How do organization move beyond experiments and pilots in Artificial Intelligence to successful enterprise -wide implementation?

As organization go down the path of implanting these new and often complex technologies, they need to develop and evolve their capabilities not just in technology but also around data practices, talent and computing resources required to train AI model.

Data is at the heart of AI

Outcome of any AI model is as good as the data used to train the system. Organizations need flexible, consistent, and scalable data storage and practices like common data consistency and data governance play a crucial role here. Organizations may also need to develop new interfaces or APIs to extract data from legacy data sources like mainframes (which quite contrary to popular belief still holds much of world’s data) and relational databases to feed their AI projects.

All this can quickly become overwhelming and poses the single biggest risk to the success of the initiative.

People and Skills

As a demand for data scientist and artificial intelligence expert increases, it raises the risk of retaining skilled talent. Organization need sustained focus on developing and retaining talent which is centered not just around training but also providing sufficiently diverse platforms to practice those skills.

Scale and Agility

Computing power is at the core an Artificial intelligence solution. There is a huge cost and scale required to train an AI model. The key to a successful transformation is move from on-premise IT infrastructure to an agile and cost-effective cloud infrastructure. At the same time organization needs to be flexible about technology changes and new discoveries in this ever-evolving arena.

That is why it is vital for to find a strategic partner with deep understanding and experience of running enterprise scale AI applications. While the partner can help create roadmaps and develop solutions, Organizations can focus on building business strategy and reducing barriers for a successful implementation of AI.