There is no denying that artificial intelligence (AI) plays a significant role in how we go about our daily lives. From predictive searches and automated translations to futuristic use cases like self-driven cars, AI has captured the imagination of managers, CXOs, tech workers and end users alike.
That said, ask any two individuals what AI is, and one is very likely to get two conflicting answers. This is not just among ordinary people but also top-level decision-makers. Many business leaders want to understand how AI will impact their business. There is also a deepening sense of worry that their companies will be left behind if they don’t learn to use AI effectively.
The worry is legitimate given the complexity of executing AI projects seamlessly and effectively. The biggest challenge I’ve found is achieving scale. An ongoing project that delivers high value often runs aground when it comes to scaling fast.
Scaling requires collaboration from multiple cross-functional teams. IT provides infrastructure and risk management, HR provides training and senior business stakeholders provide sign-offs. Unfortunately, one-off projects with limited value struggle to obtain bandwidth and priority from all these different teams. Considering these challenges, it is no surprise that many AI projects fail to deliver their intended benefits.