Hi there,
Everyone wants to talk about how they’re using AI, yet much of the evidence we see from companies applying AI in its public form is AI-generated LinkedIn posts.
In today’s post, I want to share how I think about AI process improvement and what I think a lot of firms are missing when they select AI use cases without first taking a step back to reimagine how those use cases fit within an AI-first operating model.
An operating model is just a concept of how a business is put together. It’s about how people, processes and platforms roll up into capabilities that enable a company to ship products and services to its customers.
This AI-first operating model perspective focuses every single part of operating model change on reimagining how everything is done as if you were starting from a blank whiteboard with the power of not just the best AI tools as they exist today, but what they will be like in six months’ time and beyond.
An example of the old way of approaching operating model change is how companies traditionally approached outsourcing. Let’s say they have a call centre with 500 employees. They know they have to pay for all of the systems and processes and controls and audits around operating this call centre.
The traditional way of doing this is to get a consulting firm in and get them to map out how the call centre currently works. Then get them to do a market scan to see if there are outsourced call centre providers who can replicate that capability, meet all of the different quality and cost targets that we want to achieve from this operating model change, and then give us back a nice pretty report and execution plan.
For us to go to the board and say, we want to outsource our call centre, here’s the plan, and “brand name consulting firm” helped us put this together. Can we get this spend of $10 million approved because we will save $15 million a year ongoing if we outsource this?
A more cynical take might be that the old way of thinking about operating model change involves a lot more predetermination and constraint in terms of reference and assignments of work to arrive at the preordained outcome of, oh wow, we have to outsource or offshore this thing that we’ve wanted to do for ages, and here is all the evidence to back that up, and here is the concrete plan for us to execute it, and look here, the consulting firm that helped us do this just happens to have a preferred outsourced call centre “strategic partner”!
The AI-first operating model change is a little bit different. When I talk about starting at a blank whiteboard, I’m serious. You want to be imagining how you would rebuild the capability you’re trying to rebuild. In this example, a call centre, and whether that is even the right boundary of capability you want to be rebuilding.
What are the jobs to be done? What is the right blend of human and AI agent? What are our customer expectations around the sorts of things they can just do self-service and the sorts of things where they do need to speak to either a human customer services person or an AI agent? What are all of the regulatory challenges we face around voice interaction with customers?
