CMO Shares What Moves Marketers Need To Make
Discover how Kelly Cheng, CMO at Goldcast, is using AI to build a content-led marketing engine while protecting the human judgment, original ideas, and storytelling that creates lasting demand.
Kelly Cheng
CMO at Goldcast
AI Redefinition: AI changes CMO roles from managing campaigns to building agile, learning-focused marketing teams.
Content Shift: Team moved from campaign-led to content-led model with AI boosting output and strategic thinking.
Workflow Overhaul: AI transforms single events into multi-channel content packages quickly, enhancing engagement lifespan.
Differentiation Challenge: AI struggles with demand creation; maintaining strong editorial standards is crucial for differentiation.
Structural Change: AI demands audience-focused teams over channel specialists to compete effectively in the market.
Kelly Cheng is CMO at Goldcast, where she leads the global marketing organization.
We sat down with her to hear all about how she has revamped her content marketing strategy with AI. Here's what she shared with us.
AI fundamentally redefines the role of CMOs
I’m Kelly Cheng, CMO at Goldcast, where I lead a global marketing organization spanning the full B2B GTM motion including brand and content, product marketing, growth and demand generation, marketing operations, and community.
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The team operates across the US, UK, and India, supporting both Goldcast as a standalone product and our integration into Cvent’s broader event cloud portfolio, following the acquisition.
My journey to this moment has been shaped by leading marketing teams through every major platform shift of the last decade, from content marketing to ABM to product-led growth, and now AI.
I’ve learned that the marketing leaders who win aren’t the ones who chase every new too. Adaptable leaders use these shifts to fundamentally rethink how their teams operate, what they measure, and where they create value.
AI is the biggest forcing function we’ve ever faced, and it’s pushing CMOs to redefine the role itself. The work is less about managing channels and campaigns, and more about building learning organizations that can move at the speed of the market.
How AI enables a shift from campaigns to content
The biggest change we made with AI was moving from a campaign-led operating model to a content-led one, powered by AI.
Historically, our team planned in quarterly campaign cycles, where each program had a discrete brief, asset list, and timeline. Over the past year, we restructured our operations around a continuous content engine. AI now handles repurposing, first-draft creation, and distribution prep, which shifts the team’s energy upstream to ideas, points of view, and quality control.
This resulted in a meaningful jump in output and speed, where we’re producing four to five times as many assets per source piece. But the more important change is cultural: The team now thinks in terms of audiences and narratives rather than campaigns and deliverables, which has made the work both better and more strategic.
Beyond the increase in content output, AI-assisted account scoring and personalization have also lifted engagement on our ABM programs. And we’ve meaningfully reduced the cost per qualified opportunity in paid channels by using AI to test creative variations at a scale we couldn’t before.
How to overhaul content-marketing workflows
Here's an example of how a single, hour-long webinar or event turns into a full multi-channel content package in days instead of weeks.
The workflow starts with the live event, where we capture the video and full transcript automatically. From there, AI generates the first wave of derivative assets, including a recap blog, executive summary, key quote cards, social clips, an email recap, and sales enablement snippets tailored to specific personas.
Our team layers on POV, editorial polish, and brand voice, applying human judgment.
Next, AI handles distribution prep between drafting LinkedIn posts for each speaker and our exec team, generating subject line variations for email, creating short-form video edits with captions for organic social, and writing personalized outbound notes for sales to share with target accounts.
Performance data flows back into a dashboard, where AI summarizes what’s working across formats and audiences. Those insights then inform the brief for the next program.
The result is that content that used to live for a week now drives engagement and pipeline for 60 to 90 days, and the team spends its time on ideas and strategy instead of production.
How AI can hurt differentiation
AI hasn't delivered in pipeline and demand creation.
It has helped us go faster and produce more, but it hasn’t meaningfully changed our ability to create demand in accounts that don't already know us. Generative content, AI-personalized emails, and automated outbound hit diminishing returns quickly because every competitor has access to the same tools, so the bar for breaking through is higher than before, not lower.
Early on, we leaned too hard on AI for first-draft content, and the output started feeling generic, which hurt brand differentiation. We had to re-establish stronger editorial standards and POV layers on top of any AI-generated work.
It's also worth noting that, with 95% of buyers out of market at any given time, capture is a small game. The bigger game is creating demand by shaping how people think about the category long before they’re ready to buy. That requires fewer, better pieces of content with a real point of view, not more of the same.
How content strategies should be pivoted
We’ve made three big shifts with our content.
- We anchor everything in original POV and proprietary data: research from our community, customer insights, and benchmarks no one else has. AI can remix existing content, but it can’t generate a fresh perspective or first-party data, so that’s where the moat is.
