Nearly every company surveyed by KPMG says it has an AI strategy, yet a scant 8% can point to a solid return on investment. The disparity, highlighted in the firm’s Global AI Pulse Q1 2026 report, underscores a growing frustration among executives who have poured capital into machine‑learning projects without seeing the expected bottom‑line boost.

Jon Weberg, chief executive of Profitalize, attributes the shortfall to a fundamental misstep: organizations often buy AI as a stand‑alone product instead of weaving it into the fabric of daily operations. "When AI lives in isolated silos, each tool solves only a fragment of the problem," Weberg told reporters. "Real value emerges when every decision is informed by a shared context that flows across the entire enterprise."

The Profitalize approach, dubbed AI Synthesis, treats artificial intelligence as a connected infrastructure rather than a collection of independent applications. In practice, the framework encourages data and insights to travel freely between departments—marketing feeds sales, customer‑service outcomes shape future campaigns, and operational metrics inform strategic planning. The result, according to Weberg, is a continuous feedback loop that amplifies the impact of each AI model.

KPMG’s own findings echo this sentiment. The AI Quarterly Pulse Survey revealed that 78% of leaders struggle to quantify indirect or long‑term benefits, citing skill gaps and scaling challenges as additional hurdles. Without a unified architecture, companies find it difficult to capture the broader, often intangible, advantages that AI can deliver.

Industry sentiment is shifting. PwC’s 29th Global CEO Survey noted a dip in confidence about near‑term revenue growth, a trend linked to uneven AI returns and broader economic pressures. Meanwhile, Anthropic’s 2026 Economic Index urges firms to assess AI based on the complexity and economic value of the work performed, rather than relying solely on usage metrics.

Weberg’s background in digital marketing and consulting informs his perspective. Having built websites as a teenager and later advised dozens of businesses, he observed a recurring pattern: growth initiatives receive attention, but the lack of coordination between departments hampers sustainable scaling. "The real bottleneck isn’t technology; it’s the way teams communicate and share data," he explained.

Profitalize’s platform is designed to address that bottleneck. By centralizing data streams and enabling AI models to learn from one another, the system aims to turn isolated insights into organization‑wide intelligence. Human oversight remains a cornerstone, ensuring that judgment, policy and strategic direction guide automated decisions.

Experts say the next phase of AI adoption will likely hinge on such integrated solutions. As firms continue to invest heavily in AI, the ability to demonstrate cross‑functional impact may become a key differentiator. Companies that can show how AI improves collaboration, knowledge sharing and continuous improvement across the enterprise stand to unlock value that traditional ROI metrics overlook.

In this evolving landscape, AI is no longer a simple software purchase. It is an architectural decision—one that demands alignment of people, processes and technology. Leaders who embrace a unified approach may not only close the ROI gap but also set a new benchmark for enterprise performance.

Este artículo fue escrito con la asistencia de IA.
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