Nathaniel Stich, Director of Growth Marketing at Scope3: “AI has fundamentally changed my creative process”

Few marketers know the mechanics of scaling a company better than Nathaniel Stich. Having built strategies for companies such as Audigent (acquired by Experian) and Newgistics (acquired by Pitney Bowes), he’s turned startups into enterprise-ready machines. Now, working as Director of Growth Marketing at Scope3 in New York, Nathaniel is applying that same discipline to create data-driven systems that can deliver big results.

For Nathaniel, growth isn’t just about velocity, it’s about staying true to the brand. In his own words: “Authentic marketing means communicating a fundamental truth with genuine care and effort”. AI hasn’t changed that philosophy for him, it’s simply shifted where the effort has to be placed. 

Even as AI becomes more advanced, Nathaniel is clear that marketing is becoming more human, not less. But marketers do need to pick up new skills as the ones “who thrive will be those who embrace this hybrid role: part data analyst, part creative director, part prompt engineer, all human”.

Nathaniel’s advice when it comes to AI is clear. “Have a plan,” he says, with his combining concrete goals with specialised tests and regular check-ins to support the team. His success comes from balancing measurable progress with human insight, not by chasing every new tool on the market.

Despite having such a measured approach at his foundation, Nathaniel has been no stranger to experimenting with cutting-edge AI tools. So let’s start there: what tech is most excited by?

What are the most exciting AI tools or technologies you’re currently using in your marketing campaigns?

Claude Code has transformed our approach to marketing operations. As someone who’s worked at startups where marketing often meant wearing the business analytics hat, I’ve always had just enough programming knowledge to be dangerous — I could create basic API calls and integrations that were functional but not polished enough for production environments.

Claude Code changed that equation entirely. By feeding it API credentials for our CRM, cloud storage, event management platform, and other systems, I can prompt it to identify and fill data gaps, run campaign analysis, address reporting needs, compare email performance, and then generate scheduled scripts to automate everything — all without touching our dev team’s sprint.

Here’s a concrete example: For one of our events, we needed to classify attendees by management seniority (C-Suite, VP-level, Director, Manager, Non-manager) to ensure the right mix of decision-makers and executors at each campaign touchpoint. Our CRM’s native classification struggled to return the data we needed. With a simple prompt to Claude Code, we accelerated classification and dramatically improved accuracy. We transformed our event narrative from “we met with X people” to “50% of attendees were C-level at target accounts, with 59% having budgetary control.” That’s the difference between activity metrics and business impact.

The compound effects are even more exciting. Using Claude Code to identify lookalike audiences, structure A/B tests, and analyse performance patterns across platforms, we’ve built a data-driven library of best practices. The numbers speak for themselves: our average email open rate jumped from just about 8% to around 30% across our entire database, all from insights generated through consistent prompting and analysis.

Claude Code isn’t just another tool for marketers, though. It unlocks sophisticated data analysis and automation for marketers who don’t have the skillset of a full stack developer. For me, that meant being able to quickly set up functional code without bothering dev resources. Change like that is huge for marketing teams who want to go deeper with their data and information but lack the resources, time, or development team support to do so.

What does “authentic marketing” mean to you in an era of AI-generated content? Is true authenticity still possible at scale?

Authentic marketing means communicating a fundamental truth with genuine care and effort. In the AI era, that translates to content that successfully distils brand truth into a communicable ideal. The misconception is that AI-generated equals low-effort. In reality, it can require similar hours to configure systems and prompts to produce something meaningful. The effort hasn’t disappeared — it’s shifted.

I think of authenticity as the effort of communicating an honest truth, in which case AI-generated content can absolutely be authentic. As a marketer with a high bar for content interaction, I look for pieces that are interactive, visually striking, or reveal something genuine. Believability remains the basic price of entry.

The real value of AI lies in accelerating the creative process — from ideation to creation to testing and optimisation. While AI will multiply content volume exponentially, the authenticity sniff test remains unchanged: Does this content contain a truth? Did someone put care into making it?

What’s fascinating is how the point of care has evolved. Before automation, we saw care in the craftsmanship of typesetters, sign painters, and illustrators. With traditional automation, care shifted from creation to supervision of production. With AI, care moves to the prompt itself — ensuring the automated chain understands and executes the authentic message.

There’s something unique about this moment we’re in. We’re experiencing genuine wonderment at AI’s capabilities, and that wonder represents a kind of meta-authenticity – it’s authentic precisely because the feeling is real and the effort to experiment is genuine. The number of Slack messages sharing AI-generated discoveries, the texts between friends and family marvelling at what’s possible, are proof we’re in an age where our collective awe at technological progress creates its own form of authenticity.

That shared excitement and experimentation become part of the authentic experience itself. The tools have changed, but the requirements have not: authentic marketing still demands human intention, effort, and care – just expressed through different channels.

Has AI impacted your creative process — for example, in content creation, ad design or campaign ideation? If so, how?

AI has fundamentally changed my creative process. Growing up surrounded by writers – mostly journalists – I was made acutely aware of every grammatical error and inefficiency in my writing. Professionally, I’ve always positioned myself as a better editor than writer. The truth is that creation is hard. I still remember sitting in college, staring at that blinking cursor in Microsoft Word, taunting me to start when I had either nothing to say or too much to organise.

AI gives me the ability to outsource that initial creative friction. The output might not be the final version, but it gives me something crucial: a starting point to react against. Through reaction, I discover what I actually want to create. The beauty of using AI to start is that I’m simultaneously learning about the content itself and how to better prompt the platform. The prompts, my reactions, and the iterative changes have become key signals in my creative journey.

