Every e-commerce team using Klaviyo eventually hits the same question: how much of your email marketing automation should run without you? The answer splits into two camps. One says give AI the wheel. The other says give AI a clipboard and let the human drive.

Both camps have a point. Neither is entirely right. This post breaks down what each approach actually looks like in practice, where each one works, where each one fails, and which one makes more sense depending on how your team operates.

Two models for email marketing automation

The terms get thrown around loosely, so let me be specific about what I mean.

A copilot is a tool that analyzes your Klaviyo account, surfaces problems, suggests actions, and helps you execute faster. You still make the decisions. It's a second brain that doesn't forget things.

Full automation is a system that detects problems and fixes them without asking. A flow underperforms, so the system rewrites the subject line. A segment shrinks, so the system adjusts the criteria. The human sets the guardrails upfront and then steps back.

The difference matters because it changes what your team does all day, what skills you need to hire for, and how much risk you're comfortable with.

The bottom line: copilots keep humans in the loop; full automation removes them. The right choice depends on your team size, brand sensitivity, and comfort with risk.

What copilots do (and where they fall short)

Where copilots add value

Across 20+ Klaviyo accounts I've worked with, the same pattern shows up: the data is there, but nobody has time to look at it consistently. Open rates drift down over months. A flow that used to convert stops performing. A segment grows stale. Nobody notices until the quarterly review.

A copilot fixes this by doing the monitoring work that humans skip when they're busy. In practice, that means:

Summarizing flow performance weekly in plain language instead of raw dashboards
Flagging segments that have grown too broad or too narrow compared to last month (more on this in our guide to RFM segmentation in Klaviyo)
Surfacing anomalies: a 30% drop in click rate on your welcome series, for example
Tracking whether previous recommendations actually got implemented

This is where tools like SPARKCRM operate. The tool reads your Klaviyo account, scores its health, and tells you what to fix this week. You decide whether to fix it and how.

Where copilots fall short

They don't do the work for you. If your team is stretched thin and nobody acts on the recommendations, a copilot just becomes an expensive notification system. The tool is only as good as the team's capacity to respond.

They also can't make creative decisions. A copilot can tell you that your abandoned cart flow has a 1.2% click rate and suggest testing a new subject line. It can't write copy that sounds like your brand.

In short: copilots are force multipliers for teams that already have some CRM capacity. They don't replace that capacity.

What full automation actually looks like

Where full automation works

Some parts of Klaviyo management genuinely benefit from taking the human out. Send-time optimization is a good example. Klaviyo's Smart Send Time picks the best delivery window for each recipient based on their past behavior. No human can do this at scale, and the data consistently shows it improves open rates by 5-15%.

Other areas where automation works well:

A/B test winner selection based on statistical significance, not gut feel
Product recommendation blocks that pull from Klaviyo's predictive analytics automatically
Suppression of unengaged contacts after a defined inactivity window

These are mechanical decisions with clear criteria and low brand risk. If the algorithm picks the wrong send time by 30 minutes, nobody notices. If it suppresses a contact who hasn't opened in 180 days, that's probably the right call anyway.

Where full automation breaks

The problems start when automation touches anything that affects how your brand sounds or feels to customers.

I've seen automated subject line testing produce winners that were clickbait. Higher open rate, sure. But the tone was completely wrong for the brand, and the unsubscribe rate on those emails was double the average. The automation optimized for the metric it was given, not for the outcome the brand actually wanted.

Another common failure: automated flow adjustments that conflict with each other. The welcome series automation shortens the sequence because completion rates are low. Meanwhile, the post-purchase automation extends its sequence because engagement is high. Nobody is looking at the overall customer experience across flows, because each automation only sees its own scope.

The takeaway here: full automation works for mechanical, low-risk decisions. It struggles with anything that requires brand judgment or cross-flow awareness.

Side-by-side comparison

Here's how the two approaches stack up across five criteria that matter for e-commerce CRM teams:

Control

Copilot: high. Every action goes through a human. You see what's recommended and decide what to do.

Automation: low to medium. You set rules upfront, but the system acts independently within those rules. Edge cases get handled without your input.

Brand safety

Copilot: high. No copy, offer, or timing change happens without human approval.

Automation: variable. Safe for non-creative decisions (send time, suppression). Risky for anything involving messaging, tone, or offer strategy.

Speed of action

Copilot: slower. Depends on how fast your team responds to recommendations. If the team is busy, issues can sit for days. (We cover how to reduce this firefighting cycle in a separate post.)

Automation: fast. Problems get addressed in minutes or hours, not days. This matters for time-sensitive issues like deliverability drops.

Cost and complexity

Copilot: lower setup cost, ongoing human time required. You need someone to act on the recommendations.

Automation: higher setup cost, lower ongoing time. But debugging automated decisions is harder than debugging manual ones.

Scalability

Copilot: scales linearly with team capacity. More accounts means more human hours.

Automation: scales better across accounts. The same rules can apply to multiple stores. But the initial configuration per account is heavier.

