Kana Content

This video explores the organizational dynamics of ownership regarding agentic marketing and how business leaders can effectively integrate AI into their marketing strategies. The discussion highlights the following key themes: The Role of the Chief AI Officer: The speakers note that placing the entire responsibility for AI adoption across all company functions on a single chief AI officer is not scalable. While these officers play an important role in driving the agenda and ensuring AI literacy within a company, they often lack the deep domain-specific knowledge required for every department. The Importance of Partnership: Instead of creating a binary "either/or" scenario where either marketing or the chief AI officer owns AI-driven marketing, the speakers advocate for a collaborative approach. Marketers are encouraged to partner with the chief AI officer, leveraging their own domain expertise to define use cases, structures, and centers of excellence that serve the company's goals. Marketing as an Insertion Point for AI: The speakers emphasize that marketers should seize the opportunity to own their AI initiatives rather than ceding them, as doing so could be a career-limiting move for a CMO. Marketing is an ideal place to initiate AI efforts because it involves: -High-stakes, recurring decisions where companies frequently waste significant resources. -Fast feedback loops that allow for immediate measurement of results. -An abundance of data, which serves as the essential fuel for AI initiatives. Effective Deployment: Successfully deploying agentic marketing requires building an apparatus that defines what is being measured and ensures the system can improve over time.

This first video the series discusses findings from our recent "Agentic Divide" report and the reality of AI implementation in business settings. Key topics include: Discrepancy in AI Adoption: The report claims that 70% of leaders are already running custom AI agents in production, with only 3% reporting no AI use. The speakers express skepticism regarding these figures, noting that what organizations claim to be doing often differs significantly from what is actually occurring on the ground. Measurable Business Value: Citing analysis from McKinsey, Tom and Vivek note that only 3% of companies touting AI usage are actually achieving measurable business benefit. Defining AI Usage: Much of the reported "AI usage" may stem from pressure from boards and CEOs to adopt the technology, leading organizations to classify simple tasks—such as using Claude to draft product specifications or web content—as running AI agents. Augmented Intelligence: Tom and Vivek emphasize a vision of "augmented intelligence," where AI serves to accelerate and augment human work rather than functioning as a hands-free, job-replacing replacement. Enterprise-Grade Standards: While acknowledging the utility of AI for simple use cases, Tom and Vivek stress the importance of maintaining enterprise-grade standards, including: -Careful management of data access and usage. -Implementing appropriate governance layers. -Ensuring human review of AI-generated output before it is deployed.

"What should my first marketing agent even do?" That seems to be the question most marketing teams are sitting with right now, and it opened our conversation with Daniel Murray of The Marketing Millennials. The truth is that it depends, and chances are the first agent you build won't be the last and it may not stand the test of time. You need a whole lot of consensus, time and resources to try and test things out, and a team of people working with you within your enterprise to methodically move forward together. Hear what Daniel, Kris, and Jessica had to say in their discussion on this topic and others in the replay of the webinar.

Vivek Vaidya - Chief Technology Officer, Kana presents "Your Martech stack doesn't need another dashboard. It needs a nervous system" in the Marketing & Growth track at Ai4 2026. In this presentation Vivek discusses how the average enterprise marketing team now manages roughly 90 marketing cloud services, and yet campaign velocity and attribution haven't meaningfully improved in a decade. The problem isn't a lack of data or tooling. The problem is that your stack can see, but it can't think. The next frontier in marketing technology isn't another point solution. It's an operating layer, an enterprise-grade system that provides loosely coupled AI agents that work with the tools you already have, learns from your data continuously, and coordinates action across channels in real time. Think of it as adding a nervous system to a body that, until now, has only had organs. In this session, as a serial CTO who built and sold marketing technology platforms to Microsoft and Salesforce, I will walk through what the future of agentic marketing looks like, and why an enterprise-grade AI platform is a baseline necessity for security, governance, and to future-proof your investment in AI. During the presentation I'll cover: (1) What counts as enterprise-grade AI, (2) How you should approach your transition to agentic AI in a marketing-specific context, and (3) Show you live demonstrations of what you can achieve that was never possible before. If you've ever purchased and configured marketing technology that creates more work than it eliminates, and if you're eager to understand what's truly possible in the next era of AI, my presentation will teach you how to evaluate your technology choices as you navigate the transition to Agentic Marketing.

