An Interview With Chad Silverstein

Prepare your people and processes: AI isn’t just a tech upgrade, it changes how teams work. You’ll need to think about how your team will engage with AI-generated outputs, how decisions will be made with automation in the mix, and how you’ll keep skills and knowledge up to date. Training and experimentation are essential, not optional.

In the ever-evolving and never-ending landscape of business, staying ahead of the curve is a prerequisite for success. Artificial Intelligence (AI) has gone from being a futuristic concept to a daily business tool that executives can’t ignore. In this interview series, we would like to talk with business leaders who’ve successfully integrated A.I. into their operations, transforming their companies in the process. I had the pleasure of interviewing John McGrane.

John McGrane is the Director of AI Innovation & Brand at Ars X Machina (AXM), where he helps harness artificial intelligence to drive smarter media, marketing, and creative strategies. With a diverse background spanning communications, paid media, and strategic leadership, John brings a rare blend of technical fluency and human insight to the evolving AI landscape. He’s passionate about building systems that elevate people — not replace them — and believes the future belongs to teams who can think both creatively and computationally.

Thank you so much for doing this with us! To set the stage, tell us briefly about your childhood and background. What were the early challenges you faced in your career, and how did they shape your approach to leadership?

I’ve always been driven by curiosity, especially about how things work beneath the surface. As a kid, I was less interested in what was happening and more fascinated by why it was happening and how systems, people, and processes connected behind the scenes.

That instinct followed me into my career. Early on, I made it a point to regularly engage with people outside my immediate role or department. What I found was eye-opening: most people could explain what they were working on in great detail but struggled to articulate how it connected to the company’s broader goals or why certain tools and processes were in place. That disconnect stuck with me. I kept thinking…how can you improve something if you don’t understand its larger context?

Two lessons emerged that continue to shape how I lead today. First, understanding the interconnectedness of roles across the company helped me contribute far more meaningfully. While others often got buried in the weeds, I developed the ability to “zoom out” and see systems holistically. That made it easier to spot inefficiencies, identify opportunities, and envision new paths forward.

Second, I learned that communication is an underrated superpower. It’s not enough to do great work, you have to be able to clearly explain what you’re doing, why it matters, and how it moves the business forward. Over time, I built that into my leadership approach. I don’t just hand teams tasks; I make sure everyone understands the long-term impact of their work and can communicate it with clarity and confidence. That alignment creates better results and stronger teams.

We often learn the most from our mistakes. Can you share one mistake that turned out to be one of the most valuable lessons you’ve learned?

One of the most important leadership lessons I’ve learned is that waiting too long to act on a new idea often does more damage than acting before it’s fully polished. Early in my career, I put a premium on getting things “right” before sharing them. I wanted to impress, to prove the value, to avoid criticism. So I’d hold onto ideas longer than I should have, obsessing over every detail, anticipating every possible question, and over-engineering the rollout in hopes of perfection.

Since then, I’ve adopted a very different approach based on the principles of Agile Marketing. I believe in building out loud, pressure-testing early, and getting things into the hands of the people who’ll use them, even if it’s only 70% there. I’ve found that teams respond much better to transparency and shared ownership than to delayed “big reveals.” When you bring people into the process, they don’t just follow, they contribute. And the solution you arrive at is almost always better than what you could’ve built alone.

A.I. is a big leap for many businesses. When and what first sparked your interest in incorporating it into your operations?

AI has technically been part of the marketing world for years but it mostly operated behind the scenes. In paid media, machine learning has helped with things like predictive bidding, audience segmentation, and real-time optimization. But it was often hidden in the mechanics of platforms — automated, yes, but not something most strategists interacted with directly or creatively.

That all changed for me in 2022 with the emergence of generative AI, specifically large language models. For the first time, AI wasn’t just a background function. It became something I could actively engage with, guide, and build alongside. The moment I saw an LLM instantly digest a 100-page deck and return clear, concise insights in seconds, it completely rewired how I thought about work. It wasn’t just about efficiency, it was about unlocking entirely new ways of thinking, creating, and communicating.

That experience sparked something deeper: imagination. If a model could do this with static information, what else could it help us do across media planning, creative development, data synthesis, and client strategy? Suddenly, I wasn’t just thinking about AI as a tool. I was thinking about it as a new teammate that could scale intelligence, reduce friction, and help our team spend less time wrangling complexity and more time driving impact.

That shift, from background automation to active collaboration, is what pulled me into AI deeply. And from that moment on, I made it a priority to integrate AI not as an add-on feature, but as a foundational capability across our operations.

AI can be a game-changer for individuals and their responsibilities. Can you share how you personally use AI and what are your go-to resources or tools?

