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This is part of a blog post series focusing on the AI usage gaps identified within the MIT NANDA report, The GenAI Divide: State of AI in Business 2025.
After working directly with enterprises to deploy, monitor, optimize and scale a generative AI agent, I’m struck by how much of a partnership the work is—and how critical the right expertise becomes at specific steps in the process.
So often, the first major decision point an enterprise reaches in their AI agent initiative is whether to buy a production-ready solution or build their own. But that simple either/or question glosses over the deeper and more complex challenges of getting from opportunity to pilot to production. The deciding factor for successful AI deployment is almost always a matter of well-directed expertise, not just in spinning up a passable AI agent, but in driving the project toward value realization.
Typically, the necessary expertise is a joint venture, a partnership between the enterprise and a vendor with deep experience in applying AI in the real world.
A partnership built on expertise
The recent MIT NANDA study, The GenAI Divide, found that partnering with a vendor doubles the likelihood of a successful outcome compared to trying to build a solution internally.
That doesn’t surprise me. Deploying an AI agent requires such a broad set of skills and knowledge that a single enterprise is very unlikely to have all of it in-house.
It’s true that no AI agent vendor will understand your customers or your business as well as you do. They can’t replace your knowledge of internal processes and workflows, or your insight into what’s working in your customer journeys and what’s causing unnecessary friction. And your IT team already knows the intricacies (and complexity) of your CX tech stack better than a vendor ever will. That makes your internal resources invaluable.
It’s not that a dedicated internal AI team can’t build a custom in-house solution. More so that an AI vendor likely has more experience launching an AI agent for customer service, monitoring its performance, and optimizing to drive value in a repeatable fashion. Their expertise goes well beyond the ability to build a reliable solution. And that makes partnering with the right vendor a smart decision.
Here are some of the crucial capabilities an experienced partner brings to a generative AI agent deployment.
1. Driving value with the right use cases
Everyone from your frontline agents to the C-suite probably has ideas about which use cases you should prioritize for automation with a generative AI agent. Some are high-volume, some eat up a lot of agent hours with long handle times, and some are too complex for traditional automation to deal with. A few are likely just pet peeves for people in your organization.
But choosing the right use cases to target first should be a multi-step process that considers a range of key factors. And it must be data-driven. A partner with deep expertise can help you avoid the common pitfalls, like choosing use cases that are too low-volume, too complex for your initial launch, or lacking required APIs or knowledge content. Choosing the wrong use cases can lead to months of costly tinkering before you can even think about ROI.
The right partner will ensure that you’re applying generative AI to problems where it excels—and that you’re choosing use cases that will deliver larger and faster returns.
2. Designing efficient and effective testing strategies
With a generative AI agent, testing is more than a simple quality assurance step. It’s a strategic function that verifies the AI agent’s ability to handle the complexity and variability of customer service interactions. It’s also very different from traditional software testing.
The kind of test scripts you’ve probably used with deterministic bots and virtual agents won’t address the multivariable complexity and edge cases a generative AI agent is designed to handle. To thoroughly test your agent, you’ll need to simulate a range of realistic scenarios with variation in the conversation flows, customer personalities, and specific issues to be resolved.
The right partner can design a testing methodology that mirrors real-world complexity, stress-testing the agent's knowledge base retrievals, API dependencies, reasoning, policy adherence, and conversational flow in a way that accounts for the variability of actual customer interactions.
A partner who’s experienced in this type of testing will help you zero in on the right scenarios and variations to test so you can launch with confidence—without spending weeks on end testing every scenario you can dream up. The ability to test the right scenarios at scale is crucial to reducing risk as you launch your AI agent.
3. Pinpointing and diagnosing complex issues quickly
Diagnosing issues is inherently more complex with a generative AI agent than debugging a deterministic bot. Inaccurate responses and other failures often stem from a combination of elements, such as a weak knowledge article, a problem with a required API, unclear instruction, and an ambiguous customer question or comment. So, even if you can monitor or flag all conversations for annotation and review, diagnosing the underlying causes could be a challenge. You might be left knowing what happened, but not why.
Uncovering the root causes of an AI agent’s performance issues is a specialized skill that takes time and real-world experience to develop. A partner that has deployed and optimized generative AI agents in multiple enterprise contact centers will likely be more adept at diagnosing issues than your in-house team. And they’ll know the inner workings of their own solution, which can help them pinpoint issues and propose solutions more quickly. That allows for precise modifications rather than larger, potentially destabilizing rollbacks.
4. Sustaining post-launch performance and ROI
When it comes to boosting returns on your investment, addressing the obvious problems with an AI agent’s performance, accuracy, and reliability is just the beginning. If your AI agent is only doing a decent job of resolving customer issues, you’re leaving a lot of value on the table.
Over time, you’ll find that many of the modifications that improve outcomes are small, targeted, and nuanced. For example, recognizing that key information is missing from your knowledge base is easy. But knowing how to improve your AI agent’s accuracy by restructuring your knowledge articles takes much more know-how. Similar changes, like modifying task instructions, improving memory management, adjusting API data pulls, and incorporating human judgment as a feedback loop, only drive value when they’re rooted in deep expertise.
Understanding what to tweak requires a kind of judgment that’s built on a lot of experience tuning an AI agent that’s live with customers. The right partner adds that experience to the equation.
5. Staying ahead of the innovation race
As quickly as AI technology is evolving, keeping up with every improvement, model, and new technique for applying it in the real world is a full-time project. A vendor with a deep bench of AI researchers, engineers and strategists focused on innovation and application in the enterprise can continue to enhance your AI agent’s capabilities and performance. That ensures your solution remains on the leading edge of what’s possible instead of falling behind.
Even as many enterprises are just now adopting generative AI agents, the AI for CX industry is already moving toward full customer experience platforms that orchestrate a network of AI agents. In the near future, these agents will constantly listen to and analyze interactions, engage with customers proactively, and gather new intelligence on customer journeys.
These emerging capabilities will fundamentally change how your enterprise engages with customers. That’s a lot bigger than simply building a decent AI agent that automates some of your interactions. And it’s a lot to miss out on if you don’t partner with an AI solution provider that’s already building this future state.
The bottom line is… the bottom line
The whole point of adding a generative AI agent to your contact center is to lower the cost to serve while improving your customer service delivery. On its own, that’s a tough balance to strike. It’s also just the baseline for what’s possible with an AI agent. Building and deploying your AI agent is only a small part of the process. The rest is where your biggest opportunities for value realization lie. And it requires deep expertise beyond the technical work of building the solution. That’s why partnerships beat internal builds.
Let’s be honest. A partner with deep AI expertise comes with a price. But the lack of that expertise is much more costly, especially long-term.
