AI Firms Shift From Features to Execution

Enterprise solution providers are shifting their focus from adding isolated AI features to building AI-enabled execution capabilities, according to new research from The Hackett Group. The report examines how artificial intelligence is being deployed in procurement, finance and human capital management (HCM) markets, finding that while AI is now widely embedded across these solutions, most offerings remain focused on assistive tools and workflow-level automation rather than fully autonomous execution.

AI moves from features to execution layers

Providers in these sectors are increasingly reframing AI as an execution layer embedded within enterprise applications. The strategic objective has changed from simply improving individual productivity to enabling AI to coordinate, execute and optimize end-to-end work across business processes. This transition represents a move away from stand-alone features toward architectures designed to orchestrate work across processes, systems and roles.

Meena Ibrahim, a research analyst at The Hackett Group, noted that while most solutions still focus on task automation and decision support, the long-term opportunity involves enabling AI to participate directly in end-to-end business execution.

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Current market reality and limitations

The research indicates that this market shift is taking place within existing enterprise architectures. Most providers continue to extend established SaaS applications with embedded AI capabilities; 64% align to this model. Fully agentic, AI-native solutions represent a much smaller share of the market at 36%. At the same time, agent-based AI alternatives are now a visible part of the enterprise setting, with 74% of providers reporting production deployment of basic AI agents such as copilots or conversational assistants. However, more advanced capabilities like configurable agents and multi-agent orchestration are far less common and often remain in pilot or development stages.

This reflects a broader structural limitation. Most AI capabilities remain confined to specific workflows or applications, while enterprise processes span multiple systems and decision points, and current solutions do not yet consistently enable coordinated execution across these environments.

Scaling AI requires more than technology. The research highlights a growing gap between technical capability and enterprise readiness. Providers demonstrate strong capabilities in technology infrastructure and automation frameworks, but show greater variability in governance, workforce enablement and strategic alignment. This imbalance suggests that while AI technology is advancing rapidly, many organizations may struggle to scale it effectively without addressing foundational operational challenges.

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Enterprise AI solutions are most commonly built on interconnected ecosystems rather than stand-alone platforms. Eighty-six percent of providers rely on embedded application programming interfaces (APIs) to third-party AI models, with common partnerships across leading cloud and AI providers. This ecosystem-driven model highlights the growing importance of platform integration, data architecture and orchestration in delivering scalable AI capabilities.

For solution providers, the next phase of differentiation will depend on moving beyond isolated feature-level capabilities to enable coordinated execution across enterprise processes. While current AI capabilities are often embedded within specific applications or workflows, enterprise value will increasingly depend on the ability to orchestrate AI across systems of record, data environments and decision points, supporting end-to-end process execution rather than incremental task automation. This points toward a next generation of AI-enabled architectures and emerging standards that sit above existing systems and enable more seamless coordination, integration and execution across enterprise environments.