One of the most valuable ways I use AI personally is for strategic planning and roadmapping. Whether I’m mapping out a quarterly initiative or developing a vision for how to scale AI across different functions, I often start with a clear goal and prompt a LLM to generate multiple possible roadmaps for getting there. I’ll ask for different versions: one that’s aggressive, one that’s resource-light, one that prioritizes speed, and so on. This helps me pressure-test assumptions and consider angles I might not have thought of on my own. It’s like having a strategist in the room with me 24/7.

I also use AI to sharpen my communication skills. Breaking down complex ideas into simple, clear explanations is something I think every leader should be able to do. I’ll often use prompts like “Explain this like I’m five” or “Rewrite this for a non-technical audience.” This helps me test my own understanding and ensures that the messages I’m delivering, whether internally to the team or externally to a client, land clearly. I always come back to a quote attributed to Einstein: “If you can’t explain it simply, you don’t understand it well enough.” AI helps me hold myself to that standard.

Beyond those two primary use cases, I use AI to finalize campaign details, accelerate research, summarize articles, and reframe creative ideas based on different audience personas. I rely on tools like ChatGPT, Google Gemini, and Notion AI, depending on the task. But the magic is less so in the tools, and more so in the prompts. The better the question, the better the outcome.

Ultimately, AI has become a daily collaborator. It helps me move faster, think more broadly, and communicate more clearly.

On the flip side, what challenges or setbacks have you encountered while implementing A.I. into your company?

The biggest challenge hasn’t been the technology, it’s been the human side of transformation. The world is still in the early adoption stage of AI, and that means our teams aren’t just learning new tools — they’re learning an entirely new language, navigating a rapidly evolving landscape, and in many cases, rethinking how they do their jobs from the ground up.

That’s a heavy lift. AI requires people to shift from traditional, linear workflows to more iterative, collaborative ones. It forces people to ask better questions, engage more actively with systems, and become comfortable with ambiguity. And doing all that while keeping up with their day-to-day responsibilities is the biggest challenge.

At AXM, we’ve tried to meet that challenge head-on by building in structured support. We created an internal AI training track with department-specific sessions, 1:1 coaching, and live working sessions where teams can experiment in a low-stakes environment. But even with that, the hardest part is giving people the space to integrate what they’re learning into how they actually work. You can’t just bolt AI onto old processes. In some cases, we’ve had to completely rethink our tech stack and workflow design to truly unlock AI’s value.

Another challenge that doesn’t get talked about enough is the internal mindset shift. AI changes how decisions are made, how creative is developed, and how data is interpreted. For some team members, that’s energizing. For others, it can be disorienting. As leaders, we’ve had to over-communicate the “why” behind the shift and ensure people understand that AI isn’t a replacement but a force multiplier.

Let’s dig into this further. Can you share the top 5 A.I. tools or different ways you’re integrating AI into your business? What specific functions do they serve and what kind of result have you seen so far? If you can, please share a story or example for each.

At AXM, we’re not just experimenting with AI — we’re actively embedding it across our operations to increase speed, precision, and insight. Here are five of the most impactful ways we’ve integrated AI into our workflows, along with the outcomes we’ve seen.

1. Measuring and delivering incrementality with Agile Mix Modeling (AMM). Powered by AI and machine learning, AMM is designed to give clients real-time clarity into what’s driving campaign performance and contributing to business growth. Unlike traditional media mix models, AMM continuously ingests and analyzes data, using machine learning to uncover patterns and surface actionable insights. The data is automatically ingested at a daily cadence, allowing for faster, smarter optimizations across channels — enabling clients to reallocate budget dynamically toward the best-performing tactics and boost ROI throughout the campaign lifecycle.

For a CPG client, we used AMM to measure at a market level, uncovering an optimized media mix that drove a 49% incrementality during the campaign flight.

2. LLM-Powered Research Assistants Using OpenAI. We’ve deployed custom GPTs through OpenAI to function as research assistants across both client work and new business efforts. For new business, our models automatically ingest and organize source materials. Everything from company background to audience research and call transcripts allow our team to query the information directly as they build responses.

What used to take days of manual research is now reduced to hours. These AI assistants surface insights, flag gaps, and allow our teams to focus on higher-level thinking instead of document wrangling.

3. Workflow Automation with Make. We use Make.com to power workflow automation across departments, helping us eliminate bottlenecks and increase operational efficiency. In Ad Ops, for example, incoming tagging requests are automatically parsed, ticketed, and prioritized against our agency-wide queue with no manual entry required.

On the strategy side, our internal teams receive automated Monday morning updates via Slack, including one-page pacing summaries and key insights for each client. These time-saving automations reduce administrative overhead and ensure nothing slips through the cracks.

