In touting AI capabilities of customer engagement technology, vendors typically point out how their systems use AI to enhance or optimize a business process. Their system uses AI to make better predictions about customers, for example, or to improve customer experience (CX) with more human-like interactions using natural language processing (NLP) and other generative AI (GenAI) capabilities.
While the use of AI in customer engagement technology is undeniably important to extract the maximum value from customer data, the view here is that models and algorithms behind AI are just one component of an overall AI approach. Data Readiness for AI is a comprehensive approach that ensures systems are not only infused with AI but are also equipped to supply, manage, and act on AI with the right data, insights, and execution.
Data readiness for AI posits that customer engagement technology should leverage AI to power a superior CX. But it should also ensure the best data is fueling the AI – that the data is right and fit for purpose – and that it is ready to support high value AI use cases. These AI uses may be linked to dynamic segmentation, better matching and other specific functions, or specific external systems – a call center, an ESP, online chatbot, etc.
A recent Gartner survey of marketers shows a broad use of three kinds of AI projects (see Figure 1), with AI-ready data required for all three.
In addition, modern customer data technology should also facilitate agentic AI, empowering AI agents that design and execute personalized customer journeys – whether those agents were created by the enterprise itself, by a platform the enterprise uses, or independent providers.
Figure 1: Vendors, in-house teams and business departments make up the three main categories of AI projects. 2004 Gartner survey.
There are multiple ways to create value in the market by enhancing AI, in other words. The most powerful and far-reaching way it is provide unified and high-quality data as input to AI – which improves the AI output. A second way is to take advantage of third-party AI innovations and integrate them into software products and solutions. And yet a third way to create market value is to make agentic AI actions more effective through proven, interconnected automations that agents can leverage to produce better results.
AI-Ready Data
What is AI-ready data? Quite simply, it is data that is made ready for any AI use case immediately upon data ingestion, through a robust process of transformation, cleaning, and enriching to continually provide accurate data and contextual metadata. AI-ready data powers better predictions because the data is both right (complete, accurate and timely) and fit for purpose (actionable, trusted and compliant) across the enterprise.
Autonomous data quality at data ingestion ensures high quality data from data ingress to egress – from nearly any source, to any third-party application. It is data that has been cleansed, enhanced, standardized, and matched upstream to optimize any AI system that requires high quality data to produce trusted outcomes.
AI-ready data helps control costs by eliminating the need for additional workflows and resources to standardize data before it can be used to train AI models or power segmentation. Companies avoid the many inefficiencies associated with complex AI plumbing, ensuring high performance, accuracy and more predictable results.
Downstream analytical systems are able to handle more data, generating improved analytics. Well-managed data allows AI models to function more effectively to generate more meaningful, trustworthy insights.
AI Inside
From simple to complex, there are an endless number of ways to use AI to improve CX through the use of software products and solutions. As a starting point, perhaps the initial objective is to simply integrate with a third-party innovation to produce a more human-like chatbot. Advancing on the AI maturity curve, it is also possible to infuse AI into products to bring out-of-the-box predictive models and machine learning into workflows to create next-level personalized journeys and campaigns – with optimal journeys and campaigns those where out-of-the-box models and machine learning are fed with AI-ready data.
Embedding AI innovation from a third-party predictive analytics platform into software unleashes a full spectrum of AI-driven decisioning, from descriptive analytics and evaluation to recommendations and next-best actions.
Infusing customer engagement technology with AI is a closed-loop process, where integrating AI into processes generates a better lift in performance – from detecting anomalies in data to improving identity resolution. When those results are fed back into a system that makes the data right and ready for business use at ingestion, the methodology behind a process like identity resolution is continually fine-tuned. It’s a form of captured intelligence, an AI sidecar that continuously improves both inputs and outputs.
Finally, to fully capitalize on AI innovation customer engagement technology should also support built-out integrations with key AI platforms such as Databricks and Snowflake Cortex AI.
Agentic AI
According to Mordor Intelligence, the market for agentic AI is expected to be $7.28 billion this year, reaching more than $41 billion by 2030 for a compound annual growth rate of 41 percent during the forecast period.
The rapid growth is driven by the increasing adoption of AI technologies, combined with the development of more sophisticated AI Agents capable of performing increasingly complex tasks autonomously.
To capitalize on this expanding market, modern customer engagement technology should have engines capable of producing and empowering AI agents to facilitate the creation of customer journeys. By autonomously creating triggers, messages, and next-best actions, AI agents act as intelligent surrogates for human marketers, executing real-time decisions and ensuring seamless delivery of personalized experiences across channels.
However, true agentic AI requires more than just task automation. It depends on access to high-quality data, rich metadata, and deeply integrated APIs, so that AI agents can evaluate options, make decisions, and take context-aware actions. Without this foundation, AI agents become blunt instruments – tools in search of a purpose rather than intelligent actors creating value.
A Three-Pronged Strategy to Optimize AI
A comprehensive approach to infusing AI into customer data technology and its associated outcomes is a three-pronged strategy that can be summed up as data (AI-ready data), insights (AI inside) and action (agentic AI).
Fully capitalizing on the promise of AI requires all three. More than simply leveraging cutting-edge technology, providing agents and third-party innovations with data that is right and fit for purpose is the key to using AI to transform how a business operates and engages with customers.
This series on Data Readiness for AI will continue with posts on each of the three pillars, starting with AI-ready data. For more on the Redpoint approach to AI, click here.
