Constellation Software (CSU) Investor Update summary

AI strategy and organizational approach

• AI adoption is decentralized, with each business unit experimenting and reporting progress individually; no company-wide AI metric is currently tracked.

• Panelists working on AI have strong technical backgrounds and focus on application rather than core data science.

• Duplication of AI efforts occurs due to decentralization, but this is seen as a learning opportunity.

• AI initiatives are mandated for all business units to experiment, with varying degrees of progress and focus on both internal and customer-facing solutions.

AI use cases and productivity impact

• Key AI use cases include programming efficiency, customer support, sales and marketing, and R&D, with adoption rates varying by function and business unit.

• AI tools are most effective in automating unit testing, code commenting, and documentation, but are limited by context window size for large codebases.

• AI is better at generating new code than maintaining or debugging existing code, due to training data limitations.

• Productivity gains are seen in some areas, but overall programming efficiency improvements remain modest, with some business units reporting 10-12% gains.

• Only about 3% of business units have replaced roles with AI tools; most redeploy freed capacity to new value creation.

Competitive landscape and customer dynamics

• AI-driven competition is sector-dependent; in many verticals, the organization is seen as an innovation leader rather than a follower.

• Response to AI competition focuses on customer intimacy, pragmatic evaluation of real value versus hype, and leveraging proprietary workflows and data.

• Large clients may attempt to build their own AI solutions, but complexity and regulatory requirements often favor continued partnership.

• AI enables more customization for clients, but also empowers clients to self-customize, creating a natural tension.