Artificial Intelligence is reshaping marketing and customer-focused roles, offering unprecedented opportunities for businesses. We asked 18 thought leaders about their key lessons learned from successfully integrating AI into their strategies. Discover how to effectively balance AI’s efficiency with human expertise, personalize ethically, generate AI-based images and other content, and strengthen customer-focused approaches in this rapidly evolving landscape.
Let’s explore:
AI Transforms Possibilities in Real-Time Marketing
The lesson we learned is that AI changes how we think about what is possible. Before AI, we worked in cycles such as creating campaigns, looking at results later, and making changes for the next time. Now, we can adjust in real-time based on what’s working right now.
In email marketing, my team was sending the same message to everyone. Now, AI can personalize emails based on customer behavior, and our open rates jumped from 21% to 47%. Surprisingly, our team has become more creative because AI handles repetitive tasks like sorting data and scheduling. This allows them to focus on creating content and connecting with customers.
We also realized we were not fully utilizing our customer data. AI helps us spot trends early, such as when it found a group using our tools in a new way, which led to a new service now bringing in 15% of our revenue.
My advice is to rethink your processes when adopting AI. Don’t just use it to speed up old ways; ask yourself what you could do differently with the right tools, then use AI to make it happen.
Kevin Baragona, Founder, Deep AI
Integrate AI for Seamless Automated Systems
One of the biggest lessons I’ve learned from adopting AI is that you get the most value when you actually connect everything together and make it part of your daily workflow—not just by using a tool in isolation. In my day job managing multimillion-dollar ad budgets, bringing in n8n for workflow automation has changed the game for us. We use n8n to automate Google Ads bidding, adjust budgets on the fly, create new ads, manage content, and generate reports—all with minimal manual input. It’s freed us up to focus on strategy and creativity instead of routine tasks.
I’ve seen the same thing with content. On my own project, everything from sourcing news and drafting articles to image generation and SEO happens through automated AI workflows built in n8n. This setup lets us produce a high volume of quality content with a small team.
The main takeaway for me: the real impact of AI shows up when you use tools like n8n to tie everything together into seamless, automated systems. That’s when you see huge gains in speed, consistency, and efficiency, and your team actually gets to work on the things that matter most.
Enes Karaboga, Head of Content, Caracal News
Combine AI Research with Human Expertise
Getting real results from AI starts with understanding that it only performs as well as the guidance you give it.
We experienced this firsthand when we began experimenting with AI-generated content for clients in highly technical industries. Initially, we believed the volume AI could produce would be a significant advantage. We could create pages faster than ever, optimized to match search intent and semantically relevant. Everything appeared perfect on paper. However, when we reviewed the performance after a few weeks, the engagement numbers were stagnant. It wasn’t that the content was poor—it simply lacked edge. It read as if it were written to check boxes rather than to connect with people or demonstrate any real understanding of the subject.
So we adapted our approach. Now, we treat AI more like a research assistant than a writer. We use it to analyze SERPs, identify competitive gaps, and accelerate the drafting process—but the voice, direction, and angle still come from a strategist or subject-matter expert. For one cybersecurity client, this shift made a dramatic difference. The moment we combined AI-generated outlines with human-written narratives drawn from actual conversations with the client’s team, both rankings and conversions improved significantly.
Paul DeMott, Chief Technology Officer, Helium SEO
Balance AI Efficiency with Human Oversight
The biggest lesson was this: Let AI do what it’s best at—but don’t lose the human touch.
AI is a tool, not magic. It’s like Excel: powerful, but only if the person using it knows what they’re doing. As a marketing agency owner, we were early to experiment with tools like ChatGPT back in late 2022. At first, it was just curiosity—playing around, no structure.
In 2023, we used AI more regularly, mostly for research, grammar checks, and idea generation. Still casual. We hadn’t built it into our workflows yet.
The real shift came in 2024, when we fully integrated AI into our processes. We now use it for social media content, SEO articles, ad copy, email drafts—you name it. But always with clear guidelines and human oversight. AI helps us move faster and produce more—but the final product still needs human input to feel real and relevant.
Heinz Klemann, Senior Marketing Consultant, BeastBI GmbH
Align AI with Customer Value, Not Processes
One lesson stands out: AI only works when it serves your customer, not your process. Early on, we used AI to improve internal reporting and automate audience segmentation. It made us faster, but not better. Speed alone didn’t move customer engagement. The turning point came when we shifted focus from internal efficiency to customer value. We trained AI to prioritize customer behavior, not just historical data. That meant feeding real-time data into models that could adjust messaging based on user context, not just personas.
