“How are business leaders practically using data science or AI in their marketing today, and what’s one mistake you commonly see companies make when adopting these tools?”
Here is what 5 thought leaders had to say.
Clean Data First, Let AI Sharpen Decisions
I see business leaders using data science and AI very tactically in marketing today. We use it to understand buying patterns, predict which products will trend next, and personalize experiences across email, paid ads, and on-site recommendations. AI helps us test creatives faster, optimize ad budgets daily, and identify high-value customers instead of marketing to everyone the same way. It’s less about flashy tech and more about making better, faster decisions.
The biggest mistake I see companies make is treating AI as a plug-and-play solution. They buy tools before fixing their data or defining what success actually looks like. If your customer data is fragmented or your messaging is unclear, AI just scales those problems. The brands winning with AI start small, focus on clean data, and use it to support human judgment—not replace it.
Nicolas Falourd, CEO, Cyber Techwear
Practical Reality of AI in Modern Marketing
I see business leaders using data science and AI in very practical, everyday ways, especially in personalization and timing. We use AI to analyze which quotes resonate with specific audiences, what tone performs best on different platforms, and when content is most likely to drive engagement. Many companies are also leveraging predictive analytics to refine ad targeting, optimize email subject lines, and test creative variations faster than ever before.
The most common mistake I see is adopting AI without a clear strategy or human oversight. Some brands expect AI to magically fix their marketing without first defining goals, cleaning their data, or understanding their audience. AI works best as a decision-support tool, not a replacement for judgment or creativity. When companies align AI insights with real customer empathy, that’s when marketing truly becomes more effective.
Tim Nolan, Co-founder, Quoteplicity
Turning User Behavior Into Better Decisions
I see business leaders using data science and AI in marketing in very grounded, practical ways. Most aren’t chasing buzzwords; they’re using AI to spot patterns in user behavior, predict what questions customers will ask next, and improve conversion paths. In our space, that looks like analyzing renovation cost searches, comparing project types by location, and adjusting messaging based on how confident or early-stage a homeowner appears. That kind of insight helps marketing feel more helpful than promotional.
The biggest mistake I see is companies expecting AI to “fix” weak positioning. If your value proposition isn’t clear, AI won’t magically make your marketing better, it will just optimize the wrong message faster. Tools work best when leaders define the problem first, then apply AI to refine accuracy, relevance, and timing. The human strategy still has to come before automation.
Jason Keeley, Founder, Bath Calculator
Blend Human Judgment And AI For Impact
Marketing leaders today are using data science and AI to make outreach smarter, not just faster. At Simply Noted, we analyze customer engagement data to understand which messages resonate, when to send handwritten notes, and which audiences are most likely to respond. AI helps us scale personalization in ways that would be impossible manually, turning each note into a meaningful touchpoint rather than a generic email blast.
A common mistake I see is over-relying on AI without tying it to real business goals. Companies sometimes assume the technology alone will drive results, but without clear strategy and quality data, AI can actually mislead decisions. The key is blending human judgment with data-driven insights for campaigns that feel thoughtful and effective.
Rick Elmore, CEO, Simply Noted
Start With Strategy And Data, Then Scale
I see firsthand how business leaders are leveraging data science and AI in marketing to make smarter, more impactful decisions. Today, companies use AI to analyze customer behavior, personalize content at scale, optimize ad spend, and even predict future buying trends. For example, machine learning models help us tailor email campaigns based on what art styles or artists visitors engage with most—boosting conversions and deepening relationships.
One common mistake I see is adopting tools without a clear strategy or quality data foundation. Too many teams adopt flashy AI solutions without first defining what success looks like or ensuring their data is reliable. Without clean, organized data, even the best AI tools produce misleading insights. My advice: start with a focused goal, invest in data hygiene, and then scale your AI use cases thoughtfully.
THERY Jean Christophe, CEO, MUSAARTGALLERY