## California's New Law: Food Delivery Customers Now Have the Right to Talk to a Human Representative

A new California law that went into effect last week requires food delivery companies to **provide customers with a human customer service representative** when automation fails to resolve their issues. This legislation also mandates that companies offer refunds in the original form of payment for wrong or undelivered orders.

![Backs of bike couriers with Uber Eats bags.](https://imgproxy.divecdn.com/huY7O_B00_znK-Wb_vuP1kj7OkIfbeZbnY6vdmtXmIE/g:ce/rs:fill:1200:675:1/Z3M6Ly9kaXZlc2l0ZS1zdG9yYWdlL2RpdmVpbWFnZS9HZXR0eUltYWdlcy0xMjE4MjI4MjY4LmpwZw==.webp)

*Caption: A California law requires food delivery companies like Uber Eats and DoorDash to provide customers with a human customer service representative when automation cannot solve their issue. (Tomohiro Ohsumi / Stringer via Getty Images)*

### Key Provisions of the Legislation

The law, introduced by Assemblymember Rebecca Bauer-Kahan and signed by Governor Gavin Newsom in October, puts **guardrails around how food delivery companies handle service issues** and limits their reliance on automation. While companies can still use automated systems initially, they must provide prompt access to a human representative if automation cannot address the problem.

### Impact on Customer Service Strategies

This legislation challenges the **deflection-first AI strategies** that have dominated customer service in recent years. According to Vasili Triant, CEO of UJET, this could signal a shift in the industry, with potential regulatory consequences for companies that misapply AI.

### Expert Perspectives on AI and Automation

Jeff Fettes, CEO of Laivly, emphasizes that while AI can be useful, companies must ensure it can successfully handle consumer complaints. **"The moment AI starts making customer decisions, trust becomes the product,"** Fettes noted, highlighting the need for AI systems to recognize edge cases and escalate when necessary.

### Looking Ahead: AI Governance and Regulation

California is leading the way in AI regulation, but experts stress that companies should not wait for government action. Instead, they should implement **strong AI governance** to earn public trust and avoid regulatory issues. Fettes added, **"AI is moving faster than government can right now, which makes it incumbent on brands and AI companies to lead with strong governance."**

The legislation puts guardrails around how food delivery companies handle service issues and to what extent it can rely on automation.

California's New Law: Food Delivery Customers Now Have the Right to Talk to a Human Representative

## Choctaw Nation Cultural Center Wins 2025 Excellence in Customer Service Award

At the **27th Annual American Indigenous Tourism Conference**, the **Choctaw Nation Cultural Center** achieved a significant milestone by winning the **2025 Excellence in Customer Service Award**. This recognition highlights the center's commitment to exceptional hospitality, which is deeply rooted in Choctaw culture.

### A Proud Moment for the Choctaw Nation

The conference took place in **Choctaw, Mississippi**, on the tribe's native lands, making the award even more meaningful. Cheyhoma Dugger, the **Director of Development for the Choctaw Cultural Center**, expressed immense pride in this achievement.

> "To get to go back to our home and receive this award among other tribes, among our own people, was so special and significant. And it’s something we take pride in," said Dugger.

### Why This Award Matters

This is the **first customer service award** the tribe has received at this conference, despite having won other honors in the past. The award underscores the importance of **hospitality as a core value** in Choctaw culture, demonstrating how cultural heritage can enhance customer service excellence.

The **Excellence in Customer Service Award** not only celebrates the center's efforts but also serves as an inspiration for other organizations to integrate cultural values into their service practices.

The Choctaw Cultural Center won the 2025 Excellence in Customer Service Award. The director of development said this award brings great pride because hospitality is an important part of the Choctaw culture.

How the Choctaw Nation's Cultural Center Just Won a Prestigious Customer Service Award

## How Community Engagement is Redefining Customer Experience

B2B customer experience is undergoing a transformation. While traditional CX elements like support tickets, onboarding sequences, and account management remain important, modern buyers are demanding something more social, more trusted, and more self-directed. This is where **B2B community engagement** comes into play.