We saw the difference quickly. When AI-supported messaging aligned with user actions, such as timing a trade-in offer when battery levels dropped, response rates jumped. But the bigger win was in reducing irrelevant communication. AI gave us clarity on when not to message, which built trust. The lesson wasn’t to automate more; it was to listen better. AI made that scalable, but only after we trained it on what customers valued, not what we wanted to promote.
This shift required tighter collaboration between marketing, data science, and product teams. The technology alone didn’t solve anything. The gains came from shared definitions of success, faster iteration cycles, and keeping customer friction low. AI helped, but the insight came from treating it as a listening tool, not a megaphone. That mindset changed how we use every new technology. We now start with what earns trust, not what grabs attention. The results are durable, and the customer signals are clearer.
Alec Loeb, VP of Growth Marketing, EcoATM
Fine-Tune AI on Brand Voice and Culture
One key lesson: AI works best when it’s trained on your voice, not just your data—especially in marketing and customer-facing roles.
Off-the-shelf models can generate content, segment audiences, or analyze feedback—but if they don’t reflect the brand’s tone, values, and context, the results feel off. Early on, using generic AI led to campaigns that technically performed adequately, but didn’t build trust or emotional connection.
The shift came when marketing teams started fine-tuning AI tools on real conversations, customer emails, sales calls, and support chats. Suddenly, the output felt human, relevant, and aligned. That lesson—train AI on your culture, not just your content—has been a significant one.
Vipul Mehta, Co-Founder & CTO, WeblineGlobal
Personalize Ethically with AI in Marketing
One of the biggest lessons we’ve learned is the power of hyper-personalization in marketing balanced by ethical AI practices. We worked with a retail client who used our AI agents to deliver real-time, personalized product recommendations. We achieved a 30% increase in conversion rates.
What really stood out was how quickly customers respond to relevance. When done right, it builds trust and drives results. That said, we also saw the importance of getting the ethics right. To maintain transparency and avoid bias, we built in rigorous testing and monitoring across every touchpoint.
My advice for leaders: use AI to make your customer experiences more personal, but never let personalization come at the cost of trust. Ethical AI is a brand asset.
Alexander De Ridder, Co-Founder & CTO, SmythOS.com
AI Enhances Efficiency, Humans Provide Authenticity
The most significant lesson I have learned from adopting AI is that the human touch remains more crucial than ever. While AI can accelerate content creation and manage data-intensive tasks, it struggles with authentic storytelling that resonates with people. Marketing leaders should remember that AI is a tool for enhancing efficiency, not a substitute for genuine creativity.
Utilizing AI to draft SEO-optimized articles allows my team to dedicate more time to refining tone and aligning content with customer emotions. Instead of manually writing every word, we let AI outline and generate drafts quickly. Then, we incorporate brand voice and insights that AI cannot capture. This balance has improved both productivity and engagement without sacrificing the warmth readers desire. AI liberates marketers from repetitive work but does not replace the emotional intelligence necessary for creating genuine customer connections.
Adam Yong, SEO Consultant & Founder, Agility Writer
AI Accelerates Execution, Not Strategic Planning
The biggest lesson we’ve learned? AI won’t fix a broken strategy. When we first adopted tools like ChatGPT, the temptation was to throw it at everything—blogs, social captions, even ad headlines. But we quickly realized that if the input is vague or the strategy is fuzzy, AI just makes bad ideas faster. It’s a scalpel, not a magic wand. Now, we use AI to accelerate execution—but strategy, positioning, and voice still come from humans who understand the client, the market, and what actually moves the needle.
Patrick Carver, CEO & Founder, Constellation Marketing
Test and Refine AI Integration Gradually
What I learned from integrating AI into our workflows is that you need to start small and scale smart. It sounds obvious, but it’s where most teams mess up. There’s a rush to automate everything at once, especially in digital marketing where tools promise faster audits, smarter keyword mapping, or predictive bidding. But if you don’t understand how one small AI task behaves inside your actual daily operations, everything breaks when you scale it. You get inconsistencies, lost data, or processes that become harder to fix because no one remembers what was changed.
