Business Performance (BPI) Network: Reports

Report

Id: 86
Date
Title: The Pathway to GenAI Competitive Advantage
Summary: GenAI is going to drive competitive advantage, yet 3 in 5 business leaders aren’t confident in their data-AI readiness. This report shows you how to prepare for GenAI’s next evolution.
Author: BPI Network
Body: After a year of delivering mostly marginal efficiency gains, GenAI is on the verge of accelerating functional effectiveness and competitive differentiation. Nearly 4 in 5 business leaders believe GenAI will deliver a competitive advantage over the next 18 months, such as agentic AI improving the customer experience. But not everyone will be able to reap the benefits. Some 3 in 5 leaders aren't confident in their data-AI readiness to achieve GenAI value. Our report, "The Pathway to GenAI Competitive Advantage," examines how GenAI might shape, impact and influence the way data is used for competitive advantage if properly prepared and primed. The findings of this report are based on a global survey of more than 170 business and functional leaders across industries and geographies and in-depth interviews with GenAI experts. Over 80% of respondents hold EVP/SVP/VP/GM/CIO/CTO/CISO/CLO titles, and 60% represent large corporations with annual revenues of more than $1 billion. Download your copy of the report today to see how your GenAI data readiness stacks up amongst your peers.
Image: Bpi-assetsReport-Thumbnail.png
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Created: 2025-04-18 18:07:47
Modified: 2025-04-21 20:19:28

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Id	Title	Subtitle	Description	Date	Created	Modified	Actions
34	The Pathway To Gen AI Competitive Advantage	Is Your Data Prepared and Primed to Unlock GenAI’s Next-Generation Business Value?	After a year of delivering mostly marginal efficiency gains, GenAI is on the verge of accelerating functional effectiveness and competitive differentiation. This year promises to be a landmark leap in GenAI value, but not everyone will be able to reap the benefits. At least 30% of GenAI projects will be abandoned after proof of concept by the end of 2025, due to poor data quality, inadequate risk controls, escalating costs or unclear business value, according to Gartner. Data that is not de-risked and meticulously prepared for AI increases the likelihood of initiatives failing. GenAI value is further diminished when answers to even low-complexity queries are inaccurate, unreliable, biased. In order to harness the power of GenAI, organizations first have to discover, classify, manage and secure data for GenAI to interrogate. Achieving GenAI’s next-generation business value will depend on organizations shoring up their data supply chain amid an unprecedented data deluge and security and privacy concerns. Will you be among the leaders? Or will you lag behind the competition? The Business Performance Innovation (BPI) Network and the Growth Officer Council are teaming up with intelligent information management leader EncompaaS to assess the state of data readiness in the rapid evolution of GenAI value creation. We invite your perspectives to this exploratory thought-leadership campaign as we look to highlight new challenges, needs and opportunities that can be unlocked through advances in GenAI implementations.	2024-12-11	2024-12-11 18:40:15	2025-01-29 23:38:32	View Edit Delete

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The Pathway To Gen AI Competitive Advantage

Is Your Data Prepared and Primed to Unlock GenAI’s Next-Generation Business Value?

After a year of delivering mostly marginal efficiency gains, GenAI is on the verge of accelerating functional effectiveness and competitive differentiation. This year promises to be a landmark leap in GenAI value, but not everyone will be able to reap the benefits.

At least 30% of GenAI projects will be abandoned after proof of concept by the end of 2025, due to poor data quality, inadequate risk controls, escalating costs or unclear business value, according to Gartner. Data that is not de-risked and meticulously prepared for AI increases the likelihood of initiatives failing. GenAI value is further diminished when answers to even low-complexity queries are inaccurate, unreliable, biased.

In order to harness the power of GenAI, organizations first have to discover, classify, manage and secure data for GenAI to interrogate. Achieving GenAI’s next-generation business value will depend on organizations shoring up their data supply chain amid an unprecedented data deluge and security and privacy concerns. Will you be among the leaders? Or will you lag behind the competition?

