AI’s Turning Point: Why Precision Matters More Than Broad Adoption

Artificial intelligence has moved deep into enterprise strategy, with investment and adoption expanding across functions. The financial return, however, remains an evolving part of the story. A 2026 global enterprise survey found that 95% of organizations had an AI strategy, while 8% reported established enterprise-wide ROI. The same research found that 64% were already seeing meaningful business value, suggesting that the path from experimentation to durable financial impact involves more than acquiring technology.

That distinction matters as organizations consider how AI should influence work, investment, and operating models. A separate 2026 study of more than 3,200 business and technology leaders found that 34% of organizations were beginning to use AI to deeply transform their businesses, while 30% were redesigning key processes around AI. Another 37% were applying AI with limited changes to underlying processes. These findings point toward a broader question: How specifically should AI be designed around the business it serves?

Todd Rissel, co-founder and CEO of e2Value, a web-based property valuation technology provider, has spent decades considering that question through the specialized demands of insurance and property valuation. His experience suggests that specification deserves greater attention as enterprises evaluate AI investments.

“The biggest reason companies struggle with generic systems is that they have difficulty defining their current state and agreeing on their future state,” Rissel says. For him, AI performance begins with understanding the work itself: its data, decisions, dependencies, and desired destination.

That perspective becomes especially relevant amid the enthusiasm surrounding workforce automation. Organizations facing economic pressure may view AI primarily through labor savings, with workforce reduction becoming an early measure of financial impact. Rissel sees a different opportunity in automation. More than two decades ago, his company helped automate valuation processes, with the intention of moving people away from repetitive work and toward activities requiring judgment and expertise. The lesson, he suggests, is that technology can create capacity, while leadership determines how that capacity is redeployed.

This requires a more detailed examination of how work actually happens. Insurance, for example, contains workflows shaped by years of accumulated practices, regulatory requirements, institutional knowledge, and human judgment. Some processes persist because they once represented the most practical option available. AI introduces the possibility of reconsidering those processes, yet doing so requires leaders to map the present state, define the desired future state, and communicate the transition throughout the organization.

Rissel places particular importance on middle management in that process. Executives may establish strategic direction, while operational leaders translate that direction into everyday decisions. When employees understand their responsibilities and the reasons behind organizational changes, AI can become part of a broader redesign of work. “Apprehension tends to come from a gap in understanding,” Rissel states. “If people know where the company is going, what it is doing, and where they fit, the conversation becomes much more constructive.”

The importance of context extends directly into the technology itself. Broad AI systems can process enormous quantities of information, yet useful conclusions depend on the relevance, structure, and quality of the underlying data. Rissel illustrates the principle through a simple travel example. “An AI recommendation for a little-known restaurant in a remote area can appear remarkably insightful when sufficient local information exists behind the recommendation. Without that contextual foundation, the same technology may produce a generic answer,” he shares.

Enterprise applications operate under the same principle, with far greater consequences. Valuation, underwriting, and risk assessment each depend on specialized datasets, industry terminology, regulatory considerations, and domain-specific logic. e2Value has spent more than two decades developing valuation technology around those requirements, incorporating structural characteristics, local economic conditions, construction costs, materials, labor, and property-specific information into its models.

That history gives Rissel a particular perspective on AI’s role in valuation. For e2Value, AI represents an additional analytical capability within a broader data environment, where specialized information can support risk segmentation, cost forecasting, and scenario analysis. Its work in property valuation provides a practical example of why industry-native intelligence can matter. Understanding a structure requires more than identifying its physical components. Economic conditions, local markets, rebuilding requirements, and the characteristics of the asset all contribute to the valuation question.

The same principle applies across enterprise sectors. A technology platform designed around a specific industry's workflows can incorporate its terminology, decision rules, regulatory environment, and accumulated expertise. That creates a more precise foundation for automation and decision support, while leaving room for human judgment where context remains important.

This historical pattern is familiar. Earlier technology cycles often generated ambitious expectations before organizations understood how deeply new capabilities needed to connect with existing operations. AI is presenting a similar strategic inflection point, with leaders now moving from experimentation toward questions of integration, governance, workforce capability, and return.

The emerging enterprise model points toward greater specialization. AI can become more useful as organizations connect it to the data, workflows, and decisions that define their businesses. For Rissel, that connection begins with a simple discipline: understand the work before attempting to automate it.

As enterprise AI matures, the defining advantage may increasingly come from precision-built systems that understand an industry's particular logic. The next phase of enterprise technology could therefore belong to AI designed around specific operational realities, where specialized knowledge and high-quality data give intelligent systems a more meaningful role in the decisions businesses make every day.