Walk into any large organisation today, and you will find AI activity. A pilot in customer service. A proof of concept in finance. Someone on the innovation team testing a chatbot. What you will rarely find is a single executive who can explain how these efforts connect, what they cost, or which ones actually earn their keep. Ask a chief executive how many AI pilots are running across their business and the honest answer is usually: more than I can count, and I am not sure why.
Now that we’ve identified a problem, how do we fix it?
Start with the work, not the technology
Forget “what can AI do?” The real question is which processes are slow, expensive, or error-prone in ways a machine could fix. That means mapping actual workflows: where time gets lost, where humans do repetitive judgment calls, where decisions depend on information scattered across five systems nobody has fully connected.
This step produces something concrete: a ranked list of use cases, scored on value, feasibility, and risk, not a wish list. It puts the projects most likely to work, and most likely to matter, at the front of the queue.
Skip this step, and you get what most companies have now: a portfolio of disconnected pilots, each one a fresh decision, none of them building on the last. A key reason these projects are so disconnected is a lack of guardrails. When every employee creates new AI projects in a siloed manner, there is no control over privacy and robustness.
Build the foundation once
Ideally, you want your company to be in the position where your infrastructure is set up such that after the first round of models or AI solutions, you will spend less time or energy on the next round of projects. This also enables you to focus more on the actual model than on the infrastructure. Here is why that changes the economics of AI adoption: the second project should be faster and cheaper to deploy than the first, and the tenth should be faster still. That only happens if there is a shared platform underneath every agent and workflow, one place where orchestration, security, and audit controls live.
Without this, every new AI initiative starts from zero. Someone re-solves the same integration problem, re-negotiates the same data access questions, re-builds the same monitoring dashboard. With it, each new use case plugs into existing infrastructure that has already passed a security review and gives your risk and compliance teams the visibility they need to sign off with confidence.
For regulated industries in particular, this is not optional. An AI system operating without clear logs of what it did, why, and on what authority is not a system your board should approve.
So what actually needs to be in place? Ask yourself the following questions:
- Is the data infrastructure (lakehouse/database) ready? Essentially, where is my data, and can I access it, or is it scattered everywhere?
- If I build models, how do I want to deploy? Can I develop a set of standard templates or infrastructure for deployments?
- If I am building other AI apps, can we standardise how we do this (technology, infrastructure, etc.)
- When it is deployed, how do we monitor and track the performance?
- The biggest thing companies forget is change management. Now that I have this new technology, how can I ensure people use it rather than defaulting to their old ways?
Give the organisation a memory
The third piece is the one companies discover they need only after the first two are in place: a way for AI systems to draw on institutional knowledge rather than working from a blank slate each time.
Most valuable company knowledge lives in documents, spreadsheets, and the heads of long-serving staff. An AI agent that cannot access any of that is only ever as useful as the prompt someone types into it. One that can, with proper permissions in place, becomes something closer to a colleague who has actually read the file.
The order matters more than the ambition
Identify the highest-value work first. Build one platform instead of five one-off tools. Connect that platform to what your organisation already knows. Hit the low-hanging fruit first. This might not be the most glamorous solution, but rather the one with the biggest bang for buck. Read our blog about choosing use cases here.
Companies that follow this order tend to compound their gains: each new AI use case gets cheaper and safer to add. Companies that skip it tend to accumulate pilots that never quite reach production, and executives who cannot say what their AI spend has actually bought them.
At Praelexis, we call this sequence Discover, Architect, Build, and Operate & Scale. The label matters less than the discipline behind it: know the work before you buy the tool, build the foundation once, and make every system smarter than the last.