MarTech360 Interview with Anniston Ward, USA PR, Events, and Education Manager at Metricool

If we're only measuring hours saved, we're treating AI like a productivity hack instead of asking whether it's making the work, and the people doing it, better.

Anniston, your career has taken you from social media, PR, and content work to a role that now brings together PR, events, education, and international speaking at Metricool. Looking back, which experience or transition most changed the way you think about what makes marketing communication genuinely resonate with people?

The experience that really changed how I think about what makes marketing communication genuinely resonate with people was presenting findings from our 2026 Well-Being in Social Media survey. This was the first time we surveyed this, gathering information about how people are feeling in the industry – burnout, mental health, role scope, expectations. I think we focus so heavily on output that we overlook how the people powering the industry are doing. I remember the feedback was overwhelmingly positive. Many people came up to me saying it was refreshing and that they finally felt seen. At the end of the day, that's all people want. To be seen and heard. That changed the way I approached marketing and community engagement.

Your role at Metricool sits across PR, events, and education, three areas that are often treated as separate functions. How do those three perspectives influence one another when you think about building Metricool’s presence, credibility, and relationships with the marketing community?

While these three areas can be seen as separate entities, they all have the same purpose: to be a resource for social media managers, students, digital marketers, content creators, and entrepreneurs. When it comes to PR, we want to both raise awareness of our recent studies and be a data resource that reporters and journalists can rely on. For events, we are a resource that attendees can utilize to ask questions or solve problems within their business. The same goes for education: we want to provide continued learning for all marketers, not just students. At the end of the day, it comes down to relationships. All three of these areas only work if people trust you enough to keep coming back, whether that's a journalist who calls you first, an attendee who emails you a follow-up question months later, or a student who reaches out for advice once they've graduated.

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Metricool’s latest 2026 AI research found that 95% of social media professionals now use AI, while 37% use more than four AI tools, up from 14% a year earlier. There’s something slightly ironic about that given the industry’s constant search for simpler workflows. From what you’re seeing among marketers, at what point does adding another AI tool stop making someone more capable and start making the workflow harder to manage?

For the last few years, I think we've been in a season of experimentation, just testing what's out there to see what actually sticks. And a lot of that has genuinely paid off. Some of the best AI I use day-to-day isn't even a separate tool; it's already built into platforms I was using anyway. This enhances both the quality and efficiency of utilizing AI, without asking anything extra of me.

That’s also the thinking behind things we’re building at Metricool. We recently launched Flows, which brings conversational automation directly into the platform, so marketers can automate responses to comments, DMs, and Story replies without adding another standalone tool to their stack.

Where it gets harder to manage is when you implement a brand new, standalone AI system on top of everything else. Now you're not just doing the work; you're managing a whole new relationship: learning how to prompt it, testing what it gives you, comparing that against what you'd have made yourself. At some point, all of that managing costs you more time than it saves, and you realize you could have just done the task yourself in the time it took to babysit the tool. That's the tipping point I keep coming back to.

The same research suggests AI adoption is moving considerably faster than formal guidance, with only 12% of professionals reporting a clear AI policy or specific rules governing its use. From your work with marketers, educators, and the wider social media community, what kind of guidance helps people experiment with AI confidently without turning creativity into a box-ticking exercise?

While I don’t think there needs to be a specific rulebook for every tool, there do need to be clear principles: what data should be available for public AI tools, who reviews AI-assisted content before it goes out, and when you should disclose that AI was used. Beyond that, I think guidance should be less about restriction and more about building enough trust for people to experiment, while still being honest with them about the risks that come with it. The 12% doesn’t surprise me, considering how quickly AI adoption has scaled. Marketing teams are still catching their breath, figuring out how AI actually benefits content and strategy, let alone sitting down to outline the best policies for it. The best guidance I've seen treats AI the way you'd hand off your brand's social accounts to someone new: you don't just give them the login and walk away. You show them the brand voice, point to a few examples of what's on-brand and what's not, flag which posts need a second set of eyes before they go live, and then let them post and experiment, always leaving the door open for questions, analysis, and improvement.