### What Is Community Engagement?

Community engagement represents the ongoing interaction between customers, prospects, partners, and experts within a shared space where people exchange knowledge, solve problems, and learn from one another. It's a customer experience model built on conversation, collaboration, and peer-led support—moving beyond one-way communication from brands.

In simple terms: **it's customer experience delivered through community, not just customer service.**

A **customer experience community** can exist on platforms like **Salesforce Experience Cloud**, **Khoros**, **LinkedIn**, **Higher Logic**, **Gainsight**, or integrated environments like **Slack** and **Microsoft Teams**. However, the platform isn't the strategy—what truly matters is participation, trust, and the value customers gain through each other.

### Understanding Community-Led CX

Community-led CX (community-led customer experience) is a strategy where customer experience improves through peer-to-peer interaction, shared learning, and customer advocacy within an owned or supported community. This approach scales CX by making customers and experts part of the experience delivery layer.

### What Community Engagement Is NOT

To clarify, community engagement isn't a static forum, a brand-run FAQ, or simply posting on social media. It's not "nice to have"—it's a fundamental shift in how experience is created, moving from brand-led to network-led.

### Why Community Is The Future of Customer Experience

If you're wondering, **"Why is community the future of customer experience?"**, the answer lies in three significant industry shifts:

#### 1) AI Is Scaling Responses – But Not Confidence

AI copilots and automation can help customers get answers quickly, but trust still comes from lived experience. Communities provide real-world validation—the "proof" layer that AI can't replicate on its own.

#### 2) Dark Social Is Where Buying Decisions Now Happen

More B2B influence occurs in private spaces: **Slack** groups, DMs, **WhatsApp** threads, and closed networks. This "dark social" shift means buyers are learning and deciding outside measurable channels—and community becomes the bridge between hidden conversations and brand experience. Research suggests that up to **84% of content sharing now happens through private channels** rather than public feeds.

#### 3) Trust Is Becoming Decentralized

In modern B2B, trust isn't built through brand claims—it's built through networks. Buyers trust peers, practitioners, and proven community voices. Community engagement supports this decentralized trust model by giving customers a place to share what works.

### How Community Engagement Improves Customer Experience

A strong **customer community strategy** enhances CX by enabling:

- **Faster answers through peer-to-peer support**
- **Higher product adoption through shared learning**
- **Better retention through belonging and advocacy**
- **More scalable experience without increasing headcount**

This is why **community-led customer experience strategy** is becoming a priority: it makes customer experience more social, more trusted, and more sustainable, delivering solid ROI.

### The Process Behind Community Engagement

Community engagement doesn't require enterprise-scale transformation to be effective. At its core, it's a simple operating rhythm: clear governance (who owns it and why), light-touch moderation (to keep quality high), intentional programming (events, Q&As, expert sessions), and feedback loops (turning community insight into better product and CX decisions). The goal isn't complexity—it's consistency.

### Real-World Examples of Community-Led CX

Community-led CX isn't just a B2B trend. Some of the best examples come from brands that treat community as a **core experience layer**—where customers learn from peers, build confidence, and get answers without waiting for a ticket response.

#### Salesforce: B2B Community as The Scalable Learning + Support Layer

Salesforce's Trailblazer Community, which supports its enterprise CRM and CX platforms, demonstrates how B2B communities can function as a scalable learning and support layer. Users join groups, ask questions, and learn from peers and specialists through Trailhead-adjacent community spaces. It's a strong example of community functioning as a scalable layer of enablement—not a side channel.

<iframe title="Connect with the Trailblazer Community | Salesforce" width="500" height="281" src="https://www.youtube.com/embed/PTTeyR6JtBE?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen=""></iframe>

#### Airbnb: Community As a Trust and Experience Layer

Airbnb's Host Community shows how community-led engagement can improve customer experience at scale. Hosts use the community to share operational advice, resolve issues, and learn how to deliver better guest experiences from peers who have faced similar challenges. Rather than relying solely on centralized support, Airbnb uses community to reinforce standards, trust, and service quality across a decentralized network.

