The surge of AI compels companies to reconsider their methods of assessing business value.

      TL;DR: KPMG has found that while 95% of organizations have an AI strategy, only 8% report achieving established ROI. Profitalize CEO Jon Weberg suggests this gap arises because companies tend to purchase AI as standalone tools rather than integrating it into an interconnected operational framework. His AI Synthesis framework views AI as a cohesive system that allows insights to flow across departments, fostering continuous feedback loops between marketing, sales, customer success, and operations.

      Artificial intelligence has swiftly risen to become a priority for boards, driving significant investments across various sectors. However, discussions around ROI often remain tethered to traditional software purchasing practices, where success is measured through sign-ins, subscriptions, or the performance of a single application. This viewpoint may overlook the extensive operational impact AI can have throughout an organization. As companies extend their AI investments, many leaders are beginning to reconsider how they measure AI's value.

      Recent studies indicate that this dialogue is becoming increasingly crucial. KPMG’s Global AI Pulse Q1 2026 report reveals that 95% of organizations have implemented an AI strategy, yet only 8% report receiving established ROI. While numerous organizations claim to derive significant business value from AI, the data highlight a clear disconnect between widespread adoption and consistent enterprise-wide outcomes.

      Additionally, KPMG’s AI Quarterly Pulse Survey shows that 78% of leaders cite the challenges in quantifying indirect or long-term benefits as a major obstacle in showcasing AI ROI, alongside issues like skill gaps and scaling challenges. These results suggest that many organizations are still refining their definitions and evaluations of AI success.

      Market conditions provide another layer to this discussion. According to PwC’s 29th Global CEO Survey, CEO confidence in short-term revenue growth has decreased as organizations face varying AI returns amid broader economic and operational pressures. Concurrently, Anthropic’s 2026 Economic Index encourages organizations to assess AI based on the complexity and economic value of tasks rather than relying solely on wide adoption metrics. This shift promotes the need to focus not just on usage but on how AI fosters meaningful business activity.

      Jon Weberg, the CEO of Profitalize, a firm that specializes in AI-integrated growth infrastructure, believes this disconnect arises because AI is often presented merely as a software purchase instead of being integrated into the organization's operational framework. "AI becomes significantly more valuable when every decision has context," he explains. "Context develops through interconnected systems, shared information, and ongoing learning. When intelligence exists in isolated environments, each tool is tasked with solving just a piece of the problem."

      He argues that this issue transcends technology. Many organizations operate in silos with separate departments, independent reporting structures, and disconnected workflows. Implementing standalone AI applications in such environments can reinforce these divisions, rather than fostering collaboration.

      While marketing, sales, customer success, and operations may each utilize specialized AI solutions, each platform generates its own data, insights, and priorities. Human teams often bridge these gaps through communication and experience, but AI systems require shared data and coordinated structures to cultivate the same overall awareness.

      This realization led Weberg to create AI Synthesis, a framework that regards artificial intelligence as a unified operational infrastructure rather than a series of isolated applications. He believes the most significant opportunities arise when AI systems share information across departments, establishing continuous feedback loops that allow insights from one segment to inform decisions in others. For instance, marketing initiatives can enhance sales discussions, customer interactions can shape future campaigns, and operational data can drive strategic planning within a cohesive environment.

      “If organizations establish a continuous dialogue across every function, AI can start to enhance the entire organization rather than merely optimizing individual tasks,” Weberg states. “People remain essential to the process since judgment, oversight, and direction provide the system with its purpose. AI delivers its greatest value when all parts of the business learn together.”

      This philosophy mirrors Weberg’s own career. A second-generation digital marketer, he began creating websites and building online audiences at a young age, later branching into affiliate marketing and business consulting across numerous industries. His experiences revealed a recurring theme.

      Growth strategies typically garnered significant attention, while interdepartmental communication presented opportunities for improved coordination. Consulting with organizations of varying sizes reinforced his belief that many scaling challenges stemmed from the operational connections among teams rather than from individual departments. This insight shaped the vision behind Profitalize, designed to assist businesses in establishing a more unified AI foundation instead of accumulating a range of unrelated software products.

      The broader market appears to be moving in a similar direction. Ongoing investments in the AI ecosystem reflect growing confidence that artificial intelligence will play a lasting role in business strategy. As adoption increases, organizations may stand out by how effectively AI fosters collaboration, knowledge sharing, governance, and a culture of continuous improvement across the enterprise. Human oversight remains a crucial part of this dynamic, ensuring that feedback, policies, and organizational priorities guide automated decision-making as capabilities continue to develop.

      The next phase of AI adoption might prioritize integrated business systems that simultaneously enhance all functions. Rather than evaluating each application in isolation,