Artificial intelligence can offer competitive advantages, but it’s not easy to drive success without understanding how to overcome the challenges.
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Artificial intelligence will change the way we live, work and play. In the process, it will challenge existing businesses, create new ones and fundamentally shift the economy. There’s no shortage of reports on enterprises investing in AI, but there are relatively few details about lessons learned and what can be done to improve the design of those AI plans. Here are seven ways to overcome inherent technical challenges, organizational barriers and a lack of precedent for AI pilot programs:
1. Pick a high-impact business problem
Transformative AI initiatives with significant organizational and cultural barriers require sponsorship from the highest leadership levels as they often take longer and are more complex. Start by addressing a high-impact problem that will not just get one-time executive sponsorship but ongoing executive engagement. This will also prevent the initiative from getting cut when the next budget crunch hits.
2. Understand feasible AI use cases
While headlines about the possibilities and consequences of AI abound, the reality is that current AI techniques still have several limitations. Consider an early feasibility assessment. Does the organization currently generate, store and analyze the types of data required by the machine-learning algorithm? Is the underlying process that will be improved already software-enabled? If the answer to both questions is no, chances are that even if machine learning offers potential benefits, the time and investment may not be worth the risk.
3. Know your use case and your data
Not all machine learning techniques are the same. A clear understanding of the use case and your data is imperative in picking the right machine-learning technique (or company/product). For instance, the algorithms required for targeted online advertising differ from those in healthcare. Similarly, if your use case requires extremely fast decision-making, such as applications that make real-time decisions, you must pick the appropriate algorithm. Additionally, if you’re thinking of purchasing and implementing an out-of-the-box AI capability, it’s imperative to understand what data the system was trained on and how that maps to your situation. For example, an AI capability trained on American healthcare studies may not be the best bet for a European healthcare use case.
4. Plan for IT infrastructure requirements
The IT infrastructure (network connectivity and computational power) required for machine learning applications as well as the infrastructure architecture are keys to AI success. IT infrastructure is also expensive, and once put in place, often can be hard to replace. For example, you may need to provide the ability to perform computations on a remote device (edge computing) versus centrally in the cloud (core computing). Think about what computations need to be performed and where to avoid nasty surprises later – both in terms of performance as well as the investment required for the infrastructure.[…]
read more – copyright by www.informationweek.com
Artificial intelligence can offer competitive advantages, but it’s not easy to drive success without understanding how to overcome the challenges.
copyright by www.informationweek.com
1. Pick a high-impact business problem
Transformative AI initiatives with significant organizational and cultural barriers require sponsorship from the highest leadership levels as they often take longer and are more complex. Start by addressing a high-impact problem that will not just get one-time executive sponsorship but ongoing executive engagement. This will also prevent the initiative from getting cut when the next budget crunch hits.
2. Understand feasible AI use cases
While headlines about the possibilities and consequences of AI abound, the reality is that current AI techniques still have several limitations. Consider an early feasibility assessment. Does the organization currently generate, store and analyze the types of data required by the machine-learning algorithm? Is the underlying process that will be improved already software-enabled? If the answer to both questions is no, chances are that even if machine learning offers potential benefits, the time and investment may not be worth the risk.
3. Know your use case and your data
Not all machine learning techniques are the same. A clear understanding of the use case and your data is imperative in picking the right machine-learning technique (or company/product). For instance, the algorithms required for targeted online advertising differ from those in healthcare. Similarly, if your use case requires extremely fast decision-making, such as applications that make real-time decisions, you must pick the appropriate algorithm. Additionally, if you’re thinking of purchasing and implementing an out-of-the-box AI capability, it’s imperative to understand what data the system was trained on and how that maps to your situation. For example, an AI capability trained on American healthcare studies may not be the best bet for a European healthcare use case.
4. Plan for IT infrastructure requirements
The IT infrastructure (network connectivity and computational power) required for machine learning applications as well as the infrastructure architecture are keys to AI success. IT infrastructure is also expensive, and once put in place, often can be hard to replace. For example, you may need to provide the ability to perform computations on a remote device (edge computing) versus centrally in the cloud (core computing). Think about what computations need to be performed and where to avoid nasty surprises later – both in terms of performance as well as the investment required for the infrastructure.[…]
read more – copyright by www.informationweek.com
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