AI technology: The make or buy decision

As technologists, we often face this question: Should we build a solution or should we buy one? The make or buy decision is no trivial matter.

In a world where AI will continue to define the century, correctly weighing the trade-offs between building versus buying AI solutions will be a key differentiator for you and your organization.

For example, you might be an executive concerned about the deadline for a key AI project at your organization. Is there a tool you can purchase that will help your team deliver on time? Or perhaps you're a platform engineer in charge of setting up the MLOps stack for your company. Will you build it all by yourself?

Regardless of who you are and why you need AI solutions, this blog post will help you complete a pros and cons analysis to understand the trade-offs of buying vs building.

• Buying AI solutions or building custom software: Things to consider
• Build AI solutions when you have a competitive advantage or need AI innovation
• Develop AI skills and technical expertise with skill assessments
• AI deployment: Weigh the pros and cons of building vs. buying

While building your own AI solutions may sound more cost-effective than buying one in the market, the reality is that building your own solution is never free. 

There’s always a cost, whether it be the engineering time dedicated to build the solution, ongoing operational overhead, risk of failure, obscure opportunity costs, or technical debt. In particular, when it comes to AI solutions, these costs can be significant.

Because of this, you must carefully consider the trade-offs between building and buying a solution.

When building in-house, the total cost of development often includes hidden expenses like ongoing maintenance. On the other hand, buying a solution can be more expensive upfront but typically includes benefits such as faster development times and less ongoing maintenance for your teams.

Commercial tools like Software-as-a-Service (SaaS) platforms or off-the-shelf products can drastically decrease the time it takes you to deploy AI solutions. Tools like these often provide ready-to-use, tested, and supported infrastructure that can help you get up and running quickly. 

Building a solution, however, offers more customization and flexibility, allowing teams to tailor tools to specific needs. They can even help you gain a competitive edge through creative AI innovation, especially if the available products in the market don’t meet your requirements.

Opting to build an effective solution in-house requires specialized AI skills, a very expensive skill set to hire for these days. While you can (and should) prioritize upskilling, your organization will need to continuously develop these skills to stay on pace with AI technology.

If your organization currently lacks the necessary skills, buying a solution can allow you to synergize external expertise with your organization's unique competitive advantages. This can be a more efficient use of time and resources (but you’ll still need AI skills to maintain it!).

Pro tip: When in doubt, buy your AI solutions, especially if your organization lacks the skills to deploy, manage, and innovate with them over time. It will almost certainly be cheaper to pay for a pre-made solution that can solve your problem or give you a competitive advantage.

There are still times when you'll want to build a solution yourself. I recommend doing so when you have a unique competitive advantage that will allow you to create a solution that’s more effective (e.g. cheaper or faster) than what you can buy in the market. 

Having a "competitive advantage" here means you or your team have a unique skillset—like a deep understanding of Kubernetes or proprietary knowledge—that you can leverage in your AI strategies.

Pro tip: Sometimes building a solution yourself can also give your organization a competitive edge relative to your competition, so keep this in mind during your make or buy decision process. Remember the old adage: "Buy for parity, build for competitive advantage.”

If you choose to build AI products, do so with an iterative approach. Break the project down into logical milestones and tasks that you can steadily work towards. 

The first milestone I would recommend is to build a proof-of-concept (POC) of your solution as quickly as possible. This will help you identify potential gaps and roadblocks in your planning and validate your solution's viability before committing significant resources to the project. After creating a successful POC, incorporate new findings and continue to iterate your AI solution.

Regardless of whether you choose to buy or build AI solutions, your teams need AI skills. Skill assessments are one way to assess and develop the technical skills your organization needs to build or implement AI.

Skill assessments let you benchmark your technical understanding through periodic self-evaluations, then receive recommendations on where to direct your learning next.

Check out my article on how I learned AWS using Pluralsight’s Skill IQ assessments. As I learned AWS, I used these assessments to periodically track my progress and identify gaps in my knowledge that I could revisit through brief course recommendations within the Pluralsight platform.

Pro tip: Try taking a different skill assessment each week. This will allow you to track your progress, refresh your memory, and fill skills gaps you may not have identified on your own.

Pro tip: In the Pluralsight Skills platform, each Skill IQ question contains a response option, "I don't know yet.” To get an honest assessment of your progress and skill gaps, I recommend selecting this option whenever you don't confidently know the answer to a question. These assessments are solely for you . . . there's no point in guessing and artificially inflating your results.

When you need an AI solution, your answer to the make or buy decision will depend on your resources, skills, and need for innovation.

Correctly weighing how to best incorporate AI solutions into your life, both work and personal, will be something we all need to carefully consider in the coming months and years.

Tune into some of my Pluralsight courses to stay in the loop on popular tech trends and continue to build your skills:

• Cleaning and Working with Dataframes in Python

• Python: Cleaning Data

• Python for Data Analysis