AI Sprawl: A Headache For Enterprises; An Orchestration Opportunity For SaaS Vendors

AI sprawl is now a C-Suite problem. The rapid testing and deployment of AI applications across enterprises has resulted in a proliferation of uncontrolled, uncoordinated tools, models and applications across business units and teams. Such sprawl exposes enterprises to business risks – data leakage, attack surfaces, shadow AI, regulatory exposure and rising costs. This is only set to increase as AI agents heighten the associated risk.

This sprawl remains largely ungovernable. Even large technology firms such as Amazon have acknowledged the issue, noting that the AI boom is driving duplication of tools and fragmentation of data internally. Consequently, orchestration layers that bring this sprawl under control are no longer optional, but a strategic requirement for ‘safe’ AI adoption.

The supply side is responding to this need. Aigentsphere has raised $4 million in seed funding to build a system of record for managing autonomous agents, while open-source tooling such as CrewAI and LangGraph continues to rapidly develop, and commercial AI leaders (hyperscalers, data platforms and model builders) are investing in orchestration capabilities. But AI orchestration is not just one homogenous layer. In practice, it spans three distinct but interconnected layers:

• Execution orchestration – controls agents, workflows and task coordination (targeted by model developers and workflow platforms).
• Control and governance – implements policy, builds auditability and creates identity controls.
• Data orchestration – ensures consistency, access and context management across data inputs (targeted by hyperscalers and data/AI analytics platforms).

Enterprise-grade orchestration platforms remain immature, with most capabilities embedded within proprietary ecosystems focusing on execution and data. These mostly ignore control and governance – leaving a large gap in the market which SaaS vendors are well-positioned to fill.

Some SaaS vendors have already begun to move in this direction. Domo is pursuing orchestration as a strategic priority, developing frameworks that support both agent creation and administrative control over data flows. Similarly, UiPath is evolving beyond robotic process automation (RPA), with its Maestro platform supporting orchestration across robots, agents and humans.

Software firms operating in highly regulated or compliance-linked sectors such as EHS, GRC, asset management, quality management and supply chain (and others) cannot afford to ignore this. With transparency, trust, auditability, reliability, security and control essential to establishing an independent governance layer for AI agents across enterprise systems, a pivot towards control and governance orchestration would align closely with their core value proposition.

Making the jump from AI features to cross-platform orchestration requires vendors to:

Short-term

• Establish a true control plane: embed policy engines, agent identity and access management, decision logging, and runtime monitoring as core platform capabilities.
• Invest in real-time execution infrastructure: develop workflow engines that operate dynamically across systems and support agent-driven processes at scale.
• Embed governance by design: build governance, compliance and control mechanisms into the platform, rather than layering them on afterwards.
• Introduce cost, risk and model control layers: manage execution costs for AI agent workflows, and improve visibility and control over underlying compute and model dependencies.

Medium-term

• Adopt a multi-vendor, interoperable architecture: integrate across cloud providers, agent platforms and open-source frameworks through standards (MCP, A2A), to actively support multi-agent environments.
• Strengthen data and platform alignment: partner with data platforms and invest in shared standards and infrastructure to develop consistent controls.

This upfront investment to build a cross-platform control and governance layer could also evolve into monetizable services, such as policy-as-a-service or auditability-as-a-service. Although many compliance-focused SaaS vendors are currently at a competitive disadvantage, given their relative AI immaturity, they have a strong opportunity to  establish themselves as the independent governance and control layer for AI agents across industrial systems.

For more insights into enterprise AI, agents and governance, visit the Verdantix website.