The New AI Mandate: Beyond Features to Full-Process Execution

MIAMI, FL – July 14, 2026 – A fundamental shift is underway in the world of enterprise artificial intelligence. The initial gold rush to embed discrete AI “features” into software is giving way to a more profound and strategic imperative: using AI as an execution layer to orchestrate and optimize entire business processes. New research from The Hackett Group, an ROI-led AI transformation firm, reveals that the true value of AI lies not in isolated productivity gains but in its ability to coordinate work across systems, data environments, and decision points. This evolution marks a critical inflection point, forcing leaders to rethink operational models and move from merely adopting AI to architecting an intelligent enterprise.

The findings, detailed in the firm’s “AI Solution Providers 2026 Trends, Capabilities and Strategic Insights” report, show that while AI is now widely embedded in procurement, finance, and human capital management (HCM) solutions, most offerings still focus on assistive automation rather than autonomous execution. The next phase of competitive advantage will be defined by a company's ability to weave these capabilities into a cohesive, end-to-end operational fabric.

The New Strategic Mandate: Orchestrating Intelligence

The era of celebrating AI-powered chatbots and simple workflow assistants as revolutionary is closing. The new frontier is about enabling AI to participate directly in the execution of complex, cross-functional business processes. According to The Hackett Group's research, solution providers are increasingly reframing their AI offerings away from a collection of siloed tools and toward an integrated execution layer. The goal is no longer just to make an individual employee more efficient, but to make the entire process smarter, faster, and more resilient.

This transition is still in its early stages. The research indicates that 64% of providers are extending their established Software-as-a-Service (SaaS) applications with embedded AI, a more incremental approach compared to the 36% offering fully AI-native solutions. While 74% of providers have deployed basic AI agents like copilots, more advanced capabilities such as configurable agents and multi-agent orchestration remain largely in development. This reflects a market that is evolving in step with customer readiness and the constraints of legacy architectures.

“Providers are moving beyond stand-alone AI features toward architectures designed to orchestrate work across processes, systems and roles,” said Meena Ibrahim, a 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 tangible benefits of this process-led approach are staggering. The firm’s “AI World Class” benchmarks show that organizations redesigning processes with AI as a core enabler can achieve a procurement ROI up to 3.7 times greater than their peers. In areas like purchase-to-pay, costs could plummet by as much as 80%, with staffing requirements cut by 81%. This isn't about incremental improvement; it's about a step-change in operational performance.

The Readiness Chasm: Bridging the Gap Between Technology and Operations

Despite the rapid advancement of AI technology, a significant “readiness chasm” is opening up within enterprises. The Hackett Group's research highlights a growing imbalance: while solution providers demonstrate strong technical capabilities, many client organizations are struggling to scale AI effectively due to foundational challenges in governance, workforce skills, and strategic alignment. The technology may be ready, but the organization is not.

This gap is a critical barrier to realizing AI's potential. Related studies show that 73% of Global Business Services (GBS) organizations cite process complexity as a major hurdle in AI adoption. Another 71% point to unrealistic expectations and persistent issues with data quality. Furthermore, a shortage of AI talent plagues 67% of these organizations, while 64% are hindered by inadequate change management.

This operational drag is creating a productivity crisis in key corporate functions. In finance, for example, workloads are projected to rise even as budgets and headcount shrink, creating a gap that leaders hope AI can close. A similar dynamic is playing out in HR, where a 10% increase in workload is colliding with budget cuts, resulting in a 12% productivity deficit. Simply buying more AI tools will not solve this problem. Organizations must invest in the foundational work of process simplification, data governance, and, most importantly, reskilling their workforce. Cultivating a culture of “co-intelligence,” where humans and AI work in a symbiotic partnership, is essential for navigating this transformation.

The Ecosystem Imperative and the Rise of AI Orchestrators

No company can build its AI future alone. The research underscores a critical trend: enterprise AI solutions are being built on interconnected ecosystems, not monolithic, stand-alone platforms. A striking 86% of providers rely on embedded APIs to integrate third-party AI models from leading cloud and AI companies. This ecosystem-driven model means that the ability to integrate, orchestrate, and manage a diverse set of services is becoming a core competency.

The Hackett Group itself exemplifies this trend through its strategic partnership with ServiceNow, combining its Hackett AI XPLR™ platform with the ServiceNow AI Platform. The goal is to help clients move beyond scattered experiments to identify high-impact AI initiatives and execute them at scale. This approach highlights that the future of enterprise AI will be less about building proprietary models from scratch and more about intelligently orchestrating best-in-class services within a coherent data and process architecture.

For enterprise leaders, this means the focus of AI strategy must shift toward platform integration and data architecture. The practical value of an AI capability depends less on its discrete features and more on how effectively it is woven into the end-to-end flow of work, data, and decision-making. Success requires creating an architectural layer that sits above existing systems, enabling seamless coordination and execution across the entire enterprise environment.

Beyond Copilots: The Dawn of the Agentic Enterprise

The long-term trajectory for enterprise AI points toward a future where autonomous and semi-autonomous agents actively manage and execute business operations. This vision of an “agentic enterprise” moves far beyond today’s assistive copilots. It imagines a world where multi-agent systems coordinate complex tasks like sourcing and procurement, financial closing, and talent acquisition with minimal human intervention. This requires a new generation of AI-enabled architectures that can support coordinated execution across disparate systems.

The journey toward this future will be incremental. However, the groundwork is being laid today. The introduction of benchmarks that quantify the impact of generative AI and agentic workflows across end-to-end processes provides a roadmap for this transformation. By pursuing a process-led AI strategy, organizations can achieve performance advantages of up to 75%. As companies become more adept at integrating AI into their core operations, they will unlock new levels of speed, productivity, and strategic insight, fundamentally reshaping how business value is created and sustained.