AI systems are a catalyst for digital transformation, yet, there is a clear lack of understanding of the capabilities of AI-based solutions and how it could really add value to businesses.
Author: Kamales Lardi, via www.forbes.com
However, there is still a strong misconception in the business world as to what AI is and how it contributes to the digital transformation of an organization. Often, as I work with leadership teams to develop the company’s digital transformation strategy, it is not uncommon to find strategic goals such as, “implement AI-based solutions to realize xx% revenue gains,” or “implement AI to increase productivity and efficiency.” These vague, high-level strategic goals betray a lack of understanding of the capabilities of AI-based solutions and how it could really add value to businesses.
AI can be defined as the automation of cognitive processes. The application of AI solutions supports organizations in several ways, including automation of redundant activities, digitizing processes, as well as fast analytics of data, particularly large data sets, and combining different data sets to gain rapid insights and knowledge that contribute to decision making. In addition, AI solutions enable scalability in an otherwise relatively inflexible traditional business environment. For example, scaling up hyper-personalized customer interactions and support with digital access points such as chatbots and virtual assistants.
Based on a 2020 survey by McKinsey & Company, organizations are using AI as a tool for generating value, and these companies plan to invest even more in AI in response to the Covid-19 pandemic and its acceleration of all things digital. However, a recent report by Gartner found that only 53% of companies were able to move their AI proof of concepts to production.
A Catalyst For Digital Transformation
Digital transformation is a structural redesign of an organization and its value chain. AI systems are a catalyst for digital transformation, enabling automation, optimization as well as intelligent use of data to accelerate insights and improve decision-making. However, organizations typically face several barriers that hinder AI implementation.
Instead of viewing AI solutions as a technology implementation, business leaders should focus on its capabilities and the potential business value that could be derived from its implementation. For example, digitization of processes through robotics process automation (RPA) could realize significant productivity or efficiency outcomes for the company’s operational environment. AI-based solutions also create the potential to scale up and down business environments, such as the ability to implement hyper-personalized customer interactions and support at scale through digital channels. In addition, AI solutions enable deep insights from big data sources or rapidly combine large datasets to derive intelligent insights. Companies could utilize social media and CRM data to identify new customer segments or identify dynamic products or service packages that would appeal to changing customer preferences.
To understand the capabilities and value of AI systems for business, executives first need to understand what AI solutions are. This does not mean gaining a technical knowledge of building the solutions but a working knowledge of what it is and how it could be implemented in the company. This includes understanding the differences between AI (automation of cognitive processes), machine learning (an approach to achieve AI that teaches computers the ability to do tasks with data, without explicit programming) and deep learning (a specialized technique to implement machine learning). These terms are often used interchangeably in the business environment but carry distinct meanings and implications. In our current digital environment, knowledge is democratized, and executives who want to learn more about AI systems can easily access this information from reliable sources, anywhere, anytime.
How To Deploy AI Solutions In Your Business
A starting point for any organization in AI solution deployment is to assess the current digital maturity of the organization, including the existing technology landscape and availability of reliable, verifiable data sources. At the heart of AI systems are data, and this becomes a pre-requisite for AI solution deployment, particularly for any form of data analytics application. […]
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Read more: www.forbes.com
AI systems are a catalyst for digital transformation, yet, there is a clear lack of understanding of the capabilities of AI-based solutions and how it could really add value to businesses.
Author: Kamales Lardi, via www.forbes.com
AI can be defined as the automation of cognitive processes. The application of AI solutions supports organizations in several ways, including automation of redundant activities, digitizing processes, as well as fast analytics of data, particularly large data sets, and combining different data sets to gain rapid insights and knowledge that contribute to decision making. In addition, AI solutions enable scalability in an otherwise relatively inflexible traditional business environment. For example, scaling up hyper-personalized customer interactions and support with digital access points such as chatbots and virtual assistants.
Based on a 2020 survey by McKinsey & Company, organizations are using AI as a tool for generating value, and these companies plan to invest even more in AI in response to the Covid-19 pandemic and its acceleration of all things digital. However, a recent report by Gartner found that only 53% of companies were able to move their AI proof of concepts to production.
A Catalyst For Digital Transformation
Digital transformation is a structural redesign of an organization and its value chain. AI systems are a catalyst for digital transformation, enabling automation, optimization as well as intelligent use of data to accelerate insights and improve decision-making. However, organizations typically face several barriers that hinder AI implementation.
Instead of viewing AI solutions as a technology implementation, business leaders should focus on its capabilities and the potential business value that could be derived from its implementation. For example, digitization of processes through robotics process automation (RPA) could realize significant productivity or efficiency outcomes for the company’s operational environment. AI-based solutions also create the potential to scale up and down business environments, such as the ability to implement hyper-personalized customer interactions and support at scale through digital channels. In addition, AI solutions enable deep insights from big data sources or rapidly combine large datasets to derive intelligent insights. Companies could utilize social media and CRM data to identify new customer segments or identify dynamic products or service packages that would appeal to changing customer preferences.
To understand the capabilities and value of AI systems for business, executives first need to understand what AI solutions are. This does not mean gaining a technical knowledge of building the solutions but a working knowledge of what it is and how it could be implemented in the company. This includes understanding the differences between AI (automation of cognitive processes), machine learning (an approach to achieve AI that teaches computers the ability to do tasks with data, without explicit programming) and deep learning (a specialized technique to implement machine learning). These terms are often used interchangeably in the business environment but carry distinct meanings and implications. In our current digital environment, knowledge is democratized, and executives who want to learn more about AI systems can easily access this information from reliable sources, anywhere, anytime.
How To Deploy AI Solutions In Your Business
A starting point for any organization in AI solution deployment is to assess the current digital maturity of the organization, including the existing technology landscape and availability of reliable, verifiable data sources. At the heart of AI systems are data, and this becomes a pre-requisite for AI solution deployment, particularly for any form of data analytics application. […]
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
Read more: www.forbes.com
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