More importantly, AI has demolished the barrier to entry. Instead of opening a blank document and wrestling with where to begin, I can prompt AI with our brand voice and guidelines to generate initial collateral to think through and refine. The adage – don’t let perfect be the enemy of good – has never been more relevant. AI removes the paralysis around starting and the pressure of perfection.

What’s changed most dramatically is the collaborative aspect. Rather than hoarding half-formed ideas until they’re “ready,” I can quickly generate complete thoughts to share with team members, incorporating their feedback into successive refinements. The creative process has shifted from solitary struggle to rapid iteration and collaboration. AI has accelerated creativity for me, making the journey from idea to execution faster and far less intimidating.

How do you see the role of human marketers evolving alongside increasingly sophisticated AI systems?

The data tells a compelling story about where marketing is headed. When we ran a webinar on prompt engineering for our agentic advertising platform at Scope3 – a concept that didn’t even exist 10 months ago – we had to add a second session within a week due to overwhelming demand. That’s not just interest; that’s a seismic shift in how marketers understand their evolving role.

I see the future of marketing as fundamentally agentic, with humans orchestrating AI systems rather than competing with them. The most successful marketers will become prompt engineers who synthesise across disciplines. They will weave together product and brand marketing insights, growth metrics, channel performance data, and campaign objectives into precise prompts that deliver maximum emotional resonance.

The modern marketing prompt engineer needs fluency in multiple languages: data analytics to understand what drives behaviour, systems thinking to see how pieces interconnect, creativity to craft compelling narratives, and impeccable communication skills to translate it all into clear instructions. Just as developers optimise code for performance, marketers will optimise prompts for impact.

But here’s what the data doesn’t capture and what makes this evolution exciting: everything we build still needs to connect with human emotions to matter. AI can process patterns and generate content at scale, but it takes human insight to know which patterns matter and which stories will resonate within specific communities. The most fundamental marketing skill – clear, authentic communication – becomes even more valuable when you’re the bridge between sophisticated AI capabilities and genuine human connection.

The marketers who thrive will be those who embrace this hybrid role: part data analyst, part creative director, part prompt engineer, all human.

What challenges have you faced when integrating AI solutions into your marketing stack or workflows?

After evaluating dozens of AI platforms, I’ve identified three core challenges that determine success or failure: solution-problem fit, team alignment, and brand compliance at scale.

From my experience, most AI solutions deliver about 80% of what marketing teams actually need. The real challenge isn’t finding AI tools; it’s starting with the right questions to evaluate them. While testing and learning remain critical, I’ve evolved our approach from sequential platform testing to something more systematic: we now define clear functional objectives first, then select platforms that address those specific needs.

Think of it like A/B testing at scale. We’re building repeatable prompt libraries and standardised output libraries to create apples-to-apples comparisons across platforms. It’s the equivalent of test-driving different cars on the same track – you need controlled conditions to understand true performance differences.

Image generation presents a particularly fascinating challenge. Our brand uses a sophisticated graphic library of abstract shapes with strict application rules – colour treatments, overlap parameters, gradient specifications, and dozens of other variables. What we’ve discovered through testing every major platform is that success depends less on finding a specialised tool and more on finding the most flexible platform that responds well to detailed prompting. The platforms that excel are those with robust prompt engineering capabilities that can adapt to complex requirements.

The insight here is that successful AI implementation requires building the right infrastructure for evaluation and customisation. The teams that win will be those who excel at framing evaluation questions, move fast in testing, and master the art of prompt engineering. As the market evolves, the most flexible, user-friendly tools that allow for sophisticated prompting will dominate – because they empower marketers to solve virtually any challenge rather than forcing us to hunt for niche solutions.

If you could give one piece of advice to marketing teams just starting to explore AI, what would it be?

Have a plan. It sounds simple, but when you’re juggling platform fees, usage costs, the ever-growing number of AI platforms, and the varying technical skill levels across your team, adopting the right AI solution can feel like an overwhelming puzzle with too many moving pieces to track effectively.

Start with a clear problem statement and leverage AI itself to help refine and articulate it more precisely. Let AI assist you in establishing concrete test criteria and identifying which platforms from the vast marketplace are actually worth your team’s time and budget to evaluate thoroughly.

Designate a few subject matter experts to run focused tests while maintaining regular communication channels to keep the broader team informed of progress, learnings, and potential roadblocks along the way.

The human element remains absolutely critical throughout this process. Schedule weekly check-ins where team members feel safe being vulnerable about their concerns and challenges, because there’s genuine anxiety that AI will replace rather than augment marketing jobs, especially given that marketing teams traditionally encompass such a wide spectrum of technical expertise from creative writers to data analysts.

Most importantly, establish success criteria that are as concrete and measurable as possible rather than settling for vague aspirations. Instead of broadly stating you want to “improve efficiency,” define exactly what that means for your specific context -whether that’s reducing content creation time by 40%, increasing campaign variations by 3x, or cutting weekly reporting time from 8 hours to 2 hours.

The teams that ultimately succeed won’t be those who rush to adopt AI fastest, but rather those who approach it most thoughtfully—with clearly defined problems to solve, specific metrics to track progress against, and structured support systems to help their people navigate through what can be a significant transition in how marketing work gets done.

More interviews

• Evanna Kearins, CMO at UiPath: “Embrace AI – but don’t forget the craft behind great marketing”
• Rachel Sterling, CMO at Identity Digital: “We cannot allow AI to undermine the trust we seek to build”
• Adam Rek, Head of Marketing at FLO: “We’re bingeing on buzzwords like empty calories, and the AI ones are the most overindulgent of the lot”
• Matej Kukučka, CMO at Luigi’s Box: “It’s evident that AI cannot replace the human touch”