If you are unclear on the actual process of sourcing, implementing, and managing a new AI program, look no further. As AI and ML gain traction as valuable business tools, leaders will have to recognize their importance and build a strategy to fold these technologies into their companies. Certainly every organization is different, but there are universal guidelines to consider in this process.

We have laid out five key pillars that should be a part of any AI strategy for those working in the build world.

1. Experiment early with pilot projects, but don’t get stuck in “pilot purgatory”

Pilot projects are a great way to demonstrate the value of a new technology, but there is a tendency for pilots to get stuck in purgatory. Pilots are valuable in that they mitigate risk, they allow for hypotheses and ideas to be tested, and they convince skeptical team members of the investment, but an AI or ML program will only have lasting, real outcomes when it is scaled and implemented as an integral piece of the organization.

2. Target a specific project or department and leverage on its success

Rolling out an AI package that is focused on addressing a specific corporate department, such as business development, finance, HR, or legal, gives the AI program a better chance to succeed. Even targeting the scope of work within a project– such as the supply chain or bidding process– is a great place to start. It helps to think about which department might be most ready for an AI program– and you might be pleasantly surprised to learn you’re more ready for an AI program than you would expect. Build a strong strong case in your area of influence, and expand to other departments and projects from there.

3. Ready your team to embrace a “human in the loop” mentality

Investing in an AI program is not about handing over the keys of your car to a robot and hoping for the best. In its initial phase, your AI models needs oversight and training like any other valuable employee. It needs to be taught, calibrated, and monitored by someone or a team of people in the organization– we call this a “human in the loop." This individual or group ensures the algorithm is processing your data correctly and that it is set up to produce the insight you’re looking for.

This blog originally appeared on www.br.iq. Follow this link to continue reading.