AI is no longer a concept on the distant horizon—it’s actively reshaping how companies operate, scale, and grow. From optimizing workflows to creating new revenue streams, its impact is real and accelerating.
This is a notion Nathan Yeung explored at TechExit.io Vancouver, where he shared how AI is driving business growth through efficiency, innovation, and smarter decision-making. Drawing on his work with Constellation Software, Procom, and Robert Herjavec, he stressed that success with AI is not about following trends, but about applying a clear, data-driven strategy.
Keep reading to discover Nathan’s AI-forward approach—designed to ensure AI delivers tangible, measurable value, whether you’re streamlining workflows or scaling personalization. It’s your guide to harnessing the power of AI for real, lasting success.
Key takeaways:
- Automate for efficiency: Free up time by offloading repetitive tasks.
- Personalize at scale: Use AI to deepen customer engagement.
- Make faster decisions: Leverage data to stay agile and competitive.
There’s no shortage of hype around AI—but for founders and business leaders, the real challenge is cutting through the noise to find what actually works. How do you avoid falling behind—or investing blindly?
At TechExit.io Vancouver 2025, Nathan Yeung, founder of Find Your Audience, shared a practical, strategy-first view of how AI is reshaping business growth. Drawing from his experience with companies like Constellation Software, Procom, and Robert Herjavec’s team, Nathan offered a clear framework for making AI a real driver of value—not just a buzzword.
In this article, we’ll dive into Nathan’s 4 step approach and show you how to leverage AI to scale smarter and grow faster—whether you’re just getting started or looking to optimize your existing efforts.
Step 1: Automate to Innovate
For many businesses, the most immediate and impactful way to leverage AI is through automation. But this isn’t about replacing jobs—it’s about removing repetitive, manual tasks that slow teams down, so employees can focus on higher-value work that drives innovation and growth.
As companies scale, these bottlenecks compound, making automation not just a nice-to-have, but a competitive necessity.
Nathan Yeung emphasized the importance of starting small:
“Look at the 5 to 10 things your team does every week that don’t require deep human judgment. That’s your entry point.”
Once identified, these low-complexity tasks become prime candidates for AI-driven automation. Practical examples include:
- Customer support: AI chatbots that handle FAQs, route support tickets, and reduce response times.
- Hiring and onboarding: NLP-powered tools (Natural Language Processing or NLP is a branch of AI that enables computers to understand, interpret, and generate human language) that can be used to screen resumes, generate job descriptions, and streamline training.
- Marketing operations: Automated segmentation, performance analysis, and even content creation using tools like Jasper or HubSpot’s AI features.
The impact is measurable. Companies that embrace these kinds of micro-efficiencies are already reporting up to 30–40% gains in productivity, according to Nathan. This reclaimed bandwidth allows teams to redirect focus toward strategic initiatives.
Yet despite the promise, few companies are fully prepared. Research from the Infosys Knowledge Institute found that while organizations expect an average 15% increase in productivity from AI, only 2% are truly ready across critical areas like talent, strategy, governance, data, and technology.
This readiness gap highlights the need for a broader, more strategic approach—especially as businesses begin to explore personalization at scale, the next frontier in leveraging AI for competitive advantage.
Step 2: Personalization at Scale
As businesses grow, maintaining a personalized experience across a large customer base becomes nearly impossible—unless AI is in the equation. With the right tools, companies can tailor their offerings at scale, increasing relevance and conversion without increasing manual effort.
Nathan shared that “only 1 in 3 marketers today are using AI for hyper-personalization.” That means there’s a massive opportunity for those who move early.
Industry-tested personalization tactics include:
- Recommendation engines: E-commerce and SaaS platforms can serve tailored product suggestions based on browsing or purchase behavior.
- Dynamic pricing models: Adjust prices in real time based on demand, competitor pricing, or customer segmentation.
- AI-generated content: Use customer data to customize email, ad, or website copy for specific segments.
Step 3: Build a Data-Driven Decision Engine
AI is only as good as the data behind it. Clean, structured, and actionable data is foundational for every AI use case, from predictive analytics to personalization.
“If your data is a mess, your AI will be too,” Nathan warned. He encouraged companies to treat data like infrastructure: invest in it early, keep it organized, and build governance around it.
To lay the groundwork:
- Audit your data sources: Understand where your data lives, how it’s collected, and whether it’s current and reliable.
- Create strong governance policies: Standardize metrics, naming conventions, and access permissions across departments.
- Establish feedback loops: Ensure AI tools aren’t just reading data—they’re learning from it and improving over time.
With strong data hygiene, businesses can confidently deploy predictive analytics—forecasting trends, customer churn, or supply chain risks before they become costly issues. In Nathan’s words, “AI enables you to react in real time—but only if the data behind it is trusted.”
Step 4: Prepare Teams, Not Just Tech
Many companies underestimate the human side of AI transformation. But as Nathan noted, “The conversation isn’t just about tools. It’s about people, culture, and mindset.”
New roles are emerging—AI marketing strategists, sales enablement analysts, AI compliance officers—but the real challenge is integrating AI into everyday workflows. Companies need to upskill current employees while creating a culture that embraces experimentation.
Practical ways to prepare your team:
- Host internal workshops: Teach teams how AI tools work and how to collaborate with them.
- Create cross-functional pilots: Test AI in marketing, sales, or ops, and let different departments share results.
- Reward curiosity and experimentation: Encourage employees to identify areas where AI could help their role.
Nathan also highlighted that AI readiness is increasingly part of investor conversations. “In M&A deals, we’re seeing diligence questions around AI capabilities, adoption, and the scalability of internal systems. It’s becoming a valuation driver.”
What to Do Now
To ensure AI delivers measurable value—not just theoretical promise—Nathan Yeung recommends this focused approach:
- Identify one high-friction workflow where automation could reduce time or cost.
- Clean and structure your data to lay the groundwork for deeper AI insights.
- Pilot personalization tools in your customer journey—email, website, or onboarding.
- Upskill your team through training, experimentation, and knowledge sharing.
- Track and iterate—build dashboards that monitor AI performance like any other metric.
“The companies that treat AI as a core business function—not an experiment—are the ones scaling faster and selling for more,” Nathan concluded.
Whether you’re a founder preparing for exit, a mid-stage operator trying to scale, or an investor looking for efficiency levers, AI is transforming how value is created—and measured.
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