See how Kana's Category Intelligence Hub gives category managers a single conversational interface that combines live external market signals with their own proprietary scan data — so they can get grounded, sourced answers in seconds, not days. In this visual tour, watch how Kana automatically ingests analyst reports, trade publications, search trends, and social signals, structures them into queryable intelligence, and cross-references every signal against your internal POS data. Enter every buyer meeting, innovation review, and brand planning cycle with a defensible, current view of your category.

See how Kana's OmniChannel Media Planner keeps your media strategy and campaign execution in sync — across every platform, throughout the entire campaign lifecycle. In this visual tour, watch how Kana ingests your media brief, maps audience definitions to a canonical standard across 200+ segments, scores every platform's targeting for alignment, and models optimal budget allocation using your own historical data. Strategy that holds all the way to execution.

See how Kana Audience Manager gives media planners direct control over audience creation; no SQL, no data team, no delays.In this visual tour, watch how planners upload a campaign brief in plain language and let Kana's AI agent translate it into a precise, addressable audience segment built against first-party data in minutes, not days. Kana Audience Manager is built for media planners, campaign strategists, and marketing operations teams who need to move fast without sacrificing precision.

See how Kana's Marketing Intelligence Agent helps modern marketing teams get fast, decision-grade answers — without waiting on manual exports, dashboards, or post-mortems. In this visual tour, watch how teams ask complex performance questions in plain English, surface hidden anomalies automatically, and connect top-of-funnel activity to downstream revenue outcomes. Stop proving marketing is a cost center. Start proving it drives revenue.

A study of 225 marketing, data, and AI leaders at large enterprises finds agentic marketing already in production, broadly confident, and unresolved on a single question: who owns it.

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.

How Kana's Category Intelligence solution provides you with an AI-powered intelligence layer that continuously ingests external category signals, synthesizes them with LLMs, and surfaces them alongside your proprietary scan data in a single conversational interface.

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.

How Kana's LLM Command Center helps you take control of how AI crawlers access your content with real-time bot intelligence, conversational policy creation, and granular content governance that transforms AI traffic from a threat into a strategic asset.

How Kana OmniChannel Media Planner eliminates the gap between media strategy and campaign execution with AI-powered audience mapping, integrity scoring, and budget optimization to keep every channel aligned to your brief.

How Kana Synthetic Data Generator helps you use high quality synthetic data to replace expensive, low quality third party data.

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.

Kana CTO Vivek Vaidya sat down with Experian's Head of Integrated Marketing, Steve Hartmann to discuss the role and impact of AI in modern marketing practices at the Brand Innovator's Sports Marketing Summit in San Francisco, CA. Watch as they discuss their thoughts on how AI will impact marketing moving forward.

Navin Chaddha, Managing Partner of the Mayfield Fund, has written about how AI is transforming how engineers build software and creating new job opportunities (https://lnkd.in/gwmFRGJS). He feels that now it's marketing's turn, and posits that the marketer of the future will lead with vision, taste, and judgment in setting goals and reviewing decisions - agents will handle everything in between. Kana, which announced a $15M seed round led by Mayfield in mid-February 2026, is building a suite of agents for modern marketers: the Claude Code equivalent for marketing. Kana co-founders Tom Chavez and Vivek Vaidya have spent 26 years in the boiler room of modern media and marketing. Kana is their fourth company together, following Rapt (acquired by Microsoft) and Krux (acquired by Salesforce for $1B). In this video produced by Mayfield, you can watch Navin's conversation with Tom and Vivek on their vision for Kana.

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.