What Is the Real Impact of Agentic AI on Enterprise Operations? Key Findings from Skan AI

Operations teams build the case for agentic AI payback by redesigning workflows and measuring results against an observed human baseline. Finance teams price human oversight into the cost model. Programs that skip those steps often stall in the pilot, and agentic AI ROI enterprise evidence that boards will accept is thin.

Get the full analysis in Skan AI’s whitepaper, The Real Impact of Agentic AI.

What does agentic AI ROI look like in live enterprise deployments?

Few enterprises report significant returns from AI, according to surveys from both McKinsey and Deloitte.

• McKinsey’s report tracks AI’s effect on earnings before interest and taxes (EBIT), a company’s operating profit, while Deloitte’s tracks significant agent-specific ROI.
• McKinsey’s 2025 survey found that about 6% of respondents qualify as AI high performers, meaning they attribute 5% or more of EBIT to AI and report significant value (McKinsey, 2025).
• Among respondents already using agentic AI, 10% said they currently realize significant ROI from it (Deloitte, 2025).

Why do agentic AI ROI studies report such contradictory numbers?

The MIT NANDA report is the source behind the claim that most generative AI pilots show no P&L impact (MIT NANDA, 2025). Its findings are based on interviews and conference surveys, and its authors acknowledge possible selection bias.

Other studies report contradictory agentic AI ROI numbers because they measure different things. Their methods differ on four points:

• Who answers: Vendor-commissioned surveys often sample only organizations already running AI in production. Some count ROI on at least one use case as success.
• What counts as success: One study requires sustained P&L impact six months after the pilot. Others count localized productivity gains or positive pilot results. Some also count executive sentiment.
• Self-report versus measurement: Most figures are respondent estimates. Few studies test them against operating data.
• Time horizon: First-year questions and multi-year questions produce different answers about the same programs.

These differences show why it’s important to give more weight to figures that are realized, tracked in the P&L, drawn from samples above 200, and independent of the vendor being measured.

Where does the value show up? Highest-ROI workflows by function

Start with high-volume workflows that have repeatable logic, a measurable outcome, and a write-back to a system of record, because those are the workflows where agent value shows up.

Deloitte’s 2026 agentic survey found that scaling is happening more in lower-risk, repeatable use cases with near-term return, in customer service and in technical functions such as information technology (IT) and engineering (Deloitte, 2026).

The workflows below meet those criteria, with the metric to report for each:

Judgment-heavy work with unwritten exception paths fails the criteria above. Formal documentation can omit the tacit knowledge operators use to handle exceptions. Examine those gaps before your team adopts a document as an agent’s operating procedure.

What does it cost to run agents in production?

In McKinsey’s banking example, human oversight drives most of the variable cost, at an estimated 70-75% of variable run costs for an agent performing a customer service task (McKinsey, 2026).

Token spend, the fee charged for each unit of text an AI model reads or writes, still grows with autonomy. Gartner says that routing a task to an agentic reasoning model raises provider inference costs, the charges for running the model to produce answers, by at least five times compared with a basic chatbot interaction (Gartner, 2026).

A full total cost of ownership (TCO) model carries five lines:

• Inference and tools: Token costs and spending on retrieval and APIs multiply with reasoning models and multi-step runs.
• Verification: Teams pay for sampled human review and approvals for high-impact actions. They also fund baseline evaluation.
• Integration: Teams budget for building and maintaining connectors and credentials. Write-back into systems of record also carries cost.
• Maintenance: Teams update procedures when applications or policies change. They also revise them for different process variants.
• Governance: Audit logging, model risk review, incident handling, and regulatory documentation add recurring cost.

How long until agentic AI reaches payback?

Some finance workflows pay back inside a year, while a typical AI use case takes longer, and the sources report each result for a workflow or use case rather than for a whole organization. A Gartner survey of senior finance leaders found that data extraction and report creation, along with accounts payable and receivable automation, generally deliver returns within 9-10 months (Gartner, 2026).

A typical AI use case takes longer. Deloitte found that most respondents reached satisfactory ROI on a typical AI use case within 2-4 years (Deloitte, 2025). That range applies to a use case, and none of the sources reviewed reports a realized organization-level payback figure.

Report per-workflow payback in months and program payback in years, as separate lines.

Why do traditional RPA ROI models fail for agentic AI?

RPA business cases fail for agentic AI because they counted hours removed from a scripted task, and McKinsey documented the weakness of that math: teams must maintain and upgrade bot fleets while also managing security. The platforms bots interact with change often; in one example, a regulation requiring minor changes to an application form could throw off months of work on a nearly complete bot (McKinsey, 2017).

Agents create value across the whole workflow, so the model needs layers:

• Direct value: The model counts labor cost avoided per case, net of verification and rework, against an observed human baseline.
• Indirect value: Shorter cycle times, fewer errors, redeployed capacity, and better customer and employee experience add to the case.
• Strategic value: This layer covers revenue enabled by new service models, along with faster response to regulatory change.
• Risk-adjusted value: Finance teams deduct compliance cost and account for error exposure, including the probability of pause or cancellation.

Forrester recommends setting separate targets and timelines for each value type, because productivity value arrives quickly and strategic value arrives late (Forrester, 2026). Report these KPIs to the CFO and board:

• Cost per completed case compared with the human baseline
• Straight-through processing rate and exception resolution time
• Human review rate per agent run, with its trend
• Rework and escaped-error rates
• Payback by workflow, tracked in the P&L

Does the ROI survive governance, risk, and audit scrutiny?

Finance teams can defend ROI during audit by including compliance cost in the model and retaining a traceable record of agent actions. Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027.

