Which AI model is best for customer service?

Why the 'Best Model' Question Is the Wrong Question

You've probably spent evenings comparing GPT against Claude against Gemini, reading vendor pages that each declare their model the winner. The frustrating truth: none of the credible research actually answers that question, because it's the wrong question to ask.

Consider what the evidence does show. Databricks, which builds customer service AI at enterprise scale, states plainly that data quality determines AI agent performance more than model selection does. In other words, the model is a commodity input; the data you ground it in is the differentiator. A mediocre model wired into clean, connected order and billing systems will beat a frontier model hallucinating from thin air.

The trust gap makes this worse. According to industry statistics, 55% of support teams believe AI can provide incorrect information, yet only 42% feel confident they could detect those hallucinations. If half the market can't verify what these models are doing, a published leaderboard ranking tells you almost nothing about how a model will behave on your refund policy, your tone of voice, your customers.

Even the market context is murky. Market-size estimates for AI customer service conflict across research firms — one projects $15.20 billion in 2025 growing to $141.56 billion by 2035, while other analyses land on materially different figures and timelines. When analysts can't agree on the size of the market, treat any head-to-head "best model" ranking with similar skepticism.

There's also no independent referee yet. NIST's draft AI 800-2 standard, published in January 2026, notes that consistent practices for valid, reproducible AI evaluations are only beginning to emerge — while simultaneously legitimizing automated benchmark testing as a procurement tool. The infrastructure for trustworthy comparisons is being built; the definitive rankings aren't in.

So here's the reframe:

• The best model is whichever one performs on your data and your tasks — not on someone else's benchmark suite.
• Vendor case studies are unverified; independent, with-and-without testing is the only way to see real lift.
• Grounding, guardrails, and human escalation paths matter more than the model badge.

This is precisely why SkillzBench is building sandboxed benchmarking that tests every agent skill with the same model, with and without the skill, and publishes the resulting Lift in percentage points. Before you commit to any model or skill for customer service, demand that kind of evidence — measured on tasks that look like yours.

What the Evidence Actually Says: Agentic AI, Grounding, and Hybrid Teams

The most important finding in the research isn't a model name at all — it's that how you deploy AI matters more than which model you buy. Databricks puts it bluntly: "Data quality determines AI agent performance more than model selection does" (per their enterprise AI analysis).

The evidence for agentic AI is hard to ignore. A 90-day Fini Labs study across 10 industries found agentic AI achieves 70–85% autonomous resolution rates, versus 40–60% for standard AI and just 20–40% for rule-based bots. The difference isn't cleverer conversation — it's capability. Agentic systems verify identity, issue refunds, and update accounts end-to-end rather than deflecting tickets to humans (market research shows the industry shifting from deflection to full resolution).

Grounding is what makes that autonomy safe. Retrieval-augmented generation anchors responses in enterprise knowledge, cutting hallucination risk — a real concern, since 55% of support teams believe AI can provide incorrect information and only 42% feel confident detecting it (industry survey data shows). Ziyad Basheer, founder and CEO at Aide, told CMSWire: "They ground it in their own trusted data and connected systems... The ones who stay stuck bought a tool and hoped."

Hybrid teams consistently outperform pure approaches, with AI handling routine volume while humans take complex cases, upset customers, and judgment calls. Michael Hutchison, head of customer experience at eClerx, sees agents "augmenting human agents rather than replacing them." The financials back this up:

• Average return of $3.50 per $1 invested, with top performers reaching 8x
• Cost per interaction down 68% after AI adoption ($4.60 to $1.45)
• Mature adopters see up to 30% lower operational costs and 15% higher CSAT

The catch: these returns belong to teams that measured before and after. NIST's draft AI 800-2 standard now legitimizes automated benchmark testing for procurement decisions — the same with-and-without methodology SkillzBench is building to test AI agent skills in sandboxed environments. Before committing to any model or tooling, test candidates on your own real customer service tasks and let the Lift numbers, not vendor claims, decide.

Test Before You Trust: Benchmarking Becomes Official Best Practice

For years, "test it and see" was folk wisdom in AI adoption. Now it's becoming official policy. In January 2026, NIST published draft standard AI 800-2, "Practices for Automated Benchmark Evaluations of Language Models," which explicitly legitimizes automated benchmark testing as a basis for AI procurement and implementation decisions.

