Insights on Artificial Intelligence

Just as water keeps cities and industries alive, data keeps modern organizations moving. McKinsey research shows that data-driven organizations are 23 times more likely to acquire customers, and 19 times more likely to be profitable. Data is crucial to the decision-making that supports innovation, but like water, it only creates value when it flows clearly and reliably. When data flows are opaque or unreliable, even the most advanced risk engines and AI models cannot be trusted. If data is flawed, every decision downstream is compromised.

AI projects are successful only if they keep working after they are deployed.

It may sound obvious, but one of the persistent obstacles to realizing value from AI implementation is a tendency to treat its deployment as a finish line, not a starting point. Organizations often invest heavily up front in AI systems, and then expect the same teams and processes that built and launched them to keep them running, if they invest in this stage at all. But running AI solutions reliably, at scale, with maintainable systems and predictable outcomes is a specialized discipline which requires its own expertise, processes, operational cadences, and technology.