How to use AI to ship better content, demos, and pipeline

TL;DR

• Use AI where it’s strongest: optimization. Draft with humans, then let AI repurpose to blog, carousel, SEO and AEO formats at speed. 

• Make “answer engine ops” a team habit. Audit how your brand appears in ChatGPT, Gemini, Perplexity, then fix content and channels that drive those results. 

• Track AEO traffic like a grown-up. Capture clear UTM from ChatGPT, trend unknown traffic and bot spikes week over week, and attribute what you can. 

• Choose tools the way buyers do now. Use LLMs to build shortlists, then verify on first-party sites before booking vendor calls; ship v1 in weeks, not quarters. 

• Tighten sales–marketing loops with AI. Mine call intel in Gong, enable Outreach with useful drafts, but keep editorial guardrails to avoid “AI slop.” 

Put AI in the right place in the content value chain

Workflow

1. Human experts write the anchor piece.

2. AI accelerates optimization and distribution: turn long form into LinkedIn carousels, shorts, blog trims, and SEO or AEO-friendly variants.

3. Human editorial gives final pass for taste and accuracy.

Why

AI is “top-notch” at optimization, fast variations, and channel-ready edits. Use custom Gems in Gemini and native AI inside tools like webinar platforms and Canva to speed the handoffs. Guardrails: protect customer data and follow internal usage guidelines. 

Operator tip

Build a shared “good words, banned words” list to avoid telltale LLM phrasing and keep brand voice high-trust. Milind’s heuristic: effort is a visible signal; low-effort AI reads damage credibility. 

Build “answer engine ops” instead of only SEO

Step 1: audit

Ask category questions in ChatGPT, Gemini, and Perplexity. Note which brands, pages, videos, and communities get cited. Record a baseline for your terms and competitors. 

Step 2: fix your inputs

• Reddit: contribute helpful, non-promotional answers in relevant subreddits. These threads seed many LLM answers.

• YouTube: publish concise explainers and demo clips. Gemini and others weight it heavily.

• Wikipedia: keep a clean, sourced page with accurate positioning.

• On-site content: add clear FAQs and direct answers that resolve queries fast. Today’s engines reward fast, factual response more than click-bait. 

Step 3: measure

• Use UTM source=chatgpt when visible to attribute sessions.

• Trend week-over-week spikes from LLM crawlers or unknown bots; segment and label.

• Pilot AEO modules in tools like SEMrush to guide updates; treat outputs as hints, not gospel. 

Security note

In legal and private markets, buyers expect explicit no-training commitments and visible certifications. Put security and privacy proof near AEO-targeted pages. 

Turn expert webinars into a high-velocity content factory

• Use your webinar platform’s AI to extract soundbites and chapter markers.

• Feed clips into Canva for social graphics and shorts.

• Publish a short recap post and an FAQ block that answers the exact questions buyers ask in engines.

• Iterate copy with Gemini Gems; ship multiple variants quickly, then keep the human final pass. 

Events still compound because they are opt-in

As inboxes flood and channels saturate, hosted events rise in relative value. They deliver face-to-face trust and avoid the “law of shitty click-throughs.” Tie events to post-event AEO content so the learning shows up in engines the next day. 

How Milind buys tools now: the demo stack example

Problem

Loom plus manual edits slowed demo creation, lacked interactivity, and limited engagement tracking. 

Process

1. Use ChatGPT and Gemini to generate a shortlist of demo tools.

2. Validate capabilities on first-party websites before any meeting. If a vendor’s site does not clearly show interactivity, analytics, and integrations, drop it.

3. Score on polish, setup speed, assistive build features, and price.

4. Collaborative decision with PMM and marketing ops.

5. Ship first interactive demo in five to six weeks. 

Why this works

It mirrors how modern buyers actually research: LLM shortlist, then website proof, then sales. Make sure your own site answers those checks or you will never make the shortlist. 

Keep the sales loop tight, not spammy

• Listen at scale: pull insights from Gong into messaging and roadmap notes without pulling reps off calls.

• Enable with care: Outreach can draft useful starting points, but avoid “AI slop” and gimmicky personalization. Quality control remains human.

• Personalization reality: true one-to-one at scale is an ideal; aim for relevant, effort-showing outreach that respects context. 

Team design and hiring sequence for a scale-up

1. Channel specialist for the proven workhorse (often events).

2. Seasoned product marketer who thrives in ambiguity.

3. Generalist growth marketer fluent with AI tools and rapid experiments.

4. Marketing ops to keep data, integrations, and governance clean.

Mindset shift: increase capacity with AI first, not headcount by default. 

Rapid opinions that guide the work

• What AI will likely replace: manual customer data enrichment and basic targeting.

• What AI will not replace: original creative thinking that sets the bar and the brief.

• Content bar: more content exists, but the quality bar should rise if teams use AI to compare options and refine, not to publish raw. 

Final word

Run AI where it compounds speed without sacrificing taste. Treat answer engines as channels you can optimize. Track what you can, trend what you cannot. And keep the loop between content, community, events, and sales tightly edited by humans. That is how Ontra turns expert work into durable pipeline. 

About the leader

Milind Khandare is VP of Product Marketing & Content at Ontra, the AI platform for private markets serving legal, compliance, and deal teams. He previously led global product marketing, brand, and comms at C2FO, drove product and growth marketing at SoFi, and built SMB fintech growth motions at Intuit.

About the series

This is from Breakout Sessions, where marketing leaders unpack how AI is changing GTM and the way buyers buy.