Enterprise AI

The AI dilemma: build versus buy

Today, nearly half of businesses have adopted some form of AI. Those that haven’t are eager to realize the benefits right now.

Only problem, businesses looking to take that next step usually run up against a couple obstacles.

  1. Skills: Let’s face it, acquiring data and machine learning scientists isn’t easy (in case you haven’t heard, there’s a big talent shortage).

  2. Data sets: Not all businesses have access to the quantity and quality of data that’s required to power AI.

  3. Explainability and trust: If you can’t explain how AI does what it does, how can you trust it? We call this the black box problem, and it’s a big reason why AI might present a problem internally.

  4. Time to market: Some organizations are discouraged by AI because they believe implementation and execution take too long.

  5. Scalability:  Today, most businesses have between 0-10 machine learning models in production. In the next three years, that number could be as high as 200. Many businesses lack the internal resources to scale AI capabilities.

These are very real challenges, but they shouldn’t prevent you from taking advantage of AI and the positive impact it could have on your business. In fact, these “big” hurdles aren’t even that big. They can be overcome. If you want to find out how, let’s talk.

Otherwise, you’re probably wondering whether building or buying an AI system is the best way to go. Maybe you’re wondering if one of these two approaches could mitigate some of the challenges we noted above.

Let’s find out. We’re going to break down the pros and cons of build versus buy. Then, we’ll give you our totally unbiased answer.

So, here we go.

The quick and dirty

Bottom line: If your business isn’t actively monetizing its data; in other words, selling access to it or using it in models, the buy option is likely the quickest way to market. But if you’re a company with a developed data practice and pre-existing infrastructure, building might be your best bet.

If your company has data pipelines and models in production that are seeing positive results, then you should probably keep building. Building pipelines, models, and prediction usages for the first time takes plenty of resources, so again, to go faster you might want to purchase.

Our take

When it comes to building versus buying, there are no hard rules. That’s why we think the best approach is to do a little bit of both. A combination can help you scale your AI capabilities and ensure that you get results faster.

Ultimately, before even engaging in the build versus buy debate, you need to evaluate what your organization’s capabilities are and use a hybrid approach that:

  • Enables you to move fast

  • Helps you meet (and even surpass) your revenue goals

  • Provides operational excellence

  • Drives customer centricity

  • Preserves consumer privacy

Navigating the AI landscape shouldn’t be difficult. If you have questions, we’re here to help. And if you’d like an in-depth look at building or buying AI, feel free to watch our on-demand webinar for more details.


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