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In today’s email:
Microsoft Copilot pits GPT against Claude for accuracy
Salesforce transforms Slackbot with 30 new AI features
Google adds AI-directed avatars and Veo 3.1 to Vids
Plus: why nobody can plan their way to AI success, a look at brands campaigning against AI slop, a workflow for tracking your AI search visibility, and four tools helping marketers get cited by AI.
The Top 3 Stories
1. Microsoft Copilot now pits GPT against Claude for accuracy
Microsoft's Copilot Researcher introduced Critique, routing every response through both OpenAI's GPT and Anthropic's Claude. One model generates the answer while the other reviews it before it reaches the user, flagging weak reasoning, unsupported claims and factual gaps in the process.
Why it matters: AI-generated marketing research just got a built-in fact-checker, and it works by making models compete. For teams that have been wary of dropping LLM output straight into client decks, this is the first mainstream feature that treats hallucination as a workflow problem rather than a user problem. 📰 Engadget
2. Salesforce turns Slackbot into an autonomous AI agent
Salesforce unveiled 30 new Slackbot features including meeting transcription, a desktop monitoring agent and Model Context Protocol integration connecting Slackbot to thousands of enterprise apps. The update positions Slack less as a messaging tool and more as the command layer for the rest of the Salesforce stack.
Why it matters: Marketers managing campaigns across tools now have an AI agent that orchestrates workflows without switching platforms. The real unlock is MCP, because it means Slackbot can action work inside tools Salesforce doesn't own, from analytics platforms to ad managers. 📰 TechCrunch
3. Google brings AI-directed avatars and Veo 3.1 to its Vids app
Google added natural language avatar direction and its Veo 3.1 video model to Vids, letting users create eight-second AI clips and export directly to YouTube. Avatars can now be directed with plain-text prompts covering tone, pacing and on-camera gestures, closing the gap with dedicated tools like HeyGen and Synthesia.
Why it matters: Marketers can now prototype video ads inside a single Google tool, from scripting to publishing. For small teams without a production budget, it collapses the gap between a rough idea and something testable on YouTube Shorts the same afternoon. 📰 VentureBeat
700+ teams have Viktor reading their Google Ads every morning.
Your media team opens Slack at 8am. There's a cross-platform brief in #growth: Google Ads spend vs. ROAS, Meta CPA by campaign, Stripe revenue by channel. Viktor posted it at 6am. Nobody asked for it.
Last week, one team's Viktor caught a spend spike at 2am on a broad match campaign and flagged it in Slack: "CPA up 340%. Recommend pausing and shifting budget to the top two performers." That would have burned $3K by morning. The media buyer woke up to a problem already handled.
Your strategist reviews spend trends. Your account manager checks revenue attribution. Same Slack channel, same colleague, before anyone's first coffee.
Google Ads, Meta, Stripe. One message. No Looker, no Data Studio. Anomaly detection runs around the clock. Cross-platform reporting runs on autopilot.
5,700+ teams. SOC 2 certified. Your data never trains models.
"Viktor is now an integral team member, and after weeks of use we still feel we haven't uncovered the full potential." — Patrick O'Doherty, Director, Yarra Web
More trending AI marketing news from last week
OpenAI acquires tech media company TBPN in its first move into content ownership.
WPP unifies AI efforts across creative, production and media teams.
California cements its role as the national testing ground for AI regulation.
Brands from Equinox to Almond Breeze campaign publicly against AI slop.
UK digital ad spend surpasses £40bn, driven by AI and social video growth.
MIT warns agentic commerce demands near-perfect product data from brands.
Quote of the week
You have to make a bet. You've got to commit as an organisation to AI. When nobody knows anything, when the future is that uncertain, you cannot plan your way to success."
Trending AI tools for marketers
Four tools helping marketers track and improve their visibility inside AI-generated answers:
Gauge: Unified marketing agent for organic, paid and AI search in one dashboard.
Pendium: Monitor your AI visibility score across ChatGPT, Claude, Gemini and more.
KIVA (Wellows): AI-powered keyword research optimised for Google, Bing and ChatGPT discovery.
Skayle: Track how AI engines cite or ignore your brand, then publish optimised content.
TV, podcasts & streaming
Not every brand needs to chase viral trends, and "Fruit Love Island" proves it
Gillian Follett examines why brands jumping on AI-generated viral content often backfire, and which social strategies actually resonate with audiences, listen to it here:
AI training
How to track your brand's AI search visibility using Gauge
The overview: Gauge unifies GA4, Google Search Console, keyword data and AI prompt tracking into a single marketing agent that analyses where ChatGPT, Perplexity and Google AI Overviews cite you, then drafts the content to close the gaps.
Step-by-step:
Connect your data stack
Plug Gauge into GA4 and Google Search Console so it can pull organic traffic, query and conversion data alongside AI citations.Seed your prompt set
Feed in the buying-intent prompts your customers actually ask (pricing comparisons, "best tool for X", alternatives to competitors) rather than generic brand mentions.Benchmark against competitors
Add three to five rivals and let Gauge track which prompts surface them, in which engines, and with what framing.Read the recommendation layer
Instead of exporting a gap list, review Gauge's suggested plan of attack, it ranks opportunities by traffic potential and citation likelihood.Commission drafts from the agent
Use the agentic workflow to generate briefs and draft content for the highest-priority gaps, then edit to your brand voice before publishing.
Pro tip: AI engines weight structured content heavily, so when Gauge drafts a piece, rework it into a clear problem-question-answer format with a direct one-sentence answer in the opening 100 words. That's the shape LLMs lift into citations, and it's the single biggest lever for turning a ranked page into a cited one.
The weekly deep dive
When AI agents shop for your customers, will they find your brand?
Agentic commerce is accelerating faster than most marketing teams have planned for, and the brands that prepare now will define the next wave of product discovery.
The measurement gap is closing
On 2 April alone, over a dozen AI marketing tools launched or updated. Many target the same problem: brands have no visibility into whether large language models recommend them, ignore them or misrepresent them. A new category of dashboards now tracks AI citations alongside traditional search rankings, giving marketers a unified picture for the first time.
Structured data is now a competitive moat
If an AI agent cannot parse your product information, it cannot recommend your product. Forrester reports 89% of B2B buyers now use generative AI as a primary research source, yet most teams still measure visibility through traditional search rankings alone.
Discovery is splitting into two channels
Brands now need to be findable both by humans browsing search results and by AI agents evaluating structured data behind the scenes. The gap between where buyers look and where brands measure is widening every quarter.
Our takeaway: The marketing teams that build machine-readable product data and AI citation tracking into their 2026 roadmaps will own the discovery layer that agents rely on next year.
📖 Read the full article at HBR
