Wednesday, March 20, 2019

Innovating when everything is "as a service"

I've been talking with a number of people who are more connected and smarter than me about digital transformation.  I've become convinced that the myriad and sundry tools and methods around digital transformation - cloud, ubiquitous high speed internet, IoT, AI, machine learning, robotics, augmented reality and all the rest - are going to create a major transformation in how consumers acquire and use products and services in the future, but more importantly how corporations and startups recognize value from their products and services.

This realization hit me when I was watching a short video recently where the speaker talked about how everything would be "as a service".  We are already familiar with this concept, in that many things we used to acquire and own are increasingly provided as a service.  In the not to distant future we could experience transportation as a service rather than purchase our own cars, or, my dream, lawn mowers as a service, where a trusted partner leaves a fully maintained and fueled mower at your doorstep just as you need it and picks it up the next day.

This transition into an "as a service" world changes the ownership model, the customer experience model and the revenue model, all at the same time.  What the "as a service" model doesn't fully contemplate, however, is the importance and value of data.  After all, does a mower really create interesting data that can be harvested?  Some products, like Alexa, create interesting data about the searches people conduct.  That data is valuable to create predictive analytics or to personalize offers. Where possible, we also need to consider how an existing or future product - no matter how "smart" or dumb the product is - will create data or use data - and what the data will mean as part of the value proposition.

What's all of this have to do with innovating?

In the past, we thought of disruptive new products, and occasionally thought about disrupting a channel or business model.  If the analysis above is anywhere near correct, the emerging digital transformation will require innovation on a completely different scale.  The product will simply be the kernal of a much broader offering, and the product may be something the company is more than willing to provide for free, in exchange for deeper understanding and ability to manage the data generated around the product.  This exchange of value, while it seems simple, is actually really complex in reality.

I led a small software firm years ago during the transition from installed software to "as a service".  The impact of moving from a large, one time software sale, where we'd make hundreds of thousands of dollars, to a recurring monthly retainer that required years to make the same amount of money, meant we had to radically rework our operating and revenue models, or change businesses.  We chose the latter.  And in this analysis we aren't even considering the volumes of data that can and will come streaming out of many offerings, and the complexities of managing that data and using it to create new insights.

When we innovate in the future, we need to understand exactly what the value proposition for each offer is to the customer, and to the ecosystem, and to the company making the offer.  Where does each benefit?  How does the company make money?  Where does all the data go and who owns it?  What business model supports the interaction successfully?  In other words, Doblin's Ten Types are no longer discrete options, no longer an either/or proposition, but become a framework where you may consider all of the "types" for each innovation, and add a few more, to include value for data as an example.

We're not ready but we need to be

Few large companies are doing basic product innovation well, but they are light years ahead of those that aren't really innovating at all.  A visible and relatively rapid moving tidal wave is emerging as digital transformation takes root and we create more IoT, more AI, more ubiquitous wireless broadband, more cloud storage and so on.  These things won't be siloed for long, and as they combine into a true digital transformation they open up entirely new operating models.

If companies have struggled to innovate a basic product, what will happen when innovation requires thinking through new operating models, new revenue models and new business models and channels?  The opportunities are much, much larger but will most likely accrue to those that move decisively and quickly, and the doorways to those opportunities will also close quickly.

Where is IT in this?

I asked a question to many companies about this topic in a recent event.  I asked - if your innovation opportunity creates opportunities for collecting and managing data, and gaining value from that data - who on your team could support that?  Most suggested that it would be the responsibility of their internal IT team - not the product team, not the innovation team, not the people doing digital transformation - and then went on to say that their companies simply couldn't manage the data effectively. 

It's not an internal IT team's job to think through how data generated from a new product may impact revenue and how to collect and manage that data.  But right now in most corporations NO ONE is thinking about this, and they should be.  If you though innovating a new stand alone product is difficult, wait until you try to create a new solution that is based on a product that generates or collects data and shifts a business model.  New companies can do this because they've got little infrastructure to resist it.  Established companies will need to become uber agile and able to change quickly, something most are not able to do.

The rising tide is visible from shore, but few large firms are doing enough.  They are working on the fringes of innovation and implementing some digital transformation tools without considering how these combine to create new business model and operating model opportunities.  By the time they discover just how much change is possible, it may be too late.
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posted by Jeffrey Phillips at 7:08 AM


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