2023 will be remembered as the year generative AI went mainstream after companies looked to the success of ChatGPT as inspiration for their own generative AI adoption, launches and enterprise apps. Now, as 2024 is underway, companies are looking to fully realize the promise of gen AI for the enterprise by weaving it into more of their workflows.
Yet Forrester Consulting’s survey of 220 AI decision-makers at North American firms finds that many remain concerned about the risks associated with the technology and see barriers to its adoption.
The poll highlights major roadblocks (including well-known issues like hallucinations) that leave organizations stuck in the exploration or experimentation stage, keeping them away from actually operationalizing foundation models for planned use cases.
This is something that teams will have to work on if they plan to double down on gen AI.
Given the pile of success stories on the internet, organizations across industries already understand the transformative potential of generative AI.
In the Forrester survey, which was conducted last month on behalf of Dataiku, 83% of the respondents said they are either exploring or experimenting with gen AI.
Meanwhile, a little more than 60% claimed they consider it critically or highly important for their business strategy and plan to increase investment in data/AI initiatives by up to 10% in the next 12 months.
The leaders also emphasized that they already have use cases in the pipeline. More than half of the respondents said they have identified multiple potential applications of the technology, including enhancing customer experiences (64%), product development (59%), self-service data analytics (58%) and knowledge management (56%).
“This echoes a sentiment of exploration and curiosity, where organizations are captivated by the breadth of potential applications, anticipating an inclusive embrace of the diversity of its transformative capabilities over the next two years,” the survey noted. The broader advantages expected from these applications were enhanced existing offerings, creation of new products/services and optimization of internal and external operations, the respondents added.
Despite the positive outlook, the leaders pointed out certain roadblocks in the way to successful gen AI adoption, including the risk of violating data protection and privacy laws (31%) as well as the challenge of developing the skills and governance (31%) to adeptly orchestrate the intricacies of gen AI.
More than 50% also emphasized the risk of biases and hallucinations affecting the quality of gen AI outputs.
More importantly, all these risks are further amplified when the organization fails to deliver the infrastructural prerequisites for generative AI adoption. The biggest barrier in this area, according to the survey, is the lack of a robust data infrastructure.
As many as 35% of the respondents cited inadequate infrastructure to support the consumption, storage and sharing of massive volumes of data as a pain point.
The same number of respondents also cited difficulties in integrating with existing infrastructure while 27% flagged computational limitations.
The other barriers flagged by them were around the handling of governance mechanisms (35%), interpretability and explainability of AI (25%), talent and skill gaps (31%) and the scalability of the models involved.
“Organizations can alleviate many of the implementation challenges by adopting an approach that provides them with a collaborative set of capabilities. They can achieve this with the help of AI platforms that offer prepackaged solutions for accelerated development, structured environment for easy integration, robust frameworks and security features for standardization, governance, and compliance,” the survey noted.
According to McKinsey, generative AI alone can add $2.6 trillion to $4.4 trillion in global corporate profits annually. It is also estimated that the technology AI is likely to deliver its biggest impact in the banking, high-tech and life sciences sectors.
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2023 will be remembered as the year generative AI went mainstream after companies looked to the success of ChatGPT as inspiration for their own generative AI adoption, launches and enterprise apps. Now, as 2024 is underway, companies are looking to fully realize the promise of gen AI for the enterprise by weaving it into more of their workflows.
Yet Forrester Consulting’s survey of 220 AI decision-makers at North American firms finds that many remain concerned about the risks associated with the technology and see barriers to its adoption.
The poll highlights major roadblocks (including well-known issues like hallucinations) that leave organizations stuck in the exploration or experimentation stage, keeping them away from actually operationalizing foundation models for planned use cases.
This is something that teams will have to work on if they plan to double down on gen AI.
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Organizations understand gen AI’s transformative potential
Given the pile of success stories on the internet, organizations across industries already understand the transformative potential of generative AI.
In the Forrester survey, which was conducted last month on behalf of Dataiku, 83% of the respondents said they are either exploring or experimenting with gen AI.
Meanwhile, a little more than 60% claimed they consider it critically or highly important for their business strategy and plan to increase investment in data/AI initiatives by up to 10% in the next 12 months.
The leaders also emphasized that they already have use cases in the pipeline. More than half of the respondents said they have identified multiple potential applications of the technology, including enhancing customer experiences (64%), product development (59%), self-service data analytics (58%) and knowledge management (56%).
“This echoes a sentiment of exploration and curiosity, where organizations are captivated by the breadth of potential applications, anticipating an inclusive embrace of the diversity of its transformative capabilities over the next two years,” the survey noted. The broader advantages expected from these applications were enhanced existing offerings, creation of new products/services and optimization of internal and external operations, the respondents added.
Roadblocks to adoption remain
Despite the positive outlook, the leaders pointed out certain roadblocks in the way to successful gen AI adoption, including the risk of violating data protection and privacy laws (31%) as well as the challenge of developing the skills and governance (31%) to adeptly orchestrate the intricacies of gen AI.
More than 50% also emphasized the risk of biases and hallucinations affecting the quality of gen AI outputs.
More importantly, all these risks are further amplified when the organization fails to deliver the infrastructural prerequisites for generative AI adoption. The biggest barrier in this area, according to the survey, is the lack of a robust data infrastructure.
As many as 35% of the respondents cited inadequate infrastructure to support the consumption, storage and sharing of massive volumes of data as a pain point.
The same number of respondents also cited difficulties in integrating with existing infrastructure while 27% flagged computational limitations.
The other barriers flagged by them were around the handling of governance mechanisms (35%), interpretability and explainability of AI (25%), talent and skill gaps (31%) and the scalability of the models involved.
“Organizations can alleviate many of the implementation challenges by adopting an approach that provides them with a collaborative set of capabilities. They can achieve this with the help of AI platforms that offer prepackaged solutions for accelerated development, structured environment for easy integration, robust frameworks and security features for standardization, governance, and compliance,” the survey noted.
According to McKinsey, generative AI alone can add $2.6 trillion to $4.4 trillion in global corporate profits annually. It is also estimated that the technology AI is likely to deliver its biggest impact in the banking, high-tech and life sciences sectors.
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Author: Shubham Sharma
Source: Venturebeat
Reviewed By: Editorial Team
