AI Agents, Governance Gaps, And Rising Customer Expectations
PLUS: Prompts to audit your AI governance training and build a Tier-1 AI agent pilot plan
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🗓️ August 27, 2025 ⏱️ Read Time: ~5 minutes
👋 Welcome
Today’s CX headlines are all about execution. From AI agents that can finally handle real-world messiness, to executives leaking data into AI tools, to award-winning automation platforms, the theme is clear: the gap between hype and delivery is shrinking, and so is the patience of your customers.
📡 Signal in the Noise
CX leaders must get sharper at two things: choosing AI tools that deliver measurable results, and governing how people use them so they don’t torch customer trust.
🧭 Executive Lens
This is the pivot point. The companies that marry adoption with discipline, deploying agents that work, enforcing policies that stick, and upgrading platforms that prove ROI, will be the ones who win the next wave of customer loyalty.
📌 Stories That Matter
🤖 Six tools to build AI agents that actually work
Most AI agents look slick in a demo but collapse once they hit messy, real-world customer interactions. CX Today dug into six tools designed to bridge that gap—combining rule-based logic, orchestration layers, and GenAI to build agents that actually deliver containment and resolution at scale. These aren’t toys; they’re the scaffolding for serious deployments.
Why this matters: CX leaders are under pressure to move past pilots and show real automation ROI. These tools highlight what “ready for prime time” looks like.
Try this: Don’t boil the ocean. Pick one Tier-1 use case (password reset, order status, billing check) and test a tool against your current live-agent baseline.
⚠️ C-suite and staff flouting AI usage policies—big CX risk
A new survey from Customer Experience Dive found that employees and executives alike are ignoring AI usage policies. Half of workers admitted they’d use AI tools even if rules said otherwise, and a quarter of execs confessed to pasting sensitive data into them. That’s a recipe for leaks, compliance headaches, and a trust crisis if customers find out.
Why this matters: Customers don’t care if the problem came from a frontline rep or a VP—they just know their data isn’t safe. CX pros need to treat AI misuse as a direct threat to customer trust, not just an IT problem.
Try this: Make AI governance real for employees. Tie it to customer stories (e.g., “Here’s what happens when private data gets out”) instead of dry policy docs.
🏆 Mitel CX honored for AI-driven contact center innovation
Mitel just picked up a 2025 Contact Center Technology Award for its AI-powered platform. Why? Their system is automating as much as 90% of customer interactions, routing seamlessly across channels, and layering analytics to help human agents when they do need to step in. It’s a strong example of what “AI-first CCaaS” is starting to look like.
Why this matters: Awards themselves don’t change your business, but they do show where the bar is moving. Vendors that can’t offer this level of automation and analytics won’t be competitive for long.
Try this: In your next vendor review, ask one blunt question: “What percentage of our current volume could your AI realistically handle today?”
📞 Contact center reframed as a CX growth engine
CMSWire laid out seven tactics to reposition contact centers from cost sinks into revenue-driving CX hubs. The list includes real-time monitoring, predictive analytics, and journey-based metrics—shifting the function from reactive problem-solver to proactive customer relationship driver.
Why this matters: Too many organizations still see the contact center as a place to cut costs, not create loyalty. That mindset leaves money and trust on the table.
Try this: Run a quarterly “growth review” of your contact center. Instead of looking only at handle time, track where calls could have created retention, upsell, or advocacy opportunities.
📱 Dropbox rolls out GenAI Dash download for customers
Dropbox just made its generative AI tool, Dash, available for download. Think of it as a smart search and assistant layered directly into file management—helping users find content, summarize documents, and even suggest related resources. It’s a clear example of AI becoming invisible infrastructure in day-to-day workflows.
Why this matters: If your customers live in platforms that are embedding AI (Dropbox, Google, Microsoft), their expectations for effortless, predictive support are rising too. They won’t tolerate clunky portals or outdated search.
