What are some pitfalls in assessing AI readiness?

- What are some common pitfalls that organizations might face when assessing their AI readiness and how might these be avoided? - I love that question. I think when it comes to first surveying your landscape and thinking of identifying opportunities for AI from a mechanic standpoint, you have, looking at your market trends, looking at what technology is available, you need to look at what your competitors are doing. You need to do a feasibility analysis. But more importantly, the places where I think companies often neglect to pay attention to are looking at your customer pain points. Look at your employee pain points. Where are they struggling? Where do they need help? And focusing too much on the quantitative metrics, the data, without looking at the qualitative metrics. And the best example I can use is thinking back to our web development days when we were helping all the businesses build websites and applications for their clients or for their marketing needs. And they would always focus a lot on analytics. And analytics, it's great to have analytics within your product, but I would often explain that analytics tells you the what. But by talking to your customers, you'll be able to understand the why. Why did they go to a page? Why are they using a tool? Why is an employee not producing at a certain level? Or why are they responding in a certain way? By talking to people and being human, I think you end up getting a lot more context than just looking at, okay, how much time did they spend doing something? And looking at those hard numbers. And I think just balancing the qualitative metrics, the things that are harder to measure versus the things that are easy. Because even when you're thinking of ROI, how do you measure the value of educating your workforce on AI? It is not an easy metric to measure. It's not impossible, but it's not going to be as easy as a lot of the other metrics that you can just quickly look on a chart and say, oh, we are 50% open in this metric. And so taking the time to figure out, okay, these are hard to measure, but they're important. I think that's going to be key. Also, when it comes to just a aligning your business initiatives, not considering the total cost of ownership for a lot of AI initiatives. It's one thing to just think of, okay, this is a cost for implementation, but not taking the time to say, okay, over the life cycle of this product, maybe it's going to be six months, maybe it's going to be 12 months, maybe it's going to be years. What are we expecting in terms of cost to maintain it, to stay updated with the different models? Thinking of total cost of ownership, you may not know all of the answers today, but at least asking the questions so you know to keep asking them until you have the answer is going to be important. And so those are some of the pitfalls I think companies make not really focusing on the qualitative data, not thinking of the total cost of ownership for the initiatives.