Dive Brief:
- Half of software engineering teams say agentic AI is a top investment priority for them this year, and 84% say it will be a leading investment in the next three years, according to a SoftServe report released Tuesday. The report, conducted by MIT Technology Review, surveyed 300 CIOs, CTOS and other tech leaders in December and January.
- Despite efforts to deploy the technology, agent integration with existing systems and the cost of computing resources represent barriers to AI adoption, the survey found. Just 12% of teams report widespread use of agentic AI in their organizations, the report found.
- AI experts say in the report that once fully integrated, agentic AI will become ubiquitous in areas like code generation, testing, refactoring and deployment of projects. In about three years, the report said, early adopter fields such as software expect agentic engineering to participate in overall lifecycle management.
Dive Insight:
Software development teams were early adopters of agentic AI, but their use has recently scaled up across multiple industries as tools have matured.
Most organizations expect to pour more spending into workflows in the next several years, according to the SoftServe report.
But the cost of integrating these tools is a significant factor for tech leaders, according to the State of Analytics Engineering report released today by Dbt Labs, which surveyed 363 data practitioners and leaders in December and January.
More than half of respondents said they’re increasing their warehouse and compute spend to keep up with agentic demand, compared to just 36% of leaders upping their team budgets.
Though technical integrations have gotten easier for many teams, trust in quality of data remains a concern, Dbt’s report found. Clean ownership, validated outputs and documented data models are the foundation for data teams, said Dan Poppy, senior manager of content at Dbt Labs in an email.
“Data leaders who frame governance as infrastructure are the ones who will be able to say yes to AI faster and more reliably than everyone else,” Poppy said.
Tech leaders expect productivity improvements from agentic AI will take time, with most respondents in the MIT and SoftServe report saying they expect a slight or moderate improvement in the next two years. Tech leaders foresee AI agents speeding up their teams’ delivery of software projects, and they’re hoping AI agents can manage the product development and software development lifecycles over time.
But AI failures will present different challenges than tech teams have faced before, Poppy said.
“When a traditional system breaks, you know it,” Poppy said. “If an executive asks AI for net new ARR trailing the last 30 days and the AI system fails, it can produce a plausible, confident wrong answer that reaches a board deck or a customer before anyone catches it.”
