tl;dr McKinsey's 2026 State of AI survey: 40 percent of billion-dollar companies are now scaling AI agents, up from 27 percent last year. Smaller companies sat flat at 22 percent. The gap is implementation capacity, not software cost, and there are three questions (setup, ongoing work, exit) to get answered before you buy.
Among organizations with more than $1 billion in revenue, 40 percent are now scaling AI agents, up from 27 percent last year. Among smaller organizations, the figure is 22 percent, unchanged. Software cost alone does not explain that gap.
McKinsey published its 2026 State of AI survey on August 25, based on 1,719 respondents across 97 countries.
The number that matters most to me is the split by company size. Among organizations with more than $1 billion in annual revenue, 40 percent are scaling AI agents, up from 27 percent last year. Among smaller organizations, 22 percent are scaling agents, essentially unchanged.
SHARE OF ORGANIZATIONS SCALING AI AGENTS (static chart: one line moved, the other did not) Organizations with revenue over $1B: 27% in the 2025 survey, 40% in the 2026 survey. Smaller organizations: 22% in both surveys, unchanged. Source: McKinsey, The State of AI in 2026 (published August 25, 2026; 1,719 respondents, 97 countries).The broader survey makes the contrast more useful. Forty-four percent of organizations now say AI is scaling across the enterprise, up from 38 percent last year. But the share reporting any EBIT impact is essentially unchanged at 37 percent, and only about 6 percent qualify as AI high performers.
Large enterprises pulled ahead on agents while smaller organizations stayed flat. If you run a smaller software company, that is the part of the survey worth paying attention to. The question is why.
The easy explanation is that large enterprises can afford agents and smaller companies cannot. The survey itself points to a more complicated answer.
About one in five respondents say AI operating costs, including token costs, have constrained their AI use, and McKinsey reports those constraints across the full range of AI tools. For AI agents specifically, about one in ten respondents cite cost as a constraint. Yet agent scaling moved sharply at billion-dollar companies and barely moved at smaller ones.
What separates the companies getting the most value is less about access to the technology and more about how they operate. McKinsey's high performers are much more likely to redesign workflows around AI rather than insert AI into existing ones, twice as likely to say senior leaders demonstrate commitment to the work, and twice as likely to have defined processes to measure impact.
That is why I think the constraint for smaller companies is often implementation capacity. Many agent products assume the buyer has people available to configure the system, manage exceptions, work through security and data questions, and keep the workflow current.
A 5,000-person company is more likely to have those roles already. A 200-person company may have a head of growth with three direct reports and a quarter to make.
The product can be affordable and still be impractical to run.
These products will get easier to adopt over time. But the gap widened this year: large-company scaling rose from 27 percent to 40 percent while smaller-company scaling stayed at 22 percent. That matters because the advantage is not only the software. It is also the year spent learning how to use agents inside your own workflows.
The answers to these questions lead to a more useful evaluation question than "How capable is it?"
Most vendors can now produce an impressive demo. But the right question is whether your team can put the product into production and keep it useful without creating a new operating burden.
These three questions get you most of the way there.
Setup. What does it do before your team configures anything?
Ongoing work. What will your team have to do every week to keep it running?
Exit. If you stop using it, how easily can you leave and take the history with you?
THREE QUESTIONS TO TEST WHETHER AN AI AGENT FITS YOUR TEAM (interactive: answer four questions about a vendor you are evaluating; the answers combine into plain sentences about setup, ongoing work, and exit, plus what to verify before signing; no scoring) Setup: does it do useful work before configuration? Yes means ready to use; verify in the demo what the product does before configuration, on your data. No means it requires configuration first; verify who does the configuration, how long it takes, and what it costs. Ongoing work: is the weekly work a light review? Yes means light review; verify how many minutes of review a normal week actually takes. No means ongoing administration is required; verify which role on your team owns the weekly administration. Exit: how long is the commitment? Short means leaving is inexpensive; verify the renewal terms and any price step-ups. Long means leaving costs money or time; verify the early termination terms in writing. Exit: can you export the history? Yes means the operating record leaves with you; verify the export format and whether it includes the full action history. No means the record stays behind; verify whether any history can be retrieved at all after you leave. Based only on your answers. This tool does not use vendor data or score products.A platform that can build anything is valuable if you have a team available to build on it. If you don't, flexibility can become work your team never budgeted for. Someone still has to decide what to build, configure it, and keep it current as the business changes.
Watch the demo closely. If most of it is spent showing you configuration tools, ask what useful work happens before your team touches them.
If the answer is none, you are buying a toolkit rather than a working process.
Every agent vendor will tell you the agent does the work. Ask what work is left for your team.
Who reviews its actions? How long does that take? Who fixes it when a data source changes? Who handles security questions in month three? Who takes over if the internal champion leaves?
For a smaller team, the practical test is simple: does the agent remove recurring work, or create a new recurring process?
Ask the vendor to describe a normal Tuesday three months after launch.
Long commitments are not automatically bad, but they shift more risk to the buyer.
Ask two questions: what does it cost to leave, and what can I take with me?
The second matters because the history of what the agent did is part of how you judge whether it worked. If that history cannot be exported, switching products also means losing part of your operating record.
For a smaller team, short commitments and exportable history reduce the cost of being wrong.
By those criteria, Bryn starts with a defined job, is designed to minimize ongoing administration, and is sold month to month.
Setup. Bryn watches your product, site, and CRM together, evaluates those signals against the customer profile you define, and takes an action you already approved when a meaningful pattern appears. You can customize the workflow, but you do not have to invent the job before the product can do useful work.
Ongoing work. Bryn is meant to remove the morning triage of which accounts showed intent, which matter, and what should happen next. Every action is logged, so your team can review what happened without reconstructing it across multiple tools. That audit trail also makes finance, privacy, and security review easier because there is a record of what the agent saw, why it acted, and what it did.
Exit. Every tier is billed monthly with no contract minimum, and the audit history can be exported. We give up the certainty of a long-term contract so customers can keep renewing based on whether the product is useful. Current pricing and terms live on the pricing page so readers can verify them directly.
None of this means smaller companies cannot scale agents. The fact that 22 percent already are doing it suggests the barrier is not company size alone.
But a year of no progress has a cost. Teams that started running agents in 2025 spent 2026 learning how their own data behaves when software acts on it, where human review is necessary, and which workflows are worth automating.
You can buy the software quickly. You cannot buy back that year of learning.
If agents are on your 2027 plan, get clear answers on setup, ongoing work, and exit before you let an impressive demo drive the decision.
If you're evaluating agents for a smaller team and want to compare approaches, I'm glad to talk.
Source: https://www.civic.com/field-notes/big-companies-scaled-agents