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3 Keys For Success In Getting Buy-In for AI Transformation

At their finest form, algorithms can seem like works of art. Like works of art, algorithms are seldom viable by themselves in real life (and unless they are applied properly, as we discussed in an earlier post.


Winner artificial intelligence and machine learning advocates in corporate business units know another secret to AI and ML project success - getting the right buy-in from management and relevant departments.


Top performing data science leaders know there are 3 keys to success is getting corporate management and other departments to support and cooperate with your machine learning initiative:


1. Tie technical results with corporate business goals - executives, department leaders and other key managers will support and cooperate if they realize your AI efforts will boost overall corporate business goals - increase revenue by launching added value AI products, improve performance of current solutions, improve time to market of a complex non-AI data products, increase customer satisfaction, reduce expenditure on human capital (reducing the number of data analyst teams for example) and so on.


2. Start small (machine learning POC/”black-ops”) - A POC is a very good way to start transforming your organization to an AI based company for several reasons: Budget and time - initial investment is relatively small compared to extravagant data science projects, team recruitment etc.; It’s an effective way to evaluate the amount and quality of the data you actually have; it flexes the organization enough to test data flows (and potential roadblocks); and last but not least - failure will probably not be a strategic mistake for the initiative champion.


3. Another option of course is to have an external data science consultancy like Mathematic.ai do this “black-ops”sprint for you - you will spend less, get a higher chance of success than premature in-house efforts, and have limited backfire in case the objective cannot be reached.


While other organizational change factors will be important, the above are important keys for advanced data science and machine learning start-up endeavors within organizations that weren't built around true AI.


What do you think? What other factors are important for getting machine learning into the corporate DNA? Let us know or talk to us on best practices to achieve true AI enablement!


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