AI is transforming customer service with the evolution of chatbots into robust AI Agents, and customer support professionals see where the future is headed. Capterra’s 2024 Customer Service Technology Survey found that employees predict that AI will manage around half of all call center interactions within five years.

Many CX leaders at B2C companies face a tough decision: should they buy a solution to power their customer support, or invest the resources in building their own? With the rise of AI, this question is even more important than ever.

Here, we’ll outline the key factors to consider when deciding between purchasing a subscription to an AI-powered CX system or building a homegrown solution.

Assessing Your Business Needs

Before making any changes to your tech stack, you need to first align on the desired outcome and your business needs. From there you can determine which is the best route to go.

First, define your goals. Ask yourself: What specific customer service challenges are you looking to solve? Are you aiming to improve response times, automate repetitive tasks, or personalize interactions? Perhaps it’s more about having the technology to support the business as it grows.

To do this, evaluate any current gaps in your stack by considering the following:

• What functionality does your current tech stack have?

• What functionality does your current tech stack lack?

• Does your tech stack include any AI tools, or tools that now offer AI functionality?
  

• Are there existing inefficiencies?

• Are any of the tools holding you back from scaling?

Key Evaluation Factors

As you evaluate whether it makes sense to build or buy a CX platform, these are some of the key factors in the decision.

• Budget:

  • Total cost of ownership (upfront vs. subscription costs).

  • Resources available for ongoing maintenance and improvements.

• Talent and Resources:

  • Do you have an in-house team capable of developing and managing AI?

  • Is hiring or outsourcing an option?

• Scalability Needs:

  • How quickly do you expect your customer service demands to grow?

  • Can a purchased solution handle your scale, or will you outgrow it?

• Timeframe:

  • How soon do you need results?

  • Can you afford the development time for a custom solution?

• Integration and Compatibility:

  • How well does an AI system (bought or built) integrate with your existing tech stack?

  • Does it align with your customer service workflows?

Now we’ll dig into when it makes sense to build or buy.

The Case for Buying

Buying an AI powered customer support tool makes sense for most companies. Realistically, most use cases can be met by implementing software on the market. Here’s why:

Pros

First, there’s a lot of expertise that you can benefit from the vendor you choose. They’ve likely gone through years of development and made changes based on feedback from other clients. You’ll also likely have support from experts in the system, and you may be able to hire people who have expertise with that system, whether they be agents or operations team members.

Pre-built systems can also be implemented quickly. Your organization can spend resources getting it integrated in your information system and workflows and be up and running much faster, so you can start seeing the benefits sooner.

Cost predictability is another benefit. Subscription pricing is often clear - either by seat or by usage - and will include updates. Even for options with a usage-based model, your finance team can make reasonable estimates for the costs.

Finally, many CX systems are designed to grow with your business, with a roadmap of new AI features coming in the future that can make it even more efficient.

Cons

The cons of going with an existing AI powered CX tool are the same as using any other type of existing tool. In general, you’ll get less customization. There may be limited ability to tailor the system to super-niche needs. Plus, you’ll have to rely on the vendor for updates and maintenance. The features you want may not make their roadmap if they prioritize other developments.

The Case for Building

If buying makes sense for most companies, when does it make sense to build? Larger enterprises with robust IT and development teams and a clear long-term vision for AI are the best fit for those that could benefit from developing their own solution.

Capterra found that the top five challenges of AI-enabled customer service software are maintaining customer trust, ensuring accurate information, alienating customers, maintaining customer privacy, and implementation training. These are important to consider, because developing your own AI and CX stack adds complexity to the mix.

Pros

The pros of building your own platform revolve around ownership. You can customize it to your business’s needs, building functionality tailored to your unique processes and brand identity. You’ll also have full control over your data and models, which some companies highly value.

Over time, you may potentially have a cost savings with a good return on the resources invested if you heavily use the system.

Cons

Building your own system can come with high upfront costs. A project of this size takes a significant investment in time, money, and talent. From there, you’ll likely need specialized teams for development and maintenance.

Staying compliant with privacy regulations can also be a high barrier to entry with a customized solution, and customers may be wary of systems that don’t carry certain security certifications.

Going custom can also delay the time until you see the benefits. With companies adopting AI and building into the workflows, a competitor that implements an existing solution may be able to see improvements that help them a better customer experience.

Trying to calculate the resources involved and return with a project of this scope can be tricky. On the face it may look more cost-effective, especially for companies with a high volume of users or conversations, but if the project goes beyond the scope, budget, or timeline (like most do) then it can quickly wipe out any benefits.

Outgrowing Home Grown

Even brands that at one point had a custom solution for their customer service sometimes grow out of it or find it more efficient to leverage a new software to take advantage of AI features.

Jokr, a grocery delivery service, had a homegrown customer service solution, but it realized the platform was not as flexible as it needed to serve different segments of users. When the team switched to Kustomer, it decreased wait times from 11 minutes to 5 minutes and improved their CSAT.

Alex and Ani, a jewelry company, also had a homegrown solution for their CRM and needed a tool that could provide more holistic customer views. With Kustomer, they were better positioned to handle onboarding for a new hiring cycle. And, they’re poised to take advantage of Kustomer’s AI-powered automations because they’re already familiar with the system.

Conclusion

While the “build or buy” decision impacts the CX team, it’s critical to align the decision with the overall business goals, resources, and long-term strategy. Consider your current stack, what you need from the stack, and if your business is even a good fit to consider DIY-ing it.

Whichever route you choose, it’s imperative that CX leaders evaluate your customer service needs and start exploring solutions so you can take advantage of AI.

If you’d like to explore Kustomer’s system, schedule a demo to see how our AI-native unified CX platform can work for you.