.jpeg "What do I need to buy to enable generative AI? The costs for generative AI will range from negligible to many millions depending on the use case, scale and requirements of the company. Small and midsize enterprises may derive significant business value from the free versions of public, openly hosted applications, such as ChatGPT, or by paying low subscription fees. For example, OpenAI is currently $20 per user per month. However, free and low-cost options come with minimal protection of enterprise data and associated output risks. Larger enterprises and those that desire greater analysis or use of their own enterprise data with higher levels of security and IP and privacy protections will need to invest in a range of custom services. This can include building licensed, customizable and proprietary models with data and machine learning platforms, and will require working with vendors and partners. In this instance, costs can be in the millions of dollars. It’s also worth noting that generative AI capabilities will increasingly be built into the software products you likely use everyday, like Bing, Office 365, Microsoft 365 Copilot and Google Workspace. This is effectively a “free” tier, though vendors will ultimately pass on costs to customers as part of bundled incremental price increases to their products. What does Gartner predict for the future of generative AI use? Generative AI is primed to make an increasingly strong impact on enterprises over the next five years. Gartner predicts that: By 2024, 40% of enterprise applications will have embedded conversational AI, up from less than 5% in 2020. By 2025, 30% of enterprises will have implemented an AI-augmented development and testing strategy, up from 5% in 2021. By 2026, generative design AI will automate 60% of the design effort for new websites and mobile apps. By 2026, over 100 million humans will engage robocolleagues to contribute to their work. By 2027, nearly 15% of new applications will be automatically generated by AI without a human in the loop. This is not happening at all today. GenAI Changes Innovation & Operations Who are the major tech providers in the generative AI market? The Generative AI marketplace is on fire. Beyond the big platform players, there are many hundreds of specialty providers funded by ample venture capital and a wave of new open-source models and capabilities. Enterprise application providers, such as Salesforce and SAP, are building LLM capabilities into their platforms. Organizations like Microsoft, Google, Amazon Web Services (AWS) and IBM have invested hundreds of millions of dollars and massive compute power to build the foundational models on which services like ChatGPT and others depend. Gartner considers the current major players to be as follows: Google has two large language models, Palm, a multimodal model, and Bard, a pure language model. They are embedding their generative AI technology into their suite of workplace applications, which will immediately get it in the hands of millions of people. Microsoft and OpenAI are marching in lockstep. Like Google, Microsoft is embedding generative AI technology into its products, but it has the first-mover advantage and buzz of ChatGPT on its side. Amazon has partnered with Hugging Face, which has a number of LLMs available on an open-source basis, to build solutions. Amazon also has Bedrock, which provides access to generative AI on the cloud via AWS, and has announced plans for Titan, a set of two AI models that create text and improve searches and personalization. IBM has multiple foundation models and a strong ability to fine-tune both its and third-party models by injecting data and retraining and employing the model. Conversational AI Platforms Is this the start of artificial general intelligence (AGI)? It depends whom you ask. AGI, the ability of machines to match or exceed human intelligence and solve problems they never encountered during training, provokes vigorous debate and a mix of awe and dystopia. AI is certainly becoming more capable and is displaying sometimes surprising emergent behaviors that humans did not program. The likely path is the evolution of machine intelligence that mimics human intelligence but is ultimately aimed at helping humans solve complex problems. This will require governance, new regulation and the participation of a wide swath of society.")
What do I need to buy to enable generative AI?
The costs for generative AI will range from negligible to many millions depending on the use case, scale and requirements of the company. Small and midsize enterprises may derive significant business value from the free versions of public, openly hosted applications, such as ChatGPT, or by paying low subscription fees. For example, OpenAI is currently $20 per user per month. However, free and low-cost options come with minimal protection of enterprise data and associated output risks.
Larger enterprises and those that desire greater analysis or use of their own enterprise data with higher levels of security and IP and privacy protections will need to invest in a range of custom services. This can include building licensed, customizable and proprietary models with data and machine learning platforms, and will require working with vendors and partners. In this instance, costs can be in the millions of dollars.
It’s also worth noting that generative AI capabilities will increasingly be built into the software products you likely use everyday, like Bing, Office 365, Microsoft 365 Copilot and Google Workspace. This is effectively a “free” tier, though vendors will ultimately pass on costs to customers as part of bundled incremental price increases to their products.
What does Gartner predict for the future of generative AI use?
Generative AI is primed to make an increasingly strong impact on enterprises over the next five years. Gartner predicts that:
By 2024, 40% of enterprise applications will have embedded conversational AI, up from less than 5% in 2020.
By 2025, 30% of enterprises will have implemented an AI-augmented development and testing strategy, up from 5% in 2021.
By 2026, generative design AI will automate 60% of the design effort for new websites and mobile apps.
By 2026, over 100 million humans will engage robocolleagues to contribute to their work.
By 2027, nearly 15% of new applications will be automatically generated by AI without a human in the loop. This is not happening at all today.
Who are the major tech providers in the generative AI market?
The Generative AI marketplace is on fire. Beyond the big platform players, there are many hundreds of specialty providers funded by ample venture capital and a wave of new open-source models and capabilities. Enterprise application providers, such as Salesforce and SAP, are building LLM capabilities into their platforms. Organizations like Microsoft, Google, Amazon Web Services (AWS) and IBM have invested hundreds of millions of dollars and massive compute power to build the foundational models on which services like ChatGPT and others depend.
Gartner considers the current major players to be as follows:
Google has two large language models, Palm, a multimodal model, and Bard, a pure language model. They are embedding their generative AI technology into their suite of workplace applications, which will immediately get it in the hands of millions of people.
Microsoft and OpenAI are marching in lockstep. Like Google, Microsoft is embedding generative AI technology into its products, but it has the first-mover advantage and buzz of ChatGPT on its side.
Amazon has partnered with Hugging Face, which has a number of LLMs available on an open-source basis, to build solutions. Amazon also has Bedrock, which provides access to generative AI on the cloud via AWS, and has announced plans for Titan, a set of two AI models that create text and improve searches and personalization.
- IBM has multiple foundation models and a strong ability to fine-tune both its and third-party models by injecting data and retraining and employing the model.
Is this the start of artificial general intelligence (AGI)?
It depends whom you ask. AGI, the ability of machines to match or exceed human intelligence and solve problems they never encountered during training, provokes vigorous debate and a mix of awe and dystopia. AI is certainly becoming more capable and is displaying sometimes surprising emergent behaviors that humans did not program.
The likely path is the evolution of machine intelligence that mimics human intelligence but is ultimately aimed at helping humans solve complex problems. This will require governance, new regulation and the participation of a wide swath of society.