AI Boom Forces Companies to Rethink How They Measure Business Value
KPMG finds 95% of organizations have an AI strategy but only 8% report established ROI. Profitalize CEO Jon Weberg argues the gap exists because companies buy AI as standalone tools rather than integrating it into connected operational infrastructure. His AI Synthesis framework treats AI as a unified system where insights flow across departments, creating continuous feedback loops between marketing, sales, customer success, and operations.
Artificial intelligence has quickly become a boardroom priority, inspiring significant investment across industries. Yet conversations about return on investment often remain tied to familiar software purchasing habits, where success is judged by logins, subscriptions, or the performance of an individual application. That perspective may overlook the broader operational influence AI can have across an organization. As companies continue expanding their AI investments, many leaders are beginning to ask a different question: Are they measuring the value of AI through the right lens?
Recent research suggests this conversation is becoming increasingly important. According to KPMG’s Global AI Pulse Q1 2026 report, 95% of surveyed organizations have established an AI strategy, while only 8% report achieving established ROI. Although many organizations already report meaningful business value, the findings illustrate a noticeable gap between widespread adoption and consistently realized enterprise-wide outcomes.
Meanwhile, KPMG’s AI Quarterly Pulse Survey found that 78% of leaders identify difficulty quantifying indirect or long-term benefits as one of the primary challenges in demonstrating AI ROI, alongside skills gaps and scaling challenges. These findings suggest that many organizations are still refining how they define and evaluate AI success.
Broader market conditions add another layer to the discussion. PwC’s 29th Global CEO Survey found that CEO confidence in near-term revenue growth has declined as organizations navigate uneven AI returns alongside broader economic and operational pressures. At the same time, Anthropic’s 2026 Economic Index encourages organizations to evaluate AI according to the complexity and economic value of the work being performed instead of relying on broad adoption metrics alone. This perspective shifts attention from simple usage toward understanding how AI contributes to meaningful business activity.
Jon Weberg, CEO of Profitalize, a company specializing in AI-integrated growth infrastructure, believes many organizations encounter this disconnect because AI is frequently introduced as another software purchase instead of becoming part of the organization’s operating foundation. “AI becomes significantly more valuable when every decision has context,” he says. “Context grows through connected systems, shared information, and continuous learning. When intelligence operates inside isolated environments, every tool becomes responsible for solving only a fraction of the problem.”
From his perspective, the discussion extends beyond technology itself. Many businesses already operate through separate departments, independent reporting structures, and disconnected workflows. Introducing individual AI applications into those existing environments can sometimes reinforce those divisions instead of creating stronger collaboration. Marketing, sales, customer success, and operations may each adopt specialized AI solutions, yet every platform develops insights that remain siloed unless effectively integrated.