Findings from cloud decision-makers suggest enterprises need new delivery models to operationalize agentic AI

BELLEVUE, WA / ACCESS Newswire / February 10, 2026 / SOUTHWORKS, a leading software engineering company renowned for delivering high-quality solutions at speed and scale, today announced the release of new research examining how enterprises are sourcing and scaling autonomous agentic AI capabilities.

Based on a survey of 625 cloud architects, cloud decision-makers and cloud-native Azure professionals conducted in partnership with theCUBE Research, the study finds that interest in agentic AI is rapidly moving from experimentation to execution. Nearly all respondents see value in the capabilities agentic AI presents, including AI reasoning, decision optimization, AI assistants for task execution and fully autonomous agents. However, the research indicates that most organizations lack sufficient internal capacity to scale these initiatives independently. As a result, they are increasingly relying on external platforms and service providers, underscoring a broader maturity gap between ambition and operational readiness.

"This research confirms what we've been hearing from enterprise tech leaders as they work to operationalize agentic AI across complex cloud environments: they see enormous potential in agentic AI, but few can realize it with internal teams alone," said Johnny Halife, CTO at SOUTHWORKS. "It's telling that most organizations plan to rely on external platforms or consultants to get started with agentic AI. That gap highlights the need for delivery models that accelerate execution without sacrificing ownership."

Enterprises Are Outsourcing Agentic AI to Move Faster

When asked how enterprises plan to source agentic AI capabilities, 70.9% of respondents cited agentic AI platform vendors, while 68.6% pointed to IT or consulting service providers. SaaS application integrations were also widely reported at 58.7%, along with open-source frameworks and libraries at 47.4%. In contrast, only 31.5% of organizations report plans to primarily build agentic AI capabilities in-house.

The findings suggest that speed-to-value and access to scarce AI and cloud skills are outweighing long-term internal development strategies for many organizations. Many organizations are treating agentic AI as an acquisition challenge rather than a foundational platform capability. While this approach may accelerate early progress, the research indicates it can increase long-term dependence on third-party tools and providers.

Priorities Signal Near-Term Value Over Full Autonomy

The research also examined how enterprises are prioritizing and operationalizing agentic AI. Respondents reported a near-term focus on practical use cases, such as reasoning and task assistance, while more autonomous, agent-driven systems remain a longer-term goal.

Key findings include the most important capabilities for organizations' AI strategies, ranked by priority:

• AI reasoning that can plan, optimize and justify outcomes (71%)

• AI assistants (GenAI) that help execute tasks (70.7%)

• Autonomous, goal-driven AI agents that take actions (57.6%)

• Agentic workflows led and orchestrated by AI (51.7%)

• Multi-agent collaborative systems (48%)

Over the next 18 months, respondents expect continued investment across these capabilities in a similar order: AI reasoning, autonomous AI agents that take action, GenAI assistants, agentic workflows and multi-agent collaborative systems.

Currently, AI agents are most often used to automate repetitive tasks (73.1%), assist workers in decision-making (67.5%) and help diagnose and solve business problems (65.6%). More autonomous use cases, like issuing goal-oriented objectives with minimal human guidance, remain less common.

Ambition Is High, but Enterprise Readiness Is Fragmented

While agentic AI adoption is underway across many organizations, deployment maturity remains uneven. Only about one-third (29.8%) of surveyed organizations reported having enterprise-wide agentic AI deployments built on a common framework. Similar shares said agentic AI is limited to isolated departmental use cases (29.1%) or siloed deployments across multiple business units without standardization (23.8%). An additional 17% reported that agentic AI initiatives are still in pilot or experimental phases.

This fragmentation can limit scalability, consistency and governance as deployments expand. For developers, it often translates into duplicated effort, inconsistent tooling and unclear paths from experimentation to production. The research suggests that outsourcing is frequently used as a short-term accelerator rather than a sustainable operating model.

"Our research shows agentic AI has crossed the curiosity threshold and entered an execution phase, but enterprise readiness is lagging ambition," said Paul Nashawaty, Practice Lead and Principal Analyst at theCUBE Research. "While nearly all respondents see value in agentic AI, only 31.5% plan to build these capabilities primarily in-house, and fewer than 30% have standardized enterprise deployments, forcing organizations to trade speed for long-term control as they rely on platforms and services to move forward."

Speed Introduces New Control and Governance Gaps

As enterprises move quickly to deploy agentic AI through external tools and partners, governance and security challenges are becoming more pronounced. The research found that 50.7% of organizations still rely primarily on public AI tools for AI implementation, while only 20.2% have established enterprise-wide AI deployments on common frameworks. One in ten (10.6%) reported AI use across multiple business units without shared standards.

As agentic systems become more autonomous and deeply embedded in production environments, these gaps may introduce long-term risk despite short-term gains in development velocity.

"Moving fast with AI only works if teams can keep control of what they build," Halife added. "Agentic systems touch data, workflows and decisions across the organization. Without clear ownership and governance, early speed can create friction as deployments scale."

Bridging the Gap with an Embedded Delivery Model

While external platforms and service providers are helping organizations accelerate adoption, the findings show many enterprises continue to navigate fragmented deployments, reliance on public tools and limited standardization as agentic AI moves closer to production.

SOUTHWORKS works with organizations addressing these challenges through its Development on Demand model, embedding senior engineers directly within client teams to accelerate AI delivery while maintaining ownership of architectures, workflows and governance.

The research findings indicate that agentic AI is moving from experimentation toward broader enterprise use, even as organizations continue to evaluate sourcing strategies, deployment maturity and governance models. These dynamics are shaping how agentic AI is being operationalized across teams today.

Resources

• Download the full research: https://southworks.com/enterprise-cloud-maturity-and-strategic-gaps

• Visit the SOUTHWORKS website: https://southworks.com/

About SOUTHWORKS
SOUTHWORKS is the home of Development on Demand, the new model for software engineering that's helping companies across industries move faster, innovate smarter and scale with confidence. With proven expertise in AI, cloud and scalable software solutions, SOUTHWORKS partners with leading organizations to build the right solutions-right when they're needed.

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SOURCE: SOUTHWORKS