Road Map to a Successful AI/ML Implementation - Key points from Harvard Briefing Paper

Keys to Successful AI Implementation: (Summary from the paper)

Step1: Define your why

? Adopting AI/ML is never the goal

? AI exists to support the overall business objective developed by considering the larger-picture goal.

Step2: Don't try to boil the ocean

? Identify and prioritise only specific use cases in which AI/ML could solve business problems or add value to existing products and services, do not solve all the AI/ ML use cases at once.

Step3: Move from control to consensus

? AI/ML implementation doesn’t need to come just from the top; it is a team sport, and the whole business is the team.

? Creating engaged, high-performing teams that share data, communicate openly, and learn from feedback keeps everyone invested in the mission and in the loop about what the company wants to achieve.

Step4: Break down the silos

? Eliminate silos and integrate data analytics across divisions (Marketing, Operations, and HR)

Step5: Recognize a failing project.

? AI often starts with the sandbox. Moving from a sandbox to production is super hard to do, you have to come up with templates that people can reuse to make the time moving from the sandbox to production really short.

Step6: Find the right partner

? Working with a good implementation partner can broaden your understanding of the possible and create a quicker return on investment.

Step7: Use data responsibly and ethically

? There are increased concerns about whether that data is used responsibly, which means everything from enhancing data privacy to reducing bias in the modelling.

? Organisations are advised to have an AI and data ethics board with a set of principles like 'transparency', ‘explainability,’ 'bias', oversight, nondiscrimination, 'environmental', 'social well-being' and 'accountability'.

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