The Business Performance Innovation (BPI) Network and the Growth Officer Council are teaming up with intelligent information management leader EncompaaS to assess the state of data readiness in the rapid evolution of GenAI value creation. We invite your perspectives to this exploratory thought-leadership campaign as we look to highlight new challenges, needs and opportunities that can be unlocked through advances in GenAI implementations.

2024-12-11

2024-12-11 18:40:15

2025-01-29 23:38:32

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Id	Tag	Created	Modified	Actions
3	Artificial Intelligence (AI)	2021-11-01 20:29:00	2021-11-01 20:29:00	View Edit Delete

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 authorBPI Network
 bodyAfter a year of delivering mostly marginal efficiency gains, GenAI is on the verge of accelerating functional effectiveness and competitive differentiation. Nearly 4 in 5 business leaders believe GenAI will deliver a competitive advantage over the next 18 months, such as agentic AI improving the customer experience. But not everyone will be able to reap the benefits. Some 3 in 5 leaders aren't confident in their data-AI readiness to achieve GenAI value. Our report, "The Pathway to GenAI Competitive Advantage," examines how GenAI might shape, impact and influence the way data is used for competitive advantage if properly prepared and primed. The findings of this report are based on a global survey of more than 170 business and functional leaders across industries and geographies and in-depth interviews with GenAI experts. Over 80% of respondents hold EVP/SVP/VP/GM/CIO/CTO/CISO/CLO titles, and 60% represent large corporations with annual revenues of more than billion. Download your copy of the report today to see how your GenAI data readiness stacks up amongst your peers.
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 descriptionAfter a year of delivering mostly marginal efficiency gains, GenAI is on the verge of accelerating functional effectiveness and competitive differentiation. This year promises to be a landmark leap in GenAI value, but not everyone will be able to reap the benefits. At least 30% of GenAI projects will be abandoned after proof of concept by the end of 2025, due to poor data quality, inadequate risk controls, escalating costs or unclear business value, according to Gartner. Data that is not de-risked and meticulously prepared for AI increases the likelihood of initiatives failing. GenAI value is further diminished when answers to even low-complexity queries are inaccurate, unreliable, biased. In order to harness the power of GenAI, organizations first have to discover, classify, manage and secure data for GenAI to interrogate. Achieving GenAI’s next-generation business value will depend on organizations shoring up their data supply chain amid an unprecedented data deluge and security and privacy concerns. Will you be among the leaders? Or will you lag behind the competition? The Business Performance Innovation (BPI) Network and the Growth Officer Council are teaming up with intelligent information management leader EncompaaS to assess the state of data readiness in the rapid evolution of GenAI value creation. We invite your perspectives to this exploratory thought-leadership campaign as we look to highlight new challenges, needs and opportunities that can be unlocked through advances in GenAI implementations.
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Chief Data Officers, infrastructure IT executives and others in the enterprise are now seeking new and innovative ways to gain greater control, visibility, performance and efficiency in the way they manage data to optimize its value. The program will engage data and IT executives from major companies, as well as leading system integrators, consulting firms and solution providers to understand needs, requirements and best practices in optimizing data economics, protection and logistics in today’s multi-cloud, mega-data world. Background The growing importance of the Chief Data Officer underscores widespread recognition of the essential role and value of data and analytics in the modern, agile customer-centric enterprise. This is further compounded by the significant cost, challenge and complexity of unifying, migrating, replicating, protecting, storing and governing data “ponds, lakes and dams” in today’s multi-cloud and hybrid cloud computing environments. Data has become one of the chief engines of value creation. Its role in improving analytical decision making, enhancing our ability to understand, reach and engage customers, and increasing the value of both existing and new products and services has made data a highly valued currency and asset base for enterprises operating digitally across data borders, ramparts and fences. Fueled by an explosive proliferation of new data sources—from mobile computing, social media and rich media applications and services, to the advent of the Internet of Things, which will generate some 50 million connected sensors by the end of 2020—the amount and variety of data being stored is growing exponentially. According Seagate Technology, the amount of data generated worldwide will increase to 163 zettabytes by 2025 from 12 zettabytes in 2015. In today’s multi-cloud, mega-data world, better Data Logistics