Measuring AI success involves aligning AI initiatives with business goals, tracking key metrics, and fostering continuous assessment and improvement.
Copyright: forbes.com – “How To Measure AI Success In Your Organization”
I believe that just about every business can benefit from AI by using it to create better products and services, drive efficiency in their operations, or improve customer experience.
However, not every AI initiative businesses embark on guarantees good results. In fact, according to research by Harvard Business School, as many as 80 percent of industrial AI projects can fail to generate tangible value.
Given the costs involved, this can easily lead to expensive missteps. Therefore, it’s absolutely critical that businesses understand how to measure success or failure before setting out.
Developing processes and systems for evaluating AI’s impact is essential, and in this article, I’ll highlight the questions you need to ask and the information you’ll need to gather.
Whether you’re just starting out or already have a few pilots underway and are getting ready to scale, these can help you understand and refine your approach, setting you up for the best chances at success.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
Aligning AI Strategy With Business Strategy
Firstly, it’s important to remember that to even have a chance of understanding the impact of your AI projects, you need to know that they’re aligned with your business targets and goals.
This may seem obvious, but I know from my own experience that it’s frequently overlooked. Technology is never a goal in itself; it’s a tool for achieving goals.
This means first having a business strategy in place and then identifying opportunities to deploy AI in ways that will help you achieve it.
I always recommend that businesses begin by identifying as many opportunities as possible. Then, whittling that list down to a smaller, manageable number of long-term, high-value use cases, along with some “quick win” initiatives that will let you quickly learn to deploy solutions, experiment and build confidence.[…]
Read more: www.forbes.com
Measuring AI success involves aligning AI initiatives with business goals, tracking key metrics, and fostering continuous assessment and improvement.
Copyright: forbes.com – “How To Measure AI Success In Your Organization”
I believe that just about every business can benefit from AI by using it to create better products and services, drive efficiency in their operations, or improve customer experience.
However, not every AI initiative businesses embark on guarantees good results. In fact, according to research by Harvard Business School, as many as 80 percent of industrial AI projects can fail to generate tangible value.
Given the costs involved, this can easily lead to expensive missteps. Therefore, it’s absolutely critical that businesses understand how to measure success or failure before setting out.
Developing processes and systems for evaluating AI’s impact is essential, and in this article, I’ll highlight the questions you need to ask and the information you’ll need to gather.
Whether you’re just starting out or already have a few pilots underway and are getting ready to scale, these can help you understand and refine your approach, setting you up for the best chances at success.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
Aligning AI Strategy With Business Strategy
Firstly, it’s important to remember that to even have a chance of understanding the impact of your AI projects, you need to know that they’re aligned with your business targets and goals.
This may seem obvious, but I know from my own experience that it’s frequently overlooked. Technology is never a goal in itself; it’s a tool for achieving goals.
This means first having a business strategy in place and then identifying opportunities to deploy AI in ways that will help you achieve it.
I always recommend that businesses begin by identifying as many opportunities as possible. Then, whittling that list down to a smaller, manageable number of long-term, high-value use cases, along with some “quick win” initiatives that will let you quickly learn to deploy solutions, experiment and build confidence.[…]
Read more: www.forbes.com
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