Over the last few months, if you judged AI adoption purely by LinkedIn posts and Twitter threads, you’d think everyone suddenly figured it out.
Every company appears to have a polished AI strategy. Every operator supposedly built the perfect workflow overnight. Every founder is posting screenshots of autonomous agents replacing entire departments.
It creates the impression that there was some sudden inflection point where AI capabilities became so obvious and accessible that even companies with no prior experience immediately became fluent.
I don’t think that’s what actually happened… What happened is much less glamorous.
The companies moving quickly today are often the same companies that spent the last 12–24 months experimenting quietly with smaller, less exciting use cases. They used those projects to build internal intuition. They learned where the models failed, where they worked, and how humans needed to interact with them to get reliable outcomes.
Then, when the tooling improved dramatically over the last year, they were ready.
That has definitely been true for us at Passport.
The Most Valuable AI Work Usually Doesn’t Look Impressive
Back in 2024, Passport started experimenting with Claude 3.5 on Amazon Bedrock inside a workflow designed to match shipments to the orders that produced them and correctly reconcile invoice values.
Not exactly the kind of AI demo that goes viral on social media.
But it solved a very real operational problem.
Like many companies operating in global ecommerce and logistics, we were dealing with edge cases where shipment and order data didn’t always reconcile cleanly. That created downstream customer frustration, manual operational work, and inaccuracies in invoice matching.
Using Claude in that workflow helped move our order-to-shipment match rate from roughly 85% to 93%, along with a meaningful reduction in customer complaints.
That was already a worthwhile outcome.
But in hindsight, the operational improvement was probably not the most important return on investment.
The bigger value was that we got repetitions using these systems in production.
We learned:
- How prompt engineering changes outcomes in operational workflows
- How context windows affect reliability
- How to structure inputs for better consistency
- Where models hallucinate or become unreliable
- How to evaluate different models against each other
- What kinds of workflows benefit from AI versus traditional automation
Most importantly, the team started developing intuition.
And that intuition compounds.
The Real ROI of AI Experimentation Is Organizational Learning
I think this is where a lot of companies get stuck with AI.
They evaluate experimentation entirely through the lens of immediate ROI.
If a project doesn’t save enough money in the first 30 days or fully automate a workflow immediately, they conclude the effort wasn’t worthwhile.
But the reality is that most of the value from early AI experimentation shows up later.
It shows up as:
- Better product judgment
- Better implementation judgment
- Faster iteration cycles
- More confidence evaluating new tooling
- Better instincts around what is real versus what is just a compelling demo
That matters because the companies that become effective with agentic AI are usually not the companies that waited for certainty.
They’re the companies that accumulated experience early.
Why AI Fluency Matters in Ecommerce and Logistics
This is especially true in ecommerce and logistics AI, where workflows are messy, fragmented, and filled with edge cases.
AI is not particularly useful if your business only operates in perfectly structured environments.
But international ecommerce is the opposite of that.
You’re dealing with:
- Carrier inconsistencies
- Customs complexity
- Shipment exceptions
- Localization challenges
- Fraud prevention
- Duties and tax calculations
- Constant operational ambiguity
Those are environments where human operators traditionally spend enormous amounts of time interpreting incomplete information and making judgment calls.
That is exactly where modern AI systems can become valuable — not by replacing humans outright, but by augmenting decision-making and compressing operational work.
But you only learn how to apply AI effectively in those environments by actually experimenting inside them.
The Hidden Benefit of Tools Like Querio and Notion AI
We saw a similar pattern internally with tools like Querio and Notion AI.
With Querio especially, one of the immediate benefits was that it democratized data analysis across the product organization.
More people could answer their own questions using natural language instead of:
- Writing SQL queries
- Waiting on data team bandwidth
- Relying on analysts for every exploratory question
That created obvious efficiency gains. But again, the deeper value wasn’t just speed.
People started building instincts for working with agentic systems:
- How to prompt effectively
- How to validate outputs
- Where the tools broke down
- What kinds of questions generated useful answers
- How to structure follow-up context
Over time, the organization became more comfortable interacting with AI as a collaborative system rather than just a novelty feature.
That ends up mattering a lot.
Why We’ve Been Able to Move Faster Recently
A big reason we’ve been able to move faster recently with our own internal agent is because the groundwork had already been laid.
The earlier experiments made two things possible:
1. We believed it was actually possible
That sounds obvious, but it matters.
Teams that have never successfully deployed AI internally often struggle to separate hype from reality. They either underestimate what’s possible or chase unrealistic implementations.
Prior experimentation creates a much more grounded understanding of capability.
2. We had already developed the skills needed to harness it
The team already understood:
- Prompting
- Testing
- Evaluation
- Workflow design
- Human-in-the-loop systems
- Failure modes
- Context management
Those capabilities don’t suddenly appear because a new model gets released.
They’re built gradually through experimentation.
