Just Because AI Makes It Easy to Build Doesn't Mean You Should
About this session
Just because AI makes it easy to build doesn't mean you should.
As AI dramatically reduces the time and cost required to create applications, agents, automations, and internal tools, organizations and founders are facing the same challenge from opposite directions: building solutions without fully understanding the long-term cost, business value, or market need.
For startups, the trap is building impressive technology before validating whether the problem is important enough that customers will actually pay to solve it. For organizations, the trap is investing in custom AI solutions when proven, supported platforms may already exist, often overlooking the hidden costs of maintenance, integrations, documentation, adoption, governance, and employee turnover.
Drawing from nearly 30 years of experience spanning Fortune 500 initiatives, digital transformation programs, startup ventures, and AI-powered business systems, Stéphanie Gendron shares a practical framework for evaluating AI opportunities based on business value, sustainability, and return on investment.
Attendees will leave with a clearer understanding of when to build, when to buy, when to integrate, and how to focus AI efforts on solving meaningful problems rather than simply creating more technology.
Speaker
Key takeaways
- A decision lens for evaluating when to build, buy, integrate, or automate AI solutions.
- How to assess AI projects beyond development speed: business value, adoption, maintenance, ownership, and long-term ROI.
- Lessons from startup and enterprise environments on avoiding AI projects that are impressive but fail to solve a problem people truly need solved.