New tools make the future more knowable than ever.

Are you planning for or creating your future?

If the Generative AI revolution proves anything, it’s that the lines between data types are blurring.

As we enter Digital Transformation 2.0, we will use data in new ways. A trailing indicator that’s constantly sampled and easy to control will starts to look like a real-time indicator. A clear causal link between real-time and trailing indicators will turn every business metric into something that can be directly optimized. Observational data that’s gathered simultaneously from dozens of locations in real time will begin to bridge the gap between qualitative and quantitative. 

Companies today ask a wide range of questions about their business: “What did we make?” “How many do we have?” “What did we do?” “Did it have an effect?” “Should we do something different tomorrow?” But each of these is answered on a different time scale, by a different team, possibly sitting in a different building or even a different country.

What happens when all of those questions are being answered automatically, in real time, with the results sitting on the same software dashboard? We’re looking at something like a Grand Convergence of data, in which different types of information combine to open a real, clear, useful window into the near future.

The current round of generative AI tools give us a preview of just how powerful this could be. Any number of articles, including from this author demonstrate exactly how off course decision making could get if decision makers don't know how to ask for the right sort of analysis and assess the data upon which that analysis is being done.

So the trick isn’t obtaining the data, but doing something useful with it. Will money invested in training actually impact sales rates? Will investing another 40 hours per month on bug-checking make your software more stable? Finding these kinds of causal relationships is what statisticians do for a living. And statisticians are slow, methodical, and expensive.

Machine learning on the other hand is fast, thorough, and getting cheaper every day. Artificial Intelligence is extremely good at anomaly detection, as shown by its early embrace by banks for spotting credit card fraud. Spot enough anomalies of different types, though, and eventually you’re making causal connections, especially when huge amounts of historical data are available to train on. Take a well-trained AI and feed it a stream of real-time information, and it becomes possible to optimize a wide range of business processes. This approach will soon be so powerful and accessible that it will be table stakes, even for relatively small organizations.

For example, H&M is spending heavily on IT, logistics and AI, but perhaps the more interesting thing is the investment it’s making in social media and wearables that provide inputs to its IT systems. These inputs will give the C-level direct realtime intelligence from the bottom of organizations, enabling better decision making in the moment and better forecasting across the year.

These same systems will provide H&M employees with higher levels of collaboration on the shop floor, tracking customer movement, identifying favorite customers, ensuring that there are enough staff in the dressing rooms and at the cash register. More importantly, this level of intelligence can enable dynamic pricing, optimizing for both sales volume, supply chain efficiency and consumer appetite.

Are you using your data tools to plan for or to create your future?

In coming articles, we'll explore some best practices to make sure that you know as much as you can about tomorrow, so that you can prepare for it, instead of respond to it. This isn't just a newsletter, it's a conversation and I look forward to hearing from you. What do you love? What do you hate? What's hogwash?

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