Companies Continue to Drive Investments in AI Technology and Talent Despite Economic Headwinds, Verta Insights Research Shows

Palo Alto, California –(Business Wire) — Operational AI company Verta today released the results of its 2023 AI/ML Investment Priorities Study, which surveyed more than 460 AI and machine learning (ML) practitioners to assess AI/ML investments across industries based on evolving trends. The ML spending plan benchmarks technology trends, industry developments, and macroeconomic conditions. The study, conducted by Verta Insights, the research practice of Verta Inc., found that despite economic headwinds in the broader market, nearly two-thirds of organizations plan to increase or maintain their spending on AI/ML technology and infrastructure.

“We are currently experiencing an inflection point in the AI/ML industry, with technologies such as ChatGPT and Stable Diffusion fueling interest in how companies can leverage machine learning models to significantly automate human activities with very innovative and game-changing capabilities. Our findings confirm that, Despite market volatility, organizations continue to invest heavily in AI/ML technology and talent as they position their business strategies to create intelligent experiences for their customers,” said Conrado Silva Miranda, Chief Technology Officer at Huerta.

In the study, 31% of respondents said their organizations would increase AI/ML spending in 2023 due to current economic conditions, while 32% said they would maintain AI/ML technology in 2022 and infrastructure spending levels. Only one in five (19%) say macroeconomic conditions have prompted their organization to reduce AI/ML spending this year.

When asked to name the top three drivers behind changes in AI/ML budgets in 2023, the main factors included changes in business strategy (37% of respondents), cloud migration and modernization (34%), and cost pressures and inflation ( 33%). Roughly one-third of respondents (32%) cite an increasing number of AI/ML use cases to support and increase the priority of AI/ML projects within their organization.

AI innovation is a top investment priority

The research team also asked participants about their investment strategy priorities for 2022 and 2023 across six different spending categories. The AI ​​innovation technology category topped both years, with 54% of respondents citing it as a strategic priority in 2022 and 58% in 2023. This is followed by data-related tools and infrastructure, which 51% list as a priority for 2022 and 52% for 2023. Cloud migration and modernization remains a priority, with 45% of respondents citing it as a priority for 2022 and 2023.

The most notable change in priorities identified in the study was the growing focus on MLOps and ModelOps platforms, which 43 percent listed as a priority for 2023, an increase of 8 percentage points from last year. Over the two years, about one-third of respondents consistently prioritized staffing investments, as did statistical modeling/analytics modernization.

“The increasing prioritization of MLOps and ModelOps platforms shows that the market is maturing toward an AI-driven future. We continue to see organizations invest in the cloud, data, and experimental capabilities essential prerequisites for building and training AI models. But as companies further implemented machine learning models to support digital transformation, they realized that the technical and operational requirements in a production environment were very different from the experimental nature of model development. They needed to implement stable, controlled, and highly reliable systems to scale manage, deploy and monitor models at scale, so they are shifting their investment focus to MLOps and ModelOps platforms that support these capabilities,” said Silva Miranda.

The AI/ML talent market continues to fluctuate

The findings on staffing also indicate that the labor market for AI/ML talent remains a challenge for organizations. In their public presentations, many participants in the study cited the difficulty of equipping their teams with the right skill sets to support their AI/ML initiatives.

“The biggest challenge related to our organization’s AI/ML investments in 2023 will be a lack of skilled labor,” typical comment from one attendee. The respondent went on to say that as technology continues to evolve, it becomes increasingly difficult to find people with the right skills and experience to manage and implement the company’s AI/ML initiatives. “We expect this to be the biggest challenge in 2023, and we need to find creative ways to solve it,” respondents said.

In response, many companies are increasing their budgets for AI/ML hires. The study reveals that more than 50 percent of organizations plan to increase talent spending on data science, machine learning engineering, and ML platform teams in 2023 compared to 2022.

“There’s a lot of attention being paid to layoffs in the tech industry right now, but even the leaders of the big tech companies that are doing it are saying they’re continuing to prioritize spending on AI initiatives.” Microsoft recently confirmed Investing $10 billion in ChatGPT reminds us that the race for AI advantage itself is not slowing down. Our research finds that companies are planning to increase spending across the board in 2023 on talent, technology and relatively expensive innovation to further their AI advantage /ML,” said Rory King, Principal, Verta Insights Research.

King added that MLOps and ModelOps platforms are prioritizing hiring in related functions, suggesting some companies may be addressing talent shortages by investing in tools that automate the production of ML models.

“We are seeing companies that are financially outperforming their peers prioritizing technology investments while laggards are cutting back. We are increasingly seeing leading companies recognize that they cannot hire to achieve operational excellence At the same time, they realize that closed-loop ML platforms can standardize, automate, and build resilience in the operationalization of their AI capabilities and applications, which is a force multiplier. They can automate tasks, augment AI by leveraging the technology platform “Doing more with less” by reducing the number of features and ML use cases, and reducing the costs and risks associated with brain drain and large operational support teams,” King explained.

Hybrid on-premises + cloud approach dominates

The Verta Insights study explored organizations’ approach to the technology infrastructure used to support AI/ML and found that a hybrid approach combining cloud and on-premises deployments predominates. Nearly half (48%) of respondents describe their organization’s infrastructure approach as hybrid, while 32% say they have a pure cloud strategy. Only 7 percent of respondents said they have an on-premises-only approach to AI/ML infrastructure, while another 8 percent said they currently use an on-premises-only approach but are migrating to the cloud.

Research shows that companies are increasing their spending on AI/ML technology infrastructure, including spending on cloud, compute, and storage. Nearly two-thirds (64%) of respondents said their organization plans to spend more on infrastructure in 2023 than in 2022. A quarter of respondents said they would spend the same this year as last year, while only 6% said they planned to spend less infrastructure this year than in 2022.

“The data we looked at is consistent with what we see across companies working across industries, with an overwhelming belief that we will operate in multi-cloud, hybrid ecosystems in the future. Hybrid allows organizations to keep some high-value or high-risk assets on-premises , while taking advantage of the flexibility, scalability, and cost-effectiveness of cloud infrastructure. As companies plan their AI/ML technology roadmap, they should be looking for tools , which will also support their technology stack,” said Manasi Vartak, founder and CEO of Verta.

Join the discussion of research findings

Verta will explore these and other key findings from the study during a supplemental virtual event on Thursday, February 2 at 10 a.m. PT. Individuals who register for the virtual event will receive an electronic copy of the study after it is published.

Register for the virtual event at:

About Verta Insights

Verta Insights is the research group of Verta, a leading provider of artificial intelligence (AI) model management and operations solutions. Verta Insights conducts research on trends in AI and machine learning and provides insights to help AI/ML practitioners and executive leaders prepare their organizations for an AI-enabled intelligent future.

About Weta

Verta is an operating artificial intelligence company. Verta enables enterprises to implement the high-speed data science and real-time machine learning required for the next generation of AI-enabled intelligent systems and devices. With extensive experience in data science and operational ML at Google, Twitter, and NVIDIA, Verta’s founders founded the company to fill the gap in tools for operational ML. The Verta Operational AI Platform takes any ML model and uses best-in-class DevOps support to instantly package and deliver CI/CD, operations, and monitoring while ensuring secure, reliable, and scalable real-time AI deployments. Gartner named Verta a Cool Vendor for 2022 for “Artificial Intelligence Core Technologies – Scaling AI in the Enterprise.” Headquartered in Palo Alto, Verta is backed by Intel Capital and General Catalyst.For more information, please visit or follow @VertaAI.

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