Try this: Benchmark your digital self-service experience against these consumer-grade AI tools. If Dropbox can surface files instantly, why does your customer still wait minutes to find an order status?
💡 Prompt of the Day
Audit your AI governance training
You are a CX leader tasked with auditing your organization’s AI governance training. Your goal is to identify gaps that put customer trust, compliance, or service quality at risk, then propose fixes. Follow this step-by-step structure:
Step 1 — Curriculum Review
- List the modules currently in place.
- Note which are outdated, vague, or too technical.
- Flag where content doesn’t tie directly to real CX situations.
Step 2 — Risk Behaviors
- Identify ways employees could bypass policies (e.g., using unauthorized AI apps, pasting customer data into public models, skipping fallback processes).
- Highlight the most common risks across frontline teams vs. executives.
Step 3 — Leadership Modeling
- Document examples where executives or managers ignored or bent rules.
- Assess how this undermines compliance culture.
Step 4 — Role-Based Modules
- Frontline Agents: Handling customer data, when to escalate, disclosure of AI usage.
- Managers: Monitoring compliance, coaching agents, resolving gray areas.
- Executives: Setting governance standards, modeling behavior, risk accountability.
Step 5 — Story-Driven Reinforcement
- Suggest communication strategies using real-world customer trust stories.
- Replace dry policy language with CX impact (e.g., “What happens if data leaks?”).
Step 6 — Testing & Measurement
- Recommend quizzes, scenario drills, or mystery-shop tests to track adoption.
- Define what success looks like (improved compliance scores, fewer shadow AI incidents).
What this uncovers: Where governance breaks down in real workflows.
How to apply: Tie governance refreshers directly to CX trust metrics.
Where to test: Start with frontline service and executive teams.
🛠️ Try This Prompt
Build a Tier-1 AI agent pilot plan
You are a CX transformation officer designing a 90-day pilot for a Tier-1 AI agent. Create a structured plan using this checklist:
Step 1 — Scope & Use Case
- Select one high-volume, low-complexity interaction (e.g., password reset, order tracking).
- Document expected daily/weekly volume to model ROI.
Step 2 — Success Metrics
- Containment % target (e.g., 60%+).
- Deflection rate (reduced human contacts).
- Average handle time reduction.
- CSAT/NPS shift for pilot group.
- SLA breach risk reduction.
Step 3 — Fallback Protocols
- Define handoff triggers to human agents.
- Script escalation paths and track drop-offs.
- Assign QA reviewers to evaluate escalated cases.
Step 4 — Training Data
- Gather transcripts, FAQs, macros, knowledge articles.
- Clean and tag data for accuracy.
- Define an update cycle (weekly retraining or feedback loop).
Step 5 — Customer Journey Integration
- Show how the AI connects into CRM, ticketing, and knowledge systems.
- Identify potential friction points (authentication, personalization).
Step 6 — Executive Dashboard
- Weekly reporting: containment %, CSAT, agent workload shift, cost-to-serve savings.
- Visualize trends with before/after comparisons.
Step 7 — Go/No-Go Framework
- Define thresholds for expansion (e.g., 70% containment + no CSAT decline).
- Capture agent feedback on trust/efficiency.
- Gather customer verbatims for quality assessment.
Immediate use case: Create a ready-to-launch framework for AI containment pilots.
Tactical benefit: Aligns pilot execution with measurable business impact.
How to incorporate quickly: Run the plan in one high-volume customer support queue.
📎 CX Note to Self
AI’s value in CX comes down to two levers: trust and execution. Ignore either, and you lose both customers and credibility.
👋 See You Tomorrow
What’s the biggest blind spot in your AI strategy—tools, policies, or leadership? Hit reply and let’s compare.
—Mark
P.S. Want more prompts? Grab the FREE 32 Power Prompts That Will Change Your CX Strategy – Forever to start transforming your team, now. 👉 FREE 32 Power Prompts That Will Change Your CX Strategy – Forever
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