2025 Buyers guide to Gen AI for customer operations

Navigating the rapidly shifting landscape

Choosing the right generative AI tool for your customer operations in 2025 can feel like picking out a whisper in a roaring crowd. The pace of technological change is relentless: new models, new providers, new integrations appear every quarter, if not every month. And because every company’s support needs are unique, it’s impossible to know if a vendor’s claims will hold true for your use case without testing it live. In a world where hundreds of AI applications might pop up in a single year, determining which solutions will genuinely improve your customer experience—and not just muddy the waters—is a daunting challenge.

In short: it’s tough to separate hype from value. Buying a generative AI solution is less about ticking off a feature list from a sales deck and more about applying a thoughtful mental model. You need to consider how easily you can switch providers down the line, how well the solution fits into your operational landscape, and how you’ll measure real-world results once it’s deployed.

Where generative AI will be in the next 1-2 years

The world of generative AI for customer operations is evolving so quickly that what feels cutting-edge today may be considered basic in a year’s time. Foundational models are becoming commodities, and it’s anyone’s guess which provider will lead on quality or capabilities next. The good news is that by 2026, we can expect dramatically improved language understanding, context retention, and compliance handling. Your customers will likely even start preferring interactions with high quality AI agents, given their potential to deliver instant, accurate, and empathetic resolutions.

But with that rapid evolution comes the risk of lock-in. Multi-year commitments might become liabilities if you’re stuck on a platform that can’t pivot quickly to incorporate newer, stronger models. The key will be choosing tools and partnerships that preserve your flexibility, help realising ROI quickly thus allowing you to stay on the cutting edge rather than playing catch-up.

Key considerations for choosing your Gen AI tool

1. Start by recognizing the "iceberg" of customer operations

AI-powered frontline support feels like the flashy surface layer—the tip of the iceberg. Many generative AI vendors focus on automating frontline customer interactions, and while this can offer immediate value, it often only addresses about 25% of your total operations cost (this might vary slightly by the type of company). Even if you fully automate half of your frontline conversations, you’re only chipping away at a small fraction of your overall customer ops cost (e.g. 25% * 50% = 12.5% in this case).

The big opportunity lies beneath the surface: specialist support tasks, back-office processes, compliance checks, and complex judgment calls. It makes a lot of sense to look at those processes holistically as they often depend on each other.

Depending on how ambitious you are, when evaluating a solution think beyond the immediate customer-facing use case and consider how a vendor might help you tackle the more substantial bulk of your operational iceberg over time.

2. Insist on real-world tests over vendor-proclaimed metrics

Almost every vendor claims they can deliver “up to 90% auto-resolution” and “up to 90% CSAT”. While these might be true in their best-case scenarios, they probably have zero meaning for your specific use case. The only real proof lies in a live test with a subset of your traffic. Run a controlled pilot, read at least 100 sample conversations end-to-end, and judge for yourself whether the tool actually solves issues. That real-world trial will give you insights far more meaningful than any sales slide deck can. The best tools will also give you the ability to QA conversations and define your own criteria for that.

3. Look for actual issue resolution, not just deflection

The difference between a great AI agent and a mediocre one is as stark as day and night. Top-tier agents will provide genuinely helpful, context-aware answers that solve customer problems. Mediocre bots may rely on repetitive requests for “rephrasing” or just link to generic FAQ pages. When reviewing vendors, watch out for tools that annoy or “deflect” customers rather than truly resolving their queries. With an ideal provider your deflection and resolution rates will be almost identical.

4. Ask for a proof that the AI agent will not hallucinate

To avoid major PR disasters and bad customer outcomes put it on your vendor to proof to you that their AI agent will not just make up answers or say inappropriate things.

5. Set clear measurement and ROI tracking from the outset

A good vendor should let you evaluate ROI quickly, ideally within a week’s pilot and with minimal setup effort (at least if you are mostly aiming for frontline support automation). Understand what it will take to integrate the tool into your environment—both the initial POC and the ongoing maintenance. Will the solution automatically identify knowledge gaps and self-improve, or will you be stuck manually training intents and maintaining content? Remember to factor in all dimensions of ROI: direct productivity gains, reduced overhead in hiring and training, and improved customer satisfaction—balanced against the vendor’s costs and any complexity they add.

6. Decide how much control and complexity you want to own

Some platforms make you do the heavy lifting—building and tuning your agents from scratch, maintaining training data, and constantly tweaking logic. On the flip side, they provide a great amount of flexibility in return. Others are more like a plug-and-play “AI brain,” where you only worry about giving the right business logic instructions (like you would with new human hires). Think about what skill sets you have in-house and what roles you’ll need. If your ops domain experts can’t easily update logic without engineering help, that may slow you down. Aim for a solution that aligns with the capabilities of your team and your willingness to manage complexity. The ideal solution should enable your ops SME to maintain and extend it independently.

7. Don’t tie yourself to a particular LLM provider through your vendor

The generative AI field moves at breakneck speed. LLM models from OpenAI, Google, Anthropic, and others will continuously leapfrog each other in quality and cost-efficiency. Your chosen vendor’s reliance on one particular partner could limit how quickly they can adopt the next best model. Before signing on, ask how easily they can switch the underlying models. This will ensure that your chosen AI worker can get better every time a next generation model comes out.

8. Distinguish between co-pilots and autonomous agents and understand the ROI implications

AI co-pilots, which assist human agents, sound great in theory but can be hard to measure in terms of ROI. If your customer support teams have strict productivity targets, slipping a co-pilot into the workflow won’t magically improve metrics unless you also adjust how you measure and incentivize performance. Fully autonomous agents that can shoulder entire conversations can be easier to justify: their impact shows directly in reduced handling time and improved auto resolution rates. Consider what setup best supports your operational goals and how you’ll prove the return on that investment.

9. Carefully consider pricing, contract lengths, and risk of vendor lock-in

Pricing models vary widely: some vendors may offer success-based pricing that aligns with your ROI (e.g. per successful resolution of customer queries), while others might push opaque platform fees or multi-year contracts that are hard to escape. In a fast-moving market, long-term lock-in can turn sour if the platform doesn’t keep pace with innovation. Aim for pricing structures that keep your options open and let you pivot if a better solution emerges. And if you decide to lock yourself in for >12 months then make sure you’ve live tested at least a few solutions.

10. Assess the vendor’s long-term alignment with your automation strategy

Beyond current performance, is the vendor working in a direction that fits your long-term vision? Will they challenge what’s possible today and build for tomorrow? If you foresee a world where AI agents handle not just frontline tasks but also complex back-office processes, choose a provider that’s investing in that breadth. If your future involves minimal maintenance overhead and seamless model updates, partner with a vendor who shares that philosophy. A successful relationship with an AI vendor depends on a shared direction as much as on their tool’s present-day capabilities.

Conclusion

Focus on flexibility, unlocking deep value, and future-proofing your AI partnership.

In 2025, buying a generative AI tool for customer operations isn’t about picking a single solution and sticking with it forever. Instead, it’s about understanding the full iceberg of your operational needs, ensuring flexibility to upgrade models, testing tools thoroughly in real-world environments, and planning for the long term. If you keep these considerations in mind, you’ll be equipped to navigate the turbulent waters of the generative AI market—and set your organization up for success as the landscape continues to evolve.