The highest strategic priority for the US logistics industry is “adopting new tech,” Tech.co found in a recent survey. That makes sense. Integrating time-saving software solutions like fleet management software is a big focus in an industry that’s dealing with plenty of challenges, from labor shortages to extreme weather events to tariff uncertainty.
However, AI solutions aren’t the most popular, despite the tech standing as one of the biggest and buzziest advancements of the last few years. The two biggest blockers of AI investment in the logistics sector are ROI concerns (14%) and a lack of technical expertise (13%).
Perhaps you just don’t want to over-invest in AI just yet. But if you’re just looking for a little more information on where to start, keep reading. This breakdown on the value of building vs buying AI tools may be just what you need.
In this Guide:
Logistics Companies Are Adopting New Tech
July is the second month in a row in which adopting new tech has remained a top priority for logistics companies across the US, with 16% saying so in Tech.co’s latest monthly survey.
The types of tech being picked up include popular logistics automation software like route optimization, asset tracking, and scheduling and dispatch software. Some brands, like Verizon, Samsara, and Motive, offer packages that do all those software functions, while others focus on one core offering.
When it comes to the more complex, custom-fitted solutions offered by AI technology, however, ROI concerns and technical uncertainties may become blockers for adaptation. The shipping operation OnTrac, for example, uses its own OnTrac OnRoute application for its routing needs, and even sells it to third-party independant operators.
If your company has made tech innovation a key focus for the next quarter — and you’re hoping to explore how the right AI processes could help — you have one big choice ahead of you. Do you build your own solution, or buy an existing one?
Build vs Buy: Pros and Cons
The benefits of these two tactics can be summed up in a few words: Building takes more resources but offers more control, while buying is easier but offers less flexibility.
The downsides of building include all the types of additional resources you’ll need. These include the high upfront costs for both talent and infrastructure development, a longer deploy time, and a dedicated employee (or a whole team!) who can handle ongoing maintenance and updates.
The benefits of building, however, are high. You’ll get complete customization for any of your more unique operational needs, a proprietary system that may offer a competitive advantage, and full control over your company data.
The downsides of buying? You may not be able to fully adapt the tool to highly specific workflows, you’ll be dependent on the vendor’s support and security protocols, and you won’t get that competitive edge. Instead, you’ll get your own benefits specific to buying: A speedier setup, predictable (and low) ongoing costs, and access to the vendor’s specialized industry knowledge.
The “Build or Buy” Question Checklist
Before trying to reshape your operation around the latest AI tech solutions, try taking a moment to answer a few questions. This process can help you assess if your business should build a new process or buy an existing one.
We’ll include all the questions here, but click each option to see a little more context around what your answer to that question says about your organizational needs. Or, keep scrolling down to see all our explanations together.
The Checklist:
- Is this AI capability a core competitive advantage?
- What’s the total cost of ownership (TCO)?
- Do we have the necessary in-house talent and resources?
- How specialized are our operational needs?
- How quickly do we need this solution operational?
- What are the data and security implications?
Is this AI capability a core competitive advantage?
If the process directly addresses an issue that’s fundamental to your operation, building it yourself will protect your data and align with your overall strategy.
For example, if you run a last-mile delivery service and have the resources, you may want to build a proprietary routing algorithm to speed up deliveries within your specific location. But if you just need a standard function, such as warehouse management or maintenance tracking, you’ll likely find a pre-existing solution offers the most value.
What’s the total cost of ownership (TCO)?
For more cheap processes, buying may pay off more quickly. However, each choice will bring unique costs: Building a process requires paying data engineer salaries and infrastructure costs, while buying requires a set, ongoing subscription fee (which may even increase in the future).
If you count up the total expected costs for each option, you can figure out if your return on investment (ROI) over five years will be high enough to justify them.
Do we have the necessary in-house talent and resources?
If you already have the people you need, building becomes significantly easier. In fact, a large enough company might be able to employ a full-time team of innovators who are constantly building new tools. In contrast, smaller operations are more likely to find that the “opportunity cost” of your internal team’s time may be too great.
If your operation doesn’t already have the data scientists and machine learning engineers it’ll need, the time and effort of scouting and hiring a new team will be an additional cost to consider. Some more complex tools, like supply chain risk platforms, will likely need to be bought.
How specialized are our operational needs?
If they’re truly unique, an off-the-shelf solution won’t be worth it. After all, you would have to invest in extra customization even for a solution that you’re buying. Since it will require extra time and attention anyway, you might as well start building it yourself and cut out the middleman.
For many businesses, however, you’ll be operating within a clear industry standard. A typical fleet management solution will handle all the core needs of a commercial fleet in the US, for example. The biggest fleet management systems, such as Samsara and Verizon, even offer specialized solutions for fleets such as school buses or snow plows.
How quickly do we need this solution operational?
If you need to move fast to capture a market opportunity, buying a solution could easily be worth it. You can buy, deploy, and train your employees on a SaaS (Software as a Service) platform within a week or two.
However, if you’re working with a longer timeline, you can take months or years to develop a custom solution to superpower the next stage of your business journey.
What are the data and security implications?
Building keeps your data more safe. However, logistics companies are unlikely to be working with information that’s as sensitive as the data routinely required for industries such as healthcare or finance.
In addition, buying an AI app won’t immediately expose your data. All popular generative AI solutions, from ChatGPT to Google Gemini, offer data security promises for any paid plans that allow businesses to upload proprietary documents and data.
Next Steps: Figure Out Quick Wins and a Long-Term Plan
If you’re interested in building, try exploring a few quick wins. If you can find a spreadsheet expert with a little coding or Zapier experience, for example, they may be able to create simple rule-based builds. These could be created to streamline three common fleet issues: Fuel expenditure tracking, scheduling, and fleet maintenance.
Whatever you settle on, keep the above questions in mind. This will ensure that your company balances its resource investment against the value that can be offered by a new, customized technology stack.