Metricool found that 30% of professionals feel AI-generated results can be generic or uncreative, while 14% say AI-generated content performs worse than content created without it. Do you think the emerging competitive advantage in an AI-heavy marketing environment may actually be knowing when not to use AI? Where have you personally found that boundary?

Yes, absolutely. When everyone has access to the same tools, the same search engines, even the same prompts, the thing that actually sets you apart is judgment: knowing which tasks genuinely benefit from AI and which ones still need a human fingerprint on them. For me, that boundary shows up anywhere content needs a real point of view or lived experience behind it. Our 2026 Well-Being in Social Media survey is a good example. No AI tool could have told that story, because it wasn't pulled from a dataset; it was built from real conversations with real people in this industry about how they're actually doing. The moment something needs empathy, nuance, or a perspective that only comes from having actually sat in the room, that's exactly where I draw the line

Another finding that stood out is that 54% of professionals’ measure AI’s impact through time saved, while only 6% measure content quality and 4% track post performance. Does that suggest marketers are still treating AI primarily as a productivity tool rather than a strategic or creative one? If so, what would a more meaningful definition of AI-driven marketing impact look like to you?

I do think we're still measuring AI's impact mostly through productivity and scale, and that's not necessarily a bad thing, but it can quietly dilute quality when the focus shifts to churning out more instead of thinking more deeply, strategically, and creatively. Time saved is also just the easiest data point to grab, which tells me we're still early in figuring out what AI is actually for in marketing.

We’re seeing the next step of that at Metricool through our MCP integration, which is now an official Claude connector listed in the Claude Connectors Directory. Marketers can now access Metricool and perform actions like analyzing performance or managing content without leaving Claude.

A more meaningful measure of impact would ask: did this free up time that went toward something more strategic or more human, like relationship building or original thinking? Did the actual quality or resonance of the work improve, not just the speed of producing it? If we're only measuring hours saved, we're treating AI like a productivity hack instead of asking whether it's making the work, and the people doing it, better.

You also work with professors, marketing coaches, and universities to help prepare the next generation of marketers. Given how quickly the role is expanding across content, analytics, AI, community, PR, and strategy, what do you think marketing education still underestimates about the job young marketers are actually walking into?

I think marketing education still underestimates how much of this job is about people, not platforms. Programs do a good job teaching the technical skills- content, analytics, strategy- but they don't always prepare students for how much of the actual day-to-day is cross-functional communication, adaptability, and yes, emotional labor. This role touches PR, events, education, data, community, and increasingly AI literacy, often all in the same week. Young marketers are walking into a job that requires them to be generalists and specialists at the same time, and that kind of range takes a resilience that a syllabus can't really teach. I try to be honest about that with the students and young professionals I work with.

You describe yourself as ‘your local empath,’ while much of your professional world revolves around data, measurement, optimization, and increasingly AI. Has working in such a data-heavy environment changed how you think about empathy as a marketing skill? In an industry becoming more automated, where does empathy become a competitive advantage rather than simply a personality trait?

Working in a data-heavy environment hasn't made me less empathetic; if anything, it's made me more intentional about it. Data can tell you what happened, but it can't tell you why someone felt a certain way, or what they actually need next. My job is often translating between the two: taking a data point and finding the human story underneath it. As the industry becomes more automated, empathy stops being a soft skill and becomes a differentiator, because it's the thing AI genuinely cannot replicate. It's part of why the Well-Being survey resonated the way it did. It wasn't just a set of graphs; it was people finally feeling like someone was paying attention to how they're doing, not just what they're producing.

If the next generation of marketers grows up with AI capable of writing, designing, researching, analyzing, and increasingly recommending what they should do next, what do you hope they become exceptionally good at that no tool should be allowed to decide for them?

I hope they become exceptionally good at discernment. At knowing when a recommendation from a tool is actually right for their brand, their audience, their moment, and when it's just statistically average. AI can generate options, but it can't tell you which one is true to who you are or what your community actually needs from you right now. I also hope they hold onto curiosity and their own voice. The tools can help you execute faster, but they shouldn't be deciding what you stand for, or what story is worth telling in the first place. That's still, and I hope always will be, a human call.

Thanks, Anniston!