Seven percent. That is the share of senior leaders who told KPMG they have established a measurable return on investment from artificial intelligence. The survey covered 2,100 executives across 20 countries and jurisdictions in Q2 2026. The number is not a rounding error. It is the headline finding of KPMG's Global AI Pulse report, published in June.
The same report found that three out of four respondents said they had no clear visibility into what their AI spending was actually costing them. Not a rough figure. No figure at all. KPMG described organizations where the CEO is directly accountable for AI decisions as far more likely to see results: 57% of those organizations reported meaningful business value, versus 21% where AI accountability sat lower in the hierarchy. Established ROI jumped from 4% to 14% just by moving ownership to the top.
Confidence in AI, the survey noted, is rising. Spending is holding steady. The gap between confidence and return is the story.
KPMG's findings align with a pattern that has been visible for two years. Companies invest in AI infrastructure, often in scattered tools across departments, and then discover that measuring what any of it produced is harder than buying it was. The ROI conversation stalls because no one defined what ROI would look like before the spending started.
What does this mean for the business owner who cannot afford a dedicated AI team? It means the 93% who are not seeing returns are largely running AI as an experiment rather than a workflow. The tools are live, the subscription is paid, and the output is unmeasured.
The organizations that land in the 7% share one characteristic: they connect AI directly to a specific business outcome and measure it. Not "we use AI for content." But "we reduced the time to produce our weekly market brief from six hours to forty minutes, and here is what that freed up."
Viktor is built for this kind of accountable deployment. It does not sit in a separate dashboard waiting for someone to visit it. It works inside Slack, Google Drive, Gmail, and your other existing tools — executing tasks, not offering suggestions. The output is visible, timestamped, and tied to the workflow it replaced.
A few things Viktor does in practice: it writes and schedules weekly briefings from live research, monitors competitor activity and flags changes, drafts client-facing documents from rough notes, and manages recurring operational tasks that used to absorb hours each week. Each action is logged. Each result is measurable. That is how you end up in the 7%.
Viktor runs on Claude, GPT-4, and Gemini — all three, in a single credit balance, with the platform selecting the right model for each task. No separate subscriptions. No fragmented toolstack. One place where AI work actually happens.
A Note on Security
Viktor is SOC 2 certified and compliant with GDPR, CCPA, and CASA Tier 3. Your data is stored in an encrypted vault and is never used to train AI models. Approvals happen directly inside Slack. Full details at viktor.com/security.
You get $100 of free credits to begin. No time limit, no commitment. That's enough to do real work and see what Viktor can actually do before you spend a penny. There's also $50 off your first bill. You must use this exact link to receive both benefits.
Disclosure: Some links in this article are affiliate links. If you choose to get started with Viktor using the links provided, I may receive a commission — at no additional cost to you. I only recommend tools I use and believe in.