and Performance Management (DLPM) has become essential to competing in the Data Economy. According to 451 Research, some 80 percent of companies are now utilizing two or more clouds. Three-quarters of companies say they are pursuing a hybrid/multi-cloud strategy. In this highly distributed compute environment, the requirements to understand where data resides across multiple public clouds and on-premise systems and cost-effectively store, protect and move data to its optimal location becomes critical. With the coming rise of edge computing and micro-data centers designed to bring data and applications closer to users to reduce latency and thereby improve application and workload performance, data management and logistics become even more complex. The edge is creating even more data, with increased data volumes having to be transmitted across networks and between clouds. Object-based data storage is now the dominant storage architecture globally, particularly in the cloud, and will be used to store 85 percent of the world’s data by 2025. Developed initially to handle the explosive growth of unstructured data, such as video, images, web pages, log files and IoT data, object storage saves data along with associated metadata in blocks as objects that can contain multiple information formats. It has a variety of advantages over file and block storage, including massive scalability, greater flexibility via metadata for analytics, better governance, faster retrieval, and improved cost efficiencies. In addition, continued technical advances are improving both the performance and the applicability of object storage for new use cases. A major challenge, however, is that the object-based storage systems used my various cloud providers are all different, making data synchronization and translation a challenge. The Data Logistics Mandate In this new world, Chief Data Officers and CIOs must optimize how they manage vast volumes of data across multiple clouds and data environments through better data logistics. There are more data silos, greater distribution of data, vast increases in volume, and a growing demand to understand, protect and exploit data for business growth and optimization while holding down costs. Among those logistical challenges are the need to keep track of where data resides, and cost-effectively synchronize, replicate and move data to optimize value, efficiency, security and governance based on cost differences, processing and analytics capabilities, performance advantages, and regulatory requirements. Organizations need to be able to be able to identify what data needs to be replicated and effectively transmitted and synchronized across different clouds and on-premise locations to improve the availability, security and value of data. For example, organizations increasingly need to move data to specific service providers to conduct analytics or take advantage of specific vendor technologies, lower processing and storage costs, or meet geographic requirements. Particularly when data replication and synchronization is a continuous activity, organizations need to automate this process based on established rules and policies to improve cost efficiency, eliminate errors and reduce management complexity. For object storage data, the capacity to fully utilize metadata to identify location, type and value is now a critical aspect of data logistics, as is the ability to transfer data with a minimum amount of performance degradation and data corruption. New solutions that can handle replication outside of the data path are delivering improved visibility, performance and data quality. Advancing data logistics is a major imperative for today’s data-driven organizations and their chief data officers. The mandate to improve data availability, performance, value and cost-efficiency will be a critical competitive differentiator in a multi-cloud, mega-data world. To get involved in this initiative, please reach out to <a href="mailto:dmurray@bpinetwork.org">Dave Murray</a>. 
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Chief Data Officers, infrastructure IT executives and others in the enterprise are now seeking new and innovative ways to gain greater control, visibility, performance and efficiency in the way they manage data to optimize its value. The program will engage data and IT executives from major companies, as well as leading system integrators, consulting firms and solution providers to understand needs, requirements and best practices in optimizing data economics, protection and logistics in today’s multi-cloud, mega-data world. Background The growing importance of the Chief Data Officer underscores widespread recognition of the essential role and value of data and analytics in the modern, agile customer-centric enterprise. This is further compounded by the significant cost, challenge and complexity of unifying, migrating, replicating, protecting, storing and governing data “ponds, lakes and dams” in today’s multi-cloud and hybrid cloud computing environments. Data has become one of the chief engines of value creation. Its role in improving analytical decision making, enhancing our ability to understand, reach and engage customers, and increasing the value of both existing and new products and services has made data a highly valued currency