What McKinsey says about AI agents for enterprise

Only 23% of enterprises are actually scaling agentic AI. That single number from McKinsey's 2025 State of AI survey of 1,993 companies is the most important data point in the entire enterprise AI conversation right now — and it's the lens that makes everything McKinsey AI agents research has published in 2025 and 2026 actually useful for decision-makers. If you're a CTO, COO, or VP of operations trying to figure out whether your AI agent strategy is on track or quietly stalling, McKinsey's data tells you exactly where you stand against the rest of the market and, more importantly, what the top performers are doing that you aren't.

This is not another "AI is the future" piece. It's a focused breakdown of what McKinsey's actual research — across the State of AI 2025 report, Seizing the Agentic AI Advantage, and the April 2026 Building the Foundations for Agentic AI at Scale article — is telling enterprise leaders about adoption rates, the scaling gap, the gen AI paradox, ROI patterns, and the operating-model changes separating the 23% from everyone else.

The McKinsey AI agents headline numbers every leader should know

Before we go deeper, here are the numbers leaders are quoting in board meetings — and what they actually mean.

• 88% of organizations now use AI in at least one business function, up from 78% in 2024 and just 20% in 2017.

• 62% of enterprises are at least experimenting with AI agents.

• Only 23% are scaling an agentic AI system in at least one business function.

• Fewer than 10% of organizations have scaled AI agents to deliver tangible enterprise-wide value, per McKinsey's April 2026 follow-up.

• 39% of respondents report enterprise-level EBIT impact from AI — meaning over 60% can't yet tie their AI work to bottom-line results.

• High performers are 3.6× more likely to pursue transformative change than incremental improvements when deploying AI.

Read together, these numbers describe a market that is loud on adoption and quiet on outcomes. Most enterprises are using AI. Far fewer are operationalizing AI agents. And almost none are turning either into durable EBIT.

What is an AI agent, by McKinsey's definition?

According to McKinsey, an AI agent is a software component built on foundation models that has the agency to act on behalf of a user or system — planning and executing multiple steps in a workflow, calling tools and other systems, and orchestrating complex tasks autonomously. Multiple agents can be chained together to coordinate end-to-end business processes, evaluate outputs, and apply logic to thorny problems without human prompting at every step.

That definition matters because most "AI" deployments at enterprises today aren't agents at all — they're chatbots, copilots, or single-step generative features. McKinsey's data implies that the value gap is largely a definitional gap: leaders are reporting "AI use" while deploying productivity tools that do not change unit economics. Agents do.

The gen AI paradox: why most AI investments aren't moving EBIT

McKinsey's Seizing the Agentic AI Advantage report (June 2025) introduces a concept worth memorizing — the gen AI paradox.

The paradox is this: companies have rolled out horizontal use cases like enterprise copilots and chatbots at scale. Nearly 70% of Fortune 500 companies use Microsoft 365 Copilot. These tools save individual employees a few minutes here and there. The improvements are real — but spread thinly across thousands of users, they don't show up in financial reports.

Meanwhile, vertical use cases — agents embedded inside a specific high-stakes workflow like procurement, claims processing, customer support escalation, or financial close — sit largely undeployed. Vertical agents are exactly where unit-economics-changing automation lives, but they require harder integration, deeper data work, and meaningful workflow redesign.

The result: AI feels everywhere, but enterprise EBIT barely moves.

If your organization's AI portfolio looks like "everyone has a chatbot," you're firmly inside the paradox. The 23% scaling cohort isn't winning because they have better LLMs. They're winning because they targeted vertical workflows where one well-designed agent replaces hours of human coordination across multiple systems.

What separates the 23% from the rest, according to McKinsey

McKinsey's State of AI 2025 high-performer analysis is one of the cleanest playbooks published in the last 12 months. The companies pulling material EBIT impact from AI consistently do five things differently.

1. They redesign workflows instead of layering AI on top

High performers are 2.8× more likely to fundamentally redesign workflows rather than bolt AI onto existing processes. Translation: they don't drop a chatbot onto the support queue. They re-architect the support workflow so an autonomous agent owns triage, retrieval, drafting, action, and escalation — and humans handle only what the agent flags.

