Artificial Intelligence is on just about everybody’s minds these days as the technology continues to progress. Many businesses are in the decision-making process about how the can use AI to their best advantage. They understand that tapping into transformative technology like this could give them a massive advantage over their competitors. These same firms must then decide on whether to develop solutions in-house or to seek existing platforms from third parties. 

There Are Key Points to Consider 

Deciding to do it yourself or lean on the shoulders of giants is challenging. The main points to consider are: 

How long will implementation take? The amount of preparation could be immense, depending on the features. What are you looking to do? Content curation, content moderation, sentiment analysis, facial recognition, threat detection—there’s a long list of possible applications AI can assist with. All of these represent significant investments of time and resources to implement, even in the most technically-proficient companies. It’s always possible to implement a custom version of a machine intelligent assistant, but the question remains, is it worth it? 

Ask Crucial Questions Before Starting 

  • What are the pitfalls of going it alone? Some potential problems could prove too high to handle the job in-house. There are technical and infrastructure challenges to overcome with Machine Learning, Big Data, and Natural Language Processing.
  • Can you afford to fall behind in your industry? What happens in the event one of your competitors leaps ahead of you thanks to their investment in an Artificial Intelligence outsourced solution? If the potential loss of sales is too high, bear down and find a vendor now. A reliable solution is Sigma AI’s AiCurate platform, which has all of the functionality mentioned above.
  • How much are you willing to invest (and to potentially lose?) – In some ways, your investment remains open-ended when you tackle the entire project yourself. You’ll need to appoint staff to work on your solution, and you’ll be entirely responsible for administration and maintenance on an ongoing basis.

Get It Right the First Time 

Keep in mind that the final technology will only perform at optimum levels if the proper training data is input and is of high quality. Consider the concept of “garbage in garbage out.” Poor training data will result in a poorly performing Artificial Intelligence implementation with skewed results. Unless your team has much experience dealing with this data, you may be at a severe disadvantage from the very beginning. That’s the primary reason to select an Artificial Intelligence outsourced solution. 

Relying on an Artificial Intelligence outsourced solution helps you establish fixed costs. Further, the company you select will remain responsible for patching and updating software and infrastructure and dealing with unstructured data. 

Infrastructure is not a trivial concern when dealing with Machine Learning, 

Big Data, Deep Learning, NLP, and Data Preparation. These fast-emerging technological disciplines are resources-hogs, which require copious amounts of CPU, RAM, hard drive, and bandwidth to use effectively. Each bit of complexity added to boost the requirements even further. Before long just managing the program will take considerable time and effort. If your organization can justify the cost and needs additional privacy, it may be worth it. However, most organizations will find these requisites to be overkill. 

A Broad Range of Skills Are Required 

Dependent upon business needs, there are some core competencies that all companies value in AI engineers. Engineers must be able to mine large amounts of data for patterns, possess a deep understanding of algorithms, as well as problem-solving and math skills. They’ll need to be familiar with machine learning, operate using Python, run data visualization tools, most popular is R and ggplot2, intimacy with Big Data concepts, Java and the list continues. 

You don’t have to do it on your own because the investment in this sector is immense. Third-party offerings like Sigma AI’s AiCurate platform already combine over 10 years of experience in all of these areas and the functionality to make implementation easy. 

A beneficial aspect of this type of solution is they offer API access. That means you can maintain and retain sensitive parts of your platform and use API calls to process information securely. Sigma AI has a significant advantage over competitors in that their API can quickly process data from all available sources including video, image, text, audio, and biometric data. 

Key areas of consideration when selecting a third-party include: 

  • Will the solution lower your risk? It seems likely that using an existing solution with verifiable benchmarks will always beat an unknown, undeveloped project. From a risk standpoint, going with an outside firm always make sense.
  • Will your enterprise gain the necessary insights to make the project worthwhile? If your plan fails to meet your objectives, it will be considered a failure. Further, if the investment is significant, the pressure to improve performance will be relentless. Reliance on an existing solution offloads the stress and tension.
  • How much do you save using a third-party? You can make estimates of your savings by considering the base development time it takes to “do it yourself.”
  • Can you handle security issues or should you leave it up to a vendor? Security is a complicated topic, and even the world’s largest companies struggle to get it right every time.

Experienced Technicians Are in Demand 

Most organizations find they don’t have the talent on hand to accomplish much in this arena. Top contractors and employees in all these disciplines are in high demand. That’s one of the biggest reasons that most firms are selecting outsourced solutions. Even the companies that have compelling reasons to do it themselves find they can’t make a go of it in a realistic timeframe. 

Another reason to select outsourced contractors or third-party solutions is that it’s a complicated project for anyone to undertake, especially people without experience. Tapping into companies with engineers that are experts in the field reduces the learning curve required to get up and running. They will know which approach to take and how to get there, whereas a newly assembled team, might struggle. When you use an external provider to handle the job, let’s be real, you can also part ways if things don’t quite work out as intended. That provides added flexibility that won’t be there having an in-house team do the work. 

A Small Loss of Control Is Unavoidable but You are Still the Domain Expert

The one area where outsourcing might seem riskier that doing things in-house is control. Doing it all yourself is tough and expensive, but it keeps all data right where you want it most. However, with Sigma’s API platform AiCurate, you remain the domain experts, Sigma just gives you the tools to implement the AI solution.

If you decide to use an external provider, make sure that communications between your team and theirs are effective. If they understand what you want in precise details, they’ll be able to deliver. Implementing AI and Machine Learning is a savvy decision and one that more businesses will take in the coming years. Although the subject is somewhat challenging, the results are amazing for companies that are going all in.