Gorgias AI Performance Benchmarks for High- Performing CX Teams

Insights from 20+ brands using Gorgias AI for customer support

AI is now embedded in modern customer support. For eCommerce operators, the real question is no longer whether to use Gorgias AI, but how to operate it well.

This playbook analyzes performance data from 20+ brands using Gorgias AI across ecommerce and digital-first businesses. The focus is specifically on how Gorgias AI performs within modern Al customer support teams.

We examined

• AI resolution rate
• cost per inbound message
• overall CSAT
• and how these metrics interact

The goal is simple: define what strong performance actually looks like.

Across the dataset, one pattern stands out. The highest-performing teams are not the most automated. They are the most deliberate. Moderate AI usage, paired with disciplined operating models, consistently outperforms aggressive automation.

For leaders responsible for CX, cost, and growth, this report clarifies where the real leverage sits. Scroll down to see the data and charts.

What the data shows

Across 20+ brands using Gorgias AI, a clear performance profile emerges.

Strong-performing teams typically operate at:

• 10–15% AI resolution rate
• 4.4+ CSAT
• Under $2 per inbound message (human + AI)
• 900+ resolutions per human agent per month

These are not theoretical targets. They reflect actual operating teams. Notably, none of the highest-CSAT brands in this dataset are running aggressive AI automation levels above 20%.

If you spend enough time in ecommerce CX circles, you’ll hear the same promise:

“Soon enough AI will replace your support team.”

But when you look at real performance data from ecommerce brands using Gorgias AI, the story is more measured.

The best CX teams are not replacing humans with AI.

They are using Gorgias AI to increase human productivity while protecting customer experience.

AI resolution rate by client

Across the 20+ brands analyzed, AI resolution rates range from 0% to 50.99%, with a median of 10%.

Most brands operate well below 15% AI resolution. A small group sits in the 8–15% range, while several brands are still at or near zero, indicating that AI is enabled but lightly used.

Only a limited number of brands exceed 25% automation, and just one brand in the dataset operates above 50%.

The distribution is heavily weighted toward moderate AI usage. High automation levels are the exception rather than the norm.

Cost per inbound message

Across the 20+ brands analyzed, estimated CX cost per inbound message ranges from $0.89 to $7.35, with a median of $2.30.

Within the same Gorgias ecosystem, cost efficiency varies dramatically. Some teams operate near or below $1 per message, while others exceed $5.

The gap between a team operating at $1.25 per message and one at $3.75 represents a threefold difference in cost structure.

What stands out is that this variation cannot be explained by platform access alone. Every brand in this dataset uses Gorgias AI. The difference lies in how the CX function is structured and managed.

There is no default cost profile. Operating model decisions, staffing design, and automation discipline drive the outcome.

CSAT Distribution

Across the brands analyzed, overall CSAT scores range from 2.96 to 4.90, with a median of 4.40.

A significant portion of brands operate between 4.3 and 4.7, and several exceed 4.6. At the lower end, a small number falls below 4.0.

The nearly two-point gap between the lowest and highest performers is notable.

Within the same helpdesk environment, customer satisfaction outcomes vary widely. High CSAT is achievable, but it is not guaranteed by tool adoption alone.

Here’s how the top 25% teams on Gorgias AI are performing

When we isolate the top-performing quartile in this dataset, a clear pattern emerges.

These teams typically operate with:

• CSAT of 4.5 or higher
• AI resolution rates below 15%
• Cost per inbound message between $1 and $2

Notably, none of the highest-CSAT brands in this dataset exceed 20% AI resolution rate. The strongest performers cluster in the 8–15% range.

The distinction is not aggressive automation, but controlled automation.

This does not suggest that AI harms customer experience. It suggests that unmanaged AI does.

The real role of AI in high-performing CX teams using Gorgias AI

Across the dataset, the highest-performing brands treat Gorgias AI as a support layer, not a replacement for human agents.

AI is used to manage predictable, low-emotion interactions such as order status, simple returns, and first-response drafting. Human agents remain responsible for escalations, edge cases, VIP customers, and situations that require judgment.

The objective is not maximum automation. It is sustainable performance.

Benchmark #1: AI Resolution Rate

Healthy benchmark: 10–15% AI resolution

Across the dataset, the median AI resolution rate is 10%, with an average of 13.8%. Most high-performing teams operate within a relatively narrow band.

A healthy operating range sits between 10% and 15% AI resolution. Top-performing teams typically remain below 20%, and very few exceed 25% without seeing pressure on satisfaction.

In practical terms:

• 5% of tickets → likely under-utilized
• 10–15% → strong operating range
• 20–30% → requires tight QA
• 40%+ → elevated CSAT risk unless exceptionally well governed

Moderate automation is not a compromise. It is the most consistent pattern among high-performing teams in this dataset.

