Itwerx is a service-disabled veteran-owned managed IT provider serving Seattle-area businesses, founded in 2005, and part of its AI integration work is helping clients decide, before any money is spent, whether a given problem is actually a fit for AI.
Three questions to ask before deploying AI on anything
AI can be a genuine time and money saver, and it can just as easily be a time and money waster, often on the same kind of project depending entirely on how it fits. Before deploying AI against any business problem, answer three questions:
- Is the desired end result well-defined?
- Is this a repetitious process?
- Is the available data well-organized and consistent?
AI might still help even when the fit is imperfect, but the strongest return shows up when the answer is yes to exactly two of the three, not all three and not none. That is the inverse of the old “good, fast, cheap, pick two” rule, and it works the same way: which two you have tells you what kind of help to expect.
The elimination cases come first, because they save the most money
Before the useful combinations, rule out the two extremes:
- Yes to all three: a well-defined outcome, a repetitious process and clean, consistent data describes a problem that is best solved with straightforward automation, not AI. AI might help build that automation, but it is not the answer on its own, and paying for an AI solution here is paying more for less certainty than a simpler tool would give you.
- No to all three: an undefined outcome, an inconsistent process and messy data is not a project, it is a mess. AI will not organize that for you end to end; at most it can help chip away at isolated parts of it. Treating the whole thing as an AI project at this stage is how time and budget disappear with nothing to show for it.
Where AI actually earns its keep
The useful territory is the two-out-of-three combinations, and each one points to a different kind of help:
- Well-defined outcome and repetitious process, messy data: AI is a strong tool for extracting patterns and organizing inconsistent data into something more structured, acting as a front-end filter and switchboard for other systems, as long as a person checks its results rather than trusting them blindly.
- Well-defined outcome and clean data, irregular process: this is where AI helps with data mining and business analytics, finding correlations and producing ad-hoc reports, since the process not repeating is less of a problem when the underlying data can be trusted.
- Repetitious process and clean data, undefined outcome: this is the “you don’t know what you don’t know” case, where AI works best as a sidekick for exploring underused resources or gaps you already suspect exist but have not been able to pin down.
The pattern underneath all of it
Every one of these applications still needs a foundational source of truth to work from. Think of it like driving: if you already have roads and a map, AI can help you find the destination. If you have roads and a known destination, AI can help you build the map as you go. If you have a map and a destination, AI can help you build the road. But if you have none of the three, you are lost in the wilderness, and AI assistance will be minimal at best, however good the model is.
Itwerx Corp is a service-disabled veteran-owned small business providing IT services across Seattle, Bellevue, Everett and Snohomish County. This is the kind of thing our AI integration work deals with – talk to us about yours.
