PALO ALTO, Calif.--(BUSINESS WIRE)--Verta, the Operational AI company, today released findings from the 2023 AI/ML Investment Priorities study, which surveyed more than 460 AI and machine learning (ML) practitioners to benchmark AI/ML spending plans across industry sectors in light of evolving technology trends, industry developments, and macroeconomic conditions. The study was conducted by Verta Insights, the research practice of Verta Inc., and found that nearly two-thirds of organizations are planning to either increase or maintain their spending on AI/ML technology and infrastructure despite economic headwinds in the broader market.
“We currently are experiencing an inflection point for the AI/ML industry, with technologies like ChatGPT and Stable Diffusion driving heightened interest in how companies can leverage machine learning models to significantly automate human-based activities with very innovative and game-changing capabilities. Findings from our research study confirm that organizations are continuing to make significant investments in AI/ML technology and talent, despite turbulence in the market, as they orient their business strategies around creating intelligent experiences for their customers,” said Conrado Silva Miranda, Chief Technology Officer of Verta.
In the research study, 31% of respondents said that their organizations would increase AI/ML spending in 2023 due to the current economic conditions, while 32% said that they would maintain 2022 spending levels for AI/ML technology and infrastructure. Just 1 in 5 (19%) said that macroeconomic conditions had prompted their organizations to decrease AI/ML spending this year.
When asked to cite the top three drivers behind changes in their AI/ML budget in 2023, the leading factors included changes in business strategy (37% of respondents), cloud migration and modernization (34%), and cost pressures and inflation (33%). About one-third of respondents (32%) cited an increased number of AI/ML use cases to support and increased priority for AI/ML projects within their organizations.
AI Innovation Is Top Investment Priority
The research team also asked participants about their strategic priorities for investment across six different categories of spend in both 2022 and 2023. The category of AI innovation technologies topped the list for both years, cited by 54% of respondents as a strategic priority for 2022, and 58% for 2023. Data-related tools and infrastructure followed, cited by 51% as a 2022 priority and 52% for 2023. Cloud migration and modernization was a consistent priority, cited by 45% of respondents for both 2022 and 2023.
The most significant change in priorities identified in the study was the increasing level of attention to MLOps and ModelOps platforms, which 43% cited as a priority for 2023, an increase of 8 percentage points over last year. Investments in staffing remained a consistent priority for about one-third of respondents across both years, as did statistical modeling/analytics modernization.
“The increasing prioritization of MLOps and ModelOps platforms is a signal of a natural progression in how the market is maturing towards an AI-driven future. We continue to see organizations investing in the basic prerequisites of cloud, data, and experimentation capabilities to build and train AI models. But as companies get further into their implementation of machine learning models in support of digital transformation, they realize that the technology and operating requirements in a production setting are far different from the experimental nature of model R&D. They need to implement stable, controlled and high-reliability systems to manage, deploy and monitor models at scale, so they shift their investment priorities toward MLOps and ModelOps platforms that support these capabilities,” said Silva Miranda.
Volatility Continues in AI/ML Talent Market
The findings around staffing also revealed that the labor market for AI/ML talent continues to be a challenge for organizations. In their open remarks, many participants in the study cited difficulties adequately staffing their teams with the right skill sets to support their AI/ML initiatives.
“The single biggest challenge related to our organization's AI/ML investments in 2023 will be the lack of skilled labor,” was a typical comment from one participant. This respondent went on to say that, with the constant evolution of technology, it is becoming increasingly difficult to find personnel with the right skills and experience to manage and implement the company’s AI/ML initiatives. “We anticipate that this will be the biggest challenge in 2023, and we will need to find creative ways to solve it,” the respondent stated.
In response, many companies are ramping up their budgets for hiring AI/ML personnel. More than 50% of organizations plan to increase their spending on talent in 2023 versus 2022 across data science, machine learning engineering and ML platform teams, according to the study.
“Layoffs in the tech sector are getting lots of attention at the moment, but even the remarks made by company leaders at the major tech companies who are indeed downsizing suggest that they also are continuing to prioritize spending on AI initiatives. Microsoft’s recent confirmation of $10 billion investment in ChatGPT reminds us that the race for AI superiority itself is not slowing down. Our study found that companies are planning to increase spending across the board on talent, technology and relatively costly innovation to further their advances in AI/ML in 2023,” said Rory King, Head of Verta Insights Research.
King added that increased prioritization on MLOps and ModelOps platforms over hiring in related functions suggests that some companies might be addressing the talent crunch by investing in tools that automate the productionalization of ML models.
“We see that companies who outperform their peers financially are investing in technology as a priority, whereas lagging performers are making cuts. Increasingly we see leading companies recognizing they can’t hire their way to operational excellence. At the same time, they are realizing that closed-loop ML platforms to standardize, automate and build resilience into their operationalization of AI features and applications is a force multiplier. They can ‘do more with less’ by making use of technology platforms to automate tasks, increase the number of AI features and ML use cases, and reduce both the cost and risk associated with talent churn and large support teams in operations,” King explained.
Hybrid On-prem + Cloud Approach Predominates
The Verta Insights study explored organizations’ approach to the technology infrastructure they are using to support AI/ML, finding that a hybrid approach incorporating both cloud and on-premises deployments predominates. Nearly half (48%) of respondents described their organizations’ infrastructure approach as hybrid, versus 32% that said they have a cloud-only strategy. Just 7% of respondents said they have an on-prem only approach to their AI/ML infrastructure, while a further 8% said they currently were on-prem only but moving to the cloud.
The research indicated that companies are ramping up their spend on AI/ML technology infrastructure, including spending on cloud, compute and storage. Nearly two-thirds (64%) of respondents said that their organizations plan to increase their infrastructure spend in 2023 over 2022. One-quarter said that they would spend the same this year as last, while only 6% indicated they planned to spend less for infrastructure this year than in 2022.
“The data from our study align with what we see in companies we work with across industries, where the overwhelming belief is that we will operate in a multi-cloud, hybrid ecosystem in the future. Hybrid allows an organization to keep some high value or high risk assets on-prem, while taking advantage of the flexibility, scalability and cost effectiveness of cloud infrastructure. As companies plan their AI/ML technology roadmap, they should look for tools that support whichever approach they choose today, but that also will support their technology stack as it evolves in the future,” said Manasi Vartak, Founder and CEO of Verta.
Join the Discussion of the Study Results
Verta will explore these and other key findings from the research study during a complementary virtual event on Thursday, February 2 at 10 a.m. Pacific Time. Individuals who register for the virtual event will receive an e-copy of the research study upon its release.
Register for the virtual event at: https://us06web.zoom.us/webinar/register/2016748431193/WN_R3_sZ2T0RSGZuc_G2XMYIw
About Verta Insights
Verta Insights is the research group at Verta, a leading provider of Artificial Intelligence (AI) model management and operations solutions. Verta Insights conducts research into trends in the AI and machine learning space, and delivers insights to assist AI/ML practitioners and executive leaders to prepare their organizations for the AI-enabled intelligent future.
Verta is the Operational AI company. Verta enables enterprises to achieve the high-velocity 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 established the company to fill a gap in tooling to operationalize ML. The Verta Operational AI Platform takes any ML model and instantaneously packages and delivers it using best-in-class DevOps support for CI/CD, operations, and monitoring, while ensuring safe, reliable, and scalable real-time AI deployments. Gartner named Verta a 2022 Cool Vendor for “AI Core Technologies — Scaling AI in the Enterprise.” Based in Palo Alto, Verta is backed by Intel Capital and General Catalyst. For more information, go to www.verta.ai or follow @VertaAI.