Neither approach wins on every criterion. The right choice is usually a mix of both, with clear boundaries about what gets automated and what stays manual.

Which approach fits your team

After working with CRM teams of different sizes, here's the pattern I've seen:

Teams of 1-2 people benefit most from the copilot model. They need help prioritizing and spotting issues, but they don't have the bandwidth to set up and maintain complex automation rules. A tool that says 'fix this flow, here's why' is more useful than a system that tries to fix it automatically and sometimes gets it wrong.

Teams of 3-5 people can start introducing selective automation for the mechanical stuff: send-time optimization, basic suppression rules, A/B test winner selection. Keep the creative and strategic decisions with humans.

Agencies managing 5+ accounts almost always need both. Automation handles the repetitive monitoring across accounts. A copilot tool provides the structured view that lets account managers focus on the decisions that actually require expertise.

My recommendation: start with a copilot. Add automation selectively as your team and processes mature.

Real-world examples: how teams are deciding

A DTC skincare brand I worked with last year had a two-person CRM team managing about 15 active flows and sending 3-4 campaigns per week. They tried automating their A/B testing winner selection and send-time optimization. Both worked well. Then they tried automating subject line generation based on past performance data. Within two months, their brand voice had drifted noticeably. Open rates were slightly up, but their head of brand flagged that the emails no longer sounded like them.

They pulled back the subject line automation and kept it as a suggestion engine instead. The CRM manager reviews the AI-generated options each week and either uses them as-is, rewrites them, or ignores them. This hybrid approach takes about 15 minutes per week and keeps the brand voice intact.

A Klaviyo agency managing 8 accounts took a different path. They automated monitoring and anomaly detection across all accounts using a copilot tool, then built custom automation rules for the mechanical tasks that were identical across clients: suppression policies, bounce handling, and A/B test evaluation. But every flow change, every campaign brief, and every segment adjustment still goes through an account manager. (If you're an agency, our guide to preparing for Klaviyo migrations covers how to standardize this process.)

The agency's founder told me something that stuck: 'Automation handles the stuff that's the same everywhere. The copilot handles the stuff that's different.' That's a cleaner way to think about it than trying to draw a single line between what gets automated and what doesn't.

What these examples show: the most effective implementations use automation for standardized, mechanical tasks and copilots for account-specific, judgment-heavy decisions.

Common mistakes when choosing an approach

The first mistake is treating this as an either/or decision. Most teams need elements of both. The question is which tasks belong in which category.

The second mistake is automating too early. If you don't understand why a flow is performing well, automating its optimization is risky. You might automate away the thing that was actually working. Build understanding first (copilot), then selectively automate the parts you're confident about.

The third mistake is ignoring the cost of debugging. When a human makes a bad decision about a flow, you can ask them why. When an automation makes a bad decision, you have to reverse-engineer the logic, check the data inputs, and figure out what went wrong in a system that might be interacting with other automated rules. This debugging cost is real and gets underestimated.

The pattern to follow: start with understanding (copilot), automate selectively, and account for the hidden cost of debugging automated decisions.

What to evaluate when choosing a tool

If you're shopping for email marketing automation tools to layer on top of Klaviyo, ask these questions:

Does the tool act on your account directly, or does it recommend actions for a human to approve? This is the copilot vs automation distinction. Know which one you're buying.
What data does it use for its recommendations? A tool that only looks at email metrics is missing half the picture. You want something that considers flow interactions, segment overlap, and deliverability signals together.
How does it handle edge cases? Every Klaviyo account has quirks: custom properties, unusual flow structures, multi-store setups. Ask the vendor what happens when their tool encounters something it doesn't recognize. Does it flag it for a human, or does it guess?
What does the debugging experience look like? When an automated decision goes wrong, how do you trace what happened? If the answer is 'check the logs,' ask to see what those logs actually look like. Unreadable logs are the same as no logs.
Can you turn off individual automations without disabling the whole system? Granular control matters. You might love automated send-time optimization but hate automated segment adjustments. You should be able to keep one and disable the other.

The vendors who are confident in their tool will have clear answers to these questions. The ones who get vague are usually hiding complexity behind marketing language.

Judge tools based on the level of control they give you, not the features listed on their pricing page.

Where SPARKCRM sits

SPARKCRM is a copilot, not an autopilot. It connects to your Klaviyo account via API, audits the setup, scores account health, and tells you what needs attention each week. It doesn't change anything in your account without you.

The reason for this design choice is practical: the CRM managers and agency teams we work with don't want a black box making decisions about their clients' email programs. They want clarity about what's working, what's broken, and what to prioritize. Then they want to make the call themselves.

If you're evaluating tools for your Klaviyo stack, the question worth asking is: do I need something that acts for me, or something that helps me act faster and with better information? For most teams, the second option is more honest about how CRM work actually gets done. (See also: our guide to using Klaviyo data to brief your paid media agency)

💡 Want to see how SPARKCRM scores your Klaviyo account? Request a free audit at sparkcrm.cc and get your first AuditScore report within 24 hours.