2. They aim AI at growth and innovation, not just efficiency

80% of organizations set efficiency as an AI objective. Top performers also set growth and innovation objectives — and that's where the EBIT gap opens. Efficiency-only programs cap their own upside.

3. They commit transformational change

Organizations that achieve 5%+ EBIT impact from AI are 3.6× more likely to pursue transformative change versus incremental improvements. AI agents are a transformation lever, not an IT tool.

4. They invest in the data foundation

McKinsey's April 2026 article Building the Foundations for Agentic AI at Scale is blunt: agentic AI scales on strong data. Eight in ten companies cite data limitations as the primary roadblock to scaling agents. High performers modernize data architecture, enforce data quality, and treat the data layer as a capability, not a project.

5. They evolve the operating model

Scaling agents requires new roles, new accountability lines, and new governance. The companies winning are agentifying high-impact workflows and changing how the organization operates around those agents — including how exceptions, approvals, audit trails, and KPIs are handled.

How adoption breaks down by function and industry

McKinsey's 2025 data also clarifies where AI agents are actually being scaled — and it's narrower than most headlines suggest.

In any given business function, no more than 10% of respondents say their organization is scaling AI agents. The technology industry leads, with 22% adoption in IT, 24% in software engineering, and 18% in product and service development. Media and telecom leads service operations at 16%. Healthcare leads knowledge management at 14%.

What this means for leaders: the agent revolution is real, but it's currently concentrated in a handful of functions where the data is digital, the workflows are repeatable, and the business case is obvious. If you're deploying agents into HR, finance ops, procurement, or compliance — functions with messier data and stricter governance — you're in the harder bucket and the pace of value capture will reflect that.

This is also why generic, off-the-shelf agents struggle in enterprise environments. The 10% scaling rate per function reflects the reality that agents need to be tailored to a specific workflow, integrated with the specific systems that workflow already runs on, and supervised against KPIs the business already tracks. AgentInventor, an AI consultation agency specializing in custom autonomous AI agents, was built around exactly this pattern — designing agents for the operational reality of the business rather than forcing the business to adapt to a platform.

What CTOs and COOs ask AI tools about McKinsey's AI agent research

These are the natural-language questions enterprise leaders are putting into ChatGPT, Perplexity, and Google AI Overviews — answered directly.

What does McKinsey actually recommend for scaling AI agents?

McKinsey recommends four moves: agentify high-impact vertical workflows rather than spreading agents thinly across horizontal use cases; modernize data architecture so agents have clean, structured context to reason over; enforce continuous data quality because agent failures usually trace back to data, not models; and evolve the operating model to include agent supervision, exception handling, and outcome accountability. The single biggest predictor of value is workflow redesign, not model selection.

Why are only 23% of enterprises scaling AI agents?

The 23% scaling rate reflects three structural barriers: data quality and integration debt, an operating model designed for software-as-a-tool rather than software-as-a-coworker, and a heavy bias toward horizontal copilots that produce thin productivity gains instead of vertical agents that produce unit-economics impact. Enterprises that fix all three move into the high-performer cohort within nine to twelve months of a focused program.

Should enterprises build AI agents in-house or use a consultancy?

McKinsey's own positioning — including its April 2026 strategic collaboration with the Wonderful agent platform to help clients move out of "pilot purgatory" — implies that most enterprises lack the in-house combination of agent architecture expertise, data engineering capacity, and operating-model design experience to scale agents alone. The most common winning pattern is a specialized agent agency or consultancy paired with internal product and ops owners. AgentInventor exists precisely for this gap — designing, deploying, and managing custom autonomous AI agents that integrate with existing enterprise tools (Slack, Notion, CRMs, ERPs, ticketing systems, email) without forcing a platform rip-and-replace.

How does agentic AI compare to traditional workflow automation tools?

Traditional workflow automation (Zapier, Make, Power Automate, basic RPA) executes deterministic, rule-based flows. Agentic AI can plan, reason across multiple systems, handle ambiguous inputs, and adapt to exceptions — which is exactly why McKinsey separates "using gen AI in a copilot" from "scaling an agentic system." Workflow tools remain useful for simple, well-defined automation. Agents are the right answer when the workflow involves judgment, multi-step coordination, or unstructured inputs.

