Table of contents
- Most enterprises have deployed AI, yet only a small fraction have embedded it deeply enough across functions to generate transformative business returns.
- Organizations that treat AI as a cross-functional operating layer, rather than a departmental tool, consistently outperform peers on revenue growth and cost reduction.
- AI agents are now driving meaningful enterprise value by autonomously handling multi-step workflows that generative AI alone cannot complete.
- The top reported enterprise AI benefit is enhanced decision-making, followed by cost reduction and improved customer relationships.
- Most organizations lack a mature governance model for autonomous AI agents, creating risk as agentic systems take on more consequential decisions.
- Moveworks' Reasoning Engine is designed to help resolve requests across IT, HR, Finance, and more, with human oversight built in, connecting to a wide range of enterprise systems and supporting employees across hundreds of organizations worldwide.
Enterprise leaders have been enthusiastic about artificial intelligence for years. They see its potential to make their teams more productive, lower operating costs, and give their business a competitive edge in an increasingly competitive market.
But enterprise exec teams face a major paradox: McKinsey found 88% of enterprises now use AI in at least one business function, yet a separate BCG study found only 5% see significant value from it.
So even though execs have made AI a priority, they've struggled to translate early adoption into measurable business outcomes. With AI appearing in nearly every vendor pitch, figuring out where your investment actually translates into business outcomes is a challenge in itself.
This article covers where enterprise AI delivers measurable results, why most organizations stall, and how to assess whether your organization is ready to scale.
Why 88% of enterprises use AI but only 5% see transformative returns
As highlighted above, enterprise AI adoption is now nearly universal, and investment keeps rising. Yet only 5% of companies are considered "future-built," meaning AI is integrated across core functions rather than deployed in isolated pockets.
Reported ROI reflects this: Only 6% of organizations report AI payback within one year, and most realistically need two to four years to see a return according to Deloitte. Investment is widespread, but the timeline for meaningful results requires patience.
Where enterprise AI delivers measurable impact today
Enterprise AI is generating measurable improvements across three areas: productivity, cost optimization, and customer experience.
Deloitte reports that the top benefit is improving insights and decision-making (53%), followed by reducing costs (40%). Twice as many leaders report transformative impact compared to the prior year — but only 34% are truly reimagining how their business operates.
The difference between AI adoption and AI impact tends to shrink when the system goes beyond retrieving information and starts completing the action that follows. Moveworks connects both sides: surfacing answers across enterprise systems through search, then executing the corresponding workflow. That's the shift from a tool that retrieves to a platform that resolves.
Productivity and decision-making
Productivity is where executives report the most visible early wins. 79% of executives say AI has delivered productivity gains, though only 29% can confidently measure the ROI.
The longer-term view is more encouraging, with AI projected to increase productivity and GDP by 1.5% by 2035 according to the University of Pennsylvania. That figure could climb to nearly 3% by 2055 and 3.7% by 2075 — a signal of compounding returns as AI adoption continues to mature.
Cost reduction and operational efficiency
AI has the potential to cut costs across departments by automating workflows, deflecting support tickets, and optimizing processes. Organizations that treat this as a strategic priority see it in the numbers: future-built companies achieve 5x the revenue gains and 3x the cost reductions of their peers.
Customer experience and engagement
AI is changing how organizations build and maintain relationships with their customers. Personalization, faster support, and around-the-clock service have become baseline expectations for companies hoping to drive better experiences.
38% of leaders cite improving customer relationships as one of the top benefits of their AI investments. For IT and HR teams, the same dynamic applies internally. AI agents handle tier-one tasks like onboarding workflows, benefits questions, and employee inquiry routing, which frees teams to focus on higher-value work that relies on human judgement and experience.
How AI impacts every department, not just IT
AI's enterprise impact extends well beyond IT, with HR, Finance, and Procurement all seeing tangible success. Technical teams still lead in adoption, but momentum typically spreads once a department sees results and shares them internally.
Deloitte's research suggests high-performing AI implementations tend to start with empowered employees who experiment, share early wins, and build internal momentum before broader rollouts.
That pattern generates the organizational trust that makes cross-department scaling possible, and it's what tends to separate future-built companies from the rest.
IT and service management
Before AI, much of the automation in IT departments centered around reactive ticket resolution. But using AI-driven tools, IT has the ability to become more proactive through automated service delivery.
IT and cybersecurity lead enterprise AI use cases, which means most organizations already have a foundation to build on when they're ready to extend AI into adjacent workflows and departments. That said, enterprises should invest in strong data practices, governance, and supporting infrastructure before scaling AI initiatives across additional departments and workflows.
HR and employee experience
HR teams spend a ton of time answering questions about PTO policies, benefits enrollment, and other internal processes. AI agents can potentially handle many of these repetitive, high-volume requests automatically. This supports your HR teams by freeing them up to focus on work that requires judgment, empathy, and relationship-building — all the areas humans excel at.
