5 things organizations can do upfront to increase ROI on AI and machine learning projects:-
Organizations now more than ever are expanding and enhancing service and product capabilities using Machine Learning. Return on investment remain (ROI) elusive for most organizations that haven’t completed an AI Digital Transformation journey and perhaps haven’t built up the muscle of going through building and deploying an ML-enabled product.
Although not an exhaustive list, there are a few easy steps that a firm can take to maximize ROI when undertaking more complex Machine Learning and Artificial Intelligence projects.
1. Define what success looks like
Having a clear idea on the success criteria before starting (degree of model accuracy) establishes clear goals and mitigates delivery time risk. Goal setting is important.
2. Have an early focus on Information Architecture
The more an organization can do to build a solid, transparent, secure and extensible data platform upfront, the more strategic (and costly) resources like data scientists are productive from day.
3. Identify data sources upfront
Where possible, front-load the effort to identify training and testing datasets and accelerate to data processing. The faster a team can get to iterating on models on clean data, the better.
4. Formalize an Agile Data Science methodology
Establish the methodology that data scientists, ML engineers and other cross-functional team members will adopt, unblocking any frictions that might arise later.
5. Be ‘Lean’ as well as ‘Enterprise’ grade
It’s useful to maintain two parallel Workstreams where the first operates ‘Lean’ to accelerate build and the second delivers enterprise-grade standards, laying the ground-work for production deployment.
Ultimately, there are many opportunities for managing ML and AI engagements well and many areas in a traditional corporations ecosystem to enhance and/or build out new capabilities on.
===
Thank you for talking the time to read this article. Comments and feedback welcome!
Best,
Cameron