and asset base for enterprises operating digitally across data borders, ramparts and fences. Fueled by an explosive proliferation of new data sources—from mobile computing, social media and rich media applications and services, to the advent of the Internet of Things, which will generate some 50 million connected sensors by the end of 2020—the amount and variety of data being stored is growing exponentially. According Seagate Technology, the amount of data generated worldwide will increase to 163 zettabytes by 2025 from 12 zettabytes in 2015. In today’s multi-cloud, mega-data world, better Data Logistics and Performance Management (DLPM) has become essential to competing in the Data Economy. According to 451 Research, some 80 percent of companies are now utilizing two or more clouds. Three-quarters of companies say they are pursuing a hybrid/multi-cloud strategy. In this highly distributed compute environment, the requirements to understand where data resides across multiple public clouds and on-premise systems and cost-effectively store, protect and move data to its optimal location becomes critical. With the coming rise of edge computing and micro-data centers designed to bring data and applications closer to users to reduce latency and thereby improve application and workload performance, data management and logistics become even more complex. The edge is creating even more data, with increased data volumes having to be transmitted across networks and between clouds. Object-based data storage is now the dominant storage architecture globally, particularly in the cloud, and will be used to store 85 percent of the world’s data by 2025. Developed initially to handle the explosive growth of unstructured data, such as video, images, web pages, log files and IoT data, object storage saves data along with associated metadata in blocks as objects that can contain multiple information formats. It has a variety of advantages over file and block storage, including massive scalability, greater flexibility via metadata for analytics, better governance, faster retrieval, and improved cost efficiencies. In addition, continued technical advances are improving both the performance and the applicability of object storage for new use cases. A major challenge, however, is that the object-based storage systems used my various cloud providers are all different, making data synchronization and translation a challenge. The Data Logistics Mandate In this new world, Chief Data Officers and CIOs must optimize how they manage vast volumes of data across multiple clouds and data environments through better data logistics. There are more data silos, greater distribution of data, vast increases in volume, and a growing demand to understand, protect and exploit data for business growth and optimization while holding down costs. Among those logistical challenges are the need to keep track of where data resides, and cost-effectively synchronize, replicate and move data to optimize value, efficiency, security and governance based on cost differences, processing and analytics capabilities, performance advantages, and regulatory requirements. Organizations need to be able to be able to identify what data needs to be replicated and effectively transmitted and synchronized across different clouds and on-premise locations to improve the availability, security and value of data. For example, organizations increasingly need to move data to specific service providers to conduct analytics or take advantage of specific vendor technologies, lower processing and storage costs, or meet geographic requirements. Particularly when data replication and synchronization is a continuous activity, organizations need to automate this process based on established rules and policies to improve cost efficiency, eliminate errors and reduce management complexity. For object storage data, the capacity to fully utilize metadata to identify location, type and value is now a critical aspect of data logistics, as is the ability to transfer data with a minimum amount of performance degradation and data corruption. New solutions that can handle replication outside of the data path are delivering improved visibility, performance and data quality. Advancing data logistics is a major imperative for today’s data-driven organizations and their chief data officers. The mandate to improve data availability, performance, value and cost-efficiency will be a critical competitive differentiator in a multi-cloud, mega-data world. To get involved in this initiative, please reach out to <a href="mailto:dmurray@bpinetwork.org">Dave Murray</a>. 
