Nearly nine in ten executives now suspect their employees are using AI tools to simulate productivity rather than improve it. That figure — 88% — comes from the 2026 AI at Work Report by G-P, which surveyed 2,850 business leaders across six global markets.
The wider findings are equally sobering. Nearly 70% of those executives said they are prepared to slash their AI budgets this year if projects fail to deliver measurable results. And 73% reported that at least some of their AI investments over the past twelve months failed to meet expectations.
Perhaps the most telling number: the share of leaders describing their organizations as "aggressively" pursuing AI innovation dropped from 60% to 42% in a single year. The era of throwing money at AI and hoping for the best appears to be ending.
This is not an indictment of the technology itself. It is an indictment of how most companies have deployed it. Large organizations bought enterprise AI licenses, rolled them out across departments with minimal training, and assumed adoption would translate into output. What they got instead was a workforce that learned to use AI for appearances — generating drafts that look productive, summarizing meetings nobody needed summarized, producing reports that sit unread in shared drives.
The G-P report frames this as an oversight and governance problem. Companies that scaled AI without clear success metrics, without defined use cases, and without human accountability structures now find themselves spending more while getting less done. The survey also found that AI oversight and governance workloads are rising across organizations — meaning the tools designed to save time are now generating new layers of administrative work just to manage them.
What does this mean for the business owner who cannot afford a dedicated AI team, a governance framework, and months of pilot programs before seeing a single dollar of return?
It means the enterprise approach — expensive licenses, long rollouts, productivity theater — is not the only path. Viktor was built for the opposite problem: getting real work done immediately, without the overhead.
Viktor runs on Claude, GPT-4, and Gemini — all three included in one credit balance, with the right model selected automatically. But the difference is not the models. The difference is that Viktor does tasks, not just answers. It works inside your tools — your email, your spreadsheets, your documents, your calendar — and completes work rather than suggesting how you might complete it yourself.
Where enterprise AI deployments take months to configure and still produce what executives themselves call "simulated productivity," Viktor builds working dashboards, drafts real reports from real data, manages workflows, and handles the administrative load that would otherwise consume hours of your day. There is no adoption program. No governance committee. No twelve-month pilot. The first task you hand Viktor is a real task with a real output you can evaluate on the spot.
You get $100 of free credits to begin — no credit card, no time limit, no commitment. Explore Viktor properly. Do real work. When you are ready to go further, $50 comes straight off your first bill.
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.