Are You Ready for AI?

Are you ready for AI?Artificial Intelligence (AI) is coming.  It is not a question of if, but when.  Are you ready?

While consumer AI is already in use by companies such as Netflix for movie/episode suggestions, very few companies have implemented AI.  A lot of companies claim to have AI and some offer AI solutions, be careful with what AI really means.

Artificial Intelligence refers to process automation, letting computer programs and hardware take over repetitive tasks.  Machine Learning is add-on to AI in respects of letting the machine learn from new data automatically.  AI can be enhanced through further human based programing.

AI Requirements

What does your company need for Artificial Intelligence?

To start, you need to identify one or more business processes which would add value to automate.  Start with the simplest and most repetitive tasks for your account managers, sales reps, and other operational employees.  Start with a task as simple as retrieving information needed to make a decision.  Many of our clients have discussed the concept of the virtual analyst, a robot like tool which dispenses insights instead of logging on to Tableau to retrieve.

Next, you need good data.  As the saying goes, data out is only as good as data in.  While data is never perfect, even in the most highly controlled environments, there needs to be a fair amount of stability.  While most AI can learn to deal with bad data, minimizing errors is key.

You will also need a good data science team.  You will need team players who can code using a variety of tools, such as Python and R.  You will also need resources familiar with the cloud as AWS offers many advantages, particularly for big data projects.  Even with a giant budget capable of purchasing off the shelf tools like IBM Watson and DataRobot, your data science team will bring the knowledge and experience needed to make these tools even better.

AI Suggestions

When getting started with AI, we have some suggestions.  First, start small.  It is far better to under-promise and over-deliver.  Start with a well focused use case which offers data stability and drives value to the organization. Something like the virtual analyst is a popular place to start.

Second, be flexible with your roadmap.  Count on delays, mistakes, and learnings throughout your implementation process.

Third, find a passionate, progressive executive to be your champion.  Having executive sponsorship not only helps to secure funding, but helps give the project visibility and defense at the highest level possible.  Having an executive on board helps tie the project to company wide goals.

Finally, be patient.  AI doesn’t happen overnight.  In fact, many of the most hi-tech and innovative companies with huge budgets are struggling with AI.  AI is possible, but there isn’t a well refined recipe available, yet.  Keep your eye on the prize and keep learning.

The first one to master true AI will have a huge advantage as the world of technology and analytics progresses.   It is a pretty sweet prize.


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