- We lean hard into formats that AI can’t replicate well, especially live video, podcasts, executive storytelling, and in-person events, where the human voice and presence are the product.
- We shifted from broad content distribution to building owned communities and audiences where we have a direct relationship with marketers, without an algorithm in between.
The mental model is simple: in an AI-saturated market, attention follows trust, and trust follows a recognizable point of view, a real human, and a community that values what you say.
Why humans handle POV, judgment, and relationships
So, AI now powers most of our execution and analysis: content repurposing and first drafts, audience segmentation and account scoring, ad creative variations, performance reporting, competitive monitoring, and even early drafts of briefs and emails. It’s also reshaping research, where we use AI to synthesize customer calls, win/loss data, and community conversations into insights the team can act on in hours instead of weeks.
What remains explicitly human is anything tied to point of view, judgment, and relationships. Strategy and positioning, narrative development, executive storytelling, customer and influencer relationships, and creative concepting all stay with people.
The reason is simple: AI is great at pattern matching against what already exists, but marketing leadership is about deciding what your brand stands for and where the market is going next, and that requires conviction, taste, and accountability that you can’t outsource.
The teams that win will be the ones that use AI to free up more time for exactly that human work, not less.
How AI enables a continuous-planning model
The system most leaders should be redesigning is the marketing planning and review cadence. Most teams still operate on a quarterly campaign planning cycle, conduct monthly reviews, and rely heavily on lagging metrics like MQLs and pipeline. That rhythm suited a world where producing and launching a campaign took six to eight weeks. But, in an AI-augmented environment, where a team can produce, test, and iterate on content and creative in days, quarterly planning becomes the bottleneck. Teams sit on AI capacity, waiting for the next planning cycle to deploy it.
We redesigned this by moving to a continuous planning model: a clear annual narrative, quarterly themes, and weekly editorial and program review cycles where the team decides in near real time what to double down on, kill, or remix based on engagement signals.
AI handles the data summarization and surfaces what’s working, and the team makes faster, smaller bets instead of fewer, bigger ones. This resulted in a meaningfully higher hit rate on content and campaigns because we learn and adjust weekly instead of quarterly.
Why marketing team structures must change with AI
Most marketing orgs are still built around channel specialists — a content writer, an email marketer, a paid media manager, a social lead — each owning their lane and deliverables.
Most marketing orgs are still built around channel specialists. There's a content writer, an email marketer, a paid media manager, a social lead, each owning their lane and deliverables.
This structure made sense when each channel required deep, specialized craft and a lot of manual production. In an AI-augmented world, the production work in every one of those lanes collapses in cost and time, meaning the production-focused org chart is optimizing for the wrong thing.
The redesign moves from channel-based roles to audience and outcome-based pods. Instead of a content team, a demand team, and a social team, you build small cross-functional pods that own a specific audience or motion end-to-end, with AI handling most of the production layer underneath.
The roles inside those pods shift too: you need fewer pure executors and more people who can do strategy, editorial judgment, prompt engineering, and creative direction across multiple formats.
Leaders who don’t redesign their orgs this way will end up with bloated, siloed teams trying to compete with leaner, faster, AI-native ones. And that gap will widen quickly.
Why AI adoption is a change management problem disguised as a technology problem
AI adoption is a change management problem disguised as a technology problem, and treating it the other way around costs you time and credibility.
Our first AI pilots focused on obvious wins, like content generation and email personalization, and we treated them as tool rollouts. We missed that AI fundamentally changes who does what, how work gets reviewed, and what “good” looks like, and we didn’t redesign the workflow or roles to match.
This resulted in faster outputs but uneven quality, with team members either over-relying on AI or refusing to use it, and no clear standard for either.
If I’d known that upfront, I would have started with the operating model: redefined roles, set quality bars, built editorial guardrails, and trained the team on judgment and prompting before turning the tools loose. We would have avoided about six months of inconsistent output, internal frustration, and a few pieces of content that frankly shouldn’t have gone out the door.
Why CMOs must not confuse activity for progress
Don’t confuse activity with progress. Every marketing team right now is racing to adopt AI tools. But the leaders who will come out ahead are using this moment to sharpen their strategy, their POV, and the quality of their team’s thinking. In fact, a bigger tech stack may sidetrack your efforts, if it's not managed effectively and truly valuable for your team.
My advice would be three things:
- Get clear on what only your brand can say and double down on that, because AI is going to flood the market with generic content, and the only defense is conviction.
- Invest in your team’s judgment and taste, not just their tool fluency, because the bottleneck is shifting from production to discernment.
- Protect the human work: community, relationships, original research, and storytelling. That’s where mindshare is built, and mindshare compounds when everything else becomes commoditized.
Follow along
You can follow Kelly Cheng's work on LinkedIn.
More expert interviews to come on The CMO Club!
