The Hackett Group® research examines how procurement, finance and human capital management solution providers are evolving AI from productivity tools to embedded execution capabilities
The Hackett Group, Inc. (NASDAQ: HCKT), an ROI-led AI transformation firm, today released new research examining how artificial intelligence (AI) is being developed, positioned and deployed by enterprise solution providers in the procurement, finance and human capital management (HCM) marketplace. The findings show that AI is now widely embedded across solutions in these domains, but that most offerings today remain focused on assistive and workflow-level automation rather than fully autonomous execution.
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.Share
The AI Solution Providers 2026 Trends, Capabilities and Strategic Insights report finds that providers in these areas are increasingly reframing AI from a collection of features into an execution layer embedded within enterprise applications. The strategic objective is no longer simply improving individual productivity but enabling AI to participate directly in coordinating, executing, and optimizing end-to-end work across business processes.
“Providers are moving beyond stand-alone AI features toward architectures designed to orchestrate work across processes, systems and roles,” said Meena Ibrahim, research analyst at The Hackett Group®. “While most solutions today still focus on task automation and decision support, the long-term opportunity is enabling AI to participate directly in end-to-end business execution.”
The market transition to AI in these domains remains incremental – shaped by customer readiness and existing solution architectures
The research shows that this shift is taking place within the context of existing enterprise architectures. Most providers continue to extend established SaaS applications with embedded AI capabilities, with 64% aligning to this model, while fully agentic, AI-native solutions represent a much smaller share of the market (36%).
At the same time, agent-based AI alternatives are now a visible part of the enterprise landscape – with 74% of providers reporting production deployment of basic AI agents such as copilots or conversational assistants. However, more advanced capabilities – such as 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.
As a result, the practical value of AI today depends less on the presence of discrete features and more on how effectively those capabilities are integrated into end-to-end enterprise workflows, data and decision-making.
Scaling AI requires more than technology
The research also highlights a growing gap between technical capability and enterprise readiness. Providers demonstrate strong capabilities in technology infrastructure, automation frameworks and orchestration layers, 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 governance and operational challenges (e.g., skills, data quality, process knowledge).
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.
Taken together, these findings highlight a critical inflection point in how enterprise AI delivers value in these domains and likely others. For practitioners, the research makes clear that adopting AI features alone is unlikely to deliver meaningful performance improvement. Instead, organizations must rethink how work is structured and executed, focusing on how AI can assist, augment or execute tasks within redesigned end-to-end processes.
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.
