-time order flows, macroeconomic signals and point-of-sale (POS) data, AI systems can anticipate shifts in demand more precisely. This reduces stockout and overstock risk.
Dirox's report also underscored how AI enables leaner inventory and better capacity planning.
Why Vertical AI Matters More Than Ever
Deep research AI is showing freight professionals that they don't just need more freight data. Instead, they need tools with the capacity to understand that data in its context and reach into external databases to surface even more insights.
Traditional horizontal platforms like CRMs, search engines or general-purpose large language models (LLMs) are built for flexibility across industries but lack the domain-specific logic to handle freight's complexity.
Vertical AI models, on the other hand, are trained on sector-specific data like bills of lading, RFQs, tariffs and routing schedules. A recent example is OpenMart, a startup developing vertical agents that help enterprises sell into fragmented retail markets by interpreting local purchasing patterns.
A helpful analogy: Horizontal AI is a Swiss Army knife; versatile but shallow. Vertical AI is a surgical scalpel, precise, targeted and designed to deliver clarity when the stakes are high.
Getting Started With Deep Research AI
Implementing deep research systems, however, takes planning. Beyond the tools themselves, companies need:
1. Clean And Connected Data: These systems thrive when integrated into CRMs, TMS platforms and internal document repositories. But messy or siloed data, like duplicate records, inconsistent formats or unlabeled notes, can compromise performance. Cleaning and labeling this data is a critical first step.
2. Cross-Functional Buy-In: Freight teams need alignment across operations, sales and data leads to identify meaningful use cases. Start small, like automating RFQ prep or surfacing route exceptions, and expand from there. Pilots should be scoped and evaluated with clear business outcomes.
3. Domain-Tuned Intelligence: Most off-the-shelf GenAI tools don't understand freight workflows. Teams may need to build thin wrappers around LLMs, integrate retrieval pipelines or fine-tune models to deliver useful results. Domain knowledge must be embedded into both the model and the prompts.
As deep research AI becomes more accessible, freight professionals who understand and thoughtfully integrate these tools into their workflows can enhance decision making. However, success depends not just on the technology, but on clean data, cross-team coordination and domain-specific tuning.
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