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Chief Data Officers, infrastructure IT executives and others in the enterprise are now seeking new and innovative ways to gain greater control, visibility, performance and efficiency in the way they manage data to optimize its value. The program will engage data and IT executives from major companies, as well as leading system integrators, consulting firms and solution providers to understand needs, requirements and best practices in optimizing data economics, protection and logistics in today’s multi-cloud, mega-data world. Background The growing importance of the Chief Data Officer underscores widespread recognition of the essential role and value of data and analytics in the modern, agile customer-centric enterprise. This is further compounded by the significant cost, challenge and complexity of unifying, migrating, replicating, protecting, storing and governing data “ponds, lakes and dams” in today’s multi-cloud and hybrid cloud computing environments. Data has become one of the chief engines of value creation. Its role in improving analytical decision making, enhancing our ability to understand, reach and engage customers, and increasing the value of both existing and new products and services has made data a highly valued currency and asset base for enterprises operating digitally across data borders, ramparts and fences. Fueled by an explosive proliferation of new data sources—from mobile computing, social media and rich media applications and services, to the advent of the Internet of Things, which will generate some 50 million connected sensors by the end of 2020—the amount and variety of data being stored is growing exponentially. According Seagate Technology, the amount of data generated worldwide will increase to 163 zettabytes by 2025 from 12 zettabytes in 2015. In today’s multi-cloud, mega-data world, better Data Logistics and Performance Management (DLPM) has become essential to competing in the Data Economy. According to 451 Research, some 80 percent of companies are now utilizing two or more clouds. Three-quarters of companies say they are pursuing a hybrid/multi-cloud strategy. In this highly distributed compute environment, the requirements to understand where data resides across multiple public clouds and on-premise systems and cost-effectively store, protect and move data to its optimal location becomes critical. With the coming rise of edge computing and micro-data centers designed to bring data and applications closer to users to reduce latency and thereby improve application and workload performance, data management and logistics become even more complex. The edge is creating even more data, with increased data volumes having to be transmitted across networks and between clouds. Object-based data storage is now the dominant storage architecture globally, particularly in the cloud, and will be used to store 85 percent of the world’s data by 2025. Developed initially to handle the explosive growth of unstructured data, such as video, images, web pages, log files and IoT data, object storage saves data along with associated metadata in blocks as objects that can contain multiple information formats. It has a variety of advantages over file and block storage, including massive scalability, greater flexibility via metadata for analytics, better governance, faster retrieval, and improved cost efficiencies. In addition, continued technical advances are improving both the performance and the applicability of object storage for new use cases. A major challenge, however, is that the object-based storage systems used my various cloud providers are all different, making data synchronization and translation a challenge. The Data Logistics Mandate In this new world, Chief Data Officers and CIOs must optimize how they manage vast volumes of data across multiple clouds and data environments through better data logistics. There are more data silos, greater distribution of data, vast increases in volume, and a growing demand to understand, protect and exploit data for business growth and optimization while holding down costs. Among those logistical challenges are the need to keep track of where data resides, and cost-effectively synchronize, replicate and move data to optimize value, efficiency, security and governance based on cost differences, processing and analytics capabilities, performance advantages, and regulatory requirements. Organizations need to be able to be able to identify what data needs to be replicated and effectively transmitted and synchronized across different clouds and on-premise locations to improve the availability, security and value of data. For example, organizations increasingly need to move data to specific service providers to conduct analytics or take advantage of specific vendor technologies, lower processing and storage costs, or meet geographic requirements. Particularly when data replication and synchronization is a continuous activity, organizations need to automate this process based on established rules and policies to improve cost efficiency, eliminate errors and reduce management complexity. For object storage data, the capacity to fully utilize metadata to identify location, type and value is now a critical aspect of data logistics, as is the ability to transfer data with a minimum amount of performance degradation and data corruption. New solutions that can handle replication outside of the data path are delivering improved visibility, performance and data quality. Advancing data logistics is a major imperative for today’s data-driven organizations and their chief data officers. The mandate to improve data availability, performance, value and cost-efficiency will be a critical competitive differentiator in a multi-cloud, mega-data world. To get involved in this initiative, please reach out to <a href="mailto:dmurray@bpinetwork.org">Dave Murray</a>. 