We tested using Screaming Frog’s API combined with a custom layer built on top of OpenAI to speed up internal SEO audits during client onboarding. The goal was to flag technical issues before a strategist even touched the site. On paper, it looked solid, but once it went live, the system missed context-heavy elements like JavaScript rendering and hreflang tags for multilingual setups. These gaps directly affected the quality of the audit, which led to incorrect recommendations and confusion during client handoff.
That forced us to decide between pushing for speed and risking flawed outputs or slowing down to rebuild a system that matched the complexity of our work. We added manual checkpoints, retrained the inputs using specific edge cases, and refined the tool around how our team actually works. The pilot gave us the clarity to scale with control instead of assumptions, and it saved us from embedding errors into every client audit.
Dorian Menard, SEO Strategy Director and Founder, Search Scope
Prepare Teams for Effective AI Implementation
After implementing AI in our marketing process, I realized that it is a tool that only works when your team is ready for it. Simply demonstrating a new tool and saying “get to work” will not produce quality results. Some people will rely too heavily on artificial intelligence, while others will refuse to use it at all. Therefore, you first need to conduct training for the team, explaining why AI is needed for their work and how it can improve and potentially harm it.
You should also discuss aspects such as the ethics of use, cybersecurity, and the sphere of influence (i.e., what controls AI and what is human). This will help to avoid unnecessary stress and the creation of monotonous, uninteresting content. Ultimately, each team member will understand how to use AI to avoid problems and maximize profit. Once the training is complete, you will have a team that knows how to act, does not rely solely on generated content, and looks for ways to make their work easier (rather than simply letting the tool perform tasks).
Serhii Antropov, Head of Marketing, 3DModels
Gather Employee Input Before Adopting AI Tools
One lesson that I have learned is the importance of input. If you are considering adopting AI tools for your employees to use, it’s invaluable to actually talk to those employees about it first. You might think a certain tool would be helpful, but your employees can inform you about the reality in which that tool could actually add an extra burden to their workload. Getting input has helped us to be very intentional about our AI usage, ensuring that nothing feels unnecessary.
Edward Tian, CEO, GPTZero
Scale Brand Personality Through AI-Powered Interactions
One significant lesson we learned from adopting AI is that one of its unique strengths lies in combining efficiency with a distinct human personality.
For our client in the online travel guide industry, we developed “Chat NT,” an AI chatbot powered by OpenAI technologies, designed to transform a traditional tourism website into a personalized travel assistant.
What made the difference was giving Chat NT a unique voice—modeled after Australian celebrity Abbie Chatfield—which turned routine queries into engaging, relatable conversations. This personality made users feel like they were chatting with a knowledgeable friend, not a cold machine.
We also balanced creativity with responsibility by implementing content filters to keep interactions safe and on-brand, while training the AI specifically on the client’s curated travel data. This ensured recommendations were relevant and trustworthy.
The results spoke volumes: users spent 70% more time on the site, engagement soared, and the campaign reached over 1.5 million people on social media.
Most importantly, travelers received personalized itineraries and seamless booking options, making trip planning effortless.
The key takeaway for marketing leaders: Use AI not just to automate tasks but to scale your brand’s personality and deepen customer connections. AI should amplify human warmth, not replace it.
Gursharan Singh, Co-Founder, WebSpero Solutions
Prioritize AI Training and Responsible Use
AI has become a hot topic recently, and the fear of missing out (FOMO) on AI is even more prevalent, especially among SaaS companies. The issue is that most companies are diving in headfirst without assessing their true needs and AI capabilities.
While we have been more methodical with AI integration initiatives, having responsibly introduced AI into the product before the AI frenzy began, we have gained some insights, particularly within marketing, that could be valuable to other companies.
Adding a ChatGPT Teams account and creating prompt libraries may seem like an easy win to ease teams into learning and using AI in their jobs. However, it can be an enormous waste of time and resources if buy-in hasn’t already been established and at least basic training hasn’t been prioritized. People may hesitate to use the libraries and, depending on the industry, may fear that AI could potentially eliminate their positions.
Solution:
1. Establish buy-in. The C-suite should give basic approval for employees to use AI with company-approved tools.
2. Adequately train employees on basic AI/data literacy and responsible use.
3. Train employees on AI prompting skills, as prompting and getting the best output from AI is a highly iterative process.
