Harvard Business Review just ran the numbers on corporate AI spending — and the conclusions are not what boardrooms want to see.
A piece published June 23 in Harvard Business Review synthesizes data from three of the most credible research organizations in business. McKinsey's 2025 Global Survey found that 88% of organizations use AI in at least one business function. Of those, only 39% report any measurable impact on earnings before interest and taxes. And even within that 39%, the effect is typically less than 5%. BCG's analysis is bleaker still: 60% of companies investing in AI generate no material value at all, and only 5% create substantial value at scale. Deloitte surveyed nearly 2,000 executives and found that a satisfying return on a typical AI use case takes two to four years — far longer than the seven-to-twelve-month payback window most businesses expect from a technology investment.
The HBR piece argues that not all AI spending is the same. There are five categories, two tactical and three strategic, and none of them can be measured with conventional ROI tools. The article's diagnosis is correct as far as it goes. But the deeper problem it reveals is this: most businesses are spending as if they're buying a proven piece of machinery, when what they're actually doing is funding a multi-year experiment with uncertain outcomes.
What does this mean for the business owner who cannot afford a dedicated AI team? You are not competing with McKinsey or BCG or Deloitte clients. You are not deploying AI across a business function with a three-year integration roadmap. You need something that works this week, not in 2028.
That is where Viktor enters the picture. Not as an enterprise platform requiring a deployment plan, but as an AI coworker that starts producing real output from the moment you give it a task.
Three things Viktor does that the enterprise models struggle to replicate:
Viktor runs on Claude, GPT-4, and Gemini — all three included in one credit balance, with Viktor selecting the right model automatically. The enterprise problem is partly about model selection. You do not have that problem.
Viktor acts inside your tools. It does not just produce text — it books, sends, files, researches, and delivers. The "AI impact on EBIT" problem exists because most enterprise AI sits at the analysis layer and never reaches execution. Viktor's value appears in completed work, not in reports about potential value.
There is no integration phase. The two-to-four-year ROI timeline Deloitte describes is largely driven by the complexity of embedding AI into legacy enterprise systems. With Viktor, the overhead is a credit balance and a task.
The data from McKinsey, BCG, and Deloitte describes an enterprise AI problem. It does not describe a Viktor problem. The companies generating no material value are the ones building custom models, running pilots across dozens of departments, and waiting for integration teams to finish their work. None of that applies here.
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.