Each successful project is not just a win in its own right but also an opportunity to learn how to make subsequent projects even more successful and do so even faster.
When an AI project succeeds, the real value isn’t just the win itself. It’s what that project reveals about the gap between how a process is documented and how it’s actually run day-to-day, and what closing that gap teaches SnapLogic about delivering the next project faster and better.
This post walks through what that learning process looks like in practice, drawing on patterns from SnapLogic’s AI implementations across industries, and closes with what those patterns mean for teams planning their own AI rollout.
Why evaluating AI projects matters more than usual
The only way to value technology is to use it “in anger,” in a real-world situation. This is even more true in the integration space, which operates between other systems, apps, and data. Where a standalone application is evaluated only on its own merits, an integration data fabric represents the culmination of an overall enterprise architecture project.
The reason it is important to evaluate projects is not just to notch up more customer logos, but to learn from each one and improve delivery in future engagements. That learning process is that much more important in the AI domain, where best practices are still evolving rapidly, and there simply isn’t much established “this is how we do it” knowledge to fall back on.
At SnapLogic, we’ve achieved several AI successes across various industries and domains, so I’m sharing some factors that are common to many projects like this.
Where the documented process meets the real one
The specifics of the technology and the business process vary wildly. Still, the common factors cluster around ensuring that what was built actually matches the requirements and will help users deliver concrete results.
In any technology delivery project, there has to be a phase for user acceptance, where the SnapLogic team works with the end-users on the customer’s side to understand what is needed to turn a first-draft implementation, based on our outside understanding of the process and its goals, into something that can actually go into production.
With AI, that process is even more important because there can be significant gaps between expectations of what AI can deliver and the realities of implementing an AI project in the messy real world.
In particular, it turns out quite often that the official process, the one written down in the documents that the project team had based their work on, was not exactly what the human agents were following in reality.
5 patterns that show up again and again
These are not cases of corners being cut; there is no issue of people not following instructions or anything like that. Rather, what our consultants encountered repeatedly were situations where humans were using their judgment and initiative, where messy reality did not quite match up with the clean logic of the process as it was written down.
1. Workflow timing inconsistency (operational debt)
In the manual process, operators routinely initiated the primary workflow stage before all necessary data updates or external inputs were fully available in the system, managing these gaps informally as they progressed. Because AI agents operate immediately, they will highlight instances where workflows were being run on incomplete information. Initial assessments often misidentified these as “tool errors,” but investigation will usually find that agents were simply processing the data as provided.
2. Configuration variance (knowledge debt and user variation)
When handling complex inputs, human operators might apply configuration settings inconsistently, relying on unwritten tribal knowledge or individual discretion. AI agents flag these discrepancies because automation requires consistent conditional logic. The fix here is to implement standardized default fallbacks to compensate for messy historical data entries.
3. Input format variance (hygiene debt)
Legacy processes might require dealing with critical information in unstructured, non-standard formats (such as embedding complex files within other documents). In these cases, operators often compensate by manual information gathering — basically, copying and pasting information between documents and systems. AI agents will expose any unstandardized information channels, often by failing to extract data from inherently unpredictable structures without some additional work.
4. Structural contradictions (requirements ambiguity)
Because AI agents require definitive decision paths based on internal policy, they will expose anywhere organizations have not fully standardized their own core operating rules. During testing of agentic AI solutions, these discrepancies or vague or contradictory internal policies will come to light. In these cases, AI agents are simply acting as a mirror for policy ambiguities that had previously gone unnoticed or been papered over by informal remediation processes.
5. The “human accuracy” baseline (validation debt)
Finally, internal stakeholders often initially score the automation tool’s output as low quality, insisting on a perfect accuracy standard. However, a closer look at legacy manual processes often finds calculation errors, user discrepancies, and data hygiene issues. In other words, the automation doesn’t create errors; it highlights places where historical human baselines might not be perfectly standardized, as we have seen in the previous categories.
Recommendations for success
These situations are not really specific to SnapLogic, or even to AI. Automation has always highlighted differences between the process as it is written and the process as it is run by humans. What is new with AI is the speed of implementation and the scope of what is possible.
For instance, in the past it was effectively impossible to deal with unstructured data: the mess of random PDFs, spreadsheets, images, and whatever else became the “dark matter” of enterprise IT. AI makes it possible to automate that, but in doing so, it exposes all the places where humans had been applying their judgment and initiative to messy situations.
The flipside is that because we are dealing with AI agents, not rigid prescriptive automation, we can bring those same positive abilities to the process, and save those expert humans from having to waste their valuable time eyeballing PDFs or copying and pasting information between spreadsheets.
That is how AI-enabled automation can deliver the sorts of results that CCB has seen, and that many other SnapLogic customers are also beginning to see.
We would love for you to be the next one. You can start by taking a self-guided product tour or booking a personalized demo with one of our experts.
