Innovating @ Scale with AI
AI is here and now. Enterprises that want to succeed and be counted as leaders cannot ignore AI any longer. I guess this much is clear.
But I repeatedly see organizations struggle with using the power of AI to innovate fast and at scale. The first thing a lot of them do is hire a bunch of "data scientists" and they all very quickly start working on building the greatest algorithm - but very quickly it all starts going downhill. The performance of the algorithm never seems to quite live up to the initial hype. The business leadership losses interest and within a year there is disillusionment everywhere.
Nobody is quite able to see the light at the end of this tunnel.
But it does not have to be like this. Most of building an AI solution is grunt work that can and should be automated. The first step should be automate the AI pipeline as much as possible. There are enough tools in the market (both open source and commercial) that can help do this. Once this is in place, the AI algorithm even if it is not completely ready should be exposed to the business and their feedback solicited. Early feedback from "business" is pure gold and it helps establish the much needed "trust" between the business and data science teams. Now the algorithm can be tuned and trained with larger sets of data in parallel to provide improved version(s) of the product at regular intervals.
This approach lets the business team(s) drive the development of the AI infused product and gives them a sense of ownership which is critical to the success of the product / project.
Once again, for the data science team, automate as much of the pipeline as early as possible and expose the unfinished algorithm to the business users sooner than later. Then iterate the product / project in partnership with the business.
This is the approach that will separate teams that use AI to accelerate their companies growth from those who will fall behind.
To understand more and to see how we can help your organization leapfrog to the next level using AI, please visit us @ www.roundsqr.com.
Management Consultant , Advisor - Strategy, Technology, Operations
6 年AI may also go through the same life cycle that of web development in terms of reaching a maturity level in the enterprises. Looping feedback automatically into the system can speed up. Enterprises? may not like to get in R&D mode for AI implementations as it is too expensive and time consuming. The enterprise product companies may need to adopt? integrated AI features for the enterprises to put into practice.?