An opinion piece by the CEO and co-founder of Aquant, a predictive AI and workforce knowledge management company

Shahar Chen, Aquant CEO

November 22, 2022

6 Min Read

Data is the most valuable asset in a business when taken advantage of properly. All of the information needed to drive revenue, customer engagement, and user behavior is derived from data. For companies that amass large amounts of data, implementing artificial intelligence is now crucial in order to generate actionable insights and value.

Without an AI or machine learning tool to help you manage data, you will not only miss out on numerous opportunities to improve your business, but your revenue will also take a hit.

In the 2021 McKinsey Global Survey on artificial intelligence, over half of the 1,800 surveyed said their companies adopted AI in at least one business function, while 27% noted an increase in the bottom line directly attributed to AI. The majority of businesses are no longer considering if AI is a good investment, but are now determining how and where to implement it.

However, 85% of machine learning (ML) projects fail, according to Gartner, and this trend is projected to continue through 2022. One of the primary reasons for this is because AI/ML projects are often initiated by teams that possess some, but not all, of the necessary knowledge required to execute the model correctly.

AI and ML tools are more than just a product – these investments need input from cross-functional teams that range from data analysts to product owners if they are to succeed. Internal data scientists can often build a machine learning model but taking it to production and executing the model will require additional expertise – and continuous service post-deployment to ensure the technology is operating at its highest potential.

This all leads to one very important question: Is it better to build an AI and machine learning solution in-house or buy it from a vendor? To answer this question, you must first weigh the pros and cons of buying vs. building an AI-solution and find out which offers the biggest benefit to your organization.

Ultimately, you should consider the following:

  • Which option delivers maximum organizational benefit?

  • What is the difference in deployment time for building vs. buying?

  • How much will it cost to build vs. buy?

  • What internal resources or additional consultants will be needed for each option?

  • If a quick-to-implement solution fits your organization’s needs, will it offer better ROI in comparison to building a perfect, custom solution that may take twice as much time and resources?

Pros and cons of buying vs. building

Building an AI Solution

Buying an AI solution


Customization: You can skip any unnecessary features, which will reduce overhead costs.

Flexibility: You have the opportunity to create an easily-modifiable tool that is perfectly tailored to accommodate future needs or ad-hoc requests.

Controlled Integration: You can engineer a solution that fully integrates with all of your pre-existing processes, tools, and software.

Cost Effective: AI vendors already have a dedicated or established team that know how to pull off a huge launch — no outsourcing necessary.

Quick Deployment: Many AI solutions are pre-built, which means you may only need to supply your data and have your vendor analyze and configure it to your organization’s needs.

Consistent System Upgrades: AI vendors are always maintaining and updating their solutions. They also have dedicated customer success teams ready to help answer questions or figure out additional solutions.

Seamless execution: The first reason why machine learning projects fail is that companies are unprepared and ill-equipped to see them through. When it comes time to execute, you can rely on your vendor to have the specialized knowledge to carry out the project.


Added Expenses: Custom tools are significantly more expensive due to the additional costs. You may need to outsource some of the talent needed to create such a solution, as it can be too much for one internal task force to tackle.

Time: Building an AI system in-house is often a multi-year process. Sorting through internal information sources and translating this information into usable data can be challenging. Plus, factor in the time you spend training and improving your AI models.

Less Opportunity to Customize: This can be a dealbreaker for many, as the solution is not built specifically for your organization.

Less Control: The vendor controls updates and functionalities, so you cannot add features on demand.

A case study: AI in the service sector

A leading medical device company began its data reformation in 2007. It needed to digitize its data to foster a rich and connected environment. However, after all of their information was digitized, they realized the sheer amount of information, as it was, could not provide the insights they wanted. They realized they needed a tool that could label, filter, and analyze the data for them.

Initially, the company decided to build its own AI solution. But after a few challenging builds over several years, they decided to look for help from an outside source that better understood AI. They knew they needed a service intelligence solution that equally understood both AI and the service industry. They turned to a provider that could help them transform their data into actionable service insights that was easy enough for service techs and executives to use.

Today, more companies are offering AI solutions that solve industry specific problems. In this company’s case, it was able to find a service analytics solution that met its needs, including the ability to speak service industry language, understand service-specific challenges, and offer ideas to address their specific needs.

Securing buy-in from executives

According to Gartner’s 2021 AI in Organizations Survey, the main barriers to AI implementation are difficulty measuring value and lack of understanding benefits and uses. Like the leading service organizations, if you want to take advantage of buying a solution, you are going to need to convince decision-makers. Earning a seat at the table and aligning business transformation to larger strategic initiatives is the first step.

The case for AI is more convincing every day – and as long as you identify stakeholders, understand their needs, and engage them early on in the decision-making process, you will be able to create an argument for why and how AI will benefit them and their company objectives.

Third-party vendors can guide industry players through the entire process of assembling a timely business case, helping them demonstrate the value and benefits of the project for greater buy-in. They can also ease doubts that suspicious executives may have about AI.

Once myths have been debunked and you have demonstrated the value of technology at play, you will gain enough credibility to help decision-makers see how AI specifically helps them reach larger business goals.

According to Gartner’s survey, AI is maturing from 35% usage in 2019 to nearly 50% in 2021, so in order to stay profitable, leaders need to understand the important differences between manufacturing their own solutions and investing in an out-of-the-box solution. When approaching leaders with the conversation about adopting AI, make sure to lay out the pros and cons of building vs. buying an AI solution along with thought out responses to what would offer the biggest benefit to your organization.

About the Author(s)

Shahar Chen, Aquant CEO

Shahar Chen brings over 15 years of expertise in B2B software, specifically SaaS. He co-founded Aquant to bring a powerful AI solution to revolutionize the world of field services.

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