Artificial Intelligence conjures up images of neural networks, HAL, thermonuclear war and inexplicable complexity for most – myself included. Then trying to figure out how to bring this concept into the average business – distilling it down to a level where it can augment revenue or customer satisfaction – means it will never move forward unless there is a simple first step to be had. Is AI too complicated for the average business? Lars Hard, co-founder of artificial intelligence company ExpertMaker, doesn’t think so. We simply have to readjust our viewpoint.
Mobile has ushered in a new emphasis on artificial intelligence – brought to the mass market by SIRI and other similar products – but this isn’t where it stops. We are just entering this world (thanks in part to the powerful computers we carry in our pockets), one that requires us to shed the preconceptions that have plagued our ability to really move the innovation needle. As Lars talks about throughout this episode, we need to truly innovate around new products to really embrace AI. Why do we build websites that look like newspapers. Why do we compartmentalize data into centralized tables and databases instead of smaller, localized nodes?
This is a wide ranging and mind-blowing conversation with an incredible thinker in this space. His company is trying to help usher in a new era of business AI that helps alleviate some of the stresses a business feels. However, you can see his excitement when we talk about the future and how, in all likelihood, artificial intelligence will turn conventional business on its head.
Here is a quick reference of what we covered in the show. Click on the link and the video will take you to that clip
What is ExpertMaker 2:13
Is Artificial Intelligence for the average business? 4:15
This isn’t re-creating brains 6:00
How has the definition of AI changed since you started in this industry 8:50
What is the value creation that comes from AI 10:30
How spreadsheets relate to artificial intelligence 13:45
Why we need millions of micro searches, owned by many 14:52
Why we need to rethink and redefining the way we are thinking about interacting with information 16:25
Why are databases one of the worst things that have propagated 17:40
What is the first step to take to start in AI 21:30
Why focus your efforts on the smaller developers 25:10
What is the future of big company search – does AI replace Google? 27:40
Why a single entity shouldn’t own all our experiential data 31:40
Will artificial intelligence change all industries – how? 35:45
Is data the base for successful AI 40:10
Why big data is not the answer 42:30
How do businesses start down the AI path 44:35
My key takeaways
Remove the shackles of current thinking
Too many times we look through our current lens and try to predict the future. We see this all the time in technology predictions (4 billion app downloads by 2017), revenue predictions (mobile advertising will hit $5billion by 2016) and user habits and trends. Some of these predictions are obvious linear extensions of where are today simply projected forward without a single consideration for the changes that will inevitably move us in a different direction. Think back to the iPhone – even the BlackBerry before it – did anyone see that coming? What about the iPad? Predictions were made that it would be a complete failure (although, ahem, not mine) – isn’t Apple the single largest PC producer on the planet now as a result? The point is when we make assumptions and predictions based on what we know to be true (current growth rates, adoption rates) we make false assumptions on the future.
True forward thinkers, like Lars, aren’t afraid to remove the burden of the present while examining the potential future. Put in other words, forgetting the past – the way things have been done – and focusing on the future – the way things should be done – is a fundamental first step in moving to truer innovation.
Data is not knowledge
This took me by surprise but it makes sense once I thought about it. I speak with many entrepreneurs every day who collect data (analytics on their website, information about usage patterns inside their app, checkout processes, etc.) who simply don’t have time to action on any of it. For sure, the first step in understanding how to better serve customers or constituents is to collect the data but don’t become a data hoarder – don’t keep the data buried. If you aren’t using it to further your business or deepen the relationship with your customer, don’t collect it and let it rot. You can always come back to collecting it when you realize you need to.
Lars makes a compelling argument in this episode that the concept of centralized data collection and storage is an outdated, inefficient model. Many nodes of smaller data pipes, interacting with each other and in context to a situation (a purchase, the time of day, the weather) allows for deeper engagement and a much richer experience.
What are the key metrics – those that are paramount right now to your success. How do you use this data to automate an experience that moves a customer through your purchase process or through your support process. Start here, where the impact is felt. Step 1.
Look for simple patterns in your business
When you’ve focused on the right metrics – your right number – patterns will emerge and those are key to understanding how to automate certain areas of your business, the key areas. We often look at the wrong things in the wrong places and wonder why we are missing trends or seeing them too late.
Small tends to mean nimble
The cure for lack of innovation often lands in the hands of the hungry, smaller, more nimble companies. They tend to be looking for ways to disrupt the incumbents – a little Clayton Christensen Innovators Dilemma – rather than competing with them on a seriously lopsided playing field. Small businesses become large by looking at problems differently or trying to find more efficient (or effective) ways of solving problems for customers. It is with the small companies that Lars is seeing some of the most innovative uses of artificial intelligence in hackathons and workshops they are putting on.
How do you approach solving the problem of your customers? Do you look at them through the lens of “how you’ve always done it” or are you up to the challenge of starting from scratch and looking for ways that may even disrupt your business. If you don’t, someone will. Guaranteed. Start looking at the problem you are solving from your customers eyes right now. You may be surprised at how far off you actually are from helping them.
What do you think? Also, what do you think of the new format for the episodes? Do you like the chapters for quick reference? The takeaways? What else would you like to see. Leave a comment or two below or email me.
To see my takeaways, you need to be a member
Be sure to subscribe to iTunes to be notified on all future episodes (they are free for the first week!): Audio or Video
About Lars Hard
Most stuff I do professionally relates to Artificial Intelligence, or more precisely Computational Intelligence. The absolute frontier is in Evolutionary Computing, and it can be applied on most computational opportunities like discovery of patterns in extremely complex data spaces, optimization where no traditional mathematics works or to create and build interesting digital things.