Gartner Forecasts 30% Failure Rate for Generative AI Initiatives by 2025

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New research from Gartner reveals that at least 30% of generative AI businesses will be abandoned after proof of concept by the end of 2025, due to escalating costs and data quality issues.Announced at Gartner’s Data & Analytics Summit, the findings show early adopters struggling with financial burdens, with deployments costing between $5 million and $20 million, and varied returns on investment.

TODAY’S ROADMAP

• Current Happenings in AI World

• Generative AI Adoption Faces Financial Hurdles: Gartner Reports $5-20M Deployment Costs

• Create, Edit, and Share Professional How-To Videos with Guidde

• 5 AI Tools to Boost Your Productivity

  and more…

NEWS

Current Happenings in AI World

Cloud Doubt: A recent report revealed a decline in sales for Microsoft’s AI cloud services last quarter, impacting tech stocks.

Altman Approves: OpenAI has endorsed three newly introduced bills, one of which proposes the creation of an AI Safety Institute to develop guidelines for tech companies.

Blog Buddy: Wix, the website creation platform, is introducing several new AI tools, including one that generates SEO-friendly blog posts and images.

AI Investigator: The Android “circle to search” feature — allowing users to circle an image with their finger to find more information — can now help identify the origins of a picture, including its authenticity.

AI UPDATE

Generative AI Adoption Faces Financial Hurdles: Gartner Reports $5-20M Deployment Costs

New research from Gartner reveals that at least 30% of generative AI businesses currently testing will be abandoned after proof of concept by the end of 2025.

Announced at Gartner’s Data & Analytics Summit in Sydney, Australia, the statistics highlight that early generative AI adopters are grappling with escalating costs, with deployments ranging from $5 million to $20 million.

For instance, designing a custom generative AI model, like fine-tuning a Llama model on industry-specific data, costs $5 million to $6 million upfront and up to $11,000 in recurring costs. Creating a model from scratch could cost as much as $20 million, while setting up a simple document search feature through retrieval augmented generation (RAG) costs upwards of $750,000.

Gartner suggests businesses are struggling to justify investments in generative AI due to substantial financial requirements. The firm states that regardless of ambition, generative AI “requires a higher tolerance for indirect, future financial investment criteria versus immediate return on investment (ROI).”

“After last year’s hype, executives are impatient to see returns on generative AI investments, yet organizations are struggling to prove and realize value,” said Rita Sallam, Gartner’s distinguished vice president analyst. “As the scope of initiatives widens, the financial burden of developing and deploying generative AI models is increasingly felt.”

Gartner’s research indicates that early generative AI adopters are reporting varied business improvements. A recent survey of 822 business leaders found that just 15.8% reported revenue increases, 15.2% saved costs, and 22.6% noticed productivity improvements following generative AI deployments.

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AI MEETS PRODUCTIVITY

5 AI Tools to Boost Your Productivity

• Fastmind: Create a custom chatbot on your website with one line of code and live data.

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PROMPT OF THE DAY

Expert Entrepreneur: Mastering Market Opportunities and MVP Development

prompt: You are an expert in entrepreneurship, with extensive knowledge of identifying market opportunities and developing innovative products. Your skill set includes market research, product development, and creating a minimum viable product (MVP) to validate your business idea in the market.
Develop a minimum viable product (MVP) by creating a basic version of your product that includes only the essential features. This can be done by identifying the core functionalities and designing a simple prototype that can be tested by potential customers. The MVP should be developed with the purpose of gathering feedback and validating the market demand for your product.

The task is to create a minimum viable product (MVP) for market validation, which is crucial to test the viability and potential success of a product before investing significant resources into its full development. The benefit of developing an MVP is that it allows for early feedback and validation from potential customers, helping to identify and address any issues or improvements needed before fully launching the product.

AI CRAFTED IMAGES

On top of mount Everest

Prompt:
a mountaineer equipped with climbing gear and equipment has his beard full of ice and is looking at the sky on a top of mount Everest, hyper realistic, 4k

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