Kana Content

AI agents are goal-driven systems that can perceive information, reason about it, plan multi-step actions, and execute across your tools and channels with a degree of autonomy that no prior marketing technology has offered. To take advantage of their full potential in a marketing context, you need to understand what agents actually are, how they differ from the chatbots and automation tools you are already using, and where they can make a real impact on the work you do every day. This white paper helps outline the key differences and the potential for applications in marketing.

The future of agentic marketing, and how it finally delivers on decades of broken promises shouldn’t be defined by what’s been done before — a CDP, a CRM, or a marketing automation platform in an AI wrapper trenchcoat shouldn’t be the ones telling us how it’s all going to work. And having a ton of data alone isn’t enough to fuel an agentic AI strategy. Kana sees a better way forward. We call it Build-With. We wrote a Manifesto to explain how this is now possible, why it represents a massive paradigm shift, where and how this approach matters in our process with our customers, and why we feel it's the right choice for marketers at large enterprises.

In this discussion, Tom, Vivek, and Randy discuss how, if used correctly, can become a remarkable connective tissue for data you thought was long lost, and can behave as a creativity extender for individuals or whole marketing teams.

In this discussion, Tom Vivek and Randy discuss how in the era of Agentic AI, success isn't just about "doing things faster," it’s about creating entirely new units of business value.

This Evaluation Guide document provides a discussion of the core aspects teams should consider when evaluating new AI technologies in order to determine whether they are sufficiently enterprise-grade to grow with them and fulfill modern security and governance requirements.

In this discussion, Tom, Vivek, and Randy discuss their thoughts on how AI is making the synthesis, testing and analysis of disparate and incongruous data sets possible in ways that it never was before.

In this discussion, Tom, Vivek, and Randy discuss why it's so important to think in terms of industrial, enterprise-grade AI when evaluating agentic solutions.

This Solution Overview document provides a summary of how Kana AEO solves for the challenges teams face related to ensuring their website is correctly structured and organized to be cited, referenced, and recommended by generative AI answer engines.

This Solution Overview document provides a summary of how Kana Campaign Management solves for the challenges teams face when they are blind to campaign risks until it’s too late to fix them because they're so busy manually monitoring thousands of line items in spreadsheets, missing critical errors.

This Solution Overview document provides a summary of how Kana Customer Engagement solves for the challenges teams face when, even after deploying a range of technologies and tools to help with customer tracking and retention, they still don't have sufficient information to indicate or predict when a customer will churn, so they can't prevent it.

This Solution Overview document provides a summary of how Kana LLM Command Center solves for the challenges teams face when they have no centralized tooling to monitor, manage, or monetize large language model crawler activity.

This Solution Overview document provides a summary of how Kana Analytics solves for the challenges teams face related to proving which campaigns, segments, or content drive downstream conversion and revenue outcomes when they have disparate marketing systems and data sets.

This Solution Overview document provides a summary of how Kana Media Proposal Generator solves for the challenges teams face when their sales capacity is limited by human admin hours rather than market demand because the sales team is bottlenecked by manual workflows that prevent them from matching the speed of market demand in response to RFPs.

This Checklist is an accompaniment to our Evaluation Guide on the same subject, and is intended to provide a truncated list of questions to ask when evaluating new AI technologies in order to determine whether they are sufficiently enterprise-grade to grow with them and fulfill modern security and governance requirements.