When AI handles administrative work, HR teams are better positioned to bring employees along in the adoption process. Right now, only 20% of workers consider themselves active co-creators in their organization's AI transformation, which means most employees still feel like observers rather than participants.
HR has a direct role in changing that.
Finance and procurement
Finance and procurement teams are seeing AI agents step into invoice processing, account reconciliation, and anomaly detection, handling work that previously required manual review of every transaction.
The downstream effect is time for higher-value work. Finance professionals can shift attention to forecasting, scenario planning, and identifying new revenue opportunities when AI handles the routine.
The shift from generative AI to agentic AI
As the enterprise AI conversation shifts from generative AI to agentic AI, executives are attempting to figure out what this next evolution of AI means for their teams.
The market has moved from simply using generative AI to get information and generate content to agentic AI systems designed to autonomously reason, plan, and execute multi-step workflows across tools.
The scale of that change is significant: AI agents accounted for 17% of total AI value in 2025, with that figure projected to reach 29% by 2028.
What makes agentic AI different?
Generative AI produces outputs based on a prompt. Agentic AI is designed to interpret ambiguous intent, construct a multi-step plan, validate permissions and policies against a request, then execute.
Enterprise-grade agentic AI can handle that entire chain autonomously across systems — with guardrails in place. Embedding oversight at the point of execution gives organizations a foundation to scale autonomy responsibly.
Because of a willingness to experiment, future-built companies allocate 15% of their AI budgets to agents alone, compared to 12% for companies scaling under a wider AI umbrella. For many organizations, growing investment in AI agents reflects a willingness to trust automation with higher-stakes work.
What enterprise-grade governance for agentic AI actually requires
Governance, especially in high-regulation industries, is now table stakes. With all the data protection legislation enacted over the past decade (including the General Data Protection Regulation and the California Consumer Privacy Act), businesses that don’t take data governance seriously risk getting hit with serious fines.
But despite the potential for legal repercussions, only one in five companies has a mature governance model for autonomous AI agents.
As agents take on more consequential decisions (approving requests, executing workflows, accessing sensitive data), the frameworks that govern them need to evolve at the same pace.
What separates the 5% from the rest
The enterprises generating transformative AI returns share four traits:
Top-down strategic focus: AI strategy is set by leadership and tied directly to business priorities.
Deep workflow integration: AI is embedded in the systems and processes employees already use.
Mandatory AI fluency: Teams understand how to use AI responsibly and effectively. 40% of AI ROI leaders mandate organization-wide training.
Nuanced ROI frameworks: Leading organizations track productivity, speed, adoption, customer impact, and revenue potential.
Future-built businesses that meet these criteria have the potential to achieve 5x revenue increases from AI — and those returns build as AI adoption deepens across functions.
How to evaluate your enterprise AI readiness (and move forward with Moveworks)
AI delivers the most value when it works as a single, connected layer across your organization. The organizations seeing the strongest AI ROI treat it as shared business infrastructure, integrated across teams and workflows rather than siloed in a single department:
- Palo Alto Networks saved 351,000 hours by automating workflows with Moveworks.
- Johnson Controls reduced HR call volume by 30–40%.
These results came from connecting AI across systems and departments, not from deploying isolated tools.
If you're evaluating where your organization stands, here's how Moveworks maps to each readiness dimension:
- Data readiness: Moveworks' Enterprise Search and Reasoning Engine helps teams find answers and take action from a single interface.
- Cross-department deployment: With 100+ pre-built integrations and Agent Studio, Moveworks can extend AI capabilities across HR, IT, and Finance.
- Governance maturity: Moveworks' built-in role-based permissions and policy controls help organizations scale AI adoption within their existing compliance frameworks.
- Workforce enablement: Moveworks' Continuous Measurement capability gives leaders real-time visibility into usage, adoption rates, and workflow performance, so teams can identify where additional guidance or new use cases would drive the most value.
Learn more about how Moveworks supports enterprise AI transformation.
Frequently Asked Questions
Enterprise AI refers to AI technologies designed to operate at organizational scale, integrating with existing business systems like CRM, ERP, and ITSM platforms to automate processes and drive measurable outcomes. Unlike consumer AI tools built for individual users, enterprise AI must meet requirements for security, governance, compliance, and cross-department interoperability.
AI is automating routine workflows, enhancing decision-making with real-time data analysis, and reducing resolution times across IT, HR, and Finance. The top reported benefit is enhancing insights and decision-making, followed by cost reduction and improved customer relationships.
Generative AI creates content like text, images, and code based on prompts. Agentic AI goes further by autonomously reasoning, planning, and executing multi-step workflows across enterprise systems. AI agents can handle end-to-end processes like ticket resolution, employee onboarding, or invoice reconciliation.
IT and cybersecurity currently lead in AI usage, but HR, Finance, and Procurement are seeing growing impact. AI handles high-volume requests in IT support and HR, automates financial processes like invoice matching, and enables proactive service delivery across departments.
