Modern AI is advancing rapidly, with new capabilities developing every day. While supply chain applications for AI are growing just as fast, many organizations still struggle to decide where and how to put this technology to work in their real-world environments. This has created an adoption gap in the industry: investment in AI is outpacing organizations’ ability to put it to work in ways operations teams can confidently rely on.
The potential benefits of AI’s ability to create a system of action are exciting, but the expectation of huge ROI by the C-Suite does not translate to enthusiasm from everyday users. Executives may decide to pull the trigger on a widespread implementation, but if users don’t find the tool helpful or see how it makes their jobs easier, adoption stalls.
“Not every AI solution is built for transportation,” Shipwell CEO and co-founder Greg Price points out. Using a solution that’s not designed for supply chain complexity can make users more hesitant to rely on it in their operation.
The AI Proof Problem: Deployment vs. Adoption
A recent survey from Kinaxis makes the disconnect in attitudes toward AI between the C-Suite and leadership teams clear. Across supply chain use cases, executives are far more optimistic about the ease of use and financial gain for AI than operations and IT leaders. A couple of notable examples include:
- Detecting anomalies and issuing early warnings. 32% of C-Suite respondents said implementing AI for this task would be difficult, compared to 64% of operations leaders.
- Optimizing supply chain networks. Only 19% of executives thought this was a difficult ask, while 30% of ops leaders were concerned.
This optimism disparity can lead to senior executives approving sweeping AI initiatives that their ops and tech teams find difficult or even risky to attempt. Getting teams to try an AI tool or feature is not the same as convincing them to rely on it in their daily workflow. Earning adoption from ops leaders requires proof that the tool is effective.
Before implementation, executives should answer these common questions operations leaders have about AI:
- Can it work within their specific workflow?
- Will it make their jobs meaningfully easier?
- Can it provide reliable results without the human team having to hover over it constantly to catch mistakes?
- Can they trust it enough to focus on other, more strategic parts of their job?
While executive leadership is often focused on the bottom line, what IT and operations leaders really want are the answers to the above questions, along with documented outcomes tied to specific workflows.
Overcoming Supply Chain AI Skepticism
Part of the issue lies in how executives “sell” AI to their operations teams. Rather than promises focused on efficiency and cost savings, end users want to know exactly what the tool will do and how much autonomy they should give it to act on their behalf.
There is also an education curve. Agentic AI is still new, terminology is inconsistent, and teams are learning what these systems can (and can’t) reliably do. But understanding the technology is only part of the equation. Teams also need to see how it performs within the realities of their own operation.
Every supply chain organization has different data structures and sources that live within different workflows. AI needs to be able to work across and make sense of data from all of these varying functions. It also needs to act in ways that help human teams get their jobs done.
“The first question shouldn’t be, ‘Where can we use AI?’” says Price. “It should be, ‘Where is the work breaking down?’ Start with the problems you’re facing operationally, then determine whether AI is the right way to solve them.”
Before implementing AI, stakeholders should also be clear on how much autonomy it will have. Will it recommend an action for a human to approve? Act autonomously within defined thresholds? Escalate when confidence is low? Those decisions should reflect both the risk of the task and the level of trust the AI has earned.
Difficult as it may be for AI-enthused executives to hear, the skepticism they get from operations and tech teams isn’t entirely unfounded. Technical readiness and access to the technology aren’t enough to get everyone on board.
The crucial piece many C-Suite leaders miss is the proof operations and tech teams need to be confident in an AI solution:
- What guardrails will be in place to ensure the AI acts in the way we expect?
- How does it handle a missed pickup?
- What does it do if a carrier is unresponsive?
- How does it react to a rate outside an approved threshold?
- And possibly most crucial: what does the AI do when it isn’t confident enough to act?
That proof is already emerging in real transportation operations at one global enterprise using Shipwell. 53% of monitored shipments that encountered an exception were automatically resolved by their Track & Trace AI Worker, reducing the manual intervention those exceptions would otherwise require.
Co-Innovation: The Key to AI Implementation Success
The ideal solution to the adoption gap is using AI to build automations, workflows, and tools with input from the people who will interact with it every day. Instead of choosing the solution that promises the best ROI or the highest efficiency gains, start from the challenges operations leaders say they’re struggling with right now. Including the people who understand the operational risks, exceptions, and thresholds in your specific operation will ensure an AI solution that’s set up for success from the start.
For example, Shipwell’s AI Studio allows its users to turn real-world concerns from logistics teams into workers that take action on repetitive but inconsistently resolved tasks. These run the gamut from carrier appointment coordination to ingesting settlement documentation for payment – all based on how your team operates.
A co-innovation approach gives operations teams a role in shaping how AI works, validating its performance, and defining when it should act autonomously and when it shouldn’t. Rather than asking teams to adapt their operation to the technology, the technology is shaped around the real problems they need to solve.
What Success Actually Looks Like
Successful AI adoption doesn’t have to start with sweeping transformation. It can start with one workflow that consumes too much time, one exception that repeatedly requires manual intervention, or one decision that can safely be automated within defined guardrails.
Start with a specific problem. Define what success looks like. Determine the appropriate level of autonomy. Prove the outcome in a real workflow. Then expand from there. As teams see AI perform reliably, trust grows – and so does their willingness to give it more responsibility.
AI adoption isn’t won by making promises and asking operations leaders to trust the technology. It’s earned by demonstrating real-world results that give them the confidence to make AI a valued addition to their team.
Curious to learn more? Check out AI Studio today.
