I
could not understand why Alexa wasn’t responding with the current temperature.
After shuffling across the room to see if the device was plugged in, I realized
the reason for the lack of response. I had made the request of my son’s
water bottle vs. our Alexa device (to be fair, they are very similar looking).
While my family got a good laugh at my expense, I became a bit introspective on
the importance of AI. How easy it was to assume there was a device in the
room that would provide information immediately at my request, and how quickly
frustrated I became when the data wasn’t readily available! Although I
see the growing and pervasive use of AI in the clients I meet with daily, this
personal event highlighted, for me, the new environment that we are all living
in.
Algorithms are, without a doubt, becoming more pervasive in our lives, both personally and professionally, and we are all starting to rely on AI driven capabilities whether we know it or not. The technology is providing tailored customer experiences, increased client engagement and significant differentiation for those organizations that implement it. But, according to a recent survey conducted by my employer, 88% of global c-level executives and IT decision makers believe companies incorporate AI only because it is trendy, and most admit they don’t actually know how to use it! So, how should organizations prepare for this new AI dynamic? There are 6 recommended steps for getting ready for this AI-drive world:
Step 1: Understand where you are:
Most clients I meet with continue to share that the top three challenges they face, with regards to preparing for AI, are accessing the appropriate data, determining the best way to analyze the data that they do have, and finding the right skill sets to implement AI solutions. It is so important for organizations to evaluate how their data is being captured and address the governance in place around that collection. Understanding the data, improving the data management practices and then preparing to “start small” are foundational steps.
Step 2: Determine potential:
Once an organization has an inventory of the data they do have, it is time to understand the data’s potential value and how it relates to the businesses goals and challenges. If there are gaps between the data held and the strategic objective, organizations should consider acquiring data from external sources to help close the gap. This is also the step in which organizations should consider privacy laws and their “digital ethics.” (Digital Ethics)
Step 3: Get focused:
Now that you know the business problem and what data is required to help address the challenges, it is time to get focused. Prioritize the data that matters to meeting your objectives. The amount of data we already have is overwhelming…but continues to increase. By 2020, it is expected that individuals will generate 1.5GB of data per day. That doesn’t include the amount of data generated by connected devices, smart homes, smart buildings or autonomous vehicles! The ability to focus the data is critical.
Step 4: Prototype:
The path is now clear and it is time to start training algorithms. Look for patterns and behaviors. Think outside the box. Start small. Be Agile. Evangelize early successes so that leadership sees the potential of AI (vs. considering it “trendy”).
Step 5: Operationalize
Once your prototypes add value, it is time to integrate the work into existing business processes and start measuring the impact the changes are making. At this point, you know exactly where to implement AI (bots, predictive analytics, etc.) because your data is organized and the business use case is clear.
Step 6: Do it all again.
It is important to continually repeat the steps above to sharpen the data and expand the number of AI use cases. This slow expansion allows an organization’s leadership to get comfortable with the value-add business impact of the technology, and it allows the employees to feel more comfortable with the change management aspects of the new solutions.
In summary, we should all embrace the endless potential of AI. As already noted, the technology (and required human-centered AI design) is providing tailored customer experiences, increased client engagement and significant differentiation for those that implement it. There is no hotter topic at the intersection of business and technology than AI, and the high-level steps above will help an organization get started on addressing this brave new world. My only other suggestion might be to start your AI journey by designing/developing AI-powered water bottles.
Algorithms are, without a doubt, becoming more pervasive in our lives, both personally and professionally, and we are all starting to rely on AI driven capabilities whether we know it or not. The technology is providing tailored customer experiences, increased client engagement and significant differentiation for those organizations that implement it. But, according to a recent survey conducted by my employer, 88% of global c-level executives and IT decision makers believe companies incorporate AI only because it is trendy, and most admit they don’t actually know how to use it! So, how should organizations prepare for this new AI dynamic? There are 6 recommended steps for getting ready for this AI-drive world:
Step 1: Understand where you are:
Most clients I meet with continue to share that the top three challenges they face, with regards to preparing for AI, are accessing the appropriate data, determining the best way to analyze the data that they do have, and finding the right skill sets to implement AI solutions. It is so important for organizations to evaluate how their data is being captured and address the governance in place around that collection. Understanding the data, improving the data management practices and then preparing to “start small” are foundational steps.
Step 2: Determine potential:
Once an organization has an inventory of the data they do have, it is time to understand the data’s potential value and how it relates to the businesses goals and challenges. If there are gaps between the data held and the strategic objective, organizations should consider acquiring data from external sources to help close the gap. This is also the step in which organizations should consider privacy laws and their “digital ethics.” (Digital Ethics)
Step 3: Get focused:
Now that you know the business problem and what data is required to help address the challenges, it is time to get focused. Prioritize the data that matters to meeting your objectives. The amount of data we already have is overwhelming…but continues to increase. By 2020, it is expected that individuals will generate 1.5GB of data per day. That doesn’t include the amount of data generated by connected devices, smart homes, smart buildings or autonomous vehicles! The ability to focus the data is critical.
Step 4: Prototype:
The path is now clear and it is time to start training algorithms. Look for patterns and behaviors. Think outside the box. Start small. Be Agile. Evangelize early successes so that leadership sees the potential of AI (vs. considering it “trendy”).
Step 5: Operationalize
Once your prototypes add value, it is time to integrate the work into existing business processes and start measuring the impact the changes are making. At this point, you know exactly where to implement AI (bots, predictive analytics, etc.) because your data is organized and the business use case is clear.
Step 6: Do it all again.
It is important to continually repeat the steps above to sharpen the data and expand the number of AI use cases. This slow expansion allows an organization’s leadership to get comfortable with the value-add business impact of the technology, and it allows the employees to feel more comfortable with the change management aspects of the new solutions.
In summary, we should all embrace the endless potential of AI. As already noted, the technology (and required human-centered AI design) is providing tailored customer experiences, increased client engagement and significant differentiation for those that implement it. There is no hotter topic at the intersection of business and technology than AI, and the high-level steps above will help an organization get started on addressing this brave new world. My only other suggestion might be to start your AI journey by designing/developing AI-powered water bottles.
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