Scaling AI: The 4 challenges you’ll face
Organizations of all sizes are embracing AI as a transformative technology to power their digital transformation journeys. Still the challenges around operationalizing AI at scale can still seem insurmountable, with a large number of projects failing.
I’ve worked in big data and AI with several organizations and have seen some clear trends on why AI efforts are floundering after an enthusiastic start. These are large established organizations that have done an amazing job of garnering support from their board, C-suite, business stakeholders, and even customers to embark on AI-powered transformation journeys. They have most likely set up some form of a Center of Excellence (CoE) for AI, with key hires both in leadership and technical roles, and have demonstrated the promise of AI, using a few machine learning projects in a limited scale. Then they move to scale a project into production, and they get stuck.
The reasons why scaling AI is so challenging seem to fall under four themes: customization, data, talent, and trust.