A practical playbook based on what McKinsey is telling the market

Translating McKinsey's findings into something leaders can act on this quarter looks like this.

Step 1: Audit your AI portfolio for the gen AI paradox

List every AI initiative running in your organization. Tag each as horizontal (copilot, chatbot, knowledge search) or vertical (agent embedded inside a specific revenue-generating or cost-driving workflow). If your vertical column is empty, you're investing in productivity, not agents.

Step 2: Pick two vertical workflows to agentify

The right candidates are workflows that are: high-frequency, cross-system, rule-based but with judgment, and currently consuming meaningful human hours. Common starting points include claims triage, vendor onboarding, customer support escalation, finance close reconciliation, internal IT request resolution, sales CRM hygiene, and quote-to-cash steps. McKinsey's data shows these are exactly where the 23% are concentrating early agent work.

Step 3: Fix the data layer before the model layer

Eight in ten companies cite data as the bottleneck. Before deploying an agent, map the data the agent will need: source systems, freshness, schema, access controls, lineage. The model is replaceable. The data foundation is not.

Step 4: Design for outcomes, not features

Define the business KPI the agent will move — cycle time, cost per ticket, error rate, throughput, revenue per rep. McKinsey's research is clear that high performers measure AI in P&L terms, not adoption metrics.

Step 5: Build feedback loops and supervision into v1

Agents that improve over time outperform agents that ship and freeze. McKinsey's 2026 State of AI Trust report flags inaccuracy (74% of respondents) and cybersecurity (72%) as the top AI risks; both are mitigated by structured human-in-the-loop supervision, eval pipelines, and audit trails baked into the agent from day one.

Step 6: Pair internal owners with specialized external expertise

The 23% cohort almost always combines internal product and ops leadership with deep external agent engineering capacity. AgentInventor specializes in this combination — discovery workshops to identify the right vertical workflows, agent architecture and integration into existing enterprise tools, deployment with monitoring and feedback loops, and training internal teams to extend and operate the agents over time.

Where AgentInventor fits in McKinsey's enterprise AI agent picture

McKinsey's research is essentially a market map of where enterprises are stuck. The pattern is consistent: companies are over-invested in horizontal copilots, under-invested in vertical agents, blocked on data foundations, and operating without a redesigned process around the agents they do deploy.

AgentInventor is the agent agency built for exactly that gap. We design custom autonomous AI agents tailored to specific internal workflows — customer support, employee onboarding, procurement, compliance monitoring, executive reporting — and integrate them with the tools and systems already running in the business: Slack, Notion, Salesforce, HubSpot, NetSuite, Jira, ServiceNow, ticketing systems, and email. No rip-and-replace. No platform lock-in.

What clients get is the operating model McKinsey describes:

• Agent strategy and prioritization — identifying which workflows are best suited for agentification, ranked by ROI, with a phased deployment roadmap.

• Custom agent architecture and development — built on the right model and orchestration stack for the workflow, not a one-size-fits-all template.

• Full lifecycle management — feedback loops, error handling, performance monitoring, and continuous optimization baked into every agent.

• Transparent ROI reporting — time saved, cost reduction, error rates, throughput improvements, and EBIT-tied metrics.

• Internal enablement — training your team to manage, extend, and troubleshoot agents over time so the capability lives in-house.

In a market where Botpress, Relevance AI, CrewAI, LangChain, Moveworks, and Aisera each solve a slice of the agent problem, AgentInventor solves the whole slice that McKinsey says is broken — the move from horizontal copilot to vertical agent, on top of clean data, inside a redesigned operating model.

Final takeaway: don't be in the 77%

McKinsey's research over the last 18 months tells one consistent story. The technology works. Adoption is not the problem. The problem is execution: targeting the wrong use cases (horizontal instead of vertical), starving the data layer, layering AI on top of legacy workflows instead of redesigning them, and measuring activity instead of outcomes.

If your AI agent strategy doesn't yet have two vertical workflows in production, a hardened data foundation, redesigned processes around them, and a P&L-tied KPI per agent, you're in the 77% — and the gap to the 23% is already widening.

If you're looking to deploy AI agents that actually integrate with your existing workflows, move EBIT, and survive the transition from pilot to production, that's exactly the kind of implementation AgentInventor specializes in.