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AI is already beginning to heavily integrate into our world through intelligent products like self-driving vehicles, IoT, wearables, digital home assistants, and more. Businesses are also beginning to see it as an accelerant for digital innovation; adding AI functionalities to make data-driven decisions, automate processes, improve operational efficiency, and drive competitive advantage.

With AI adoption tripling in 2019 and the global market forecasted to reach $3.9 trillion in value by 2022, enterprises are starting to understand the importance of AI-powered software as well. However, companies looking to take advantage of AI must first ask themselves: is it better to develop AI technologies in-house (build) or purchase an off-the-shelf AI-powered solution (buy)?

This question has long-plagued developers and there is no “one size fits all” answer. In this guide, we will break down the decision-making criteria for building vs. buying AI-powered platforms to help your company make the call.


Breaking Down the Build vs. Buy Decision

The Rise of AI-Enabled Tech

AI adoption has been growing steadily for years, with 85% of companies reporting using or actively evaluating AI in 2019. Companies are using AI to power software development projects, streamline customer service, optimize HR, and even improve supply chain operations.

AI benefits departments company-wide by streamlining the following functions:

  • Automating mundane, repetitive tasks
  • Intelligent reporting for deep analysis and data-driven decision making
  • Behavior tracking to enhance the customer experience
  • Reducing friction and increasing engagement through automated processes

Although half of companies identify as mature users of AI, many are in the early stages of adoption and lack institutional support. There are many contributing factors, but the main bottlenecks for AI adoption are lack of specialized skills, difficulty finding use-cases, and a company culture that isn’t open to AI.

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As AI technology becomes more of a business imperative, what can companies do to break through these bottlenecks? This is where the build vs. buy decision comes in.


Defining Build vs. Buy Decision Criteria

Companies seeking the advantages of AI have to be strategic about how they go about implementing it. Making the wrong choice can be costly and time-consuming, which is why AI projects are more likely to fail than traditional development. But when done right, AI can yield powerful gains for your organization.

The first step in your AI journey is to identify your business needs. What problem is being solved and what are the inputs/outputs? What solution would solve this problem and does the solution already exist on the market?

Next, defining your decision-making criteria can help determine whether building or buying AI is the right choice for your project.

  • Driving Competitive Advantage: Will the technology provide an asset or platform/software-as-a-service?
  • Project Requirements: Does the project meet leadership needs, customer requirements, compliance and security protocols, and other important objectives?
  • Financial Impact: What is the project budget and expected ROI?
  • Time-to-Market: What is the necessary speed to develop and deploy?
  • Project Maintenance: What are the resources and cost of support for software, hardware, and human capital?

Use your criteria as a compass to weigh each factor against the bottom line and determine what the scope of your project is. From there, evaluate your project against the pros and cons of building vs. buying.


Building AI In-House

Buying an off-the-shelf AI solution is convenient, but there are times when companies have to build their own solutions. Companies who leverage AI as their core business model or have an in-house AI solution already may find building additional functionality is more advantageous. Companies have the flexibility to modify the project as needed and customize it based on stakeholder needs.

On the other hand, building AI in-house is a big commitment, especially as projects grow in complexity. Even with the support of open-source software, 80% of companies say it takes more than 6 months to deploy in-house AI into production. Project timelines get pushed back due to challenges with finding specialized skill sets, quality production, and unforeseen issues. This results in frequent project delays, longer project turnarounds, and costlier builds.


Buying AI-Enabled Solutions

Instead of sinking money into months or years-long projects, many companies may choose to invest in off-the-shelf AI solutions so they can go to market quicker and save money on development costs.

Buying offers a host of advantages since software vendors take over trickier issues like integrating the AI application into existing ecosystems and training workers to use the specialized tools. Pre-made tools also provide peace of mind since security is usually built-in and continuously upgraded to safeguard your data.

Buying AI solutions is most beneficial for the following situations:

  • Building software is not part of your core business.
  • There are already solutions on the market that address your business needs.
  • Internal resources are limited and/or software development requires specialized and difficult to find technical skills.
  • Fast deployment is a bigger priority than a fully customized product.


Choosing the Right Approach for Your Business

The AI build vs. buy decision comes down to your personal business needs. There are instances where building an AI solution is the most pragmatic decision, but more experts agree commercial platforms are generally a better business decision.

Companies looking to leverage customizable, data-driven AI through an intelligent communication platform can turn to Remitter. Our off-the-shelf solution is designed to streamline digital outreach and payments in your collection process, all enhanced by AI. Complete with compliance and secure data encryption, Remitter is committed to providing an AI-powered solution in a matter of weeks, all while reducing your operational cost.

If you’re interested in learning how Remitter incorporates AI into all aspects of your receivables process, schedule a demo to learn more. 

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