With massive transformative potential for both enterprises and SMBs, generative AI is a force unlike any other.
But are Fortune 1000 companies ready to harness this potential? And what’s preventing them from achieving AI success?
A recent eye-opening study by ClearML and the AI Infrastructure Alliance (AIIA) and brought to us by VentureBeat, takes us behind the curtain.
They’re revealing the triumphs and tribulations of generative AI adoption among top-tier organisations. And they’ve got some incredible insights.
The Exciting Landscape of Generative AI
Generative AI isn’t just a buzzword. It’s quickly becoming a catalyst for innovation, productivity, and revenue growth (to name a few).
According to the study, an astounding 81% of C-level executives consider unleashing AI a top priority. This is while 87% are planning to adopt AI technologies like xGPT/LLMs by 2024.
Sounds promising, doesn’t it? But here’s the catch:
59% of organisations actually lack the resources to meet generative AI expectations.
It’s not just about the lack of funds; it’s about the dearth of technology, talent, time, and crafting an effective strategy to make AI work.
Moses Guttmann, co-founder and CEO of ClearML, says, “While most respondents said they need to scale AI, they also said they lack the budget, resources, talent, time, and technology to do so.”
The Hurdles on the Path to AI Transformation
The journey to successful AI adoption is paved with obstacles.
Pointing to a number of critical problem areas, the study reveals that:
- Talent: 42% of enterprises are in desperate need for expert AI personnel.
- Technology: 28% see the lack of a unified software platform as a significant barrier.
- Time: 22% are hampered by the excessive time spent on data collection and preparation.
Beyond these challenges, governance has emerged as a concern, with 54% of top executives admitting that their failure to govern AI applications led to significant financial losses.
“It was found that 54% percent of CDOs, CEOs, CIOs, heads of AI, and CTOs reported that their failure to govern AI/ML applications resulted in losses to the enterprise, while 63% of respondents reported losses of $50 million or more due to inadequate governance of their AI/ML applications,” VentureBeat reports.
The need for customisation, data preservation, security, and performance looms across the board, presenting numerous challenges that require intricate solutions.
But. Despite the obstacles, the expectations are soaring. More than half of the respondents expect a double-digit increase in revenue from AI and ML investments.
“With growing interest in materialising business value from AI and ML investments, we expect that the demand for increased visibility, seamless integration and low code will drive generative AI adoption,” asserts Guttmann.
The Future of Generative AI: unleashing the potential
The study paints a picture of an industry poised for greatness but constrained by resource limitations and strategic challenges.
ClearML’s new Enterprise Cost Management Center and the forthcoming calculator to understand gen AI costs are significant steps toward addressing these challenges.
What’s clear is that generative AI is no longer a future concept; it’s a present reality. It’s a burgeoning field with the power to redefine how enterprises function, innovate, and grow.
The hurdles are real, but the desire to overcome them is palpable.
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