Vultr reveals insights for bridging the gap between AI ambition and maturity

Vultr reveals insights for bridging the gap between AI ambition and maturity

Transformational AI enterprises are leading the charge, outperforming operational organisations across key business metrics, according to Vultr’s new report.

Vultr, one of the world’s largest, privately-held cloud computing platforms, has released a new industry report, The New Battleground: Unlocking the Power of AI Maturity with Multi-Model AI.

The new study reveals a clear correlation between an organisation’s AI maturity and its ability to achieve superior business outcomes, outpacing industry peers in revenue growth, market share, customer satisfaction and operational efficiency.

Commissioned by Vultr and conducted by S&P Global Market Intelligence, the research surveyed over 1,000 IT and Digital Transformation decision-makers responsible for their organisation’s AI strategy across industries, including healthcare and life sciences, government and public sector, retail, manufacturing, financial services and more.

Of the respondents surveyed, almost three-quarters (72%) are at higher levels of maturity of AI use. The report also includes a qualitative perspective on AI use by enterprises of varying sizes through in-depth interviews with AI decision-makers and practitioners.

“As organisations worldwide capitalise on strategic investments in AI, we wanted to look at the state of AI maturity,” said Kevin Cochrane, CMO of Vultr’s parent company, Constant. “What we’ve found is that transformational organisations are winning the hearts, minds and share of wallets while also improving their operating margins. AI maturity is the new competitive weapon, and businesses must invest now to accelerate AI models, training and scaling in production.”

The age of multi-model AI

The number of models actively used within an organisation is a reliable measure of its deployed AI capabilities and overall AI maturity. The data reveals that advanced AI adopters leverage a multitude of models simultaneously as part of a multi-model approach.

On average, the number of distinct AI models currently operational stands at 158 with projections suggesting this number will rise to 176 AI models within the next year. This growth highlights remarkable acceleration in AI adoption across industries, underscored by the 89% of organisations anticipating advanced AI utilisation within two years.

AI proficiency and maturity is the new business performance battleground

AI is poised to permeate throughout the enterprise with 80% adoption anticipated across all business functions within 24 months. This will include AI being embedded across all applications and business units.

As AI builds on its new foothold across businesses, there will be an immense impact on enterprise-wide performance. According to the report, those with transformational AI practices reported they outperformed their peers at higher levels. Specifically, 50% of transformational companies are performing ‘significantly better’ against industry peers than those at operational levels, while a large majority of AI-driven organisations say they improved their 2022/2023 year-over-year performance in customer satisfaction (90%), revenue (91%), cost reduction/margin expansion (88%), risk (87%), marketing (89%) and market share (89%).

Meanwhile, nearly half (40-45%) of organisations say AI is having a ‘major’ impact on market share, revenue, customer satisfaction, marketing improvements, and cost and risk reduction.

“AI’s transformative impact is undeniable – it’s devouring industries and is becoming ubiquitous in every facet of business operations. This necessitates a new era of technology, underpinned by a composable stack and platform engineering to effectively scale these innovations,” said Cochrane.

AI spending is expected to outpace IT spend

To fully harness AI’s potential, 88% of the enterprises surveyed intend to increase their AI spend in 2025 with 49% expecting moderate to significant increases. Findings related to key infrastructure, partner and implementation strategies include:

  • For cloud-native applications, two-thirds of organisations are either custom-building their models or using open-source models to deliver functionality.
  • In 2025, the AI infrastructure stack will be hybrid cloud with 35% of inference taking place on-prem and 38% in the cloud/multi-cloud.
  • Thanks to the skills shortage, 47% of enterprises are leveraging a partner to help them with strategy, implementation and deployment of AI at scale. Only 15% are leveraging hyperscalers such as AWS, GCP, or Azure.
  • Open, secure and compliant are the top attributes of cloud platforms for scaling AI across the organisation, geographies and to the Edge.

“For years the hyperscalers have dominated the infrastructure market, relying on scale, resources and technological expertise, but that is all about to change,” added Cochrane.

“Over the next decade, everything will be rebuilt with AI at the core, with organisations integrating the principles of cloud engineering into their operations. As a result, we will see the rise of AI specialists and independents as they empower organisations to do transformative work and gain a competitive edge.”

Challenges to scaling AI across all enterprises

As the race to AI heats up, it will not be without its share of obstacles. Budget limitations, building or obtaining AI algorithms, lack of skilled personnel and data quality are among the top hurdles organisations say they must resolve to graduate to the next stage of AI maturity.

For those at a transformational level of maturity, governance (30%) becomes much more of an issue, while company culture is the larger issue for those still in the accelerating stage.

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