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09/07/2026 1:41 pm
TL;DR: AI is changing how customers optimise rewards and point pooling, while loyalty teams still face 12-month deployment backlogs that slow monetisation and margin protection, according to Comarch and Loyalty360. The governance challenge is no longer campaign creativity but whether loyalty platforms can adapt fast enough to support revenue, incrementality, and customer behaviour shifts.
NHIMG editorial — here’s why we think this discussion matters
Questions worth separating out
Q: How should organisations govern AI-driven loyalty abuse without slowing down growth?
A: Organisations should treat loyalty rules, customer identity data, and exception handling as governed runtime assets.
Q: Why do legacy loyalty platforms create control risk for customer engagement programmes?
A: Legacy loyalty platforms create control risk because they force policy changes through long delivery queues, while customer behaviour and abuse patterns change much faster.
Practitioner guidance
- Map loyalty controls to business change latency Measure how long it takes to update offers, pooling rules, and redemption logic across the live stack.
- Review access to loyalty decision engines and customer data Identify who can change rules, inspect transaction data, or override exceptions in the loyalty platform.
- Stress-test incrementality against AI-assisted abuse patterns Simulate habitual-buyer discounting, point-pooling manipulation, and reward optimisation scenarios before approving campaigns.
What to expect at the briefing
Comarch's full webinar covers the tactical loyalty operating model this post intentionally leaves at the strategic level:
- The exact incrementality levers used by top-quartile brands to reduce unnecessary discounting.
- A practical blueprint for bypassing long IT queues without replacing the entire loyalty stack.
- Board-facing monetisation framing for turning loyalty from a cost centre into a revenue driver.
- Discussion with named speakers on data integration challenges and current loyalty strategy pressures.
👉 Register for Comarch and Loyalty360's webinar on AI-era loyalty growth →
AI-driven loyalty programmes: what happens when IT queues fall behind?
Explore further
09/07/2026 1:46 pm
AI pressure turns loyalty governance into a runtime control problem. The core issue is no longer whether a loyalty programme has enough features. It is whether the organisation can detect and constrain AI-driven optimisation before margin leakage becomes normalised. That shifts the debate from campaign creativity to operational governance, where identity-linked data, rules engines, and exception handling must be managed as a live control plane, not a static marketing asset. Practitioners should treat loyalty abuse as a governance design issue, not a one-off fraud event.
A few things that frame the scale:
- Only 44% of developers are reported to follow security best practices for secrets management, exposing a significant developer behaviour gap, according to The State of Secrets in AppSec.
- The average estimated time to remediate a leaked secret is 27 days, while 75% of organisations still express strong confidence in their secrets management capabilities.
A question worth separating out:
Q: Who should be accountable when loyalty logic affects revenue, customer trust, and data use?
A: Accountability should sit with a cross-functional owner group that includes marketing, finance, security, and platform teams. Loyalty logic now affects identity-linked data, economics, and operational risk, so no single function can own the outcome without shared control over policy, access, and telemetry.
👉 Read our full editorial: AI-driven loyalty growth is outpacing legacy IT deployment cycles
