Conversations around AI in the supply chain have shifted in the last couple of years. When generative AI first emerged, people often framed it in terms of its potential. Recently, though, a range of applications in predictive analytics, autonomous routing, and real-time optimization have moved from theory to reality.
What’s Related
Still, logistics pros sometimes struggle to implement AI for several reasons, not least of which is that successful implementations require serious groundwork. While the outcomes from AI projects vary radically from company to company, they all need to start with the same foundation: transforming how your organization structures its data, teams, and operations.
How does this transformation look? This isn’t an easy question to answer. Supply chain and logistics companies should embark on a major initiative to connect siloed systems and data into a usable ecosystem. This reset enables faster movement, delivers better outcomes, and ultimately unlocks AI’s potential across the business.
Here are four important takeaways we learned.
Maneet Singh of Odyssey Logistics
1. Integrate Your Data Before You Deploy AI
AI depends on clean, connected data. Without it, results are flawed or irrelevant.
If, like many logistics companies, you have multiple TMS platforms and various warehouse and ERP systems, your first step should be a major consolidation project. For most companies, the end goal should be to build a unified data lake. This is a massive harmonization and normalization project, and it will take discipline, prioritization, and strong governance.
To build a workable data lake, you’ll need to build data definitions from the ground up, harmonize language across your business units, and, in many cases, hire a dedicated data governance lead.
With a solid data foundation, AI can deliver real results: better forecasting, real-time decision-making, and customer insights that were previously out of reach.
2. Drive Leadership Alignment Across the Organization
Even though data governance is the priority, little can be accomplished without leadership alignment throughout your project. This alignment applies to all levels of management. While executive support helps set direction, execution depends on strong, ongoing engagement with middle managers. Their proximity to day-to-day operations means they have the power to either accelerate your project or torpedo it entirely.
Winning and maintaining buy-in on your AI project could be its own article. A key factor will be the ability to show progress early and often — short sprints, clear wins, and frequent communication. Your approach should be collaborative, not top-down. An effective starting point is to engage your business leaders early in the planning phase, ensuring they feel that their input is actively shaping the project’s direction. This will also ensure that your AI project isn’t perceived as an esoteric experiment, but rather as a value-driver, connected to the key business concerns of your leadership.
3. Build Employee Feedback into the Process
Employee support is closely related to leadership buy-in. AI rollouts should be framed as evolving partnerships with every employee at your company rather than one-time events.
Making this shift depends on having a strong system for incorporating employee feedback. Such a system can include built-in checkpoints — predefined places to pause and collect ideas on what’s working with the project and what could use strengthening.
“This approach also helps reduce fear around AI by giving employees a front-row seat to how it works and the value it brings, fostering transparency and trust instead of uncertainty.”
Some of these checkpoints can include finished modules of your AI project. For example, when you launch a route optimization tool, it’s wise to pause and seek employee feedback, and to drum up awareness and support for the wider rollout to keep momentum going. This keeps your employee base aware and engaged in what your AI project is trying to accomplish, step by step. This approach also helps reduce fear around AI by giving employees a front-row seat to how it works and the value it brings, fostering transparency and trust instead of uncertainty.
4. Make Security and Compliance the Backdrop
A driving factor in any AI project is security and compliance. AI can’t operate effectively in unreliable environments, and it can quickly become a major liability if left unchecked. That’s why security and compliance can’t be separate tracks — they need to be embedded across your project from day one.
With every step in rebuilding your data stack, cybersecurity and regulatory requirements need to be top of mind. Every application, every integration, and every tool should meet the standards that your customers and regulatory bodies expect.
This attention may seem constricting, but in the long term, it will keep your implementation at a sustainable pace and avoid the backtracking that slows down so many AI transformation efforts.
Setting the Stage for Successful AI
With AI, the real work happens before the first model is trained. It starts with aligning teams, building clean data, and focusing on outcomes that matter.
This background work sets the stage for AI, enabling fast, strategic, and connected decisions. By carefully teeing up your AI project with these four points, you can ensure your implementation won’t get stuck in the pilot phase. With the technology in place and your teams on board, your business will be ready to move.
Maneet Singh is CIO at Odyssey Logistics. He has over 20 years of IT experience and focuses on strategy spanning, project delivery, M&A, risk management, and global team leadership. He is responsible for Odyssey’s technology and cybersecurity strategy, managing Global IT operations, and leading major IT transformations to support growth initiatives.
