Strategic CX & GenAI: A Practical Checklist for Design Leaders

Purpose: Use this as a quick checklist to pressure-test roadmaps, vendor pitches, and consulting proposals before you commit budget — or reputation.

Joahna Kuiper / https://betterimagesofai.org / https://creativecommons.org/licenses/by/4.0/

Incentive Misalignment (Follow the Money)

Consultancies and vendors often publish research that points back to the things they sell. That can inflate promises and hide the messy parts of implementation. If a firm makes a lot of money from AI services, expect their forecasts to be very optimistic.

AI can also make leaders sound more confident while becoming less accurate — especially when a polished slide deck wraps shaky AI-generated analysis. Treat “authoritative” projections like marketing until the unit economics stand up to independent scrutiny.

Quick check: Who gets paid if we choose this path? What (kind of) numbers are they basing their promises on?

Platform Lock-In Risk (Path Dependence by Design)

AI stacks — models, orchestration tools, plugins — create dependencies. Pricing, features, and access are controlled by the provider. Remember, if terms change, you pay to re-platform.

Lock-in can also devalue your craft: outputs get commoditized, and platform economics start driving your priorities. Build for portability from day one: exportable data and evals, open APIs, and contract terms that protect your exit options. For example, what happens when you are forced to leave a platform that doesn’t hold up to EU law? Or when you feel the need to abandon US dependencies, etc. etc.

Quick check: Can we export our data, prompts, fine-tunes, and evaluation results tomorrow — without the vendor?

Model & Data Governance (Safety, Privacy, Compliance)

Hallucinations, bias, and IP leakage aren’t “bugs” — they’re governance problems. With the EU AI Act and expanding privacy laws, your interfaces must support explainability, disclosure, human oversight, and real-time bias monitoring. “Governance” is a tricky word. It conjures images of legalese, unnecessary reporting, and lawyers. My take? Treat governance as a design requirement:

• Run model-risk reviews (where can this be wrong, and who catches it?).
• Minimize data by default.
• Make sure your system knows exactly what each customer has allowed you to do, and it uses that permission at the moment you act (personalize, target, train a model, etc.). Build this into your cusotmer data platform (CDP) decisioning stack so you never move faster than what’s legally allowed.

Quick check: If a regulator or customer asks “why did the system do that?”, can we show our reasoning and controls?

Example

User: EU customer
They allow: Email personalization
They deny: Ads & model training

• Email tool may personalize subject lines using first-party signals (not third-party since they’ve blocked ads!).
• Ad platform sync is blocked; user never joins retargeting audiences.
• ML pipeline must exclude this user’s data from training sets.
• On-site experience falls back to “popular now” modules instead of behavior-based or personalized content.
• etc. etc.

These are direct user experience attributes and features — not just “lawyer stuff”.

Labor Impact (Beyond “Augmentation” Talk)

Displacement is already here in creative and knowledge work. Demand and rates have fallen in several categories (especially creatives); layoffs tied to automation keep showing up. Even when jobs stay, work changes: more QA, new workflows, and morale risk.

Don’t rely on “creativity” as the safety moat. Durable advantage sits in relationships, context, and organizational navigation — things that don’t easily commoditize and that shape real outcomes.

Quick check: What roles change, who needs upskilling, and how will we protect quality and morale?

ROI Opacity (Mitigate “Transformation Theater”)

Beware trillion-dollar headline estimates that extrapolate today’s capabilities without accounting for plateau effects, regulatory drag, or commoditization. Require verifiable comparisons at the journey level. Can you get a full disclosure of your advisors’ AI revenue dependencies; and pair that with a rigorous CX ROI frame that separates hard savings, soft benefits, and revenue impact (retention, cross-sell, referrals), anchored to leading and lagging indicators?

Solution: Use a three-tier measurement ladder and a 3–5 value-driver business case for every initiative.

Three tiers of metrics

• Transaction: Did the touchpoint improve? (e.g., checkout errors ↓, page loads ↑)
• Journey: Did the end-to-end flow improve? (e.g., trial → activation ↑)
• Relationship: Are we deepening loyalty? (e.g., 90-day retention ↑, Customer Lifecycle Value (CLV) ↑)

Tiny example — Fix checkout friction

• Transaction: Payment failures ↓ 6% → 3%
• Journey: Purchase conversion ↑ 2.5% → 3.0%
• Relationship: 90-day repeat rate ↑ 20% → 22%

Value drivers (pick 3–5):

1. More conversions → revenue up
2. Fewer payment tickets → service cost down
3. Faster resolution → productivity up
4. Higher repeat rate → CLV up (more value per customer over time)

Quick check: Every experiment ladders from point → flow → relationship, and every initiative states how it makes or saves money. That kills “transformation theater” that consists of fluffy powerpoint decks, and instead connects design to customer experience reality.

Conclusion: Lead with Governance, Portability, and Proof

Strategic CX leadership when technology changes fast (as in the GenAI era we’re currently in) means:

• Governing incentives (interrogate the pitch),
• Designing for portability (avoid lock-in),
• Treat AI governance as design,
• Re-architecting roles (people and process first),
• Demanding audit-ready ROI (metrics that survive finance).

Decide as if you’re changing the operating model — because you are. Every tech-driven CX bet has contractual, cultural, and financial consequences. Lead with clarity, keep your options open, and prove the value.