4. Hold workshops and lunch & learns to help employees determine which tasks to entrust to AI first.
5. Stress the importance of having a “human-in-the-loop” at every step of the way.
Lea Thomas, SEO and LLM Visibility Optimization | AI Consultant, Teachable
Simplify AI-Generated Content for Better Engagement
We rolled out AI to clean up quote presentations and speed up plan matching. It undoubtedly got faster. However, the drop-off rate then increased. People started bouncing off the quote screens more quickly than before. Why? Because the AI-generated summaries assumed clients read the entire thing. They did not. One plan’s rate breakdown was buried under auto-generated comparisons that were technically accurate but visually unengaging.
So we cut 70% of the content and made the AI produce just two questions: “How long will this take to set up?” and, “What will I save in the first 30 days?” Conversion increased. Our time-on-page decreased, but our call-to-sign ratio climbed from 14% to 21%. People want straightforward answers. AI tends to over-explain.
Teach your AI to communicate like a person who only has 15 seconds to explain a deal. Otherwise, it writes to impress, not to sell.
Benjamin Tom, Digital Marketing Expert and Utility Specialist, Electricity Monster
Leverage AI to Strengthen Customer-Focused Strategies
One of the biggest lessons from adopting AI in marketing is that speed means nothing without relevance. Initially, AI was used to scale content like emails, social posts, and ad copy. As a result, output increased rapidly, but performance did not improve. Click-through rates declined, bounce rates remained steady, and engagement stayed flat.
The mistake was not in the technology itself, but in treating AI like a content machine instead of a strategic tool.
Things began to change when AI prompts were redesigned around real customer signals. These included pain points from sales calls, shifts in cost-per-lead, and patterns in campaign performance. Instead of asking AI to write something generic about a new service, the prompts became more specific and tied to what certain audiences actually care about. This approach made the output sharper and more aligned with what people want to hear.
AI works better in customer-facing roles when it’s used to stress-test messaging before it goes live. For example, feeding a landing page into a model and asking it to respond like a skeptical CFO or a busy ops lead can surface blind spots early. While not perfect, this method is superior to guessing. It transforms AI into a feedback engine, rather than just a content generator.
The main takeaway is that AI doesn’t replace strategy. Instead, it strengthens strategy when built on real context.
Josiah Roche, Fractional CMO, JRR Marketing
AI Amplifies Human Insight in Sales
One of the most significant lessons I have learned from adopting AI is that AI doesn’t replace human insight; it magnifies it.
In marketing and sales, speed and precision are everything. We built our GTM-1 Omni platform to integrate agentic AI into every part of our outbound process, from identifying in-market buyers using buying intent signals to personalizing messaging frameworks in real-time. But what really stood out was how this technology didn’t eliminate the need for human judgment; it sharpened it.
When our sales representatives get AI-curated talking points tailored to buyer behavior, their conversations become stronger, not scripted. When marketers receive feedback loops from every campaign we run, their decisions become faster, not riskier. AI gave us scale and clarity, but the human element still drives the connection. That synergy is where real growth happens, and that’s the lesson that has shaped how we go to market every day.
Vito Vishnepolsky, Founder and Director, Martal Group
Balance AI Analysis with Human Expertise
The most transformative lesson we’ve learned from our AI journey is that artificial intelligence must solve real business problems rather than simply being deployed as a shiny new technology.
At our core, we’re a matchmaking service connecting eCommerce businesses with the right 3PL partners. Early in our AI adoption, we became enchanted with advanced algorithms that could theoretically make perfect matches, but we quickly discovered that technology without human context creates more problems than solutions.
For example, we initially built an AI recommendation engine that prioritized low shipping costs above all else. The data looked great on paper, but we saw a spike in customer complaints about service quality. The lesson? Our AI wasn’t accounting for the nuanced relationship factors that make 3PL partnerships successful.
We’ve since evolved to a hybrid approach. Our AI now ingests and analyzes thousands of data points across shipping performance, geographic specialization, and vertical expertise, but the final recommendations incorporate human expertise. This balance has increased successful matches by 37% and dramatically improved customer satisfaction scores.
For marketing and customer-focused leaders specifically, I’d emphasize that AI works best when it augments rather than replaces the human touch. When we implemented AI-powered customer service tools, we specifically designed them to handle routine inquiries while escalating complex issues to our specialists. This approach freed our team to focus on high-value interactions while still maintaining our personal touch.
The lesson is clear: successful AI adoption requires starting with the customer problem, not the technology. The moment you flip that equation is the moment you lose sight of what truly matters in your business.
Joe Spisak, CEO, Fulfill.com