Its report cites escalating costs and unclear business value, alongside inadequate risk controls (Gartner, 2025). Four regimes shape compliance cost:

• EU AI Act: The Commission’s AI Act Service Desk treats agents as AI systems. Article 50 transparency rules apply from 2 August 2026 to agents intended to interact with people or generate content; high-risk requirements apply from 2 December 2027 or 2 August 2028 (European Commission, 2026).
• GDPR: Article 22 restricts solely automated decisions with legal or similarly significant effects; under the contract and explicit-consent exceptions, it requires a right to human intervention (EUR-Lex, 2016).
• HIPAA: HHS guidance says the Privacy Rule generally limits covered entities to the minimum necessary protected health information, with exceptions such as treatment disclosures (HHS guidance).
• SOC 2: The AICPA’s TQA Section 9561 covers how a service organization’s AI use affects SOC 1 and SOC 2 examinations (AICPA, 2026).

Net compliance staffing and audit-evidence costs against the gains, then account for error liability. With a case-level record of inputs, outputs, actions, and rationale, auditors can treat a reversal as evidence. When an adjuster overrides an agent’s claims recommendation, the adjuster logs the reason and the process owner updates the governing procedure before the next run.

Why does agentic AI stall in the pilot, and how do leaders reach scale?

Many pilots stall when the agent meets exceptions its inputs never described. Only 31% of CIOs report having guidelines for when an AI pilot should not move to production (Gartner, 2026).

The usual inputs each miss part of the work:

• Documents and interviews: SOPs and interviews can omit workarounds.
• Mining approaches: Process mining reaches end-to-end depth only after integration into every system of record, and it misses work that never reaches an event log. Task mining needs no integrations but captures individual tasks and misses the end-to-end process.

Scaling needs an input that records how work runs across applications, including handoffs and exception paths, and keeps updating as processes drift.

Use gated stages to test readiness for scale. The illustrative durations below come from the worked example, rather than measured deployment timelines:

Worked example: a net ROI model for one production agent

This model prices one claims-intake triage agent at a regulated insurer, with oversight and rework inside the cost line. The inputs are illustrative. Replace each one with your observed baseline:

• 10,000 cases a month, handled today in 12 minutes at a fully loaded $60 an hour, or $12 a case
• 70% steady-state containment, with the remaining cases staying with people
• $0.40 inference and tool cost per agent-handled case
• 15% of agent runs sampled for a 4-minute review
• 2% of agent runs fail and cost $24 to fix
• $15,000 a month for the platform and its monitoring and governance
• $350,000 upfront, then two shadow months before containment ramps to 25% and 50%, reaching 70% in month 5

At steady state the agent avoids $84,000 of labor a month and costs $25,360 to run: $2,800 inference, $4,200 review, $3,360 rework, and $15,000 platform. Net benefit is $58,640 a month. Shadow months cost $19,200 each, for platform and review.

Cumulative cash position by month:

The agent pays back in month 10, and in this model containment rate moves the payback date more than token price does. If undocumented exceptions cap containment at 50%, monthly net falls to $37,600 and payback slips to month 14. Raising review from 15% to 40% adds $7,000 a month, while doubling inference cost adds $2,800.

How Skan AI grounds agentic AI ROI in regulated enterprises

Enterprises need to ground AI agents in the operational context to realize ROI, and Skan AI supplies that context via the Context Graph of Work, a continuously updated record of how enterprise work gets done.

That record spans people, applications, tasks, decisions, policies, and outcomes. Skan AI combines end-to-end process depth with zero-integration capture, which observes work without connecting to the back end of each system behind it.

Built with privacy by design, Skan Virtual Assistant uses computer vision at the desktop level to capture human-system interactions on screen, and map processes across mainframes (large legacy business computers), virtual desktop infrastructure (VDI, remotely hosted desktops), legacy desktop applications, web applications, and spreadsheets. At the same time, Skan PRISM applies pixel-level redaction, which blurs sensitive screen content, inside the customer environment before metadata, the descriptive data about each action, reaches analytics.

How the Skan AI platform helps deliver agentic AI with real impact

Skan AI Blueprint, Intelligence, and Agents run on one Context Graph of Work. Blueprint identifies where to act, Intelligence analyzes and improves those areas in depth, and Agents execute the approved work through agentic operating procedures.

• Blueprint: Blueprint maps operational activity, and Automation Explorer ranks AI and automation opportunities into a roadmap tied to measurable outcomes, in weeks.
• Intelligence: turns observation of the areas Blueprint identifies into end-to-end flows and variant analysis (a comparison of the different paths cases take through a process), with drift detection (spotting when the way work runs changes over time), which produces the pre-agent baseline the KPIs above require.
• Agents: Skan AI Agents executes approved work under Agentic Operating Procedures (AOPs), which are machine-usable procedures built from observed work that encode decision rules, exception handling, escalation thresholds, and success criteria. Teams evaluate each agent against a human baseline before promotion, and the governance layer records inputs, actions, decisions, and rationale for case-level review.

Enterprises can choose on-premises inside the customer firewall, private cloud, or Skan AI hosted. Skan AI holds SOC 2 Type 2, ISO/IEC 27001, and ISO 42001 certifications, and is HIPAA, GDPR, and California Consumer Privacy Act (CCPA) compliant. The neurosymbolic design, which pairs machine-learning models with explicit rules, preserves an explainable path from observed activity to recommendation.

Test the model on one of your own workflows using The Real Impact of Agentic AI. Request a Skan AI demo focused on your process and the evidence needed to assess its operating outcome, or read the companion ROI analysis.