After 25 years leading digital transformation for Fortune 500 clients including Canada Post, CIBC, WPP, and Invesco, Karla Congson spotted an opportunity. Enterprise organizations were transforming their operations with AI, and she recognized that mid-market companies needed access to the same capabilities. Enter Agentiiv: the agentic AI company she built to bridge the gap.
Today, Agentiiv is changing how mid-market businesses use AI and is helping them grow and stay competitive in a world where technology is changing the landscape (and fast!). It’s also become the official technology partner of the Vector Institute and Karla has been named one of Canada’s Top 50 Women Over 50 and an RBC Women of Influence Entrepreneur Award winner in the “Ones To Watch” category.
In this interview, Karla shares what women in business need to know about AI, how to navigate the technical landscape without a computer science degree, and why the future of AI depends on who gets to build it.
1. Women business leaders are hearing conflicting messages about AI. Some say it’s overhyped, others say it’s urgent. What should women in leadership positions prioritize when it comes to AI strategy?
My recommendation is to start with AI literacy for yourself, not because you need to become a data scientist, but because strategic decisions require understanding of a given subject.
Spend three months getting your hands dirty with AI tools. Test something, break something, and see where the technology adds value versus where it’s just expensive automation.
Then identify one high-impact use case where AI can solve a problem your organization is facing. Look for repetitive, high-volume work that drains your team’s capacity then let AI handle those tasks so your team can focus their day to day on things that require judgment, relationship building, and creative problem-solving.
I often see leaders treating AI as an IT initiative when it should be viewed as a business transformation that happens to use technology. Your competitive advantage won’t come from the models you choose but from understanding your operations well enough to know exactly where AI creates leverage and having the courage to redesign workflows around that reality.
2. Women business leaders often face different challenges when implementing new technology. What should women in leadership positions understand about navigating AI adoption that’s specific to their experience?
Women leaders sometimes face more scepticism when championing technology initiatives, especially something as technical as AI. That scepticism rarely shows up as overt bias. It comes through in the meeting where someone suggests bringing in a technical expert to validate your recommendations, or the board member who directs technical questions to your male colleagues instead of you. My advice is to know the technology deeply, not at a surface level but thoroughly enough that you can challenge vendor claims, understand implementation implications, and make technical trade-offs confidently.
One advantage women have is understanding the human side of technology adoption. The technical piece is straightforward compared to getting people to trust and use AI in their day to day.
Use that strength deliberately. While other leaders focus purely on technical capabilities and efficiency gains, you can build AI strategies that account for adoption challenges upfront. Frame it as change management, risk mitigation, or adoption strategy and make it clear you’re solving the hard business problem that determines whether AI initiatives succeed or fail.
Trust your judgment about what problems AI should solve in your organization. You know your business, your team, and your customers better than anyone pitching you solutions. If something feels like technology for technology’s sake, or if an AI implementation would damage relationships that drive your business, trust that instinct. The best AI strategy isn’t the most technically sophisticated one but rather the one that solves real problems without creating new ones.
3. Agentiiv is focused on democratizing AI access, particularly for mid-market organizations. Why does access to this technology matter beyond just competitive advantage?
Mid-market companies are the backbone of Canada’s economy, and they deserve access to the same tools that are redefining productivity for larger organizations. The brilliant teams at mid-market firms don’t suddenly become less capable because they operate with different budgets and team structures. They just need access to the same capabilities.
There’s something bigger at stake here as well. Who gets to shape how AI works matters tremendously. If AI development and access remains concentrated in large tech companies and well-funded enterprises, a relatively small group of people will make decisions that reshape how everyone works. We need diverse organizations solving diverse problems with AI to ensure the technology serves a broader range of needs.
4. You chose to bootstrap Agentiiv instead of raising venture capital. What does that decision reveal about how you think about building an AI company differently?
The conventional path pushes companies to raise money, scale fast, dominate the market, and worry about profitability later. That model works for some companies, but it also creates pressure to prioritize growth metrics over customer success.
Bootstrapping forced us to focus on delivering genuine value from day one. We’re approaching some major financial milestones with zero outside funding because every dollar we’ve made came from clients who saw measurable results and decided to expand or refer us. That feedback loop keeps you honest about whether your product works.
This approach also lets us maintain the culture that defines Agentiiv. For us, and at least right now, staying independent has meant we’ve gotten to define success on our own terms. We’re proving we can build a technically sophisticated AI company that competes with well-funded players while maintaining values like transparency, accessibility, and genuine client partnership.
5. What are you seeing in how organizations approach AI adoption that makes the difference between success and struggle?
Organizations that succeed treat AI adoption as a change management challenge alongside the technology deployment. They spend significant time helping people understand what AI will and won’t do because people need to trust AI before they’ll use it.
That trust comes from transparency about what the system does, clear boundaries around where humans still make decisions, and proof that AI enhances their work rather than threatening it. I’ve seen companies invest substantial resources in sophisticated AI tools that sit unused because leaders focused entirely on the technical implementation without addressing the human side of the equation.
Successful organizations also start with practical, high-value use cases. They automate the repetitive work that everyone complains about but that nobody wants to do, proving AI works in low-risk environments before tackling complex, judgment-heavy processes. This builds confidence and organizational understanding gradually. Speed of iteration matters more than perfection. The organizations making real progress with AI right now are running dozens of small experiments, learning what works for their specific context, and moving on quickly when something doesn’t deliver results. They’re building organizational muscle around AI adoption through hands-on experience rather than endless planning cycles.