The 3 Unspoken Benefits of Buying AI

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Artificial intelligence (AI) is known for its benefits to businesses in improving efficiencies, reducing human errors, and even boosting revenues. With AI systems, you can help make customer service operations more efficient, recruit faster, make quality assurance technicians more effective, and HR managers a hundred times more productive by automating time-consuming tasks.

However, when it comes to implementing AI technology, companies are often faced with the dilemma of whether they should buy an off-the-shelf solution or build from scratch. Some companies think that their only option is to build AI solutions from scratch when engaging the job market. This couldn’t be further from the truth. Purchasing prebuilt AI solutions for specific problems is an entirely valid option, and it can sometimes fit your needs better than a “build it yourself AI.”

What Does “Buying AI” Mean?

So what does buying AI mean? When you buy AI, it can mean two things. The first is you could be purchasing a larger software platform with AI integrated into it. For example, HubSpot’s customer relationship management (CRM) software is an extensive software system with a small piece that does AI-powered lead-scoring. You may already have these larger software platforms but are yet to utilize the AI features.

The second way you can “buy AI” is to license AI-powered solutions that solve a specific problem, like removing human error from a recruiting process. One example is AI-powered sentiment analysis services that generate sentiment predictions given some text data. While many of these prepackaged software solutions need a fair amount of customization on your data, some can be used right out of the box.

Examples of targeted AI-powered solutions:

  • Virtual AI assistants
  • Facial recognition systems
  • Sentiment analysis tools
  • Language translation services
  • Product recommendation engines
  • Speech recognition systems

Examples of general software platforms with integrated AI: 

  • Customer relationship management (CRM) software (e.g., HubSpot)
  • IT operations management software (e.g., Splunk)
  • Hiring and recruiting AI tools (e.g., Recruiter.com)
  • Word processing documents (e.g., Microsoft Word, Google Docs)

While these buy options are available to companies, it’s often unclear why a company would buy a solution instead of just building everything out from scratch. Let’s discuss the three unspoken benefits of buying AI solutions to understand this better.

The 3 Benefits of Buying AI

1. It’s Overall Less Work

One of the most significant benefits of buying AI solutions is its convenience. You don’t need to hire a team of data scientists and manage them, nor do you need to understand natural language processing and machine learning algorithms to use these solutions. In most cases, you won’t have to worry about maintaining and monitoring the underlying AI models, which can be a challenge with a basic IT infrastructure. When you buy AI solutions, it’s often the responsibility of the vendors to ensure that their models work as expected and that any customization is straightforward to implement.

Further, the fact that you can use your existing engineering and IT teams to help evaluate, customize, and integrate the prepackaged solutions into your business systems makes buying AI an attractive option. It lowers the barrier to entry when it comes to adopting AI and realizing its gains for your business.

2. Your Costs Are Contained

When you build solutions from scratch, you often need to hire data scientists and support engineers such as software and data engineers to assist with developing and productionization of these AI solutions. You may also have to employ other software engineers and user interface designers to build out the user-facing components.

None of this comes cheap, especially if you’re hiring full-time employees. In fact, the cost of hiring a single data scientist can be north of $150,000 in the United States, which you’ll pay year after year. Also, as you get into development difficulties, you may have to hire additional staff or outsource parts of the work. More importantly, if you don’t have sufficient projects for these developers to work on over the years, it becomes an expensive investment.

In contrast, with prepackaged solutions, you’ll be paying a monthly or yearly fee for using these systems. You may also incur one-time support costs to integrate these systems into your workflow if you don’t have an IT or engineering team. But these costs are more predictable as you often know what you’re paying for. Plus, many of the off-the-shelf solutions will be cheaper than hiring all the different engineers. Moreover, you can always try to find a solution that fits your budget, even if you have a small budget to start with. Plus, off-the-shelf solutions often come with different package levels depending on usage.

The bottom line is that, while off-the-shelf solutions may not be fully customizable to your business needs, costs can be a significant driver in trying to use something already prebuilt than trying to build one internally.

3. You’re Less Likely to Be “Stuck” with a Legacy Solution

As organizations grow and times change, so do the needs of an organization. This change in need may prompt organizations to consider alternative software solutions, including AI-driven ones. This is much easier to do when you’ve bought an off-the-shelf solution as your organization is not emotionally attached to it. You can test out alternatives and pick the one that best serves your current needs.

In contrast, if solutions were painstakingly built in-house, you’re less likely to swap out your current solution with an alternative as you took all the trouble to make it. Why would you want to waste away all that hard work? You may try to live with the solution even though it’s inadequate or improves upon it by making incremental updates.

This could all lead to unwanted consequences, such as losing your competitive advantage against other businesses because you’re using an antiquated software solution that hampers productivity or is inaccurate. You may also see employees resign as the tools to do their jobs are just not cutting it to keep them effective.

Therefore, buying an AI solution can have benefits beyond just costs where you may be more willing to swap solutions in and out depending on your needs over the years.

How to Buy Wisely?

While many businesses may think that using AI to create value for their business is impossible and only meant for big tech companies, the availability of prebuilt AI solutions levels the playing field. Companies no longer need to rely 100% on building AI solutions from scratch.

AI capabilities are available within various applications, from virtual AI assistants to language translation services to IT operations. Also, you can always start by buying AI solutions to see if it’s beneficial for your business before thinking about investing in a more customized home-grown system.

Plus, as we’ve seen from this article, buying AI solutions also ensures that your costs are more predictable, you don’t need a lot of in-house AI talent, and it’ll allow you to swap solutions in and out as needed. However, before you buy your own AI tools, it’s essential to evaluate the solutions adequately so that you don’t start by investing in the wrong ones.

 

Kavita Ganesan is the founder of Opinosis Analytics.

 

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Kavita Ganesan, author of "The Business Case for AI: A Leader's Guide to AI Strategies, Best Practices & Real-World Applications," is an AI advisor, strategist, educator, and founder of Opinosis Analytics. She works with senior management and teams across the enterprise to help them get results from AI. With over 15 years of experience, Kavita has scaled and delivered multiple successful AI initiatives for Fortune 500 companies as well as smaller organizations. She has also helped leaders and practitioners around the world through her blog posts, coaching sessions, and open-source tools. Kavita holds degrees from prestigious computer science programs, specifically a master’s degree from the University of Southern California, and a Ph.D. from the University of Illinois at Urbana Champaign, with a specialization in Applied AI, NLP, Search Technologies, and Machine Learning. Kavita has been featured by numerous media outlets including CMSWire, Verizon, SD Times, Techopedia, and tED Magazine.