4. Client-Facing Knowledge Distribution with Google NotebookLM. Keeping clients up to speed on media trends is core to our value proposition. With Google NotebookLM, we’re able to organize complex POVs and “State of the State” reports in a more interactive and digestible format. The tool’s notebook-style interface makes it easy to structure content, while built-in features like mind mapping and audio podcast generation give clients new ways to absorb key insights.

For busy client stakeholders who can’t always sit down to read a 50-page deck, the podcast output has been especially impactful. It allows them to listen to strategic POVs on the go — turning dense reports into an on-demand audio experience.

5. Deeper Audience and Platform Intelligence from Partner Ecosystems/ AI isn’t limited to the tools we build, it also exists across the platforms and partners we work with. From audience intelligence via Resonate to more precise targeting from partners like Viant, we actively collaborate with vendors to stay ahead of their AI roadmaps and feature releases.

This proactive approach allows us to participate in early betas, integrate AI capabilities into client campaigns sooner, and unlock deeper insights around behavior, intent, and media performance. It’s a force multiplier amplifying our strategies with best-in-class external intelligence.

There’s concern about A.I. taking over jobs. How do you balance A.I. tools with your human workforce and have you already replaced any positions using technology?

We get the concern. Headlines make it sound like AI is here to replace everyone. But at AXM, we see it differently. We believe AI doesn’t replace people, it enhances what they can accomplish with scale and speed.

Our philosophy is simple: technology is only as powerful as the humans guiding it. We use AI and automation to eliminate repetitive, slow, and error-prone grunt work so our team can focus on what humans do best: creativity, strategy, and building real relationships.

Tools like Agile Mix Modeling™ (AMM) are a great example. Yes, AMM uses machine learning to automate data collection and modeling, but the “what now?” insights still come from our people. That human filter is non-negotiable. AI gives us speed and scale, but our people bring the soul and direction.

Have we replaced roles with tech? No. But we’ve evolved roles. Analysts now operate more like strategists. Media planners are more agile, creative, and impactful. In short, we’re not shrinking the team, we’re supercharging it.

Looking ahead, what’s on the horizon in the world of AI that people should know about? What do you see happening in the next 3–5 years? I would love to hear your best prediction.

In the next 3 to 5 years, AI won’t just improve how we work, it will fundamentally reshape how we interact with the digital world. One of the biggest shifts I see coming is the slow decline of the traditional, browser-based internet. As AI agents become more capable, they’ll begin acting as intermediaries between us and the web. We’ll rely less on scrolling through websites or searching through endless tabs, and more on intelligent agents that understand our intent and take action on our behalf.

Today’s digital experiences are largely built for eyeballs and designed to attract attention, generate clicks, and hold people’s focus on screens. But when AI agents are the ones “surfing” the web, that model breaks. These agents don’t care about beautiful landing pages or clever navigation, they care about efficiency, relevance, and results. They’ll scan the web, compare offers, summarize reviews, execute purchases, and book appointments without a person ever opening a browser.

This shift from “attention internet” to “agent internet” will disrupt the core mechanics of digital marketing. SEO, SEM, website UX. None of it will matter in the same way when a bot is the end user. And as AI agents begin to dominate digital interactions, the competition for human attention will move elsewhere.

That’s where I see a counterbalancing trend: a return to the real. I expect a resurgence in high-emotion, in-person experiences, experiential marketing, real-world activations, pop-ups, events, OOH, and even direct mail. Brands will need to create moments that agents can’t replicate: touch, context, nuance, and emotion. When AI is optimizing everything digital, the most powerful differentiators will come from what feels human, physical, and memorable.

We’re also going to see AI evolve from simply generating responses to executing complex, multi-step tasks. We’re already testing AI agents that can research a prospect, generate a personalized pitch, and draft an email without human input. But that’s just the beginning. Over time, these agents will coordinate cross-channel campaigns, manage creative testing, monitor performance, and even communicate directly with other agents. The internet won’t just be filled with human activity, it will be filled with AI agents negotiating, optimizing, and executing on behalf of their human counterparts.

In parallel, all components of AI, including machine learning, computer vision, LLMs, edge computing, and even hardware, will fuse into integrated systems that are far more intelligent and autonomous. These systems will be capable of managing long-term, multi-step projects, reacting to real-time data, and adapting to business goals on the fly. That’s not science fiction. It’s already starting to take shape in enterprise workflows.

So what’s my boldest prediction? Within five years, we won’t go to the web. Instead, the web will come to us, mediated through intelligent agents. Just as we no longer visit physical libraries to find information, relying instead on intelligent systems that retrieve and synthesize content for us, we’ll soon experience the internet in a similar way. The winners in that world won’t be the ones with the best websites or the best ads — they’ll be the ones with the best orchestration between people and AI, and the best real-world presence to create emotional, lasting connections when everything else becomes invisible.

