Build or Buy: Agentic AI Frameworks for Supply Chain Automation

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Learn about the benefits of agentic AI frameworks for enterprise supply chains, plus the pros and cons of 3rd party and in-house agentic solutions.
January 23, 2025
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Agentic AI is an emerging breed of artificial intelligence with the capacity to make autonomous actions to achieve specific goals. It is built to adapt to new information, learn from past outcomes, and make real-time decisions without the need for constant human oversight. 

While this  level of autonomy is highly useful in a variety of increasingly popular automation contexts, this aspect of agentic AI frameworks is especially valuable in environments like enterprise supply chains, where a high level of complexity and the threat of unanticipated disruptions can make around-the-clock human intervention difficult to scale. 

Agentic AI offers considerable transformative potential in areas like demand forecasting and dynamic pricing, promising significant improvements in efficiency, revenue, and customer satisfaction. However, the decision to purchase a third-party solution or build one in-house is a critical one that must be navigated if enterprise leaders want to maximize the value of their investments. 

In this blog, we will weigh the pros and cons of each approach to help stakeholders land on the decision that aligns with their objectives.

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The Benefits of Agentic AI Frameworks for Supply Chain

Agentic AI transforms supply chain operations by enhancing demand forecasting capabilities and enabling real-time dynamic pricing that keeps pace with shifting markets. Other benefits include:

  • Efficiency and Cost Savings: Automate manual, time-consuming tasks and repurpose employee time for high-value activities.
  • Increased Revenue: Optimize pricing strategies to maximize revenue.
  • Customer Satisfaction: Anticipate customer needs by ensuring equipment is available and functional when required. Reduce delays and subsequent customer frustration while repurposing employee time to focus on customer relationships. 
  • Employee Satisfaction: Reduce manual overhead and improve morale, enabling employees to focus on their areas of expertise and aligning their workloads with meaningful contributions.
  • Scalability: Agentic AI systems are scalable and can operate around the clock, delivering the same quality outcomes for businesses with 5 stores or 500.

To Build or to Buy: A Look at the Benefits and the Drawbacks

The choice to leverage an external vendor’s AI capabilities or to build your own solution in-house comes in most respects boils down to a choice of convenience or control.
Opting for an external vendor offers the convenience of speed and specialized expertise that allows you to tap into a wealth of resources and knowledge without the need to build infrastructure or hire a dedicated team of AI experts.These vendors bring proven, ready-made solutions to the table and can significantly reduce the time and effort required to get a solution up and running.

On the other hand, building an AI solution in-house gives you full control over every aspect of the system. You have the flexibility to tailor the solution specifically to your business’s needs, from the data it uses to the exact way it operates. This level of customization can result in a highly specialized AI model that fits seamlessly into your operations and evolves with your changing business objectives.

The following chart highlights other key differences between the two approaches:

Choosing the Right Path for Your Business

When deciding between buying or building an agentic AI framework, businesses should weigh several crucial factors. Consider the urgency of implementation and immediate benefits versus the long-term vision of the company.

For quick deployment with less upfront investment, third-party solutions may be the optimal choice, providing immediate access to advanced AI capabilities and enabling faster time-to-market.

However, for organizations with a focus on long-term strategy and more bespoke requirements, building an in-house solution can offer unparalleled customization and control. This approach allows the AI framework to evolve alongside the business, ensuring that it meets specific requirements and workflows.

Businesses must also assess their tolerance for vendor dependency and data security concerns, as these factors can significantly influence the decision. By aligning the choice with strategic priorities and available resources, organizations can make an informed decision that best supports their long-term goals.

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A Middle Path: Building Bespoke Agentic AI Frameworks with a Data Partner

Ultimately, the choice between building and buying an agentic AI solution should reflect your company’s strategic priorities, resource availability, and risk tolerance. Whether opting for the immediacy of a third-party solution or the tailored precision of an in-house build, the key lies in aligning the AI framework with your business objectives to harness its full transformative potential in your respective supply chain.

It is important to remember, however, that building a solution in-house doesn’t necessarily mean relying on your internal data teams to push that solution across the finish line.

Strategic data partners like Hakkoda give companies looking for the best of both worlds a third option: combining the flexibility and specificity of a custom-built solution with the speed and expertise of external support.

Hakkoda also brings a host of specialized industry understanding and advanced tooling to the table, allowing your team to focus on core business priorities while ensuring the AI system is fine-tuned and integrated effectively into your operations. 

In this way, businesses can achieve the tailored precision of an in-house solution without the full burden of development and maintenance—all while accelerating time to value and minimizing the risks associated with a complex AI deployment.

Ready to explore how agentic AI can transform your supply chain and logistics operations? Talk to one of our experts today.

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