Before 2020, the adoption of digital customer service tools like voice-to-text and self-service portals was slow. Then it jumped — up to 25 times faster than before the pandemic, according to McKinsey research. Over 80% of customer interactions are now expected to move to digital channels. That shift isn’t slowing down, and it’s rewriting the rules for how businesses of any size need to handle customer experience.
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What that means in practice is that the old playbook — email response within 24 hours, phone lines open 9-to-5, a basic FAQ page — no longer meets what people expect. Customers today arrive with research already done, often across multiple channels, and they expect the person or system they’re talking to to know what they’ve already done. Half of customers say promotional emails, ads, and social media posts have only two to five seconds to capture their interest. That’s a tight window, and it puts pressure on every interaction a business has with its customers.
This article walks through what the research actually says about the shift in customer experience, where businesses are getting it wrong, and what a practical, working model looks like. Here’s what you actually need to know.
What the Research Reveals About Modern Customer Experience
The term you’ll hear a lot is “AI-first customer experience.”
What I tend to notice is that businesses either leap too far into full automation or hold back entirely. The research points to a middle ground that works better than either extreme. Building a brand that customers trust depends on getting that balance right.
What Happens When Customer Experience Falls Short
When customer experience is inconsistent or slow, the consequences show up in hard numbers. The research from Freshworks on the Hobbycraft case study puts it clearly: organisations that get the balance right see real gains.
That’s not a tech company in Silicon Valley. It’s a craft retailer that unified its social, email, and voice channels and let AI handle the predictable questions — delivery status, stock availability — while human agents took over when the conversation needed judgement. The 25% satisfaction lift and the 82% first-contact resolution rate are the kind of numbers that show up in the bottom line through repeat business and word-of-mouth referrals.
On the flip side, 72% of organisations now expect seamless experiences across digital and physical touchpoints, according to Adobe’s Digital Trends report. If a customer has to repeat themselves from one channel to another, or if a chatbot can’t hand off to a human without losing context, that’s where satisfaction drops. In a market where one in four customers already use AI-powered platforms as their primary source for information and purchase decisions, falling behind on experience means losing relevance.
Where Businesses Get Digital Customer Service Wrong
Treating AI as a Complete Replacement for Humans
Some organisations roll out a chatbot, automate everything, and assume that’s the job done. But the research shows that customers still want a human for complex or sensitive issues. 43% of customers are willing to interact with an AI agent, but that number drops when the issue involves money, emotion, or nuance. The mistake is treating AI as a cost-cutting off switch rather than a triage system. The better approach is to let AI handle the first layer — routine questions, order lookups, password resets — and escalate to a human the moment the conversation shifts in complexity.
Ignoring the Need for Cross-Channel Context
A customer might start on a chatbot, move to email, then call. If each channel treats them as a brand new person, frustration builds fast. 72% of organisations expect seamless experiences across digital and physical touchpoints, but many still operate with disconnected systems. The fix is a shared customer data platform that records every interaction, regardless of channel. 71% of organisations already have shared customer data platforms, so the infrastructure is becoming standard. Using it is the next step.
Underestimating How Much Customers Research Before Contacting You
Customers today are not passive. They arrive with multi-product, multi-service, and multi-interaction scenarios. They’ve already visited your website, read reviews, maybe checked a competitor. If your support system can’t pick up on that context, the interaction feels like a waste of time. AI agents that draw connections across knowledge sources and past interactions can handle this. But if the system is limited to a narrow knowledge base, it will fail on anything beyond the most basic query.
Rolling Out AI Without a Pilot Phase
Roughly one-quarter to one-third of organisations are running limited pilots of generative AI in customer support. The ones that skip the pilot phase often end up with a system that frustrates customers — giving wrong answers, misinterpreting intent, or escalating too late. The research suggests a phased rollout: start with one channel, one type of query, and measure resolution rates and satisfaction before expanding.
How to Build a Customer Experience Model That Actually Works
The research points to a structure that balances automation with human judgement. It’s not a single tool or platform — it’s a design approach that starts with understanding what each type of interaction needs.
