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Start with a business problem and a realistic view of what AI can contribute. RPA2AI’s advisory work connects adoption plans with technology selection, organizational readiness and executive priorities.

A useful starting point is a defined task whose quality and cost can be assessed. Ask what a successful result would look like in daily use, and involve the people who will rely on it. Kashyap’s article on enterprise evaluation explains why general model rankings cannot settle those business questions. Read more about evaluating AI for enterprise use.

A pilot can leave data silos and process bottlenecks untouched. Scaling requires an enterprise plan that connects individual applications to the wider workflow.

Consider the supporting systems as well as the model: information must reach the application, outputs must enter existing processes, and someone must maintain the connections. A successful demonstration does not establish that these arrangements are ready. Kashyap’s guide to the enterprise AI technology stack sets out the components behind a working application. Read more about the enterprise AI technology stack.

Measure the cost of a completed business outcome, including tool calls, retries, data access and human review. A low model price alone does not establish value.

Look for costs that grow with an agent’s behavior, such as repeated attempts or unnecessary use of expensive models. Cost controls should be designed into execution, with visibility into where spending occurs. Kashyap’s cost-optimization article offers a practical basis for examining those decisions before usage expands. Read more about controlling AI agent costs.

No. People remain responsible for goals, difficult judgments and consequences. Agentic systems make those responsibilities more important as software gains the ability to act.

Oversight needs an organizational framework, with responsibilities that people understand and can carry out. Deciding to keep a person involved is a starting point; that person must know how the review fits the wider process. Kashyap’s governance-framework article explores the organizational work required to make oversight effective. Read more about an AI governance framework.

RPA2AI is an independent industry analyst firm. Its work covers enterprise AI strategy, technology selection, investment due diligence, governance, organizational change and professional education.