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Build Versus Buy: Deciding on AI Adoption for Your Business

Adopting artificial intelligence (AI) into your business can drive innovation, efficiency, and competitive advantage.  However, one of the crucial decisions you’ll face is whether to build your AI solution in-house or buy a ready-made solution from a vendor. This “build versus buy” decision involves several considerations, each with its own set of advantages and challenges.

Key Considerations in the Build vs. Buy Decision

  1.  Cost and Resources
  2.  Customization and Control
  3.  Scalability and Maintenance
  4.  Expertise and Innovation
  5.  Speed to Solution
  6.  Risk of Project Failure

Cost and Resources

When deciding whether to build or buy, cost and resource allocation are primary concerns.  Building an in-house AI solution requires significant upfront investment in talent, technology, and time.  Your business must be prepared to invest in hiring data scientists, engineers, and AI specialists.  Additionally, developing and training AI models from scratch involves substantial time and computational resources.

On the other hand, buying a pre-built AI solution can offer a more predictable and sometimes lower initial cost.  Vendors typically spread the development costs across multiple clients, making the solution more affordable for individual businesses.  However, recurring subscription fees and potential costs for customization can add up over time.

Customization and Control

Building your AI solution gives you full control over the design, development, and deployment processes.  This control allows for a high degree of customization tailored to your specific business needs and goals.  However, this approach requires a deep understanding of AI technologies and continuous investment in updates and maintenance.

In contrast, buying an AI solution often means sacrificing some level of customization.  Vendors offer configurable options, but they may not align perfectly with your unique requirements.  It is important to vet vendor claims thoroughly to ensure the solution meets your needs.  (For more insights, see our paper on Vetting Vendor AI Claims).

Scalability and Maintenance

Scalability is another factor in the build versus buy decision.  In-house solutions offer scalability tailored to your business growth, but they require ongoing maintenance and updates to keep pace with technological advancements and changing needs.  This maintenance can become resource-intensive over time.

Vendor solutions often come with built-in scalability, allowing your business to expand usage as needed without worrying about infrastructure upgrades.  Maintenance and updates are managed by the vendor, freeing your internal resources for other initiatives.

Expertise and Innovation

Building an AI solution internally can drive innovation within your organization, fostering a culture of continuous learning and development.  It allows your team to experiment with advanced technologies and methodologies, potentially leading to unique competitive advantages.  However, this approach demands a high level of expertise and a commitment to staying current with the latest AI trends and advancements.

Buying an AI solution leverages the vendor’s expertise and experience.  Vendors offer solutions backed by extensive industry knowledge and research.

Speed to Solution

One of the significant advantages of buying an AI solution is the speed to solution.  Implementing a ready-to-go AI solution can drastically reduce the time needed to start realizing the benefits of AI.  This quick deployment can lead to faster financial gains, as the solution can start driving efficiency and improving decision-making almost immediately.  In contrast, building an in-house solution can take months or even years before it becomes fully operational, during which time market opportunities may be missed.

Risk of Project Failure

A high percentage of internal AI projects fail to deliver the expected results.  According to some studies, up to 85% of AI projects do not achieve their intended outcomes due to a variety of factors such as lack of expertise, insufficient data quality, and poor project management.  This risk makes the buy option particularly attractive for businesses looking to minimize uncertainty and ensure a higher likelihood of success.  Leveraging a vendor’s proven solutions can mitigate these risks and provide a more reliable path to achieving your AI goals.

How First Analytics Can Help

To assist businesses in making informed decisions about AI adoption, First Analytics offers comprehensive Analytics Strategy services.  These services help you develop a clear roadmap for integrating AI into your business processes, ensuring alignment with your overall business goals.  This  approach involves assessing your current analytics capabilities, identifying gaps, and providing a detailed plan for building or buying AI solutions that best meet your needs.

We can also help if you pursue the “build” direction, through our Custom Solutions and Services, Analytics Enablement, and Flexible Analytics Resourcing offerings.

Finally, our Collaborative Analytics Lab provides a unique environment for your team to work alongside our experts on real-world AI projects.  This collaborative approach not only accelerates the learning curve for your team but also ensures that the AI solutions developed are practical, effective, and aligned with your business objectives.  By leveraging our Collaborative Analytics Lab, you can pilot AI initiatives with reduced risk and greater confidence in their potential success.


The build versus buy decision for AI adoption is complex, requiring a careful assessment of your business’s needs, resources, and goals.  Both approaches have their merits and challenges, and the right choice depends on your specific circumstances.   We are ready to meet you where you are and lend our experience as you make these decisions.  You can contact one of our team members directly if you would like to learn more.


Disclosure: most of this post was written by an AI trained specifically on the business of First Analytics, with human prompting.  Check out our generative AI disclaimer.


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