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In all things success depends on previous preparation, and without such previous preparation there is sure to be failure.
The C-Suite is Nervous
If you’re in charge of piloting an AI project, you’re probably nervous too. Executives need to have a position and a vision for AI in their company. The gains that other companies are sharing is putting pressure on executives to get a win from AI. The problem is, smart people are telling them they aren’t ready yet. They need data audits and controls before any real project can be deployed.
That tension between getting a win and going slow is pushing the needle on timeline and budget. Executives want swift action, IT teams need a well thought out plan and a lot of preparation. And while the preparation is taking time and money, it is a crucial step that may make or break a deployment.
Right now if you were to deploy AI into your organization and give everyone access to a corporate LLM account, you would very likely have a big issue on your hands. First, I think you would have the majority of employees only use the chat functionality for simple searches. Then you might get a few power users, these people would be writing scripts, analyzing data, and building agents & skills. So, one class of employee barely using it, and the other that is potentially going wild. This is the scenario that most SMB’s are facing today. Which means the prep work is vital to having clean data, human training, AI policy, AI controls, and a corporate sponsored use case.
If you try to deploy without preparation as I explained, or you choose the wrong use case, you could be left with a nervous executive suite that doesn’t have tolerance for a botched implementation.
For instance, if your CEO queries AI for corporate projections, or even project status, and the result that comes back doesn’t account for old data and missing information - then you could find yourself under some serious scrutiny. One wrong word from the AI and it could deeply scar any trust the executives had in the system.
Make it Successful
Here’s how to get ready:
Assess the environment → Data, Security, Governance, Processes, Integrations
Identify Risks → Data Silos & Duplications, Manual processes, Missing standards
Test the AI → Search for insight, analyze workflows, push security boundaries
Find a winning use case:
Can you simplify something with high manual effort? → Reporting
Can you automate repetitive tasks? → Proposals
Does it have a measurable ROI? → Customer Service Copilots
Run a pilot program:
Find one problem to solve.
Constrain to a small group of people.
Find and measure the highest ROI project.
These AI projects are fairly high-stakes. As CIO’s or IT leaders within our company, we need to get it right the first time, so we can support adoption and trust from the very beginning. I speak with a lot of organizations that have “winged” projects in the past with no real buy-in or plan. Just migrate the thing, and upgrade the hardware. What’s different with an initial AI project is the workflow implications which are supported by knowing the AI has been implemented properly and the data is reliable. If we deploy and immediately erode trust, we run a big risk with future AI project success.
Until next week,
—Jared
Text Me: 314.806.3912
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