E-commerce teams are handing more and more decisions over to algorithms: product recommendations, send times, targeting, bidding, CRM journeys.
The discomfort rarely comes from the technology itself. It comes from the feeling of losing control over decisions that directly impact revenue.
Why «If / Then / Else» Rules Are No Longer Enough
For a long time, CRM and parts of digital marketing relied on simple scenarios:
- If a customer abandoned their cart → send a reminder.
- If a customer has not ordered in X days → send a follow-up.
- If a customer belongs to segment A → show them a specific message.
This logic works as long as:
- customer journeys remain relatively linear,
- data volumes stay manageable,
- the number of signals remains limited.
In 2026, that is no longer the case:
- multi-device, multi-session journeys,
- increasingly complex behaviors,
- multiple intent signals, sometimes weak or indirect,
- data volumes that make static rules insufficient..
Algorithms and AI are therefore less of a « nice to have »and more of an operational necessity to absorb this complexity.
The Real Source of Discomfort: A Black Box Making Decisions for You
When a model decides:
- which product to highlight,
teams face an uncomfortable reality: they do not always understand why one decision was made over another.
This is not a bug. It is the very nature of modern models:
- across volumes of signals that are impossible for humans to process manually,
- with interactions between variables that are difficult to explain simply.
The risk, if you focus too much on understanding every individual decision, is missing the real question:
« Is what the algorithm does actually creating value, or is it simply shifting behavior that would have happened anyway ? ».
The Trap of Reassuring Metrics
When visibility decreases, teams tend to cling to simple indicators:
- conversions observed after exposure,
- flattering attribution reports.
These numbers are reassuring, but they only answer a limited question:
« What happened after the action ? ».
They do not answer the central question:
« What would have happened if we had done nothing ? ».
This is where the confusion between correlation and value creation becomes dangerous:
- a message can generate many sales from customers who would have purchased anyway,
- an automation can look highly effective simply because it mainly targets customers who were already very engaged.
A/B Testing as the Minimum Standard for Governance
When the inner workings of algorithms are no longer fully understandable, there is only one healthy way to judge their impact: comparison.
In other words:
- compare a population exposed to the algorithm with a similar population that is not exposed,
- compare journeys with and without automation,
- observe behavior across a sufficiently representative sample.
This is not unnecessary sophistication. It is the minimum standard for governance.
- Without testing, algorithms make decisions with no counterbalance.
- With testing, you may not control every decision in detail, but you do control the proof of its overall impact.
How to Stay in Control Without Understanding Everything
For a Shopify store, the goal is not to become a data scientist. The goal is to build a simple framework :
- Clarify what the algorithm is supposed to optimize
- LTV? Incremental revenue? Purchase frequency?
- Avoid objectives that are too generic (« engagement », « clicks »).
- Always require a test or holdout mode
- For every new automated scenario, keep a percentage of customers unexposed.
- Compare their behavior over time, not only immediately after the action.
- Choose the right success metrics
- Do not settle for uplift on opens or clicks.
- Look at cumulative value, purchase frequency, and margin.
- Make impact reviews a habit
- Run a monthly review of automated scenarios.
- Make explicit decisions: stop, adjust, or scale.
What This Changes in Team Culture
Handing decisions over to algorithms requires a shift in mindset:
- You accept that you will not understand everything,
- but you become uncompromising about the quality of the evidence,
- and about the quality of the input signals.
The question is no longer:
«Do I understand every choice the algorithm makes? »,
It becomes :
« Can I prove that what it does creates value I would not have created otherwise? ».
Within this framework, AI is no longer a worrying black box. It becomes a demanding collaborator: it makes decisions at scale, but under the control of clear governance.
