Kellie Hackney, ANZ Regional Vice President for Zendesk, identifies ‘contextual Intelligence’ as the dominant trend poised to disrupt both Australian and international markets, projecting that it will set a new CX standard by 2026. This paradigm moves beyond static systems, focusing instead on the real-time infusion of AI, data, and human insight to create experiences that are truly adaptive, personal, and predictive.
In this exclusive interview with CXFocus Editor, Mark Atterby, Kellie Hackney unpacks the forces redefining customer interactions. In her responses, she highlights how cosmetics retailer, Lush, leveraged their AI agent to achieve a remarkable 60% First Contact Resolution rate while maintaining a 93% CSAT score.
Mark Atterby (MA): Kellie, what fundamental CX trends do you anticipate will reshape the Australian and international markets over the next 12 to 20 months?
Kellie Hackney (KH): Looking ahead to next year, we are seeing a significant trend and shift toward ‘contextual intelligence’. We believe this will set a new standard for Customer Experience (CX) in 2026 and fundamentally change the landscape.
Let’s unpack this a little.
We predict the Australian market next year will be significantly disrupted by the rise of ‘contextual intelligence’. This concept is centred on the real-time infusion of AI, data, and human insight, bringing these three elements much closer together within the organisation.
The practical outcome of this integration will be the ability to craft a customer experience that is truly adaptive, personal, and predictive. Unlike static systems, ‘contextual intelligence’ will effectively connect past interactions and leverage them to shape every future touchpoint.
MA: How should organisations approach and manage the necessary transformation?
KH: A crucial point we frequently discuss with customers is not just the shift in the technology stack and what’s becoming technically possible, but how they manage this transformation from a human expertise and workforce perspective.
Traditionally, customer touchpoints—the points where customers interact with a company—have been met by a human customer service agent. Historically, that human could act as a crucial pivot moment. If the underlying systems were flawed (the data wasn’t accurate, the procedures weren’t right, or the returns policy was incorrect), the agent could apply “band-aids” and fix the issue in the moment.
However, as organisations shift to digital customer experience and engagement platforms, this reliance on the human agent to correct systemic errors becomes increasingly redundant.
MA: Given that the transition to digital customer engagement means interactions are increasingly managed by AI agents, smart routing, or automated workflows, what specific steps must organisations take to enable ‘contextual intelligence’?
KH: The transition to digital customer engagement means that interactions are now often met by AI agents, smart routing, or automated workflows. This fundamental shift means the technology and data within the business must be perfectly ready at every customer interaction point.
Consequently, we are seeing a significant evolution and growth in:
- Digital customer journey mapping experts: Professionals who design seamless, end-to-end digital pathways.
- Content creation and curation specialists: The accuracy of content now truly matters, as AI models rely on it.
- Data and governance experts:AI must be trained to understand all of the internal knowledge that governs company behaviour, including what products and services are sold as well as Internal procedures and policies. This comprehensive set of governing information needs to come together to enable effective digital engagement. Therefore, a major part of this transformation is the evolution of roles and the workforce in CX, alongside the technical changes. We spend a substantial amount of time working with customers on this change management element, as it is critical for business success.
MA: What is a compelling use case that clearly demonstrates the power and trajectory of contextual intelligence in customer experience (CX)?
KH: Contextual Intelligence moves beyond isolated AI tasks and into complete, real-time understanding of a customer’s situation, allowing for autonomous resolution. Imagine a telco company utilising this technology. It knows everything about the customer, the services they subscribe to, tenure, history and geographic location.
The system also monitors and analyses the company’s internal data, immediately detecting an internet outage impacting the customer’’s specific service area.
This leads to autonomous, predictive action. Instead of waiting for the customer to call in and report the issue, the contextual intelligence platform:
- Detects the problem based on the product/service the customer has subscribed to.
- Autonomously initiates the solution, such as booking a service technician.
- Proactively communicates the resolution plan to the customer
The system essentially avoids the customer service interaction entirely by providing a proactive solution.
MA: Are there any examples of companies currently deploying or trialling contextual intelligence within their operations?
KH: Yes, there is. Lush, a global cosmetics retailer, offers a compelling example of how AI is helping them achieve advanced customer experience goals.Lush faced a significant challenge common to high-volume, global businesses – handling a massive influx of customer inquiries across:
- Diverse time zones: Round-the-clock support needs.
- Multiple languages: Ensuring consistency across all markets.
- Recurring issues: While the exact topics vary (like product ingredients or delivery status), the underlying questions were highly consistent.
This high volume and variation created a critical risk: customers frequently had to repeat themselves or endure lengthy waits for resolutions due to disconnected information across different touchpoints.
To solve this, Lush implemented a solution designed to unify information and provide instant, consistent service. Lush implemented an AI agent they named ‘Marvin’ Powered by Zendesk, Marvin was designed to address the high volume of common, repetitive customer enquiries.
Key deployment and results:
- Rapid Launch – Went live in 16 languages from day one and continues to grow. Provided immediate, consistent global support.
- Contextual Tagging – Marvin tags incoming tickets with all relevant context before escalation.Human agents receive an information-rich ticket, eliminating the need for customers to repeat themselves.
- First Contact Resolution (FCR) – Achieved a 60% FCR rate, meaning the agent resolved the issue immediately.Highly efficient resolution of common issues.
- Customer Satisfaction (CSAT) – Maintained an outstanding 93% Customer Satisfaction score.Proved automation did not compromise the customer experience or loyalty.
- Cost Savings: Saved hundreds of thousands of dollars on baseline operational costs.Demonstrates clear ROI alongside CX improvements.
Customer Loyalty is rapidly becoming one of the most important metrics in modern CX. Lush’s high CSAT score (93%) is a powerful indicator of this loyalty. We are seeing a shift in organisations’ focus towards these key metrics when judging the effectiveness of their customer service delivery and results.
MA: Since digital engagement removes the human agent, how do organisations effectively measure the success of an interaction—specifically, how do they assess whether the tone, sentiment, and final outcome were positive for the customer?
KH: To address the challenge of measuring digital success, we utilise Quality Assurance (QA) technology that layers directly over AI.
This technology is designed to monitor and evaluate 100% of all interactions processed through the platform—both fully digital and human-assisted. Instead of manually spot-checking individual human agents, this system provides CX leaders with comprehensive intent data, sentiment analysis, and actual resolution tracking across the entire digital and human-powered service centre.
Essentially, this creates a digital way to audit the experience, ensuring that quality and successful outcomes are consistently measured, regardless of whether the touchpoint was automated or human-led.
