Breaking Down the Economics of AI [Infographic]

Use Cases & Projects, Scaling AI Catie Grasso

According to an Accenture research report, three out of four C-suite executives believe that if they don't scale AI in the next five years, they risk going out of business entirely. Those able to leverage data science and machine learning techniques with efficiency and agility to improve business operations and processes (while also uncovering net new business opportunities) will gain a competitive advantage.

In order for Enterprise AI to become pervasive and not drag businesses underwater due to rising costs and depleting revenue, organizations must consider the economics in a holistic way, understanding both gains and costs. Check out the infographic below for examples of tangible costs that can interfere with an organization's ability to scale Enterprise AI and embed it across people, processes, and technology, as well as the ways organizations can aim to reduce these costs.

GM1802-DAC+Updating+Economics+of+AI+Infographic_v04-1

 

You May Also Like

Explainable AI in Practice (In Plain English!)

Read More

Democratizing Access to AI: SLB and Deloitte

Read More

Secure and Scalable Enterprise AI: TitanML & the Dataiku LLM Mesh

Read More

Solving the Ocean Plastic Pollution Problem With Data

Read More