Most Companies Are Thinking About AI Backwards
I think many leadership teams are approaching AI with the wrong mental model.
They want:
- A guaranteed ROI case
- A fully polished implementation
- Zero mistakes along the way
But competence with AI doesn’t work that way.
You become competent by:
- Running experiments
- Finding weak points
- Building pattern recognition
- Developing organizational intuition
- Learning where systems fail
That process is messy by definition.
The irony is that the companies demanding certainty before experimenting are often the ones falling behind the fastest.
Meanwhile, the companies gaining leverage from AI today are usually the ones that were willing to look inefficient while learning.
The Goal Is Not One Good AI Use Case
If you’re leading a company right now, I think the goal should be bigger than finding one successful AI workflow.
The goal is to build a company that is genuinely confident and competent working with agentic AI.
That means creating space for teams to:
- Experiment
- Fail
- Iterate
- Test tools
- Build intuition
- Share learnings internally
Because eventually, the differentiator won’t just be access to AI models.
Everyone will have access to the models.
The differentiator will be organizational fluency:
- Who knows how to apply them
- Who understands their limitations
- Who can operationalize them effectively
- Who can adapt as capabilities evolve
Despite how it may look on LinkedIn, there is still a window to get ahead here.
But it’s closing quickly.
Now that the urgency around AI is obvious to everyone, the advantage increasingly belongs to the teams that already spent years quietly getting their hands dirty.
How Passport Thinks About AI in Ecommerce and Logistics
At Passport, we help ecommerce brands grow globally through better localization, cross-border logistics, and conversion-ready international experiences.
As AI continues reshaping ecommerce and logistics, we think the biggest opportunity is not just adopting new tools — it’s building organizations that know how to apply them thoughtfully in real operational environments.
That’s especially important in international ecommerce, where complexity compounds quickly across shipping, duties, localization, customer experience, and compliance.
The companies that win with AI won’t necessarily be the ones chasing the flashiest demos. They’ll be the ones building the operational intuition, workflows, and internal capabilities needed to apply AI consistently and effectively over time.
That’s the mindset we’re building toward at Passport.
We combine hands-on global ecommerce expertise with practical AI experimentation across logistics, operations, support, and internal tooling to help brands scale internationally with less friction and more confidence.
If you’re thinking about how to simplify global expansion, improve the international customer experience, or better prepare your organization for the next generation of AI-powered ecommerce, talk to a Passport expert.
Frequently Asked Questions
What is agentic AI?
Agentic AI refers to AI systems that can take actions, make decisions, and execute multi-step workflows with varying levels of autonomy. Unlike traditional AI tools that simply generate outputs, agentic AI can reason through tasks, interact with systems, and help automate complex business processes.
Why is AI experimentation important for businesses?
AI experimentation helps organizations develop practical experience with AI tools, workflows, and implementation strategies. Even when early projects generate modest returns, they build the institutional knowledge and operational intuition needed to successfully deploy more advanced AI solutions later.
How should companies measure the ROI of AI initiatives?
Companies should evaluate AI initiatives based on both immediate business outcomes and long-term organizational learning. Early AI projects often create value through improved decision-making, faster iteration cycles, stronger implementation skills, and greater confidence evaluating new technologies.
How can ecommerce and logistics companies use AI effectively?
Ecommerce and logistics companies can use AI to improve shipment tracking, customer support, fraud detection, localization, customs compliance, demand forecasting, and operational decision-making. AI is particularly valuable in environments with large amounts of unstructured data and frequent exceptions.
What are the biggest challenges of implementing AI in logistics?
Common challenges include data quality issues, fragmented systems, complex workflows, regulatory requirements, and the need for human oversight. Successful AI implementations typically combine automation with human expertise rather than attempting to eliminate human involvement entirely.
Why do some companies adopt AI faster than others?
Organizations that have spent time experimenting with AI often develop stronger internal capabilities, including prompt engineering, workflow design, model evaluation, and change management. This experience allows them to move faster as AI technology evolves.
What skills do teams need to work effectively with AI?
Teams benefit from developing skills in prompt engineering, output validation, workflow design, human-in-the-loop systems, AI evaluation, and context management. These capabilities help organizations apply AI more reliably and effectively across business operations.
What is organizational AI fluency?
Organizational AI fluency is a company’s ability to understand, evaluate, implement, and adapt AI technologies effectively. It includes technical skills, operational processes, governance practices, and the experience needed to identify high-value AI opportunities while avoiding common pitfalls.
Authored by Ilan Rotenberg
Senior Director, Product | Passport
Ilan Rotenberg, a seasoned engineering and product pro, boasts six years in software product management. Fueled by a passion for elevating user experience, Ilan excels in unraveling user problems, fostering product adoption, and streamlining customer flows. Armed with a Master’s in Mechanical Engineering from the University of British Columbia, Ilan has left an indelible mark, co-creating products with clients such as Airbus, the United Nations, Toyota, Rhode Beauty, and Clove.