For a lot of organizations, building tools for every corner of the business makes a lot of sense. There’s merit in building and owning tools that support sales or analytics teams. But when it comes to artificial intelligence (AI), the decision to build in-house versus buying from an external provider is trickier, and needs more thoughtful consideration.
Ahead, we’ll get into what to consider in the AI buy vs. build debate for any organization looking to level up their customer service efforts. We’ll go through what AI can do for a customer service org, its benefits, why business needs are important to building vs. buying, and the top factors to decide what’s right for any customer service organization.
What AI can do for your customer service organization
AI is used in customer service organizations to support customers with queries, issues, or concerns with a product or service. While still new and somewhat intimidating, the technology is meant to increase speed to resolution for customers, optimize workflows and processes for live agents, and save businesses time and money.
Key benefits of AI in CX organizations
There are a lot of reasons for customer service orgs to adopt AI. It’s often quick and a very seamless integration into any and all customer-facing efforts. Virtual assistants, for example, help solve customer issues such as needing company information or checking on an order. AI can still ensure there’s a human connection, too.
That said, consider the following benefits of AI for customer service teams:
1. Customer satisfaction. AI is 24/7. Customers have needs beyond a business’s regular hours. AI can boost overall customer satisfaction by being available all the time.
2. Streamline workflow. AI can take a look at and help optimize teams’ workflow and processes, suggesting solutions for efficiencies.
3. Personalization. Agents can have customer information at the ready from a customer’s file to ensure they’re suggesting or supporting what a customer specifically needs.
4. Manage support demands. AI has the ability to handle volume of requests or queries in a way a live agent may not. Here, AI frees up time for live agents to focus on more complex customer issues.
5. Decrease costs. There are a number of tasks AI can automate that save time and money. AI can also ensure efforts are allocated and resourced for high-priority work.
How to determine AI’s role in your CX org
Before we get to the build vs. buy, it’s important to understand what role AI will play and to whom it will serve. A vital question to ask is: what do you need to know before you integrate AI into your business’s customer service organization?
The following will help anchor your decision, and remain as key guides in your AI journey.
- Goals. Consider what the overall goals of your business and customer support may be. How will AI help?
- Resources. While AI has the functionality to help support customers, it takes certain people to get it there. What resources do you have to ensure proper implementation?
- Ethics. AI takes a lot of training—both a company’s current and historical data, as well as anything else relevant from other sources. What ethical considerations need to be had before integrating AI into your business?
10 factors to decide what AI solution is right for your CX organization
Building an AI solution requires time, resources, and a lot of training to ensure it’s specific to your business’s needs. Buying an AI solution from a third-party vendor means you don’t have to do all of that, and may actually end up saving some cost.
There’s an argument for both but consider the following factors when weighing your decision to build a custom generative AI solution or to a ready-made one from another vendor.
- Time
It will take a lot of time to build an AI solution from scratch. That time, however, may mean more personalization or specification.
Buying a ready-made AI solution, on the other hand, can reduce its launch market timeline, and allow the organization to refocus that time on integrating and optimizing the AI for their specific needs rather than starting from scratch.
- Cost
In a lot of ways AI can save a business some serious dollars. In a build situation, there may be more costs associated because of certain complexities and specificities during the build. A cost-per-resolution for an in-house build, according to Forethought’s AI in CX Benchmark Report, was as high as $12 per. A dedicated vendor cost was $8.
Buying from a vendor saves on the upfront costs that often come with a custom build. Here, too, there will be less spent on the resources for that build.
- Expertise
With a custom build, there is no doubt that the AI will be trained on a company’s existing data and nuances. However, custom builds may have less expertise on hand to maintain, and even update, the technology.
With a bought AI solution, businesses can rely on the technology to be refined by specialists who ensure the tool’s natural language processing (NPL) and any customer support applications are up-to-date.
- Scalability
AI vendors equip their tools to run at scale. If your business has a set amount of customers, with no flux of inquiry volume, a build may be more appropriate for your needs. However, businesses with growth, and therefore more customers, would benefit from considering a purchased AI solution to handle whatever flow and demand that comes to their customer service org.
- Performance
No matter the technology, there will always be knots to untangle at some point. With a build, it’s not as easy to understand how well the tool will perform, and may need some certain (and costly) testing to understand and improve it.
Purchased AI has testing and iterating included from the vendor. They are invested in the performance of the tool and optimize often, even for small improvements that can be felt over time.
- Reliability
Reliability of the tool goes hand-in-hand with performance. In-house builds may require more testing and more resourcing to ensure the tool is working the way it needs to.
Vendors will continue to test and support the AI tool to ensure reliability of performance, using datasets to refine new and complex customer interactions, making those more effective.
- Integration and support
Once AI is built, it will take time to integrate, teach, and learn the tool. It will also need support. All of that needs to be resourced properly with a custom build to ensure its efficacy. If resources who built the tool leave at any time without proper documentation, it may be difficult to support any upgrades to the tool.
Purchased AI from a third-party vendor comes with ready-made support and documentation, even integration help, as well as ongoing contact for any maintenance that may come up.
- Compliance
Building an AI tool specific to a company’s needs can be exceedingly helpful. But AI moves at a fast-pace, as the technology is consistently and constantly evolving, which means keeping an eye on any legal and compliance updates is imperative. Even more so with a custom build.
AI that has been purchased through a third-party vendor addresses any regulatory requirements, which may include data privacy laws. This may be challenging for in-house build teams if they do not have dedicated expertise and resources.
- Security
Customer security is of the utmost importance, particularly in today’s digital world. So much customer data is available to bad faith actors if a tool is improperly secured. In-house teams building AI will need to resource this properly and efficiently, ensuring the technology is safe and secure to house any customer data.
Vendors, on the other hand, are already trained to do this, and to support their AI customers by implementing sturdy security measures to protect sensitive customer information.
- Competencies
While an in-house AI solution may hold nuances of a business and its customers, it may also take away resources in an effort to keep it properly maintained. Purchased generative AI solutions will always have dedicated vendor support so a business can continue to focus on core competencies like delivering excellent customer service.
An example of a core competency is decreased deflections: Using AI trained on their own historic data, according to Forethought’s report, companies handling 5000-10,000 monthly tickets achieved the greatest difference of deflection from 17% to 40%. An in-house development had the lowest overall deflection rate of 16%.
Conclusion
AI is a fast-paced technology with a lot of power to drive organizations toward efficiencies and perhaps save some money along the way. AI has been used very effectively in customer-oriented organizations of businesses. So, for a customer-facing technology, building vs. buying AI becomes a more nuanced discussion.
There are pros and cons for both, but a generative AI solution from a third-party vendor will always come with support, dedicated resourcing, and maintenance so businesses are freed up to keep customers happy and satisfied.
