Digital technology is changing the innovation game

Digital technology is changing the innovation game

Digital technologies are beginning to have a real impact on the methods, approaches, and rates of our innovation outputs. Social technologies are giving us real-time understanding.

We continually learn that intuition and ‘gut feel’ on research set up and gathered weeks or more often months ago, has a hidden cost as it rapidly goes out of date. This ‘knowledge’ is becoming out of date before we can gain from it and sometimes highly dangerous to follow, or believe in in the more volatile market conditions of today’s, those that are rapidly changing. We need to get closer to ‘real-time.’

This reliance on rapidly out-of-date understanding cannot be the basis for any justifications for high-stake bets when it comes to innovation. We need to change our thinking and design in the digital insight part more specifically within and along the innovation process. Technology in all its forms is altering the innovation game but are we adapting to this radical change potential? We need to embrace it.

We have, in the past, partly because of levels of uncertainty. We have been overly reliant on ‘poor’ limited data and tended to take a safer route to market from this limited comprehension of the dynamics within the market. We are trying to achieve well-defined problems based on limited validation and simply place “solutions” into the pipeline for innovation. We attempt to work methodically along the timelines irrespective of changes constantly occurring, often impervious to the market conditions change, often going on before our eyes, with the end result, a disappointment and a lot of wasted energy and resource.

We continue to couch our bets and gravitate to solutions that are as close to what we currently offer, and that just keeps reinforcing the incremental route regretfully.

Today we can radically alter this innovation discovery by getting as close to ‘real-time’ if we care to invest in it. We need to invest in the enablers of technology to deliver constantly updated data, to allow greater analysis of choices and potential, closer to ‘go/no-go’ decision points.

Digital threatens (thankfully) this entire incremental pathway.

Markets continue to have even greater uncertainty and we need to respond in very different, faster, well-informed ways. Competition is changing, markets are blurring and customers are becoming far more vocal. To respond we need to reconceive innovation into a constant and rapid learning approach, listening and observing from these sources.

The need is to become far more iterative in feeding back what we hear; full of experimentation, testing, and adjusting, we must lend ourselves to adopting a far more minimal viable product approach, where we continually test assumptions, checking what is valued about the concept, gained constantly from the customer or market feedback.

Digital is also pushing us to shake off complacency since customers are increasingly further connected and interacting in new ways, making the constant choice, adapting to best experiences, imaginative offers and certainly not being as brand loyal for most of our products as previously. We need to find different and adaptive paths to ‘plug into’ and join the conversation, to help shape that conversation and through these exchanges, recognize how the brand and reputations become increasingly important in all things innovating.

Digital tools are changing how customers discover, evaluate, explore, make their purchases and use products and then how they react, share, interact and make their connections to our brands. The whole process is highly dynamic and reliant on the ‘network and social media effect’, and the job of the innovator is to translate this and respond in fast, nimble, agile ways, to fulfil the needs and capitalize in responsive ways. This demands a radical redesign of the innovation process and the whole customer engagement process. Most of our organizations today, really lag on this engagement, let alone on building a more adaptive and agile innovation process being ‘fed’ by the technology solutions we have available to us today.

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The need is to “test to learn” and then adjust and adapt, some thoughts include:

  • Without a doubt, it is finding and using more techniques and tools that help you make far greater rapid prototyping, testing, piloting and learning from these understandings and how they can be quickly translated back into the innovation process to improve on what you have known, into one of what you have become aware of. “Testing to learn” will become the new innovation mantra and the accepted innovation process; a dynamic, fluid and rapid experimenting that makes it a constant learning one.
  • We are today, partly down this route of early learning, with the help of software that can capture ideas, offering a sound vehicle for communicating these and a place to gather and define solutions that can be taken forward. Yet we have to push the path of constant evolution and that will continue to come at increasing speed from technology to keep providing new knowledge so as you can constantly improve on your eventual value proposition but that is going to require an ever-increasing agile organization and adaptive system.
  • We will need to find different ways to redesign the manufacturing process (more outsourcing) to have more adaptive points where we can intervene and change product design further upstream. We will need to find different ways to ramp up or dampen down finished goods and that is going to push ‘agile’ even further. The ability to balance “scale, speed, and scope” will become paramount in this.
  • We will need to learn to focus on the seizing of emerging opportunities in quicker ways but also take a much tougher approach to divestment, so as to release resources from declining positions of advantage. The whole life-cycle thinking needs challenging and thinking through.
  • We will need to push for “agile systems” where we can design new applications, find better communication and collaborative social technologies to reduce delays in getting the right information to the right people to advance the process and reduce the duplication of work or the search for the information needed to complete the job. We need to focus much more on reducing innovation process cycle time for faster deployment. We must be constantly adaptive not blindly following an outdated innovation process, that is full of rigidity.
  • We will also need to invest in more data-driven front ends to build a more sophisticated process of discovery. The whole concept of divergent experiments and convergent testing leading to validations will become the way to think of the innovation process as a re-looping process. This will require a critical mindset change from a linear one on how you set it, in the design of the process and the use and application of the digital tools you deploy to move this through this constant, ongoing iterative process. Nothing can remain ‘static,’ we need to make it ‘highly’ dynamic, iterative and fluid, a constant learn and adapt.

Rapid innovation matters and it will become even more central in 2017 as digital takes hold.

Rapid-iteration needs to replace the fixed product release date mentality we see in much of our innovative software. We are in a world, driven by digital that requires us to constantly adapt to new learning that is coming from real-time market feedback. The way we learn from this and our rapid innovation application process will be all about this continually testing new assumptions to improve our (final) product and service propositions. This is where the cloud provides the ability to constantly update and release, so we the innovators benefit.

This, I believe, requires a rethinking of the innovation process, as we will need to transform the existing methods and make them increasingly digital and social, in real time, feeding (as close to) real-time data and insights. We need to focus on the rates of innovation output from our learning and improvement in the eventual offering, not on the ‘classic’ input and output metrics from the past, as our constant bearing. It is outcome learning as the driver for metrics It is how we design this in a more comprehensive manner will be a real challenge in 2017.

Are we in a pivotal year of the rapid innovation application process? Will we finally move toward a greater fluid and looping innovation process? One that is fully designed and adopt as our new innovation discovery to the execution process based on the transformational effect of technology. One that is a constant path of innovation understanding and evolution that fits far more with these digital transformational times we are going through. They certainly do need to form a closer partnership for us to gain the transforming value.

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