4. Pick the right tools

The final step is choosing the right tools. AI tools used to be geared toward academic research and proof of concepts. But now a new generation is emerging, allowing organizations to build reliable AI fast for a reasonable cost.

Since the software and hardware used for AI is going through rapid development, make sure the solutions you choose are scalable and future-proof – to avoid costly maintenance. Choose tools based on goals, budgets, available in-house competencies, time to market and total cost of ownership. AI can make business much easier, but the tried-and-true rules of business still very much apply.

Finally, since AI is bound to affect multiple aspects of your business, go for a tool that makes collaboration across the organization possible.

• Ensure the tool/platform is future-proof to avoid costly maintenance.

• Align current and possible future in-house AI competencies with the tool/platform.

• Make sure the tool/platform can handle not only the data types that you have today, but also data types you may want to use in the future.

• Review the technical capabilities of the platform. Traditional Machine Learning can solve far fewer problems than Deep Learning.

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