The AI Reality Check

with Kyle Cruickshanks

A lot of companies are treating AI like a magic bullet that will fix all the problems within their organization. Business Intelligence Team Lead Kyle Cruickshanks is talking to us and sharing why it’s not that simple – and how you should be approaching AI strategically to set your organization up for success.

Transcript:

Today we’re talking to Kyle Cruickshanks, leader of our Business Intelligence team, about how organizations can apply AI more strategically to bolster the performance of their top performers. Kyle – can you tell us what you’re seeing across client organizations who are deploying AI?

Well, we’re seeing a lot of interest and enthusiasm from organizations who are keen to make real strides using AI. The problem in many of these cases is that they’re treating AI like a magic bullet that can fix pretty much anything they come across in terms of broken processes across their organization.

That sounds like something we can all relate to. So instead of just crossing their fingers and hoping AI fixes everything in their org, what should company leaders be doing?

The first step isn’t really related to AI at all, it has to do with diagnostics. Ask yourself: ‘How long does it take to get an answer about a core business KPI?’ Frankly, if your answer is measured in days, you aren’t ready for AI—you have a foundational data problem. AI doesn’t tend to fix messy data that takes days to wrangle; it just makes the problem worse, faster. That’s why you use Business Intelligence or other functions as your diagnostic tool, to find the ‘flat tires’ in your operations before you invest in a new engine. You have to fix the process first, then you can add the horsepower.

Yes, There’s a lot that can be accomplished with AI – but what is the BEST way for companies to approach integrating it into their organizations?

I think the biggest ROI from AI doesn’t come from giving a generic tool to everyone. It comes from creating a personalized co-pilot for your best people, trained on their strengths and success patterns.  The goal is to amplify excellence instead of trying to fix mediocrity and have these users champion the solution across the organization, especially when it comes to agentic and generative AI use cases.

So, on the flip side, what’s a potential pitfalls that AI can bring to an organization  if they’re rushing to implement just because of the hype?

I think the biggest one would be, as AI makes decisions—routing support tickets, answering customer inquiry—you have to govern those decisions, not just the data. If you can’t explain to a customer or a regulator why your AI did what it did, you don’t have an asset; you have a liability. 

So true. Thank you Kyle. That’s a very helpful insight. We hope you can find value here as you navigate AI use in your organization.