How leading organisations are moving from generative AI pilots to autonomous business impact — and why most aren’t ready for what comes next

The Inflection Point

For three years, enterprise AI looked like a conversation. You asked a question. The model answered. You decided what to do next.

That era is ending.

In 2025, a new architecture emerged at the frontier of enterprise technology: agentic AI — systems that don’t wait for instructions, but set goals, plan sequences of action, execute across multiple tools and systems, and adapt in real time when conditions change. The shift from generative to agentic AI is not an incremental upgrade. It is a category change. And the distance between organisations that understand this and those that don’t is widening faster than most boards appreciate.

Gartner projects that 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026 — up from less than 5% today. In their most optimistic scenario, agentic AI could drive approximately 30% of enterprise application software revenue by 2035, surpassing $450 billion, up from just 2% in 2025.

The opportunity is real. So is the execution gap.

What Agentic AI Actually Means for Business Leaders

Most executives encounter the term “agentic AI” in vendor decks and confuse it with advanced chatbots or automation. The distinction is consequential.

Traditional AI — including most generative AI deployments — operates in a request-response loop. A human defines a task, the system completes it, a human reviews the output and decides the next step. Agentic AI breaks this loop. A human defines an objective; the agent decomposes it into a task sequence, selects and orchestrates the tools required, monitors progress, identifies failure points, and self-corrects — all without step-by-step human direction.

A 2025 joint research study by MIT Sloan Management Review and BCG, drawing on 2,102 respondents across 21 industries and 116 countries, found that 76% of executives now view agentic AI as more like a coworker than a tool. This is not a semantic observation. It has profound organisational implications. You do not manage a coworker the same way you manage a tool. You do not govern one the way you govern the other. And the accountability frameworks that apply to one do not map cleanly onto the other.

The most consequential finding from IBM’s 2025 research into agentic AI operating models is a sharp divide in enterprise strategy: organisations focused on optimising existing processes with agentic AI are achieving measurable efficiency gains — but organisations designing entirely new workflow capabilities around autonomous decision-making are achieving net-new business impact that process-focused peers cannot replicate.

This is the strategic fork in the road. Efficiency versus reinvention. And the organisations that confuse the two are investing capital in the wrong direction.

The Performance Evidence

The business case for agentic AI, properly deployed, is among the most compelling in enterprise technology:

Organisations project an average ROI of 171% from agentic AI deployments, with U.S. enterprises forecasting 192% returns. Among current adopters, 66% report measurable value through increased productivity.

The primary use case — deployed by 71% of organisations — is process automation. But the organisations capturing the highest returns are not automating processes. They are replacing them.

A healthcare revenue cycle management provider deployed agentic AI across its prior authorisation workflow — historically a 30-day process requiring constant human follow-up across insurer systems, clinical documentation, and compliance checks. The result: approval times fell from approximately 30 days to three days, dramatically reducing treatment delays and enabling staff to redirect attention from administrative tracking to patient care exceptions.

A global retailer deployed inventory agents that autonomously monitor sales velocity, weather patterns, regional demand signals, and supplier lead times — then execute replenishment decisions without human approval below defined thresholds. The outcome: a 22% increase in e-commerce sales in pilot regions, with significant reduction in out-of-stock incidents and lower operational costs through reduced unnecessary warehousing.

These are not efficiency stories. They are operating model stories.

The Execution Gap: Why Most Organisations Will Underdeliver

Despite compelling evidence, the path from agentic ambition to agentic impact is obstructed by three structural failures that most enterprise deployments do not adequately address.

The governance vacuum. Agentic systems make decisions. In many deployments, the accountability for those decisions has not been assigned. When an AI agent makes an error — and they do — who is responsible? The vendor? The IT team that deployed it? The business leader who approved the use case? The absence of a governance architecture designed specifically for autonomous systems is the most common cause of agentic AI deployments being pulled back after incidents that could have been anticipated.

The data readiness deficit. Agentic AI is only as reliable as the data environments it operates in. In the banking sector, AI — including agentic AI — showed clear potential in 2025, but fragmented processes, legacy systems, and unstructured data kept many institutions from scaling. This pattern repeats across industries. The agent is capable. The data architecture it needs to operate reliably does not exist.

The operating model inertia. McKinsey’s 2025 State of AI survey found that AI high performers are nearly three times as likely to have fundamentally redesigned individual workflows compared to their peers — and that intentional workflow redesign has one of the strongest contributions to achieving meaningful business impact of all factors tested. Most organisations deploy agentic AI into existing workflows. The organisations achieving transformative returns design new workflows around agentic capabilities from the outset.

The Leadership Imperative

Three decisions separate the organisations that will capture agentic AI’s value from those that will spend the next three years running expensive pilots.

Define the human-agent boundary explicitly. For every agentic use case, specify which decisions the agent makes autonomously, which require human review, and which are reserved for human judgment regardless of agent confidence. This is not a technical specification. It is a leadership decision with legal, ethical, and competitive implications.

Build governance before scale. Agent oversight frameworks, audit trails, escalation protocols, and error accountability structures must be designed before deployment, not after the first incident.

Measure outcomes, not activity. The temptation in agentic deployments is to report on actions taken — emails sent, decisions made, tasks completed. The metric that matters is business impact: cycle time, error rate, revenue influenced, cost removed. If those metrics are not improving, the deployment is not working, regardless of how active the agents are.

The question MIT Sloan and BCG pose for every leadership team is the right one: “Are we simply adding a new tool to our business, or are we introducing a new, nonhuman actor into our organisation?”

The answer determines everything that follows.

Quantility AI Perspective: We advise leadership teams on agentic AI strategy, operating model design, and governance frameworks — with accountability tied to business outcomes, not deployment metrics. The organisations that will lead their sectors in five years are making these decisions today.