Editor’s Note: This is Part 1 of our 2026 predictions. Part 2 will focus on the role of data readiness in the rise of agentic AI as another key trend to watch in 2026.
Signs that we have reached a critical mass of digital engagement are everywhere. Consumers default to a smartphone as the primary device for banking, shopping, navigation, and entertainment. QR codes are now mainstream for countless customer experience use cases in retail, healthcare, travel, and customer service. Mobile wallets and tap-to-pay are driving the shift to a cashless economy, and digital identity, passkeys, and digital logins are now commonplace. It’s becoming almost impossible to leave your smartphone at home and still function as a productive member of society.
This crossover to digital engagement frames several emerging trends that will take root in 2026, centering around data readiness as a key to meeting customer expectations for brand interactions. Here are three predictions based on the digital crossover:
- Hyper-Personalization Becomes the Norm
Digital-first customer interactions are the basis for a tipping point in moving from basic personalization to hyper-personalization. A study on healthcare consumer engagement from Engagys, a healthcare consulting and advisory firm, illustrates the urgent need for brands to respond to a rise in digital engagement with more relevant personalization.
The study showed that digital engagement by healthcare consumers – use of portals, email, and text – has risen for two consecutive years, while non-digital channels saw a decline. Older customers are driving the change, with more than 80 percent of consumers between 60-and-69 using a smartphone as their primary communication device.
To meet this digital tipping point, health plans are investing in personalization, with a focus on cost efficiency and member familiarity. Plans identify multichannel orchestration, preference management and “next best action” design as top objectives, reflecting a data-driven mindset.
Healthcare payers are moving from the middle to the upper levels of the personalization capabilities maturity curve, where more advanced personalization – like Medicare age-in campaigns and activating members for annual wellness visits – take root. Similarly, retailers are advancing from basic personalization (persona-based product recommendations) to more advanced personalization (real-time recommendations and decisions; multi-stage, multi-trigger, multi-channel journeys).
Driving the shift to advanced personalization is the recognition that data-driven personalization will yield out-sized gains in CX improvement, where the quality of experience is measured by greater loyalty and higher revenue. A Forrester study on the ROI of CX transformation showed that for every percentage point increase in the Forrester CX index, companies can earn from $10 to $100 million in incremental revenue.
- Proof point: According to Gartner, the effective delivery of a personalized CX makes businesses 60 percent more profitable compared to companies that are not customer focused.
- Data Readiness is a Must-Have for Organizations to Compete
According to Gartner’s Evolution of Data Management survey, IT professionals identify investments in AI-ready data as their No. 1 priority over the next two-three years, with data quality and governance as a close second.
One reason is that even as organizations generate reams of customer data, not enough is used to fuel differentiated AI or CX experiences. In an Invesp survey, 87 percent of marketers said that data is their organization’s most under-utilized asset – with 54 percent claiming that a lack of data quality and completeness is the biggest challenge to data-driven marketing.
To achieve the hyper-personalization demanded for digital-first engagement requires data readiness – making customer data ready (complete, accurate, timely) and fit-for-purpose (actionable, trusted, compliant) across the enterprise. Data readiness eliminates the persistent customer experience gap, measuring the distance between the experience consumers expect versus what brands can deliver.
Applying the principles of data readiness to all customer data as it enters the system builds a unified customer profile that provides brands with a deep, contextual, real-time understanding of a customer.
A persistently updated, complete profile makes the difference between fragmented, static experiences and truly orchestrated, omnichannel personalization. It gives brands the ability to act on fresh, accurate data in real time, anticipating customer needs, optimizing engagement across all touchpoints, and driving measurable growth.
- Proof point: According to Gartner’s Personalization Customer Survey, 44 percent of customers block a brand when it communicates in a way the customer perceives as irrelevant or annoying – and 40 percent will stop doing business with the brand altogether.
- Proof point: Enterprises that invest in data readiness, particularly in relation to AI, achieve 26 percent higher business outcomes compared to those that do not prioritize this area. This includes improvements in revenue generation, cost optimization, and customer experience.
- Enterprises will Adopt a Best-in-Class Data Management Strategy as a Foundation for Data Readiness
According to Gartner, most enterprises have a dozen or more data management solutions with overlapping functionalities – a sign that existing architectures are fragmented and complex. With gaps and overlaps in functionality, it is no wonder that the biggest issue teams are running into with integrated AI systems is poor data quality (missing/stale/ inconsistent), with 56.3 percent reporting that as the top issue per a 2025 AI & Data in Marketing Survey by Chiefmartech. Facing these challenges, 35 percent of data and analytics leaders surveyed by Gartner said they need to “significantly overhaul their data management architecture.”
This need to simplify operations will drive the market toward a converged data management platform, complemented with best-in-class components to address key gaps in data readiness. The old way to build, manage, and operationalize data stores with hundreds of data pipelines has been upended by the need for operational simplicity where success is not measured by data deliverables, but by data products that are easy to find, use, and maintain. As Gartner clearly states, “the question that matters is whether a data team can deliver AI-ready data on time and in full.”
Data and Analytic leaders have challenging objectives in the need to 1) simplify their data management platform while 2) improving data readiness and 3) avoiding vendor lock-in and new data silos. This is why they need flexibility to swap out capabilities within their data management platforms with independent software vendor (ISV) solutions that are best in class, particularly to get data right and fit-for purpose. For example, incorporating leading components to perform customer data cleansing, matching and identity resolution is the best way to resolve complex data quality issues, better readying a company’s data for AI and CX use cases.
- Proof Point: According to the 2025 Gartner Chief Data and Analytics Officer Agenda Survey, 56 percent of senior D&A leaders said that their primary responsibility is to optimize the technology landscape
Conclusion
The combination of a consumer-led digital engagement tipping point and the increasing familiarity and comfort level of consumers with AI interactions will result in several trends taking root in 2026. We will see hyper-personalization become a CX standard, and data readiness will be essential – particularly to support AI initiatives with AI-ready data.
Finally, the clear need for data readiness – and operational simplicity – will put more pressure on enterprise companies to move toward simplified yet best-in-class data management, both for operational and cost efficiency and to allow the enterprise to deliver more relevant real-time experiences.
