Like a best man speech at a wedding reception, business data is often unstructured and messy.
Take insurance, for example. “Sometimes the auto insurance customer database is not directly connected to the homeowner database or to the life insurance database. And so if you want to get a holistic view of the customer, what sounds like a trivial event is not trivial,” Alex Singla, senior partner at McKinsey and co-leader of its AI arm QuantumBlack, told IT Brew.
Singla helps clients across industries deploy AI to solve business problems—and that, in simple terms, can come down to connecting datasets to decision-making algorithms.
Despite uncertainty on AI ROI, many orgs have spent or plan to spend on AI in 2025 (78% of global senior leaders, according to a Deloitte poll last year).
Following McKinsey’s Media Day on September 8, Singla spoke to us about how IT teams are being impacted as companies try out AI—and how one role for IT, amid these AI deployments, might be especially data-driven.
This interview has been edited for length and clarity.
How can IT evaluate whether an AI tool is actually useful in a certain context, or whether it’s just being pushed by executives who are excited about AI?
I think IT needs to be part of that decision-making process and not have it being thrust upon them. And the reason I say that is because any of these solutions and tools come with a level of [questions]: How will this interact and play within my own infrastructure? And how will it actually connect and work?…IT needs to play a role in understanding what that will look like.
IT can [also] do an amazing job understanding what data needs will be required to make that AI solution work: How good is our data? Where does it reside? Will it need to be real-time data, or not real-time data? If it’s real-time, what are the implications of that, in terms of not just where it resides, but how much it will cost.
If it’s real-time data feeds, and it sits on a cloud platform, all of a sudden I’m consuming much greater cloud consumption than I might have typically [consumed], and therefore I need to track what the cost will be. So, as I think about the value, it’s not just the upside of what that AI solution might provide, but it’s also the cost side of that equation.
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What kinds of data sources are commonly used in AI deployments that an IT pro should already be ready to connect with or understand?
Many AI solutions will benefit from your own finance data, your customer data, your product data; if it’s maybe an efficiency play, you have people data. Those are, in our view, “no-regret” things to get structured in ways in which AI can leverage and use them over time. Like finance, for example: At any point somewhere you’re going to have to connect the impact back into the finance systems, right? So, how it connects to that will be important. Product and customer are usually enormous data sets that are not always clean; let’s put it that way. It takes real work to get them in good shape.
What does it look like to clean up data?
The data is in so many different spots. The data is not always captured consistently, and so you get a lot of messy data, or gaps in the data…And the more messy that is, the more expensive it becomes, particularly when that data sits in a cloud.
Is IT being asked to do everything? And what are ways to make sure an IT professional doesn’t get overwhelmed with this process?
What I sometimes see is people get so excited about the opportunities but they don’t fully appreciate how long some of these things take to build—not just from a technical perspective, but then rollout and scale across the organization. I think the technical professionals can continue to reinforce just the time it takes to do some of these things.
Like the data-engineering side and getting the data structure in good shape: That’s often the long pole in the tent. Building the models and building the solutions—of course, you need great, smart people to go do that, but that’s usually a solvable problem. It’s getting the data structured up front, getting access to it, and then rolling it out and scaling it, which has less to do with technology, and has become such a huge part of driving an impact.