AI Agents for Customer Service: Should You Build or Buy?
23 May 2025
4 min read
AI agents are having a moment. With new tools from OpenAI and others, it’s never been easier to build your own custom AI agent. Many of these tools are designed to automate the exact kind of repetitive administrative tasks that customer service teams handle every day.
To build AI agents effectively, teams need expertise in prompt engineering, natural language understanding, and machine learning to ensure the AI agent is accurate, flexible, and integrates well with existing systems.
Pair that automation with the conversational strengths of large language models (LLMs), and the appeal of building your own AI agent becomes hard to resist.
But should you build one – or buy a ready-made solution?
A Cautionary Tale
We recently spoke with an e commerce brand that set out to build their own AI customer service agent. A few weeks in, they hit a wall. The scope had grown, complexity piled up, and it became clear that it would take far longer – and far more resources – than expected.
So they paused, explored the market, and discovered several mature, off-the-shelf solutions built specifically for ecommerce.
Long story short: they bought a solution, got up and running quickly, and were able to deploy the solution efficiently. They haven’t looked back.
Brands who Buy see more success than those who Build
A report from MIT discovered that companies which purchased AI tools were more successful than enterprises that tried to build their own systems. Building from scratch does require a level of expertise that most companies simply don't have and can't afford to bring in.
As the report says: "Organizations that successfully cross the GenAI Divide approach AI procurement differently, they act like BPO clients, not SaaS customers. They demand deep customization, drive adoption from the front lines, and hold vendors accountable to business metrics. The most successful buyers understand that crossing the divide requires partnership, not just purchase."
So it's not just about signing up for a free trial and hoping for the best. it's about building proof of concepts, and ensuring that a solution can work for your specific needs.
What’s Involved in Building an AI Agent?
The first question is: What do you want your agent to do?
If your goal is simple – like answering FAQs or pre-purchase questions (e.g. “What’s your return policy?” or “What materials is this made from?”) – you might be able to build it yourself. That involves:
Connecting the agent to your knowledge base
Creating guardrails for appropriate responses
Ensuring your documentation is AI-readable
Putting checks in place to avoid hallucinations
Testing the agent's performance to ensure reliable answers
Maintaining control over the agent's logic and behavior
Customizing responses using code for advanced scenarios
Integrating relevant business data for more accurate and data-driven responses
If you’re confident, you could build this in-house. Otherwise, several platforms can help. But here’s the catch…
Generic vs. Personalized Agents
Basic FAQ bots are generic. That means the answer is the same, no matter who’s asking. Your return policy doesn’t change based on whether the customer is a 21-year-old browsing hoodies or a grandmother buying a birthday gift.But what about questions that do depend on who’s asking? “Where is my order”, “What’s happening with my return?”, “Why haven’t you applied loyalty points to my account?” – these require personalized responses from customer service agents who can handle interactions across every channel your customers use.That’s when things get complex.To personalize, your AI needs real-time access to systems like:Order trackingEcommerce platformsWarehouse managementLoyalty programsA simple question like “Where is my order?” might require multiple integrations and logic layers. What if the order was marked “delivered” but wasn’t received? What if it hasn’t shipped yet? You’ll need to connect with ecommerce platforms, order management systems carriers, apply conditional logic, and possibly trigger escalation workflows.All for one “simple” query.
Do You Have the Resources?
It’s possible to build a personalized agent – but it requires significant technical resources. That includes:
Engineering time for integrations and logic building
AI expertise to avoid pitfalls
Ongoing maintenance as systems and policies evolve
And remember: even if you build it, you’ll likely still be using third-party AI infrastructure or an ai platform. When selecting an ai platform, it's crucial to prioritize a secure platform that ensures robust security, privacy, and compliance standards to protect your data and operations. Will you also build your own LLM stack? Probably not.
What’s the Value of Customer Service AI?
AI in customer service delivers value in two main ways:
Cost savings – Handle more tickets without growing your team.
Better, faster service – Instant, 24/7 responses improve customer experience and retention, while reducing response times to boost customer satisfaction.
If a customer messages you and your competitor, guess who wins? Usually the one who replies first. Faster responses help retain customers by improving customer satisfaction and ensuring their needs are met quickly.
As you weigh your options, ask: How soon do we want to see those benefits?
If building takes a year but a vendor can get you live in two months, delaying comes at a real cost.
Staying Competitive
Industry leaders and other ecommerce brands are already reaping the rewards of AI agents. If you start building from scratch today, how far behind will you be by the time you launch?
Check in on your competitors. If they’re already ahead, you may need to focus on speed rather than customization. If you’re leading, focus on customization to maintain your advantage.
The Advantages of Buying
Yes, implementation takes effort – you still need to configure the system for your brand, policies, and tone – but the underlying infrastructure is already built.
And if you choose the right provider, you’ll get:
Industry-specific expertise
Faster implementation
Support and best practices baked in
A robust agent platform or ai agent platform that is AI-powered, giving you the power to manage customer service operations efficiently
Look into what onboarding help is available. Is the setup done for you? Are there guides? A responsive customer success team?
Then compare the cost of the platform to what you’d spend hiring more agents or tying up your engineering team for months. These solutions are designed to meet business needs, and the calculations often favor buying.
Build vs. Buy Checklist
Here’s a quick checklist to help you decide.
You should build if you answer “yes” to most of the following:
Do you have the technical resources for a long-term project?
Are you more comfortable using your team’s time than spending money?
Are most of your support questions simple and repetitive?
Are you ahead of competitors when it comes to AI adoption?
Can you launch something valuable within 12 months?
Can you maintain and improve the agent over time?
If you answered “no” more than “yes,” buying is likely your best bet.
Curious about what’s already out there?
You’re in the right place – start with DigitalGenius. Talk to our team and see what a ready-made solution can do for your brand.
