July 15, 2022

Beginners Guide To Implementing AI: Proving ROI

use cases

Throughout our beginner’s guide to implementing AI series, we have covered the multiple ways that organizations can dive into the world of the Cloud and AI. None of these parts work unless we are getting a real return on investment that translates to real dollars and change within an organization. Some companies may be on an AI journey, but are they seeing the enormous value it brings? The answer is sometimes no. Now at the tail end of our journey,  we need to think about “how do we prove ROI for AI use cases”.

ROI and AI

To start, we need to think about how return on investment and artificial intelligence are related. In recent years, we have seen a major increase in the implementation of AI in businesses. McKinsey found that 56% of respondents reported they adopted AI in at least one function in 2021. This was an increase from 50% in 2020. When companies invest in AI, they expect to see an increase in ROI. As CEO Bob Picciano explained,” ROI from AI is possible, but it must be accurately described and personified according to a business goal”. Now we must ask ourselves, do investments in AI deliver real ROI results? Nearly half of the respondents surveyed by Domino Data Lab’s recent Resurvey, expect double-digit growth as a result of data science. 79% of respondents claimed that data science, ML and AI are critical to the future growth of a company, 36% of them calling it the ‘single most critical factor’.

With the implementation of AI, in the case of supply chains, product delivery can now be easily predicted. With such insights, data scientists will no longer have to spend hours sending weekly email updates to customers. Instead, AI will provide real-time updates. As a result, this will impact the overall success of a business. This is because businesses can now efficiently communicate with their customers building loyal and reliable relationships. In return, this often will lead to an increase in ROI. 

For example, in use cases that involve delivery time, it is important to think about:

How much can we deploy in AI to predict delivery?  

Can this AI automatically update us with key information?

If we acknowledge these two steps we can reduce work time and add accuracy and value to the customer. Because of this insightful AI engineering, customers now have a value that they can share with future customers. This won’t just provide the delivery time of the product but can now alert customers with up-to-date info. This will put ROI on the top line leading to customer satisfaction. 

With this use case example, there are three important ROIs to consider:

  1. Cost 
  2. Revenue
  3. Customer satisfaction 

These three different ROIs will help to clearly measure what’s happening with the expectation of: 

  1. Reduction in effort
  2. Adoption of the specific data or whatever needs to see an increase and go up
  3. There should be customer appreciation because of this specific case scenario 

Often there is a clear expectation on how companies should utilize use cases. ROI  is an incremental journey that will face failures but is never a waste of time. Businesses must start small, realize ROI, know how to accurately measure it, and deploy something that can truly revolutionize a company.

Learn how investing in AI can increase your business’s ROI by contacting [email protected] or by requesting a demo today! 

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