If you had to pick just one AI tool that you feel is essential, one that you haven’t mentioned yet, which would it be and why?

One tool I’m keeping a close eye on is Google NotebookLM. At first glance, it might seem like another version of ChatGPT, but it offers a few standout features that set it apart, especially for people like me who consume information from a wide range of formats.

One of the most valuable features is its ability to pull in YouTube videos as source material. I often rely on long-form videos like interviews, roundtable discussions, product demos, and conference panels to stay current. Being able to feed that video content into an LLM opens up a completely new way to interact with spoken information. Instead of passively watching and taking notes, I can now query the content directly and extract insights on demand.

Another feature I really appreciate is the Mindmap view. As a visual thinker, it’s incredibly useful to see how all my data sources connect. The tool automatically builds a visual map that links key ideas, topics, and references across your uploaded materials. It helps you understand patterns, spot gaps, and structure your thinking in a more intuitive way.

The Audio output feature is another standout. You can convert any output into a podcast-style dialogue where two AI voices discuss the material. At AXM, we’ve used this to repackage dense presentations like QBRs, POVs, or State of the State decks into short audio segments. It’s a simple but powerful way to meet clients where they are and give them another way to absorb information — especially when they’re on the go or in between meetings.

NotebookLM may still be evolving, but its versatility and focus on multimodal interaction make it one of the most exciting tools in the space right now. It’s a great example of how AI can move beyond speed and efficiency, and start enhancing comprehension and accessibility too.

For the uninitiated, what advice would you give someone looking to integrate AI into their business and doesn’t know where to start?

Start small, but be intentional. One of the biggest pitfalls companies face when approaching AI is thinking of it as a quick add-on rather than a shift in how the business operates. You don’t need to overhaul everything at once, but you do need a clear foundation: where AI fits, what problems it can solve, and how your people will use it.

At AXM, we recommend starting with four simple focus areas:

1. Understand where AI fits: Begin by understanding how AI already interacts with your business. In paid media, for example, AI is now a factor in planning, buying, analytics, and reporting. Ask your internal teams and external partners to explain where AI is already at work. The goal is to bring visibility to what’s often running quietly in the background.
2. Define clear use cases: It’s easy to get distracted by the flashiest AI tools, but the real value comes from solving meaningful problems. Start by identifying whether you’re trying to save time, improve accuracy, or unlock new creative approaches. Focus on a single use case that matches your business needs whether that’s automating reporting, generating content, or improving personalization.
3. Take inventory of your tools: Take a step back and look at the technology and workflows already in place across your business. Many teams are using tools with AI baked in — sometimes without even realizing it. Whether it’s automating data analysis, generating reports, or optimizing content, there may already be AI capabilities at work under the hood. The key is to assess how these tools are being used, what value they’re delivering, and whether they’re helping you move toward your broader goals. Once you know what’s working (and what’s not), you can make more informed decisions about where to go next.
4. Prepare your people and processes: AI isn’t just a tech upgrade, it changes how teams work. You’ll need to think about how your team will engage with AI-generated outputs, how decisions will be made with automation in the mix, and how you’ll keep skills and knowledge up to date. Training and experimentation are essential, not optional.

To get started, ask yourself and your media team a few core questions:

• How are we using AI today in planning, buying, or analytics?
• Where are the biggest opportunities to improve efficiency?
• What areas offer room to innovate?
• Are we using purpose-built AI platforms or patching things together?
• How are we staying up to speed with how fast AI is evolving?

You don’t need to have every answer right away. Just start with one team, one process, or one tool and build from there. The businesses that succeed with AI aren’t necessarily the fastest — they’re the ones who integrate it with intention and build the capabilities to scale it in a way that works for their people.

Where can our readers follow you to learn more about leveraging A.I. in the business world?

https://www.linkedin.com/in/johnbmcgrane/

AXM (Ars X Machina) | LinkedIn

This was great. Thanks for taking time for us to learn more about you and your business. We wish you continued success!

About the Interviewer: Chad Silverstein is a seasoned entrepreneur with 25+ years of experience as a Founder and CEO. While attending Ohio State University, he launched his first company, Choice Recovery, Inc., a nationally recognized healthcare collection agency — twice ranked the #1 workplace in Ohio. In 2013, he founded [re]start, helping thousands of people find meaningful career opportunities. After selling both companies, Chad shifted his focus to his true passion — leadership. Today, he coaches founders and CEOs at Built to Lead, advises Authority Magazine’s Thought Leader Incubator.

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John McGrane Of Ars X Machina (AXM): How We Leveraged AI To Take Our Company To The Next Level was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.