Kevin Novak, Managing Partner & Founder at Rackhouse Venture Capital.
Recent disruptions in key trade routes, from commercial shipping issues in the Red Sea to automotive production delays following floods in Europe, have exposed weaknesses in our supply chains. Traditional automation has delivered efficiency gains but introduced a hidden fragility: Systems that work perfectly within normal parameters can still break down exactly when we need them most. Having spent over a decade building and investing in AI-driven logistics and data infrastructure, I’ve seen how systems optimized for efficiency can crumble in the face of real-world volatility—unless they’re designed to adapt in real time.
AI’s Value In Crisis Response
I believe the biggest impact AI can have on supply chain management is in crisis response and operational issue recovery. In the world of non-AI tech, process automation often goes hand-in-hand with rigidity. When things remain within normal parameters, these systems deliver efficiency and profitability. But when real-world complications arise, the solution can default to “pick up the phone, call your counterparty and figure it out.”
The rise of generative AI has fundamentally changed this dynamic. These systems can self-solve a significantly larger number of problems, and I’ve found them to be uniquely suited to handling ambiguous situations. They can often deduce the right fix and implement it, either autonomously or with minimal human oversight. Additionally, long-form natural language and/or audio is a totally acceptable medium with which to interact with GenAI products. This means that even in situations where a phone call needs to be made or a human answer is needed to answer a question, AI tools can get the answers they need while continuing to manage incidents at machine speed.
Creating More Resilient Markets Through Technology
AI and data technology are also transforming supply chain management by creating technology-driven marketplaces that give buyers and sellers more options for finding partners. Companies like Uber Freight and Flexport have digitized freight brokerage, enabling more reliable transactions at market-clearing prices.
We can see this marketplace revolution playing out across multiple segments of the supply chain. I’ve seen companies my firm works with developing this technology in a myriad of ways, including all-in-one infrastructure platforms for last-mile logistics, freight marketplaces that drive cost savings and efficiency gains for retailers in specific regions, AI-powered inventory modeling and more. The result has been more standardized data models and automated matching that can make it easier than ever to understand fair value and find counterparties.
In my experience, liquid markets are reliable markets, and technology is bringing liquidity to traditionally offline marketplaces. And the numbers show the ROI potential: A 2021 report by McKinsey found that early adopters of AI-enabled supply chain management were able to reduce their logistics costs by 15%, improve inventory levels by 35% and enhance service levels by 65%.
Implementation Challenges And Solutions
The first key challenge of integrating AI technologies into the supply chain is technical adoption and data centralization. In order to be effective, your AI tools need access to machine-consumable data that represents the current state of your system, ideally in a centralized server warehouse or data lake. While there are AI systems being developed that could remove the necessity of such centralized information, this sort of centralization can give valuable simplification to the problem and increase your odds of success for AI-accelerated workflows.
The second challenge is the inherently delayed nature of the payoff for an investment in technology and AI. One fundamental truism I’ve learned in my days building startups is that operations investment tends to pay out linearly (i.e., immediately and in direct proportion to your input investment), whereas research and development investment pays out exponentially (i.e., initially quite sublinearly but, with sufficient investment and scale, paying out with considerably more leverage than operational investment). Keeping this in mind can help you build your budget and update stakeholders accordingly.
Finally, the biggest challenge many companies face when implementing AI in their supply chain is one of trust. For automation to be maximally effective, it needs to influence and steer a company’s most critical supply chains, ideally responding with a speed that human operators are unable to match. Take measures to ensure your AI tools have solid safeguards and human oversight to prevent critical errors and cyberattacks, and be transparent with shareholders to help build their confidence in the steps of your implementation process.
Moving Forward: A Balanced Approach To Adoption
I believe the path forward will require both vision and practicality. Companies should make vision-centric decisions about AI investment while taking an evidence-based approach to implementation. Success means complete adoption of AI, but that adoption must be earned through demonstrated ability to handle real operational challenges.
The data is clear: Leaders in GenAI adoption and data-led innovation—those who view GenAI capabilities as the primary driver of their automation investments—are seeing “72% greater annual net profits and 17% more annual revenue growth than peers.” But perhaps more importantly, they’re building datasets and operational experience that could be hard for others to catch up to. While the investment patterns might be delayed and trust must be earned gradually, I recommend starting the process now. I believe the next generation of supply chain management will be led not by those who just have the best technology but by those who have learned how best to use it.
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