Gen AI: The End of BI?

Imagine that you have a question about a specific event for which you need to go the public library and look at newspaper archives. You might go to an index, narrow your search down by region and date and sift through a bunch of local newspapers column by column and hopefully find your answer - if it’s there, it should take a few hours. It’s a scene out of a detective film from yesteryear. Now imagine a sci-fi version of the same film where you pull up a device in your home and ask it the question - and the system comes back in seconds to give you your answer.

One of the biggest challenges of innovation work is being able to think different instead of better.? Better is how we're tuned to thinking. It's what Henry Ford called a 'faster horse'. Doing what we do already, but with improved performance. Cheaper, faster, more efficient, and so on. If you sell ground coffee in supermarkets - better leads you down the path of better supply chains, stocking improvements, store promotions, and packaging improvements. It also typically includes tinkering with the product mix - for taste and flavour, or branding and positioning efforts. It doesn't however take you to the point of thinking 'what if we sold ready to drink coffee directly to customers using a globally scalable model?' Which is why coffee brands at companies such as P&G were taken by surprise with the success of Starbucks, and all the other now-ubiquitous coffee chains. And ironically, a few decades later while Starbucks were trying to figure out how to make their model better - better retail, better service, better experiences, better product mix, they didn't think different either. For example, could we give the same experience of fresh coffee to consumers at home?? Nespresso did, and suddenly the coffee chains were losing a section of clients to home brewed coffee on kitchen counter friendly coffee machines. This is about the art of thinking different.

The folks at ARIA also had an interesting phrase - at the recently concluded CogX , they were talking about research that creates 'not just a new product but a new industry'. Creating a new industry requires a fundamental rethink of something that we will all now do differently. This too, can be at varying levels of scale. Google maps changed how we navigate on roads, globally.? Social media changed how we stay in touch with friends and family. Mega changes, or what Suleyman Mustafa calls 'waves' are even more seismic in their change impact. The Internet, the automobile, the printing press are examples of things that effectively changed the course of civilisation. It created 'different' at a truly global scale.

Which brings us nicely to AI. It seems like AI could be the next big megachange that alters the course of our collective history. It has potential implications that have captivated the worlds greatest leaders, scientists, historians, and business people. Within the broader sweep of AI systems, Gen AI has become particularly topical - largely because of its easy accessibility. And given what Gen AI is good at, it's natural that people are drawn towards areas like summarising documents, writing essays, and in chatbots and virtual assistants. All of which are excellent examples of getting better. But how can we think different? Where could Generative AI change business significantly? Sometimes it helps to reframe the question, and as I like to say, look at the problem "sideways".

I asked myself this question - what is the super-power of GenAI? And it's this: Generative AI understands language and context. It can decipher your questions and intent, no matter how you ask it. And it can translate your question to a set of specific instructions to any other complex system. For example, rather than pore through the manual of a car or any domestic appliance, you could just ask a question. 'How do I replace the engine oil'? And Gen AI can act as the intermediary and fetch you the answer. So you don't have to go looking through the index and the dozen references in the manual you have to scroll, to find the right answer.

Let's extend this a bit more. Think of GenAI as your interface to any business system. In any enterprise, there are dozens of major systems, and possibly a hundred different smaller systems and tools that are used to run and manage the business. All of them hold data and information that you need. Gen AI should be your interface to all of their data. No more having to run SQL queries, or following specific and often complex processes to extract insights from them. These complex instructions usually require technical knowledge, so you're also reliant on your data team. Historically this has led to an entire industry around reporting. BI (Business Intelligence), MI (Management Intelligence), Analytics, predictive and prescriptive analytics, real time data, all of these terms are supported by a plethora of products and armies of data engineers and analysts. An outcome of this is just-in-case reporting. Every business generates encyclopaedic amounts of reports, just in case somebody needs some piece of data. Not only is it inefficient, as most reports go unread, it also has a poor signal to noise ratio, since you have to wade through a lot of information before you find what you need.

Why not end all of this often-mindless reporting, and focus on what you really want to know. If you're in retail and you want to know which stores had the highest or lowest footfall in the past 24 hours, or where the inventory is currently at its highest, simply ask the question, and you should get an answer. Assuming that the data exists somewhere in the business, GenAI should be designed to create your interface with your data. Companies such as seek.ai and Thoughtspot are looking to do just this. I recently saw a demo at client where the queries were quite ambiguously structured, such as 'which ski resort destinations have the highest profitability' - so you're expecting the system to pick out 'ski resorts' or 'beaches' without specifically naming locations. The solution also uses Gen AI to decide what the best format for presenting data is.

It's not as simple as implementing ChatGPT inside the business. The heavy lifting of stitching together data from multiple systems, and relational tables still needs to be done and won't be done by GenAI. But GenAI can act like your executive assistant for data and insights, and trigger the right systems and processes, so all you need to do is ask a simple question in natural language. But I think this is how tools will be set up, and this potentially is an area where GenAI can help us be different, not just better, in businesses large and small.

If you want a reminder of how much of a change this can make, think about the humble remote control. Earlier, if you wanted to find a program on your TV system, or on Netflix or Amazon, you would need to navigate to a virtual 'keyboard' and type out the search by navigating your way around the keys. Clunky and time consuming by any yardstick, but it's what we were used to.

Something like this:

Sky tried to improve this by creating the alphabetical bar. Better, but not different.

Then came the next generation of Sky Boxes, with a voice search built into the remote. Now suddenly you didn't have to type, or navigate. You just pressed the blue button and 'said' the name of the program. And the system found the match(es) and showed you the results. I think that qualifies as different, rather than better.

Imagine a similar kind of power for your business - a simple and intuitive way of asking for complex information. Which customers have the highest amounts outstanding? Which suppliers have negotiated the most favourable terms of payments? What was the age and gender split of people who visited our website last month? All you need to do is ask the question, and the AI system will do the rest. Now, isn't that different??