![Airbnb CX](https://www.cxtoday.com/wp-content/uploads/2026/01/Screenshot-2026-01-06-112229.png)

### When Do You Need Community Engagement?

You likely need a **customer community strategy** if you recognize any of these signals:

1. **Your customers ask the same questions repeatedly**
   If support and CS teams see recurring themes, community-led CX can create peer-led answers and searchable knowledge that scales.

2. **Product adoption depends on education and best practices**
   If customers succeed by learning "how others do it," community engagement becomes a powerful adoption accelerator.

3. **You're struggling to differentiate on CX alone**
   When competitors offer similar features, experience becomes the battleground—and community adds a moat through belonging and shared expertise.

4. **Your brand is being discussed in private channels you can't see**
   If influence is happening in dark social spaces, building a trusted customer experience community gives you a presence where decisions are shaped.

### The Next Evolution of CX

The future of CX isn't just automation or personalization—it's connection.

In a world shaped by AI, dark social, and decentralized trust, **community engagement** is quickly becoming the operating system behind modern customer experience.

If traditional CX is about service, **community-led CX** is about ecosystem—and that ecosystem is where the next generation of B2B customer loyalty will be won.

Community engagement is reshaping customer experience through trust, peer support, and shared learning. Here’s why it’s becoming essential.

Unlock the Future of Customer Experience: How Community Engagement is Revolutionizing CX

## The Secret to Getting Through Customer Service Calls

Expert **Amas Tenumah** reveals the key phrases that can help you bypass automated AI systems and connect with a **real person** to resolve your issues quickly.

![The Secret to Getting Through Customer Service Calls](https://static-media.fox.com/fmcv3/prod/fts/3q3mmvsw4zzq83uy/0r6c9np0cqef9ipg.jpg)

### Why This Matters

Many companies now use **AI-powered customer service systems** that can be frustrating to navigate. These automated systems are designed to handle common queries, but when you have a complex or urgent issue, speaking to a human agent becomes essential.

### What to Say

According to Tenumah, using specific phrases can trigger the system to transfer you to a live representative. While the exact wording may vary by company, here are some effective strategies:

- **Clearly state "I need to speak to a representative"** - This direct request often works better than vague complaints.
- **Mention "technical issue" or "billing problem"** - These keywords can signal the system that your issue requires human intervention.
- **Ask for "account verification"** - Many systems will transfer you to an agent for security-related matters.
- **Use the word "escalate"** - This indicates your issue needs higher-level attention.

### Understanding the System

Modern customer service AI is programmed to recognize certain patterns and keywords. By understanding how these systems work, you can **strategically communicate** to achieve your goal of reaching a human agent.

### Expert Insight

Tenumah emphasizes that **persistence and clarity** are key. "Many people get frustrated and give up too quickly," he notes. "But with the right approach, you can significantly reduce your wait time and get the help you need."

### Practical Tips

1. **Be prepared** - Have your account information ready before calling
2. **Stay calm** - Frustration can make the process more difficult
3. **Listen carefully** to the automated options
4. **Repeat your request** if the first attempt doesn't work

### The Bigger Picture

As **AI continues to transform customer service**, learning how to effectively navigate these systems becomes an essential skill for consumers. While automation offers efficiency benefits, there will always be situations where human judgment and empathy are necessary.

Expert Amas Tenumah shares what to say to bypass the AI systems and speak to a real person about your issue. 

Unlock the Secret: How to Bypass AI and Speak to a Real Customer Service Agent

In the customer service sector, Europe may have found its alternative to OpenAI-powered agents with the French solution **Yampa**. We spoke with its founder, Martin Huet.

**Yampa** is a French startup that offers an **enterprise-grade agentic platform** for customer care. The company recently announced the opening of **Y.core** to enterprises — its sovereign orchestration platform designed to create, deploy, and supervise fleets of autonomous AI agents.

