A Soft Economy Will Slow AI Project Deployments
We may see signs of AI weakness in Accenture's bookings and deal pipeline this Thursday Dec. 18th when ACN reports.
There is a cost associated with deploying AI initiatives (I am referring to all permutations of AI, not only Gen AI).
Prepping for an AI project is an extensive and expensive process. Data must be acquired, cleansed and stored in an accessible manner so that it can be utilized for advanced automation initiatives.
Data has to be lifted out of data silos that reside in various corporate departments.
This assumes the CEO has mandated that various departments share their data. If the AI project is bottoms-up, it may be next to impossible to exert enough influence on another department to acquire their data.
Internal politics have killed a number of automation projects before they ever got off the ground.
Once acquired, data must be cleansed, labeled and stored in an accessible manner.
Enterprise Data can be stored inside of data lakes where Devs, Data Scientists and business users can work together to glean insights from the data and to build tools, apps and various products for internal and external use.
Companies can’t begin to build robust ROI cases until they know what they have for data and how they may utilize it to generate operating efficiencies and revenue.
Steps 1 and 2 may require that a company hire people with data management experience, along with Stats Phds, to execute on AI projects. A company can’t simply hand an AI project to a group of people that are not qualified. As it is, 80% of all predictive / “AI” models do not see production.
Companies simply won’t take on AI projects if they feel their business is slowing due to a soft economy.