For decades, enterprise software procurement followed a predictable pattern. Finance teams would identify a need, evaluate vendors, negotiate contracts, and then spend months or years working around the inevitable gaps between what the software promised and what their business actually required. This approach worked because traditional software (TradSaaS) was basically static, it did what it did, nothing more, nothing less.
The advent of agentic AI fundamentally breaks this model. To successfully implement AI agents, you need to stop thinking like you're buying software and start thinking like you're hiring employees.
The conventional software procurement process was built around acceptance of limitations. You'd find a tool that solved maybe 70% of your problem, knowing full well that the remaining 30% would require creative workarounds. The standard approach looked like this:
First, you'd map your requirements to available features, accepting that perfect alignment was impossible. Then came the inevitable compromises:
"Well, it doesn't handle our specific billing model, but we can export the data and manipulate it in Excel before importing it back."
Next, you'd assign human resources to fill the gaps. Someone's job became taking data from System A, reformatting it manually, and uploading it to System B. Another person would spend hours each month reconciling discrepancies and exceptions that the software couldn't handle automatically.
Finally, you'd use contract renewals as leverage, hoping to convince vendors to build the features you actually needed. This cycle could take years, and even then, vendors often prioritized features that served their broader market rather than your specific use case.
The entire process was linear and reactive: "This tool will handle scenario X, and when it doesn't, we'll figure out a workaround."
Traditional procurement thinking breaks down when applied to AI agents because it misunderstands their fundamental nature. Unlike static software, AI agents (when built correctly) are dynamic systems that learn, adapt, and improve over time. They don't just execute predetermined functions and workflows, they evolve, develop understanding of your specific business context and processes and prepare themselves for changes and corner cases.
When you try to evaluate AI agents like TradSaaS, you're essentially asking: "What can this agent do on day one?" This is the wrong question entirely. The better question is: "How will this agent learn to work within our specific environment, and what will it be capable of after six months of operation?"
Static software requires you to conform your processes to its limitations. AI agents conform to your processes and eliminate the limitations over time.
Think about how you hire employees versus how you buy software. You hire the best candidate for the job at present but also look for adaptability, and the ability to learn your organizational nuances and adapt with time. You provide training, context, and feedback, expecting continuous improvement.
This is exactly how successful AI agent implementation works. You're not purchasing just a fixed set of capabilities, you're bringing on digital team members that will develop expertise in your particular business model now and in the future.
Consider how this changes your evaluation criteria:
The shift from buying to hiring creates dramatically different operational realities:
The hiring analogy reveals another critical consideration: you want to hire people who are specialists of their role, not generalists that have some understanding of the job role.
The same principle applies to AI agents, and this is exactly where many implementations fail. Generic AI is good with language and is geared to solve everyone's problem, their depth of understanding is wide but not specific.
At streamOS, our finance-specific agents arrive pre-trained on accounting principles, compliance frameworks, and financial operations. Rather than starting from zero, these agents immediately understand concepts like ASC 606 compliance or complex contract terms, allowing them to focus their learning on the nuances of each client's particular business model. The result is faster adaptation and more sophisticated understanding of company-specific processes.
This distinction explains why some organizations see immediate value from AI agents while others struggle through lengthy implementation periods. Agents built with domain-specific knowledge can start contributing meaningfully from day one, then rapidly develop expertise in your particular business model and thrive within the inevitable nuance. Generic agents, by contrast, must first learn the domain before they can even begin learning your processes.
Instead of comprehensive requirements documents, focus on providing rich context about your business processes. Rather than extensive feature comparisons, evaluate existing specialization, learning velocity and adaptability.
Most importantly, build feedback loops into your implementation plan. Just as you would schedule regular check-ins with new employees, establish systematic ways to guide and refine your AI agents' understanding of your business.
The organizations that thrive with AI will be those that embrace this fundamental shift in thinking. They'll stop trying to force AI into the old software procurement box and start building relationships with digital team members that grow more valuable every day.
The question isn't whether your AI can handle every scenario today, it's whether it can learn to handle scenarios you haven't even encountered yet.