Yampa has raised a €3 million seed round led by Partech, alongside BPO experts and tech entrepreneurs, to turn this conviction into an industrial reality. Martin Huet, founder of Yampa, tells us more.

![Photo of Martin Huet](https://emag.directindustry.com/wp-content/uploads/sites/3/martin-huet.jpg)
*Martin Huet*

> “Moving to a fleet of AI agents means you structure automation the way you structure teams.”

## Can you present Yampa, its mission and key features?

> **Martin Huet:** “Our mission is to give companies the ability to **create, deploy and operate their own fleet of autonomous AI agents**, fully adapted to their processes and constraints. These agents don’t just answer questions; they actually do the work: they gather information, make decisions within predefined policies, trigger actions in back-office systems, and escalate to human teams when necessary. They operate across voice, email, chat, SMS and APIs, in multiple languages, all orchestrated by our platform, **Y.core**.

> Y.core is the **command center** for this fleet of agents. It connects to CRMs, helpdesks, telephony and internal tools (Salesforce, Zendesk, Freshdesk, Intercom, Microsoft Dynamics, GLPI, custom APIs, etc.) and is compatible with several LLM providers such as OpenAI, Anthropic and Mistral. On top of orchestration, it brings the “enterprise layer” that large organizations need: monitoring, versioning, access control, governance, and quality and compliance workflows.”

## What is your market positioning?

> **Martin Huet:** “In terms of positioning, we focus on **mid-market and enterprise customers** with complex operations: multiple channels, legacy tools, regulatory requirements, and often a mix of in-house and outsourced teams. This is precisely where traditional chatbots or simple “AI add-ons” struggle, and where a structured fleet of AI agents brings the most value.

> We for example have great use-cases in **IT and tech services**, where we’ve automated a large share of technical hotline tickets. And also in **e-commerce and retail**, where multilingual email and ticket triage is a major pain point.”

## What are the advantages of this solution over competitors?

> **Martin Huet:** “Our main advantage is that we don’t sell a generic bot: we provide a **tailor-made agentic platform** that is configured and industrialized for each client. We combine deep AI expertise with decades of BPO and call-center experience to help organizations design the right agents, connect them safely to their systems, and run them in production as a real operational layer – not just as an experiment.”

## How does agentics fundamentally transform the customer relationship compared to traditional automation approaches?

> **Martin Huet:** “Traditional automation, IVRs, form-based workflows, basic chatbots, pushes work onto the customer. You navigate menus, repeat your story across channels, and hope the system eventually lands you in the right queue. It’s “self-service” in theory, but often feels like abandonment.

> Agentics is the opposite: instead of making the customer adapt to the system, an AI agent takes ownership of the request. It understands natural language, asks clarifying questions, pulls data from internal tools, fills in forms, updates tickets, and follows up, like a digital employee whose job is to get the problem solved, not to recite information.

> For the customer, the experience becomes: “I explain once, in my own words, and the system handles the rest.” For the company, it’s a shift from managing channels and scripts to managing outcomes: resolution rate, time-to-resolution, and satisfaction. In our deployments, this reduces what I call “customer silence”, those long stretches where no one knows what’s happening with a case, which is one of the biggest drivers of frustration.”

## According to you, why should companies move from isolated chatbots to fully autonomous fleets of AI agents?

> **Martin Huet:** “Most companies today have “islands” of automation: a chatbot on the website, an IVR on the phone, maybe an AI assistant for agents. Each solves a slice of the problem, none owns the full journey. That fragmentation shows up in KPIs: high transfer rates, repeated contacts, and inconsistent experiences across channels.

> Moving to a fleet of AI agents means you structure automation the way you structure teams. For example, you might have an email triage agent that classifies and routes every incoming message in your CRM. Or a voice agent that answers calls 24/7 and manages after-hours incidents. Or even a billing agent that can negotiate payment plans within defined guardrails.

> Each agent is specialized, but they share the same platform, data and governance. That’s what Y.core provides: a way to design, deploy and supervise dozens of agents as if they were a single team, rather than a collection of disconnected bots.