Benchmark #2: Cost per Inbound Message

A healthy benchmark sits between ~$1 and $1.75 per inbound message.

Across the dataset, the median cost per inbound message is $2.30, with an average of $2.96. The most efficient teams operate closer to the $1–$1.50 range.

For context, cost was estimated using:

• $2,500 per human agent (including management)
• $0.90 per AI resolution

The gap between a team operating at $1.25 per message and one at $3.75 represents a threefold difference in cost efficiency.

Importantly, that gap does not reflect platform differences. Every brand in this dataset uses Gorgias AI. The variation reflects operating model decisions, staffing structure, and automation discipline.

You don’t need AI to win (but it helps)

One of the more surprising findings in this dataset is that several of the highest-CSAT brands use AI for less than 20% of resolutions.

Strong customer experience remains fundamentally human. AI is an accelerator, not the engine.

A well-run, human-first team with:

• Clear processes
• Strong macros
• Proper staffing ratios
• Weekly QA

…can achieve best-in-class satisfaction.

But when AI is layered in correctly and systematically:

• Response times improve
• Agent productivity increases
• Cost per message declines
• Teams scale more predictably

The difference is structural readiness.

AI amplifies a strong CX operational model. It does not fix a weak one. In organisations with clean workflows and clear escalation paths, AI increases throughput and removes repetitive load. Where processes are inconsistent, automation simply scales the inconsistency.

AI creates leverage. It does not replace operational discipline.

The pro-level CX Gorgias AI operating model

Based on the highest-performing teams in this dataset, a strong operating model consistently includes three elements: clear targets, defined structure, and disciplined governance.

Target metrics * CSAT: 4.6+ * AI resolution: 10–20% * Cost per message: ~$1–$1.75

Team structure * 70–80% L1 agents handling high-volume workflows * 10–20% L2 specialists managing escalations and complex cases * 1 team lead per 8–12 agents * Gorgias AI handling repetitive Tier 1 flows

Top teams are not attempting to automate everything. They define clearly which interactions AI customer support owns and which remain human.

That clarity is what protects both efficiency and experience.

The 5 moves top teams make

Across the dataset, the strongest teams consistently demonstrate five operational habits:

1. Automate the top 3–5 ticket types: Typically: order status, returns, exchanges, shipping updates, basic product questions
2. Keep AI in the 10–20% resolution band: Enough to drive efficiency. Not enough to erode experience.
3. Segment work by complexity: Ensuring high-volume tickets and escalations are clearly separated.
4. Measure cost per message weekly: This is the clearest efficiency signal in CX operations.
5. Treat AI like a junior agent: It requires training, QA, monitoring, and ongoing refinement.

When AI resolution goes too high, CSAT drops

The dataset shows a clear inflection when automation levels rise significantly above the median.

Median AI resolution across the dataset is 10%. The highest-CSAT teams stay below 20%.

But a few outliers illustrate the risk of over-automation.

Example: Brand 1

• AI resolution: ~51%
• CSAT: ~3.7

Example: Brand 2

• AI resolution: ~30%
• CSAT: ~3.6

In both cases, automation is significantly above the dataset median, while satisfaction falls below the 4.40 benchmark.

This does not mean AI is harmful. It means unmanaged AI hurts the overall CSAT. This is where structured AI agent management becomes critical.

When AI becomes the dominant interaction layer rather than a support layer, experience quality becomes more difficult to control.

Contrast: High-CSAT Teams with Moderate AI

• Brand 3: ~4.79 CSAT, 0% AI
• Brand 4: ~4.71 CSAT, low AI usage
• Brand 5: ~4.67 CSAT, ~10–15% AI

These teams have something in common. They:

• Use AI selectively
• Keep complex interactions human
• Maintain strong overall satisfaction score

The pattern across the dataset is consistent. AI performs best as a controlled efficiency lever rather than a primary channel.

A simple target

For teams aiming to operate in the top tier of this dataset, three metrics consistently appear together:

• AI resolution rate: 10–15%
• Cost per message: ~$1–$1.75
• CSAT: 4.6+

These ranges are not theoretical. They reflect the operating profile of the strongest-performing teams analyzed.

The objective is not to maximise any single metric in isolation, but to keep them in balance.

In conclusion

AI on its own is not the future of customer support. AI-empowered teams are.

The brands leading in CX are not those with the highest automation rates. They are the ones that balance speed, cost, empathy, and judgment.

Across this dataset, that balance consistently reflects moderate AI usage paired with strong human ownership. Efficiency improves, costs stabilise, and customer experience remains protected.

Operational maturity in AI customer support is not defined by how much you automate, but by how intentionally you do use it.

If you’re evaluating how your AI performance stacks up, book a call with our team to review your AI agent management strategy.