India's largest platform and marketplace for AI & Analytics leaders & professionals

Sign in

India's largest platform and marketplace for AI & Analytics leaders & professionals

3AI Digital Library

Creating the Bridge of Translation between AI Technologists and Business

3AI May 12, 2023

Featured Article:

Author:  Abhishek Tandon, Director, Customer Success for Fosfor, LTIMindtree

Like we saw in the previous article “Crossing the AI Adoption Chasm“, there is a big gap in the objective of the technologists driving the AI project and the business user seeking value from it. This gap is causing major adoption issues as both sides are able to defend their stance on the relevance (or lack of) of the solution from their lens. However, just like there are language translators to navigate foreign lands, there is a need for a bridge of translation between the AI project teams and the business teams.

The bridge of translation is what I call the success hierarchy. It is a simple 5 step process which helps understand the requirements in great depth and hence puts business problem correctly in from of the technologists to solve.

Below is how the Success Hierarchy acts as a bridge between the two:

Business MotivationSuccess HierarchyAI Approach
Problem statementWhyWhy AI to address the problem statement
Need / urgencyWhatWhat can AI solve
Current Business approachHowProposed Solution Approach
Time to solveWhenProject Timelines
Functional Experts neededWhoAI Resources Needed

The change hierarchy is designed to layout exactly what is needed by the business and parallelly establish why an AI solution is needed to solve for it. The biggest mistake made by practitioners today is that they are extremely focused on what the AI technologies can do without really thinking about what is the business need it is going to solve. Hence, they tend to start with the “How” rather than the “Why”.

Business users on the other hand are interested in solving for the “Why”. Therefore, any project irrespective of size or scale needs to start with the Why and end at the Who.

The success hierarchy should not just limited a person or two. There are 3 key roles that need to be mapped to the success hierarchy.

Sponsors: People who are buying the product

Managers: People who are going to manage a team of people who will use the product

Users: People who will actually use the product

The motivation of each of these persona types is completely different. Therefore, if the solution adoption plan is not created keeping in mind the “why – what – how – when” for each of them there will be a serious gap in adoption when the solution is released. Mapping the persona to the change hierarchy will give you a structure that looks something like this:

 SponsorManagerUser
Why   
What   
How   
When   
Who   

With this structure in place you will be able to completely capture:

  1. The needs of each of the personas
  2. The discrepancy in the expectations of the personas
  3. The gap between the proposed solution and the needs of the personas

Hence, the change hierarchy across persona will be critical success barometer for any AI solution that is being built.

This structure covers two critical points:

  1. Participation from all the right levels
  2. Clarity of outcome across the levels

Point two above in turn ensures that when an adoption program is being created, the right messaging is being applied to the personas depending on the outcome they had enlisted earlier.

Thus, the Change Hierarchy becomes a perfect bridge between the AI project teams and the Business.

Title picture: freepik.com

    3AI Trending Articles

  • Understanding Language Model Evaluation Metrics: A Comprehensive Overview

    Featured Article: Author: Mradul Jain, AB InBev Large language models, such as GPT, Llama, Bard, etc. have gained immense popularity for their ability to generate coherent and contextually relevant text. Evaluating the performance of these models is crucial to ensure their reliability and utility. To accomplish this, a range of metrics have been developed. In […]

  • Transforming Indian Judiciary with GPT4

    Featured Article: Author: Vivek Gupta, Founder & CEO, Softsensor.ai Indian judiciary suffers significant backlog with 35 million cases across many courts. Assuming that each case requires about 100 pages of drafting, we are looking at about 3.5 billion pages to be drafted. Each page takes about 30 mins, we are looking at 100 billion minutes […]

  • Data for AI -Optimizing AI Governance and Implementing Key Performance Indicators for Success

    Featured Article Author: Prabhu Chandrasekharan Artificial Intelligence (AI) is revolutionizing industries by driving automation, optimization, and innovation. As AI systems become more complex, establishing robust governance frameworks focused on ‘Data for AI’ is essential. This ensures data quality, security, and neutrality, leading to reliable AI outcomes. Effective AI governance hinges on several Key Performance Indicators […]

  • Can modern AI systems solve any problem on their own?

    Featured Article: Author: Puneet B C, Data Scientist – Computer Vision, AIRBUS First, let’s briefly understand what AI is. Artificial Intelligence is basically an algorithm or a rule that is used to make decisions automatically. Making a decision is to generate a desired output for the given input. In order to achieve this, the algorithm […]