How conversational AI is reshaping analytics

Traditional content analytics has always been about users sifting through volumes of data, examining dashboards, submitting complex queries, spotting trends and patterns, and crunching numbers. 

That conventional approach delivers results if you know what you’re doing, but it also throws up barriers to efficient, impactful analysis. That’s because users often need a level of technical knowledge to not only apply analytic tools, but to interpret data — which leaves many teams waiting on their technical counterparts to deliver results.

In worst cases, that inefficiency ends up delaying the implementation of meaningful changes to content strategy, and leaves brands racing to catch up with their customers. 

But what if there was an easier way? What if we could get accurate analytic insight by simply asking questions? 

In the era of natural language processing and agentic artificial intelligence (AI), we can. 

In this post, we’re going to discuss the potential of agentic AI tools to support content analytics workflows. We’ll explore the benefits of conversational AI to analytics (and some limitations), and what it means for the future of content operations.

Conversational AI analytics defined

AI-based conversational analytics refers to the integration of a generative AI (GenAI) agent within an analytics platform to support self-service user interactions. 

In other words, users prompt the AI agent, via an interface, to extract insight from analytics data — rather than poring over statistics, numbers, and dozens of different dashboards themselves. 

In a conversational AI-based analytics platform, there’s no need to jump between different platforms or even scrutinize the fine detail of different metrics. Extracting insight is as simple as prompting the AI agent with questions like: “What were my page views last week?”, “Which audience segments like this piece of content?”, “How did this keyword perform?”, “What’s the conversion rate for this call to action?”, and so on. 

The evidence suggests this approach is taking off with users. Conversational AI is an increasingly popular form of AI deployment — and one that’s gathering pace in industries around the world.

The global market for conversational AI is expected to reach around $79.4 billion by 2033, growing at over 22% per year in that time. Meanwhile, a McKinsey report from 2025 suggests that 92% of companies will invest in GenAI over the next three years, with over 70% of employees expecting the technology to change the way they work. 

Why does this matter?

The principles of content analytics are straightforward: use data to generate insight about content, and use that insight to effect meaningful change. That said, finding the right data and the right tools to do that job is often challenging. 

The problem is, brands face a shifting landscape of analytics needs. In this environment, new trends, new technologies, and new market influences push new metrics into demand constantly, which forces content teams to hang on tight in an unpredictable data landscape, and act fast to capitalize on insight. 

That typically means integrating more tools and dashboards to capture the latest data — which can, in turn, lead to overly complex, slow-to-respond analytics solutions with fragmented toolchains.

Research suggests that brands are starting to feel the strain: Only 53% of marketers are factoring analytics into their decision-making processes, while 56% now need technical support when activating that data. 

In other words, despite an abundance of data, it’s increasingly difficult for brands to connect content performance to business outcomes — a situation that not only undermines the value of an analytics solution but can make its users lose confidence in it. 

But that’s no longer the only way to approach content analytics. 

How conversational AI tools help

You may have read about the potential for AI to transform content creation, but here’s how it makes a meaningful difference to analytics solutions. 

Speedier, cost-efficient insight

Conversational AI delivers meaningful analytic insight faster because it doesn’t impose the same complex, number-crunching requirements as conventional analytics tools. Nor does it need users to navigate multiple platforms and disconnected data points: insight is accessed via a single interface. 

Ideally, users can have a conversation with their analytics platforms, and receive a plain-language response in seconds. 

Analytics for everyone

In an evolving data landscape, conversational AI isn’t just a speed boost, it’s also an accessibility advantage. By removing the need for technical expertise, conversational AI makes insight available to everyone, not just experienced analysts or data-savvy team members.

This democratization has the potential to change the way content teams and developers work together. Writers, editors, designers, and marketers can use data to drive their decisions, leading to stronger content and more cohesive collaboration with teams working in the back end. 

Single source of truth

The accessibility and versatility of the conversational AI interface means that it can be positioned as the default interface for analytic insight. 

That establishes the conversational AI as a single source of truth: the gateway through which everyone understands they have to access data. That focus can help content teams put out consistent, accurate information, but also build a distinctive brand voice and avoid messaging fragmentation. 

Strategic value

Conversational AI won’t replace the skill and expertise of human analysts but it can enhance those factors. There will always be situations where human expertise is critical to the analytics workflow, including the verification of results, the interpretation of ambiguous data, and the connection of data to wider trends and market patterns. 

In the meantime, conversational AI can automate the more routine, mundane, and time-consuming aspects of analytics — and free up human analysts from hours at the keyboard scrutinizing dashboards. Instead, they’ll be able to focus on higher-value work, such as shaping content strategy and alignment with business objectives. 

Powering change

We need to move content analytics out of the era of fragmented toolchains and into the new age of agentic AI, where workflows are defined by efficiency, flexibility, and interoperability.

Digital experience platforms can power this kind of change and address many of its challenges by providing entirely composable ecosystems in which to test, integrate, and refine new technologies and tools, like conversational AI.

In Contentful, for example, data flows freely across every part of the tech stack, giving brands the space and the scope they need to explore new solutions to content analytics challenges and integrate new features effortlessly. Leveraging the inherent flexibility of our platform, we’ve put AI at users’ fingertips via an array of native automations that enable content teams to action analytic insight fast.

If you’re ready to learn more, you can browse demos of our AI-powered content management automations, explore our latest features, or contact sales to discuss AI possibilities for your analytics solutions. 

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