In recent years, artificial intelligence has proven to be invaluable to businesses looking to improve or expand their product offerings, performance, and relationships with clients. But when deciding how to implement AI into a business, there are many factors to consider including existing team size and expertise, cost, and how prepared the company is to incorporate AI. Building a team in-house is highly expensive and time consuming, and in most cases, companies would be better off leveraging an outside firm that can create, host, and support a custom learning model. 

Challenges of creating an in-house team

On a successful AI team, there cannot solely be data scientists and machine learning engineers. Database engineers, backend developers, and creative data visualization developers are also required. However, there is currently an AI-skills gap, with a shortage of available talent and a high demand for positions as companies move to implement AI in their processes. According to technology company, Tecent, worldwide there are millions of AI jobs required but only about 300,000 AI professionals. There are current initiatives working to close this talent gap including increasing resources for digital, math, and technical education, and large tech giants like Amazon investing internationally in facilities and labs dedicated to AI. However, AI professionals cannot just have a computer science degree and jump into a leadership position at a company with a nascent AI strategy. They also need to have on-the-job experience. Companies wanting to develop their own AI team will instead need to train their current employees until there are more professionals in the workforce equipped to fill these positions. Besides the team itself, a company needs to provide the correct data and infrastructure for launching an AI initiative. A business should have both relevant data for the problem it is trying to solve as well as clusters of machines for training the relevant data and developing algorithms. When adding up the costs from developing, hiring, training, and equipping a team, building a new AI platform becomes highly expensive and time consuming.   

The solution: partner with an outside AI company 

Many companies have smaller in-house tech teams, making outsourcing an AI initiative a more viable option. Most major cloud providers have AI offerings with pre-existing models that can be more accurate than what a company could build on its own. However, these models are not customized and tend to be too generic to provide the desired ROI. Instead, businesses should utilize predictive analytics for a bespoke product that incorporates their own data and process. This is when partnering with an outside firm capable of building these custom models would be the most beneficial. Outside firms are already equipped with the proper teams and technology for an AI platform and are experts at what they do. Vertical-specific partners can provide the same value of building an in-house team at a much lower cost while also including model ownership and training. 

AI is a vehicle that can help many businesses improve their strategies and is becoming a vital edge for companies over their competition. The largest barrier to AI adoption is the skills gap, but there are also challenges with data, company culture, equipment, and company resources. Given the limited AI talent that is currently available, some companies are forced to constrain their employees into single tasks, hindering their freedom to learn and grow in the industry. Building a team is highly expensive and many companies simply cannot afford the resources required. Instead, partnering with an outside AI firm saves you time, money, effort, and stress. Trill AI is a trusted partner that will provide the custom models focusing on your vertical so your business can focus on what it does best. 

Let’s see what we can build together.

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