→ Scroll right to see all columns
| Approach | Cost Efficiency | Personalisation | Human Touch | 24/7 Availability | Best For |
|---|---|---|---|---|---|
| Traditional (Human-Only) | Low — scales with headcount | High — but inconsistent | High | No — unless shifts are added | Complex, high-emotion issues |
| AI-First (AI-Only) | High — low marginal cost per interaction | High — consistent across all interactions | Low — can feel robotic | Yes | Routine, repetitive, low-stakes queries |
| Hybrid (AI + Human) | Medium — AI handles volume, humans handle complexity | High — AI provides context, humans add empathy | High — where it matters | Yes — AI covers off-hours, escalates when needed | Most businesses — balances efficiency with satisfaction |
Start With the Routine, Then Layer In Complexity
The most effective approach is to map out every customer interaction your business handles. Identify the ones that follow a pattern — delivery status, account balance, store hours, return policy. Those are the ones AI can handle reliably from day one. The Hobbycraft example shows that roughly 30% of repetitive inquiries can be automated without affecting satisfaction. Once the system is handling those well, you can start adding more complex scenarios — multi-product questions, troubleshooting, and eventually, personalised recommendations.
For businesses that need to set up an ecommerce or service platform quickly, tools like Shopify offer built-in AI features for customer engagement, inventory management, and multichannel sales. Having a solid platform underneath your CX efforts makes the automation layer easier to integrate.
Design the Handoff Between AI and Humans
The point where a conversation moves from AI to a human agent is the most fragile moment in the entire experience. If the AI can’t summarise what’s already happened, the human starts from zero and the customer has to repeat themselves. The fix is to design the handoff with a structured summary — what the customer asked, what the AI tried, what information was gathered, and why escalation was needed. LLMs like GPT and BERT make this possible by interpreting context and intent, but the system has to be built to pass that context along. Without it, the hybrid model fails.
Measure What Actually Matters
First-contact resolution rate, customer satisfaction score, and average handling time are the three metrics that matter most in a hybrid model. The research shows that 82% of tickets resolved at first contact was achievable for Hobbycraft, and that drove a 25% satisfaction lift. But don’t just measure the AI or the human side — measure the handoff too. How many interactions escalate? How long does the handoff take? Does satisfaction drop after the handoff? Those numbers tell you whether your hybrid model is working or needs adjustment.
What’s Coming Next: Agentic AI
About a third of organisations are prioritising agentic AI — systems that don’t just respond to queries but take independent action on behalf of the customer. Within the next 18 months, about half of customer interactions in support and post-purchase support are expected to be handled directly by agentic AI, according to the Adobe Digital Trends report. 63% of organisations expect agentic AI to give employees more time for strategic and creative work. For businesses that haven’t yet built the foundation of a hybrid model, the window to catch up is narrowing. The basic structure — AI handling routine work, humans handling complex work — is the same, but the AI side is becoming more capable and more autonomous. If you’re still running a human-only model, the gap between what customers expect and what you deliver will only widen.
For businesses exploring how to integrate these tools, a MagicFit AI platform can help generate content, ad creative, and personalised customer messaging across channels — supporting the kind of omnichannel consistency that customers now expect.
Frequently Asked Questions About Digital Customer Experience
Do I need a dedicated AI platform to start improving customer experience? ▾
How do I know which customer interactions should stay with humans? ▾
What if my customers are older and prefer talking to a human? ▾
How long does it take to see results from an AI-first CX approach? ▾
Can small businesses with limited budgets adopt AI-first customer experience? ▾
Is agentic AI safe to use for customer-facing interactions? ▾
The Direction Customer Experience Is Taking
The shift toward AI-first customer experience is not a temporary trend. Digital solutions were adopted up to 25 times faster during the pandemic, and over 75% of digital operations leaders now expect customer services to be fulfilled via online channels in the future. The productivity benefits are estimated at over 25% above current levels for organisations that get the model right.
What’s changing is the balance. The first wave of AI in CX was about answering questions. The next wave — agentic AI — is about taking action. Within 18 months, roughly half of customer interactions in support are expected to be handled directly by agentic AI. Businesses that wait until the technology is fully mature before adapting will find themselves trying to catch up while their competitors have already built the systems and the customer relationships that come with them.
Remember: this article is general information only. For advice on your specific situation, speak to a qualified professional.
If this was useful, you might also want to read Conscious Consumerism: How Values Are Shaping NZ’s Market.
Sources and Further Reading
Building a Brand That Resonates: Authentic Storytelling for NZ Consumers — How brand authenticity and customer connection work together in practice.
Beyond Silicon Valley: Building a Thriving Tech Ecosystem in NZ — The infrastructure and talent considerations that support digital-first business models.
Freshworks (2024). AI-First CX Revolution. 🔗
McKinsey & Company (2024). Innovation Through the Digital Disruption of Customer Service. 🔗
Adobe (2024). Digital Trends Report: AI Is Reshaping Customer Experience. 🔗
