A Session with Harish Abbott, CEO & Co-Founder, Augment, at Optym LTL Convergence 2025
Artificial intelligence is no longer just another productivity tool. According to Harish Abbott, CEO and Co-Founder of Augment, AI is entering a new phase where machines don’t just help people work faster but take ownership of the work itself.
Speaking at the Optym Convergence Conference, Abbott outlined how this shift is already underway across industries and why trucking executives should view AI not as a dashboard or feature, but as a scalable operational teammate capable of reshaping freight execution from the ground up.
A Technology Shift Unlike Any Before
Most industries experience transformational technology only once or twice in a lifetime. Steam engines reshaped distance and cities. Electrification enabled continuous production. Automation unlocked mass manufacturing. Digitization created the modern knowledge economy.
AI stands apart because of its speed and scope.
Technologies like personal computers and the internet took nearly a decade to reach mass adoption. Generative AI reached that milestone in roughly two years. Entire markets from autonomous vehicles to software engineering have shifted in under 24 months.
For trucking leaders, the message is clear: this transition will not be gradual.
Why Freight Requires Purpose-Built AI
While AI adoption has accelerated rapidly in software, healthcare, and legal professions, freight presents a very different challenge.
Trucking operations are inherently multi-party and highly coordinated. Every load involves carriers, brokers, customers, terminals, drivers, and siloed systems that must remain synchronized in real time. On top of that, freight data is notoriously inconsistent — appointments, detention rules, accessorial, and routing logic vary by company and customer.
This complexity is why Abbott argues that general-purpose AI tools are insufficient for freight. To be effective, AI must be purpose-built for transportation workflows, data structures, and operational realities.
From Software to AI Teammates
Rather than positioning AI as another system to manage, Abbott introduced a different operating model: AI as a teammate.
At Augment, that teammate is designed to work the way employees already do —communicating through existing tools like email, chat, and collaboration platforms, without forcing teams to learn new interfaces or workflows.
This enables what Abbott described as a human-in-the-loop model, where AI handles repetitive, high-volume operational work while humans retain judgment, customer context, and exception management.
The Four Layers of an AI Teammate
Abbott broke down the AI teammate concept into four foundational layers that trucking executives should recognize immediately:
- Skills: Just like a new hire, AI must be able to read and write emails, handle calls, interact with TMS, and communicate across existing channels.
- Workflows (SOPs): Instead of code, teams define Standard Operating Procedures in plain English. These SOPs instruct AI on how to dispatch loads, schedule appointments, collect invoices, escalate issues, and obtain approvals — mirroring how work is actually done today.
- Knowledge: The most valuable employees accumulate tribal knowledge over time. AI can institutionalize that knowledge by continuously learning from documents, emails, call transcripts, and system data — making organizations more resilient to turnover and growth.
- Judgment: Judgment remains human. When AI encounters ambiguity or gray areas, it escalates decisions to the right person and records the outcome. Over time, this builds organizational intelligence without removing human control.
Abbott summarized today’s AI agents succinctly: they are hardworking interns with excellent memory, not executives or managers.
Why End-to-End Ownership Matters
A key risk in AI adoption is deploying disconnected point solutions — one AI for emails, another for calls, another for updates. Without shared context, these systems create operational noise, broken handoffs, and accountability gaps.
Instead, Harish emphasized task ownership over task automation.
Rather than automating individual actions, AI should own complete workflows — such as dispatching, appointment management, or customer updates — only involving humans when judgment or approvals are required. This dramatically reduces errors, delays, and manual system interactions.
Measuring AI Impact in Real Operations
For executives evaluating AI ROI, Abbott offered a practical metric: TMS touches.
Every human interaction with a TMS requires time, context, and cognitive effort. When AI begins handling updates and transactions directly, those touches decrease —providing a measurable, business-relevant indicator of productivity improvement without relying on abstract benchmarks.
AI as an Accelerator for Optimization
One of the most compelling implications for trucking leaders is how AI teammates improve optimization outcomes.
By capturing structured data from emails, calls, and customer interactions, AI continuously enriches planning inputs. That information can then flow into optimization systems — improving routing, appointment selection, and network decisions in near real time.
In practice, this means faster cycle times, fewer errors, and decisions that reflect both operational constraints and real-world execution.
Where This Aligns with Optym’s Vision
Abbott’s perspective closely mirrors Optym’s long-standing philosophy: human judgment paired with mathematically rigorous optimization.
AI teammates excel at removing repetitive work, capturing data, and executing defined processes at scale. Optimization engines — built on decades of transportation-specific research — use that data to determine the best possible decisions under complex constraints. Humans remain firmly in control, providing context, oversight, and strategic direction.
Together, this combination enables trucking organizations to move beyond analytics and into execution at scale, without sacrificing transparency or trust.
A Defining Moment for Trucking Leadership
Harish closed with a clear message for the industry: this is a rare opportunity.
Purpose-built AI has the potential to reduce human workload, institutionalize tribal knowledge, accelerate execution, and unlock profitable growth while keeping people at the center of decision-making.
For trucking executives navigating tight margins, labor constraints, and rising complexity, AI teammates are no longer theoretical. They are quickly becoming an operational necessity.
