Vultr, the world's largest privately held company Cloud Computing Platformhas released a new industry report, “The New Battlefield: Unleashing the Power of AI Maturity with Multi-Model AI.” This groundbreaking new research reveals a clear correlation between an organization's AI maturity and their ability to achieve superior business outcomes in revenue growth, market share, customer satisfaction and operational efficiency that outperform industry peers.
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“Over the next decade, everything will be reimagined with AI at its core, and organizations will integrate cloud engineering principles into their operations. This will result in the rise of AI specialists and independent AI companies, enabling organizations to perform transformative work and gain competitive advantage.”
The study, commissioned by Vultr and conducted by S&P Global Market Intelligence, surveyed more than 1,000 IT and digital transformation decision makers at U.S.-based companies responsible for their organization's AI strategy across a range of industries, including healthcare and life sciences, government/public sector, retail, manufacturing, and financial services. Nearly three-quarters (72%) of those surveyed stated that their AI use is at a high level of maturity. The report also includes a qualitative perspective on AI use by companies of various sizes across the U.S. through in-depth interviews with AI decision makers and practitioners.
“As organizations around the world leverage their strategic investments in AI, we wanted to explore AI maturity,” said Kevin Cochrane, CMO of Vultr's parent company Constant. “What we found is that transformative organizations are winning the hearts, minds, and wallet strings of their customers and clients while improving their operating margins. AI maturity is the new competitive weapon, and companies need to invest now to accelerate the scaling of AI models, training, and production.”
The era of multi-model AI
The number of models actively in use within an organization is a reliable indicator of deployed AI capabilities and overall AI maturity. Data shows that companies with advanced AI adoption leverage many models simultaneously as part of a multi-model approach.
The average number of AI models in production today is 158, and that number is projected to grow to 176 within the next year. This growth represents a significant acceleration in AI adoption across industries, as evidenced by the fact that 89% of organizations expect to be highly advanced in their use of AI within two years.
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AI proficiency and maturity are the new battlegrounds for business performance
AI is poised to permeate the enterprise, with 80% adoption expected across all business functions within 24 months, which includes embedding AI in every application and business unit.
As AI gains new footholds across business, it will have a profound impact on overall enterprise performance. According to the report, companies implementing transformative AI practices report outperforming their peers at higher levels. Specifically, 50% of transformed companies are performing “significantly better” than their peers at an operational level, while the majority of AI-led organizations say their 2022/2023 year-over-year performance has improved in customer satisfaction (90%), revenue (91%), cost reduction/margin expansion (88%), risk (87%), marketing (89%) and market share (89%). Meanwhile, nearly half of organizations (40-45%) say AI has had a “significant” impact on market share, revenue, customer satisfaction, improved marketing, and cost and risk reduction.
“The transformative impact of AI is undeniable. AI is taking industries by storm and permeating every aspect of business operations. Therefore, to effectively scale these innovations, we need a new age of technology underpinned by composable stacks and platform engineering,” said Cochrane.
AI spending is expected to surpass IT spending
To realize the full potential of AI, 88% of companies surveyed plan to increase their AI spending in 2025, with 49% anticipating a moderate to significant increase. Survey findings on key infrastructure, partners and implementation strategies include:
- For cloud-native applications, two-thirds of organizations are custom-building models or using open-source models to deliver the functionality.
- By 2025, the AI infrastructure stack will be hybrid cloud, with 35% of inference happening on-premise and 38% in the cloud/multi-cloud.
- Due to a skills shortage, 47% of companies are leveraging partners to help with AI strategy, implementation, and scale adoption, with only 15% leveraging hyperscalers like AWS, GCP, or Azure.
- Openness, security, and compliance are the most important attributes of a cloud platform for extending AI across organizations, geographies, and the edge.
“For years, hyperscalers have dominated the infrastructure market by relying on their scale, resources and technical expertise, but that's all changing,” Cochran added. “Over the next decade, everything will be restructured around AI, as organizations integrate cloud engineering principles into their operations. This will result in the rise of AI specialists and independents that will enable organizations to carry out transformative work and gain competitive advantage.”
Challenges in implementing AI in any business
As the race to AI intensifies, various obstacles will stand in the way. Budget limitations, building or acquiring AI algorithms, lack of skilled talent, and data quality are some of the major hurdles that organizations will need to overcome to progress to the next stage of AI maturity.
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