Building a Successful Enterprise AI

Artificial intelligence (AI) is among those disruptive technologies that have changed people’s lives. Its influence on businesses and industries has also increased over the years. Corporate spending on AI has increased by 55% between 2020 and 2021, a sure sign that AI is becoming an indispensable tool for businesses.

An enterprise AI isn’t the same as consumer AI applications, however. The predictions system employed by streaming services, chatbots, self-driving cars and others are few examples of this type of AI application. Artificial intelligence built for an enterprise platform is different. However, both rely on data. After all, the level of AI applications you can reach will rely on the depth and quality of data. That’s why having an annotation tool platform that can give you the quality of data you need for your build is critical.

Enterprise AI Compared With Consumer AI

Consumer AI applications, like Netflix or Spotify’s recommendation system, often require a tremendous amount of data from millions of users. To train this type of AI, developers use a machine learning algorithm that looks for patterns in user behaviour data. The data is collected and fed into the algorithm which finds the most relevant results for a specific subscriber.

In contrast, enterprise AI tackles problems with a much smaller data set. The issues that the enterprise AI addresses are often more specific to the industry or the company. The algorithm is usually customised and more nuanced. Data-driven businesses use enterprise AI and its subset, machine learning (ML), to better understand the data that drives their operations. Enterprise AI can also help businesses automate specific processes for their day-to-day activities, including helping a company achieve digital transformation.

The AI projects typically include various data sets, machine learning models, APIs and other processes. An enterprise AI merges these processes, including storage, data processing, model orchestration, AI models, monitoring and integration under a single infrastructure.

Building a Successful Enterprise AI

An enterprise AI will profoundly impact the organisational workflow and how businesses operate. This technology can help enterprises save money, improve efficiency, create insights and penetrate new markets. Below are tips on how to build an enterprise AI for your organisation.

Create An AI Team

All successful quests start with building a team. Skilled individuals are invaluable, more so when they’re the right fit for the team you’re making. The right AI team members know data and concepts and how to deploy AI techs in the real world. The members should also know how to navigate the ins and outs of implementing AI in your organisation.

Your team shouldn’t just be making presentations on what an AI should do; when they chalk up wins and get busy building momentum early on, you know you have an ace team. Moreover, recruit team members whose skill sets are relevant to your organisation’s needs. For example, find out whether you’ll focus on data science or the engineering side. Researchers are more likely to be suited for trend-based modelling, while engineers are suited for physics-based modelling.

Ensure Collaboration

Researchers are usually hired by industries looking for solutions to specific problems. These problems typically require new research in developing either unique or customised algorithms. Engineers, however, can use a variety of tools that can speed up development and deployment.

Ideally, you should find people who can do both; ContactOut personal email finder will allow you to find the ideal candidate.. However, such people are few; instead, you can evaluate whether you need to focus on data science or the modelling side. Most likely, you’ll need both—different skills are required to come up with a solution that can benefit the whole enterprise.

Collaboration, therefore, is one of the key points to building a successful enterprise AI. Cooperation among the stakeholders involved, like the product team, the data science and the applied research team and the engineering team, is vital to your project’s success. Teamwork has to start from the initial stage of identifying what’s needed for a successful deployment.

Communication should be friction-free. The atmosphere should be conducive to a healthy exchange of ideas. One way of ensuring teamwork is to create a collaboration process. Working together, the product managers, researchers/data scientists and engineers can quickly pinpoint problem points, propose alternatives and offer a solution.

Optimise Opportunities

Build a road map for what you want to achieve with the AI at the outset. Artificial intelligence can be complex, so it’s vital to have your organisation’s goals in mind—you won’t lose your way and get distracted by the small details. To maximise an enterprise AI’s capabilities, a leader must understand the AI’s distinguishing characteristics, pinpoint cases where AI is most needed and build algorithms that directly address these cases.

By identifying, understanding and prioritising opportunities that can arise, you can direct the AI’s development to better suit your organisation’s needs. Combine AI experience with client knowledge, trends and quality data; you can determine which AI’s capabilities can serve your enterprise’s interests.

Enterprise AI is rapidly becoming a vital part of any enterprise that wants to be competitive. An enterprise AI, however, is different from a consumer-facing AI. Data sets are smaller, and algorithms are often customised for specific industries.

Building a successful enterprise AI starts with forming a team, ensuring that team members collaborate seamlessly and identifying and understanding an AI’s capabilities. That way, your organisation can optimise any opportunities that an AI presents.