AI readiness, not the technology, is the real constraint most organizations are facing, whether they realize it or not.
AI keeps getting easier to deploy and experiment with, across technical and non-technical teams alike. That accessibility is exposing something more fundamental: most organizations are not structurally prepared to use AI in a way that creates real operational value.
In this conversation, Josh Anderson, CTO, and Sam Gonzales, VP of Systems Engineering, come at the problem from different angles and land in the same place. The question is not whether AI works. It does. The question is whether the organization has the clarity, data, and systems to use it well.
AI is not going to come in and create data out of thin air. — Josh Anderson, CTO, DecisionPoint Technologies
This is the diagnosis stage of the modernization journey. It assumes you have already taken an honest look at where you are on the curve. Here the focus narrows to what AI specifically demands before it can deliver.
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AI readiness starts with a clearly defined problem, and most organizations never get that far. As Sam Gonzales puts it, the common pattern is wanting to leverage AI without grounding it in a specific, measurable business challenge.
The intent is understandable, since AI is visible and the pressure is real. But starting from the technology instead of the problem makes the work drift into loosely connected experiments that never compound.
Sam’s rule is blunt: do not chase a technology in search of a problem. Start with the problems worth solving, then ask whether AI is even the right tool, because some problems already have a perfectly good non-AI solution.
Once there is a real problem, the next question is whether the data exists to solve it, and that is where most AI ambitions meet reality. As Josh Anderson notes, there is a persistent hope that AI can compensate for missing data.
The opposite is true. AI depends entirely on the data it receives, so if that data is incomplete or inconsistent, the output will be too. The gap shows up in operations constantly: a team knows a problem exists, a delay, an inefficiency, a variance, but they are not capturing the time, frequency, or variability behind it, so there is nothing for AI to work from.
The practical takeaway is to treat AI readiness as a data initiative first. Before asking what AI can do, ask what data is required to answer the questions you actually care about.
AI readiness rarely stays contained. As Sam describes it, AI is the brain, but a brain needs senses. The decisions you want it to make depend on inputs that reflect what is actually happening in the business, and in many operations those inputs are thin or captured only periodically.
To feed AI, organizations start adding the technologies that gather better data: barcode scanning, RFID, IoT sensors, and integrations that move data closer to real time. Josh frames the same point from the architecture side. AI is powerful at connecting disparate sources and finding correlations across them, but it still needs access to the data in the first place.
AI becomes a catalyst for modernization, not because anyone set out to transform their systems, but because the value of AI cannot be realized without it. Leaders should expect AI initiatives to grow in scope as these dependencies surface.
None of this requires a massive, high-risk program. Both Josh and Sam land firmly on the opposite approach. Unlike the old SaaS-era rollouts that were hard to dip a toe into, AI can start small.
As Josh puts it, you do not need to invest three quarters of a million dollars before seeing the first benefit. Add one focused data stream, apply AI to a narrow, well-defined use case, see what value it produces, and scale from there.
Sam makes the complementary point that the technologies feeding AI deliver gains on their own along the way, so every step of the journey pays off. The discipline that matters is tying each use case to a measurable outcome and letting each iteration inform the next.
AI is also reshaping how organizations weigh building versus buying software, though it has not removed the tradeoffs. As Josh explains, AI lowers the cost of producing software but does not change the long-term responsibility of owning it.
Building still demands maintenance, governance, and alignment with the business, and those matter more, not less, as development speeds up. Buying still offers stability and scale.
The real question is where customization creates genuine value and where standardization wins, which comes back to how well an organization understands its own processes. That decision is the subject of the next article in the series.
AI readiness, not the technology, is what ultimately determines whether organizations see results. AI does not introduce entirely new problems. It applies pressure to ones that already exist: gaps in data, unclear problem definition, and disconnected systems.
For leaders, that shifts the goal from adopting AI to making sure the business is set up to use it, clear on which problems matter, what data is needed, and where incremental progress can create real impact.
If you are working out where AI fits, start upstream. Revisit modernization readiness to place yourself on the curve, or see how DecisionPoint helps organizations get the data and systems foundation right.
