Enterprise software has traditionally been deterministic, meaning you’ll get the same output every time if you give it the same input. This predictability has underpinned enterprise systems for decades, enabling confidence, auditability and control. But AI agents introduce a probabilistic model of computing. Their outputs are shaped by context, data, and inference rather than fixed rules. Depending on phrasing, inputs, or recent learning, the same prompt might yield slightly different results.
This shift from deterministic to probabilistic systems marks a foundational change in how enterprise teams need to think about much of their core technology. Quality assurance practices must evolve from confirming exact, repeatable outcomes to evaluating confidence levels, variance and business tolerances for error. In some cases, “close enough” is acceptable, while absolute precision remains non-negotiable in others. Knowing when and where each applies is critical.
Understanding When, Where, and How to Deploy AI Agents
AI agents bring in data, evaluate context, and take action. But unlike traditional software, their responses can vary. This makes them ideal for tasks where flexibility of the outcome is acceptable. Examples include summarizing content, routing requests or proposing recommendations. They also augment human workflows and enhance productivity without entirely replacing human judgment.
However, AI agents are not suitable for every scenario. When absolute precision is required, such as in payroll processing or regulatory reporting, deterministic software remains the right choice. Enterprises must also avoid deploying agents where governance, data quality or compliance structures aren’t mature.
Additionally, the introduction of AI agents introduces new risks. Many companies struggle to control data flows between applications, and AI agents add a new layer of complexity. Without strong governance, enterprises risk fragmented, uncontrolled agent deployments that create operational and security vulnerabilities.
The solution is structured, risk-aware AI governance that mirrors integration management best practices. This includes lifecycle management for deploying, monitoring, updating, and retiring agents. Enterprises must also establish enterprise-wide visibility to maintain consistent security and compliance oversight.
Integrating AI Agents into the Enterprise
At Dispatch Integration, we’ve long helped organizations overcome cross-functional challenges to orchestrate people, processes, and data. AI agents represent a fourth pillar. These agents are not just automation tools; they are digital task forces that can act, decide and interact within business workflows. Their role must be strategically incorporated into enterprise systems, with clear alignment to business outcomes.
AI should be treated as a new class of digital worker, not just a capability. The starting point isn’t what the AI can do, but what the business needs done. By identifying jobs to be done, organizations can determine where AI agents can free up, augment or amplify human effort. By focusing on the job to be done and the nature of the output of these jobs, businesses can also assess whether deterministic vs agentic approaches are the most appropriate. The most effective strategy is incremental, adding value in controlled, low-risk areas before scaling more broadly.
Amazing new enterprise technologies are making it easier to build advanced agentic solutions. In our opinion, the real effort isn’t about the technology; it’s about understanding the nature of human work and translating this into agentic or deterministic workflows. Our team at Dispatch helps teams understand where AI fits, how to deploy it safely, and how to manage it over time.
By taking a structured, incremental, and business-first approach, your enterprise can unlock the benefits of agentic AI while managing risk and maintaining operational control.
Cameron Hay is the CEO of Dispatch Integration, a data integration and workflow automation company with clients in Canada, US, Europe and Australia. He has over 30 years of leadership experience in various technology-oriented industries.
