AI & Enterprise Value
For the foreseeable future, at least until the labs heap AGI upon us, the generation of real economic value by AI in the enterprise will be coming from the deployment of custom agentic systems to automate, create or augment concrete business processes.
Most professionals today interact with AI through general-purpose interfaces—ChatGPT Enterprise, Claude, Microsoft Copilot, or on-prem models with a UI on top. These tools are impressive individual productivity enhancers. Top coding agents write production code. Agentic interfaces like Claude Cowork run autonomously and produce increasingly decent business outputs. But they face three structural challenges with no clear solution in sight.
1. Their effectiveness depends on the willingness, proficiency and consistency of the individuals using them.
2. The jagged frontier. Building a tool or system that is actually effective at everything is so far impossible. Current foundation models work surprisingly well at some tasks while failing unexpectedly at others that may seem similar or even simpler, making it difficult to intuitively predict where AI will help versus hinder performance.
3. Even if used effectively, the productivity gains and economic value generated with them is extremely difficult to measure and attribute to specific business processes.
On the other hand, the deep integration of AI capabilities through agentic systems in business workflows does have the potential to transform them from the ground-up.
However, with hundreds to thousands of processes to potentially rethink, where to start and which to prioritize is a tricky question without a cookie-cutter answer.
Based on our direct experience and research, a set of key characteristics provide early indicators that a specific process will accrue higher value from its redesign as agentic. Here is the TOP 10:
Heterogeneous inputs and arbitrary documentation formats Processes requiring interpretation of unstructured documents—contracts, correspondence, images, mixed-format files—exploit LLM multimodal capabilities that traditional automation cannot match.
Multi-system integration and data aggregation needs Workflows spanning three or more systems with data synthesis requirements benefit from agents’ orchestration capabilities.
Complex rule-based logic with frequent exceptions Processes with branching decision trees, exception handling, and conditional logic streams are ideal.
High volume, repetitive task patterns Transaction volume provides the denominator for ROI calculations. Some industry frameworks cite minimum 2 FTE equivalent workload as the viability threshold.
Sequential dependencies where step completion accelerates subsequent steps Workflows with “waterfall” characteristics—where output from step N becomes input for step N+1—benefit from agents’ ability to maintain context and build upon prior work.
Back-and-forth communication with multiple stakeholders Processes requiring iterative exchanges both internally and/or externally —clarifications, approvals, information gathering across parties—exploit agents’ conversational capabilities with near-zero latency interaction.
Parallelization potential Workflows with independent subtasks that can execute simultaneously maximize agent efficiency.
Tolerance for gradual autonomy Workflows where incremental automation delivers incremental value—versus all-or-nothing requirements—enable the recommended phased approach.
Existing SOPs and process documentation Well-documented processes with clear standard operating procedures provide the ground for agent development and codification.
Clear, verifiable success metrics: Processes with objective success criteria (accuracy rates, processing times, compliance scores, unit tests..) allow agents to self-evaluate and improve. This is one of the main reasons why coding agents are the most evolved and arguably autonomous AI systems. Code compiles, runs and goes through unit tests.
At Visa Consulting, our AI Labs practice helps financial institutions, fintechs, and merchants identify, architect, and deploy agentic systems that deliver measurable business value in payments, consumer finance, and commerce.
If your organization is navigating where to start with agentic AI — or how to move from pilots to production — reach out.
The 10 characteristics are useful. High volume, documented processes, and verifiable success metrics give you a clean starting point. Without those, you're guessing. And guessing with agents gets expensive fast.