The standard is candid about why this matters. NIST notes that automated benchmarks are "particularly useful when organizations face constraints on time, expertise, or resources" — which describes most customer service teams evaluating AI tools today. At the same time, the agency cautions that "consistent practices to support the validity, transparency, and reproducibility of AI evaluations are only beginning to emerge."

That caution is warranted. Industry voices have been warning for a while that deployment without measurement is the norm, not the exception. Martin Taylor, co-founder and deputy CEO at Content Guru, observed in industry commentary that "businesses never baselined what they were starting from" — meaning many companies deploy AI without ever measuring their existing first-contact resolution or handle times to compare against.

The gap is real: 55% of support teams believe AI can provide incorrect information, yet only 42% feel confident they could detect those hallucinations, according to aggregated industry research. Testing before deployment is the obvious bridge across that gap.

Mark Hughes, co-founder and CEO at Solidrad, argues the difference between AI success and failure "comes down to whether the team actually has visibility into what the AI is doing" — and that "there has to be some third-party oversight." Vendor-reported case-study metrics, which often lack clear methodology, make independent evaluation even more important.

This is where with-and-without testing earns its place. Rather than trusting a vendor's claim, you run the same model on the same tasks twice — once with the skill or component installed, once without — and measure the difference, or "Lift," in percentage points. It's the same logic as Taylor's baselining advice, applied at the component level.

SkillzBench is building exactly this kind of sandboxed, same-model benchmarking for AI agent skills, alongside security scanning — a necessary precaution given that published research found roughly 26% of agent skills carry at least one vulnerability.

For a customer service team, the practical checklist before any deployment looks like:

• Baseline current performance on first-contact resolution and CSAT before AI goes live
• Run candidate models and skills on your own real support tasks, not generic demos
• Compare with-and-without results to isolate what each component actually contributes
• Security-scan any third-party skills before connecting them to customer data
• Keep a third party or independent benchmark in the loop for ongoing visibility

Testing is no longer optional diligence — it's becoming the standard of care. The teams that measure before they trust will be the ones whose AI actually resolves tickets instead of generating new problems.

Your 90-Day Playbook: Deploy Narrow, Measure Outcomes, Scale Slowly

Most teams skip the baseline, making it impossible to prove AI value later. Martin Taylor, co-founder of Content Guru, notes that businesses frequently fail to measure their starting point before deploying new technology according to industry analysis.

Your first week is dedicated to establishing ground truth. Track your current first-contact resolution rate and Customer Satisfaction (CSAT) scores for three full weeks. This data becomes your control group. Without it, you cannot distinguish between genuine improvement and natural fluctuation.

Next, identify your pilot scope. Choose high-volume, low-complexity tasks where the risk of error is minimal. Order tracking, billing inquiries, and password resets are ideal starting points. These workflows allow you to test system reliability without exposing your brand to significant reputational damage.

Set strict confidence thresholds for escalation. If the AI model’s confidence score drops below a defined limit, the system must immediately route the conversation to a human agent. This is critical for sensitive interactions like refunds or complaints. Research indicates that 55% of support teams worry about AI providing incorrect information, yet only 42% feel confident detecting those hallucinations.

Measure outcomes, not just automation volume. Latané Conant, CMO at Parloa, advises against asking how many tickets were automated. Instead, ask if customer life became easier and if that drove loyalty as cited by CMSWire. Focus on these core metrics:

• First-contact resolution rate: Did the issue actually get solved?
• CSAT score: Did the customer feel helped or frustrated?
• Human escalation rate: Are you routing the right conversations to humans?
• Customer effort score: How many steps did the customer take?

Scale only when these metrics improve or remain neutral. Do not expand to complex tasks until your narrow pilot demonstrates stability. This incremental approach protects your team’s bandwidth and builds trust in the system.

Before you commit budget to a specific model or platform, test candidates on your own real data. Skills, proven. Join the SkillzBench waitlist to test candidate models and skills on your specific customer service tasks before making a decision. This ensures you deploy solutions that work for your unique workflow, not just generic benchmarks.