How to invest to capitalize on four top CX trends

With its success driven by everything from the latest business trends and technologies to consumer psychology and developments in the consumer tech space, CX is an undisputed driver of business outcomes. In the AI age, however, enterprises are racing to differentiate in a crowded marketplace where customer expectations are heightened.

The CX Network report 4 CX trends to capitalize on in 2026 explains how four major trends are changing CX and how practitioners must pivot to respond.

The highlighted trends are: 

1. AI-first journeys, which have re-written the rules of discoverability.
2. Voice AI becoming a mainstream interaction technology.
3. AI agents and workers facilitating service and CX on a new scale.
4. Agentic AI powering a new wave of hyper-personalization.

This article outlines the top investments organizations need to prioritize, how to invest to support better data capabilities, and how to ensure investments meets both customer and organizational needs. 

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The top CX investments to make right now

To capitalize on leading CX trends such as those outlined above, Ekaterina Mamonova, head of broker proposition at Liberty Specialty Markets, says new technologies are reshaping how organizations engage, serve, and retain customers. “The winners of 2026 will not be those who simply ’adopt AI,’ but those who invest deliberately in the foundations that make hyper‑personalization, behavioral insight, and AI‑driven experiences scalable and effective,” she adds. 

Mamonova’s five strategic CX investments are: 

1. A clean, unified first‑party data foundation

Hyper‑personalization succeeds only when data is accurate, consistent, and connected. With many generative AI pilots failing due to messy or incomplete data, organizations are shifting rapidly from third‑party cookies to first‑ and zero‑party data strategies. 

AI performance collapses with poor data quality, first‑party data provides richer behavioral signals for precise predictions, and clean pipelines enable real‑time insight and compliant, traceable decision‑making.

Where to invest

1. Centralized data lakes and feature stores
2. Automated data quality checks
3. Strong governance and lineage tracking
4. Real‑time ingestion and transformation layers

2. AI‑powered decisioning engines for real‑time personalization

The next wave of CX is powered by next best experience engines, i.e. systems that determine the right message, channel, and action for each customer in each moment. This shift from “batch campaigns” to “dynamic, AI‑driven interactions” is already delivering measurable results.

Where to invest

1. Propensity and churn models
2. Channel optimization models
3. Customer value and CLTV models
4. Orchestration layers connecting analytics to operational logic

This capability transforms customer experience from reactive to proactive: anticipating needs, reducing friction, and driving lifetime value.

3. Purpose‑built voice and agentic AI

This year sees a pivot away from large, generic AI copilots toward specialized CX‑focused agents. These lighter, more configurable models outperform general‑purpose tools because they are trained on domain‑specific data, can handle compliance constraints, and are easier and cheaper to scale. 

Emerging use cases include: voice AI for complex service interactions, autonomously optimized messaging and recommendations, compliance‑driven content checks, AI agents supporting operations, risk assessment, and service recovery

Where to invest

1. Smaller, vertical‑specific models
2. Guardrails reflecting brand, risk, and regulatory requirements
3. MLOps/DevOps capability for rapid deployment and monitoring

The future belongs to AI that is accurate, explainable, specialized, and adaptable.

4. Operating model and culture change to support AI adoption

Technology is only half the equation. Many AI initiatives stall because organizations don’t address the human, structural, and cross‑functional elements required to embed AI insights into real workflows. More than half of the effort behind successful AI programs is change management, enablement, and adoption. Organizations that align incentives and empower teams will see the fastest returns.

Where to invest

1. Cross‑functional governance and unified customer contact policies
2. Shared KPIs across marketing, care, billing, and sales
3. Training and capability building for frontline teams
4. Embedding AI insights directly into agent and employee tools

5. A “proof of value” mindset with measurable ROI

After years of experimentation, AI is shifting from hype to hard accountability, which means a need to prove ROI. With up to 50  percent of AI projects failing to scale, based on McKinsey’ research, organizations must prove commercial value early and continuously. 

Where to invest

1. Universal control groups to measure lift
2. A/B testing across journeys
3. Dashboards tracking model accuracy, savings, and revenue impact
4. A two‑speed approach: small lighthouse pilots + long‑term foundations

Investing to improve data capabilities 

Data is the foundation of all digital experiences and without accurate, unified and accessible data, competitive experiences cannot be delivered. 

Jaslyin Qiyu, CMO and head of CX for Singapore and Australia at Cigna Healthcare, says: “The most cost-effective data capability improvements begin with inventory and optimization of existing assets rather than purchasing new datasets.”

She explains: “Many organizations sit on fragmented but valuable data across disparate systems – CRM platforms, support ticketing, e-commerce transactions, marketing automation – that collectively could deliver comprehensive customer insights if properly unified. Practitioners should audit what data already exists, assess its quality and accessibility, and prioritize integration initiatives that connect these siloed sources into a coherent view.”

Not only does AI demand high-quality data, but it can be used to enhance and ensure data quality. 

For example, Qiyu says that rather than resource-intensive manual cleansing, generative AI and machine learning can systematically identify duplicates, standardize formats, flag anomalies, and enrich incomplete records – often at a fraction of the cost of traditional data management approaches. “This allows smaller teams to achieve data hygiene previously requiring dedicated operations staff,” she says. 

On the most suitable tools, she adds: “When considering external data subscriptions, practitioners should evaluate open platforms that facilitate data sharing or aggregation. Some industry consortia and platform ecosystems offer access to benchmarking data or enriched customer profiles at marginal cost compared to proprietary datasets. The key consideration is interoperability: platforms that expose APIs and support standard data formats deliver better long-term value than closed systems, even if the latter appear feature-rich initially.”

Throughout these investments, Qiyu says the privacy-convenience calculus remains paramount. “Customers increasingly expect transparency about how their data drives personalization,” she says. It means organizations that clearly communicate data usage, offer meaningful consent mechanisms, and demonstrate tangible benefits, for example by explaining how they use customer browsing history to surface relevant content, while presenting options to disable anytime, build trust and justify data-driven experiences. “The alternative – opaque data practices or purely extractive personalization – erodes confidence regardless of technical sophistication,” she warns. 

Investing to meet both customer and organizational needs 

To ensure investments meet the needs of both business and customer, Amory Somers Vine, director of CX for Expereo, says there are five factors to consider: 

1. Time to value: Focus on the quick wins, implement, then iterate and improve.  
2. Change management for internal teams: Identify your change champions and super users and ensure they are fully engaged and leveraged to inform, test, and support adoption.
3. Test with customers: Choose your group of trusted customer advisors and enable them to co-create and learn with you. 
4. Clear adoption programs: Take the time to ensure you are putting every effort into clear communication and well supported adoption plans to make process changes as easy as possible for customers.
5. Celebrate success: Sharing success and lessons learned with teams and with customers maintains the momentum and fosters further creativity, use cases and adoption initiatives.

For more insights on the top CX trends and what practitioners need to do to capitalize on them, download the CX Network report 4 CX trends to capitalize on in 2026

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