With AI automation achieving better success in automating highly repeatable tasks we can expect to see companies allocating the surplus time this creates to innovation activities.
In a recent study of businesses engaged in utilising (AI) cognitive systems, the Harvard Business Review essentially concluded that moonshots were fool hardy and instead businesses should focus on using AI to automate workflows that were relatively simplistic in nature. These workflows could be business side processes such as helping a procurement team quickly isolate the best supplier for a project or could be on the customer experience side such as helping a car buyer book a test drive.
As opposed to ‘making cancer history’ or creating business oracles that are complete subject matter experts. This approach has a few upsides, firstly it helps the business deliver on AI projects that can have demonstrated success, helping to maintain momentum but importantly the opportunity this optimisation of regular workflows creates for companies gearing their culture towards innovation is that their employee’s time will be freed up to engage in innovation oriented tasks and thinking.
A case study cited as part of the study was the MD Anderson Cancer Centre which in 2015 launched an AI project to end cancer. A bold moonshot that came at the peak of AI inflated expectation to cite the Gartner Hype Cycle. In 2017 the project was put on hold after costs exceeded $62 million USD and was yet to be used on a single patient. Parallel to this they also instituted AI pilots helping with less ambitious tasks such as identifying patients that needed help paying their bills, making hotel and restaurant recommendations for families of patients and helping staff address IT problems, all with much greater success.
Working on a handful of AI projects ourselves, this is something we have seen as well, that the complexity in applying AI to tasks that seem straight forward at first is never just that and there is always hidden complexity to be found.
Another observation we’ve made is that a common response to attempting to get non innovation employees to engage is design thinking style insight capture or pilot innovation promotion is that “they are busy doing their own job”. This time constraint is understandable, employee’s job descriptions and workflows have been tightly optimised to ensure they are being as productive as possible against dedicated KPIs.
But in linking these two ideas together, what if we saw AI as a way of freeing up their time to engage in lateral thinking or opportunity spotting? Isn’t that the real purpose for innovation in our lives? To free us from the mundane so that we can engage in more rewarding activities?
There is a cynical side to AI adoption which is rationalising employees out of the equation to reduce costs and enjoy a better margin but this is potentially dangerous short term thinking. In the process of doing this, vast amounts of non digitally captured customer knowledge will be lost.
Instead the application of AI to business process and customer experience workflows opens up the opportunity to implement broad adoption of innovation & design thinking behaviours to be inserted into the time AI frees up for employees, making full use of the customer knowledge contained in their collective minds. As well as making time available for them to engage in field trips, contextual observation studies and side projects that can yield new perspectives not currently found in the collective mind of the organisation.
We also need to consider the organisational politics around AI. It will invariably create a climate of fear as more use cases show success and people naturally begin to wonder if they will be replaced next. Something that is sure to destabilise and even damage the cultures companies are so dependant on for innovation to exist and work so hard to create.
So instead, by automating workflows that free up employee time to engage in innovation & design thinking, companies will be able to enjoy the benefits of improved employee satisfaction and a reinforcement of the company’s most valuable asset, its culture.
The assumption here is that every employee can and will want to engage in thinking beyond their current set of workflows. So this may not play out for everyone at first but in promoting employee engagement in innovation & design thinking we are essentially promoting a path for them which is more self actualised in nature. It takes their everyday reality from a large number of mundane tasks and broadens it to include tasks which are far more gratifying in nature.
So in this way AI can be a force for building an innovation culture, by overcoming the time constraints employees face in their day to day and making it possible for them to expand the possibilities of how they participate in helping their organisation move forwards.
A thesis we have at Half Machine is that AI could make every category a winner take all category as the company that utilises it most effectively first experiences a compounding benefit that sees them attain an unassailable advantage. Amazon is beginning to show signs of this to a degree but for the majority this now sounds a bit futuristic to us. Instead we think it will be a combination of AI plus people driven innovation & design thinking that will be key, with AI facilitated culture as described in this article being the killer App of the next decade.
This is a guest post by Damien Hughes, Experience Strategy Partner at Halfmachine– a future-focused innovation and design company that uses a human-centered, design-based approach to help organisations create new offerings and build better capabilities. Occupying the intersection of innovation, technology, and business, Half Machine is committed to the exploration of technological possibilities aimed at enhancing the human experience.