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Chief Data Officers, infrastructure IT executives and others in the enterprise are now seeking new and innovative ways to gain greater control, visibility, performance and efficiency in the way they manage data to optimize its value. The program will engage data and IT executives from major companies, as well as leading system integrators, consulting firms and solution providers to understand needs, requirements and best practices in optimizing data economics, protection and logistics in today’s multi-cloud, mega-data world. Background The growing importance of the Chief Data Officer underscores widespread recognition of the essential role and value of data and analytics in the modern, agile customer-centric enterprise. This is further compounded by the significant cost, challenge and complexity of unifying, migrating, replicating, protecting, storing and governing data “ponds, lakes and dams” in today’s multi-cloud and hybrid cloud computing environments. Data has become one of the chief engines of value creation. Its role in improving analytical decision making, enhancing our ability to understand, reach and engage customers, and increasing the value of both existing and new products and services has made data a highly valued currency and asset base for enterprises operating digitally across data borders, ramparts and fences. Fueled by an explosive proliferation of new data sources—from mobile computing, social media and rich media applications and services, to the advent of the Internet of Things, which will generate some 50 million connected sensors by the end of 2020—the amount and variety of data being stored is growing exponentially. According Seagate Technology, the amount of data generated worldwide will increase to 163 zettabytes by 2025 from 12 zettabytes in 2015. In today’s multi-cloud, mega-data world, better Data Logistics and Performance Management (DLPM) has become essential to competing in the Data Economy. According to 451 Research, some 80 percent of companies are now utilizing two or more clouds. Three-quarters of companies say they are pursuing a hybrid/multi-cloud strategy. In this highly distributed compute environment, the requirements to understand where data resides across multiple public clouds and on-premise systems and cost-effectively store, protect and move data to its optimal location becomes critical. With the coming rise of edge computing and micro-data centers designed to bring data and applications closer to users to reduce latency and thereby improve application and workload performance, data management and logistics become even more complex. The edge is creating even more data, with increased data volumes having to be transmitted across networks and between clouds. Object-based data storage is now the dominant storage architecture globally, particularly in the cloud, and will be used to store 85 percent of the world’s data by 2025. Developed initially to handle the explosive growth of unstructured data, such as video, images, web pages, log files and IoT data, object storage saves data along with associated metadata in blocks as objects that can contain multiple information formats. It has a variety of advantages over file and block storage, including massive scalability, greater flexibility via metadata for analytics, better governance, faster retrieval, and improved cost efficiencies. In addition, continued technical advances are improving both the performance and the applicability of object storage for new use cases. A major challenge, however, is that the object-based storage systems used my various cloud providers are all different, making data synchronization and translation a challenge. The Data Logistics Mandate In this new world, Chief Data Officers and CIOs must optimize how they manage vast volumes of data across multiple clouds and data environments through better data logistics. There are more data silos, greater distribution of data, vast increases in volume, and a growing demand to understand, protect and exploit data for business growth and optimization while holding down costs. Among those logistical challenges are the need to keep track of where data resides, and cost-effectively synchronize, replicate and move data to optimize value, efficiency, security and governance based on cost differences, processing and analytics capabilities, performance advantages, and regulatory requirements. Organizations need to be able to be able to identify what data needs to be replicated and effectively transmitted and synchronized across different clouds and on-premise locations to improve the availability, security and value of data. For example, organizations increasingly need to move data to specific service providers to conduct analytics or take advantage of specific vendor technologies, lower processing and storage costs, or meet geographic requirements. 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To get involved in this initiative, please reach out to <a href="mailto:dmurray@bpinetwork.org">Dave Murray</a>. 
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Business Performance (BPI) Network

Report

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default

Total Time: 4 ms Total Queries: 28 queries

Query Explain:

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Included Files

Total Time: 4 ms
Total Queries: 28 queries