> The practical impact is higher autonomous resolution on real-world processes. For instance, we’ve seen 50% of technical hotline tickets resolved end-to-end by AI, significant productivity gains on multilingual email management, and 24/7 instant pickup on all calls in emergency scenarios.”

## You claim that Y.core can handle thousands of interactions in real time. What is the technological secret behind this scalability?

> **Martin Huet:** “There is no single “secret algorithm”; it’s an architecture choice. Y.core separates three concerns:

> 1. **Conversation orchestration**, Stateless services that manage turns in the dialogue and context retrieval, built to scale horizontally.
> 2. **Business logic & workflows**, Deterministic workflows that decide which tools to call, which policies apply, how to route and escalate.
> 3. **Integration layer**, Adapters to CRMs, ticketing systems, telephony and internal APIs, with their own scaling and resiliency rules.

> On top of that, Y.core is LLM-agnostic: we can route different use cases to different models (OpenAI, Anthropic, Mistral, or private deployments), and we aggressively pre- and post-process to reduce unnecessary calls. That’s what allows us to support thousands of concurrent conversations without collapsing your infrastructure or your budget.

> The benefits are concrete: you can absorb peaks (product launches, billing cycles, seasonal spikes) without hiring waves of temporary agents; you can open 24/7 coverage or new languages with minimal marginal cost; and you maintain consistent quality even when volumes are unpredictable. In some emergency and property management cases, this has meant 100% of calls answered instantly, day and night.”

## You also announce impressive results: 65% of calls handled, 73% of emails automated. What real productivity gains can your clients expect, and in what timeframe? So far, companies implementing AI tools have not been satisfied and have not achieved significant productivity gains (see MIT study). How do you respond to this?

> **Martin Huet:** “These figures are in line with what we see on mature, well-scoped use cases: clear processes, clean data, and strong sponsorship on the client side.

> To give a concrete example, in one deployment we publicly highlight, our voice agent now resolves about 65% of after-hours calls autonomously, with on-call costs divided by three and a 40% drop in escalations in just two months. On the email side, we’ve seen 50% of messages automatically classified and routed in CRM for a proptech client, freeing teams from low-value triage.

> In terms of **timeframe**, a typical pattern is:

> • **0–2 months**, First agent live in production on a tightly scoped use case; 20–40% of volume handled or accelerated by AI.
> • **3–6 months**, Automation rates climb as we expand coverage and iterate on edge cases; productivity gains of 30–50% on the targeted process are common.
> • **6–12 months**, Additional agents go live on adjacent processes and channels; overall, a significant portion of Tier 1 and repetitive Tier 2 workload is absorbed by AI.

> Regarding the MIT / Stanford research: the best-known study on generative AI in customer support found around 14–15% higher productivity for human agents using an AI assistant, with the biggest gains for less-experienced staff. That’s real, but incremental, and it matches what we see when AI is used only as a “copilot” that suggests answers.”

## Sovereignty is a key issue. How does Yampa represent a credible European alternative to American solutions?

> **Martin Huet:** “First, we’re a European company, founded in France, financed by European and international investors, and built from day one with European regulatory and cultural constraints in mind. On the technical side, Y.core is hosted in Europe and designed so that customer data doesn’t need to leave the EU. The platform is LLM-agnostic: we can work with American models where appropriate, but we also integrate European providers such as **Mistral**, and can support private or on-premise deployments for clients with strict sovereignty requirements.

> Our security, governance, and auditability approach is aligned with GDPR and the upcoming **AI Act**, not adapted after the fact. More broadly, I think sovereignty is not just about where the servers live, but about **control and expertise**. If Europe wants strategic autonomy, it needs not only foundational models, but also industrial platforms and teams capable of deploying them safely at scale in critical business processes. That’s exactly the layer Yampa aims to represent.”

In the customer service sector, Europe may have found its alternative to OpenAI-powered agents with Yampa. We spoke with Martin Huet.

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