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Rotman Insights Hub | University of Toronto - Rotman School of Management Groundbreaking ideas and research for engaged leaders
Rotman Insights Hub | University of Toronto - Rotman School of Management

The era of human and machine innovation

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Angèle Beausoleil

In today’s environment, organizations that don’t keep up with customers evolving needs are doomed. What is the best way to get a handle on these evolving needs?

The first step in understanding your customers is to accept the fact that you know very little about them. That way, you will remain open to learning. This point is critical, because customers are continuously evolving and adapting.

Once you are aware that you have a lot to learn, you can begin to observe customers: Simply watch, listen and engage with them. The concept of empathy is critical here — and must be practiced. Document what they say and do, and how they respond to different situations and contexts. Then, gather your best insights by grouping them into themes and categories that make sense to your company. Convert these insights into educated guesses, and you can then begin to test assumptions. As indicated, this all starts with being humble about what you know and allowing yourself to be a bit strategically naïve up front.

What is the more commonly-embraced approach to innovation today?

Through my academic research and by engaging in the innovation process for over 25 years as an industry executive, I have learned that most companies tend to jump right in to innovation in direct response to a negative situation. The trigger could be a financial company facing changes to foreign-investment policies that narrow or eliminate a market for its current services; or a beverage company seeing its sales decline due to a growing number of entrepreneurs providing newer, tastier choices. For these and most other situations, the important questions to ask before jumping in are:

1. What problem are we really trying to solve?
2. Who is directly impacted by this problem?
3. Why do we believe it is a problem, and why does it matter to our customers — and to us as a company?

The aim of these questions is to kick-start a dialogue — not a directive. Once the questions and potential answers are explored, then you can begin to orient yourself towards the type of innovation you need to pursue.

Every innovation team needs to be super clear about exactly what they are seeking to change, because there are at least five types of innovation. If you seek to change what you offer, you are pursuing product or service innovation; if you want to change who you are offering your productto, that is market innovation; if you seek to change how you design and deliver your product, that is process innovation; if you seek to change how and where you offer your product, that is positioning innovation; and if you seek to change the why, how and what of your offer, that entails paradigm or cultural innovation.

Every innovation team needs to be super clear about exactly what they are seeking to change.

Talk a bit about the mix of quantitative and qualitative methods that creates the best innovation.

When you observe human behaviour in a natural setting, the end result is a detailed narrative description that constitutes qualitative (thick) data. Combining this with numerical, pattern-validating data can be very powerful. The thick data explains the why and the how of the numerical (big) data, which provides the what.

Even in our increasingly digital economy, field work is critical. You must observe your customers in their natural state, behaving as they do without any artificial probing. The observations that come from this provide insights that, when further researched, can lead to an innovative solution. The good news is that if it is well researched and well stated, your problem is already half-solved.

You have devised a basic recipe for innovation. Please describe it.

My basic recipe involves a few key ingredients and three stages or steps. The ingredients are:

  • A group of problem owners or users;
  • One perceived problem or need; and
  • A handful of risk-taking problem framers and solvers.

The three steps or stages are: problem or need finding; problem framing; and problem solving. This recipe is easy to customize, as all organizations have access to multiple perceived needs or problems. At least one should be researched, framed and re-framed, solved and then formally brought to market.

Here are the directions:

1. Ask yourself what needs to change. Is it your product/service, market position, process or culture?
2. Gather a team that represents all of the key stakeholders that would be involved in that change across functions, systems and markets.
3. Prepare an innovation intent framework that is part need-finding, part problem-framing and part problem-solving.
4. Collect and combine need-finding data.
5. Form insights.
6. Wrap your insights into problem-framing ideas (prototypes) and let stand until all stakeholders have had a chance to reflect.
7. Whisk customer feedback into the prototype mixture.
8. Prepare a final prototype for implementation.
9. Bake the innovation and test for rejection or adoption.
10. Save your recipe and continue to experiment with new ingredients.

Apart from the usual suspects, name two companies that get how innovation works, and two that have missed the mark.

Adidas is one example of a company that innovates through collaboration — with Japanese and British fashion designers and hip hop artists for shoe and clothing design, and more recently, with plastic a recycling company for textile innovation. Nature’s Path is a Canadian company that started with a few breakfast cereals and now has a portfolio of more than 150 products to suit the evolving tastes of consumers and organic food choices.

Companies who have missed the mark include Target, which failed to understand the needs of Canadian shoppers and provide a merchandise mix and pricing strategy needed for north of the U.S. border. Also, Nortel was once a leading telco that was slammed by increasing changes introduced through digital technologies. It failed to evolve its business model to keep up and lacked proper integration of its acquisitions. This could be seen as a paradigm or cultural innovation fail.

Marketers and researchers can now see how people think and observe the context where their choices are being made.

You have said that the cognitive process involved in sense-making is moving from being mostly in the head to a collaborative process that occurs partly in the head and partly in computer-based tools. Please explain.

Over the past 20 years, we have basically outsourced our short and long-term memories to technology: We put our meetings and appointments in our Outlook calendars and we share moments on our Facebook and Instagram accounts. What is so exciting about this shift is that technology has made the inner world of our thinking much more visible. Marketers and researchers can now see how people think and observe the context where their choices are being made. Technology is also facilitating our ability to make sense of those thoughts, actions and choices. We can now outsource our processing power to machines that can numerically identify patterns at a faster rate than our brains ever could.

Describe how innovators are tackling the ongoing data tsunami via the emerging field of visual analytics.

As both a designer and a design thinking educator, I am always eager to introduce the next generation of mixed research and analysis methods that involve human-machine integration. Visual analytics is an emerging and interactive way of collecting and visually processing data sets using computer processing with human perception. The input is data and the output is visual displays such as text clouds or maps of correlated words, phrases and themes.

With this technology, Design researchers can now collect data from field visits and interviews and input it into platforms that can detect patterns. A simple example is plugging in your field notes into the Wordle platform to see the word frequency count. Long unstructured texts can be analyzed in a variety of ways to surface themes and ultimately, insights. Another notable visual analytics tool is Tableau, which offers big data narratives—converting numerical sequences of data into powerful stories and strategies. I see this as the evolution of business intelligence software.

How do AI and machine learning fit into the innovation and design thinking picture?

My colleagues and I embrace the possibilities of what machine learning and artificial intelligence can offer us — now and in the future — but it is important to remember that technology is still a mediator or enabler for humans.

As a result, innovation will always involve humans, but I predict that pairing human-centric methods with AI tools will be very powerful. Using design thinking to actually design these AI systems will be critical, and that’s one of the key challenges ahead. Even if you have the best technology, innovation doesn’t just happen; it is designed by humans for humans.

Angèle BeausoleilAngèle Beausoleil is an assistant professor of business design and innovation at the Rotman School of Management. She teaches the School’s business design practicum, creativity and business innovation, design research and data storytelling, and leads the International Design Study Tour. She has also taught at UC Berkeley’s Haas School of Business and the University of British Columbia’s Sauder School of Business and until recently, was the academic in residence at Creative BC.