Stocks and flows of knowledge

One way that I’ve found very useful to articulate how leaders can catalyse innovation in an organisation is to consider organisational knowledge in terms of ‘stocks and flows’. In economics and accounting a stock represents a quantity of something, measured at a specific time, but which may have accumulated over a period. Flows are measured differently, usually per unit of time (such as over one year) and so are analogous to speed or the rate at which something is happening. As an example, an accountant might measure a stock of capital, but then also look at the incoming flow of new investment and the ongoing depreciation of capital to assess true value. Or another accounting example might be that a stock would relate to the value of a given asset at a point in time, and the flow would be a measure of transaction value (or sales) during an accounting period.

Image:Magic5ball at English Wikipedia, Public domain, via Wikimedia Common

The value of the stock at any given time will be impacted by the rate of flow in, and the rate of depreciation or flow out.

We can think about organisational knowledge in the same way. Leaders can consider the ‘stocks’ of knowledge that are held within the organisation but also the input flow of fresh ideas, perspectives and inspiration, and the output flow of new concepts, propositions or prototypes. 

As the flow of inputs from external sources increases, so the stock of knowledge and ideas from which innovations (whether that be optimisation-focused ideas such as process innovation, or breakthrough product and service ideas) can be drawn increases. The outflow is the application of that knowledge into the generation of new propositions, products, capabilities or prototypes which can benefit the market.

In order to catalyse innovation in a company leaders should pay attention not only to what the organisation knows now (its stocks of knowledge) but the rate at which new perspectives and thinking is brought into the organisation (the input rate), and the rate at which existing knowledge and understanding is applied to generate innovative solutions or outputs (the output rate). This is represented in the diagram below. 

There are several elements related to stocks and flows which I think are under-utilised and are often areas of underrated value.

FLOW:

When looking at the flow of inputs into an organisation too many businesses focus on in-sector examples, or ‘best practice’, or what the competition is doing. Larry Page once said:

“For a lot of companies, it’s useful for them to feel like they have an obvious competitor and to rally around that. I personally believe it’s better to shoot higher. You don’t want to be looking at your competitors. You want to be looking at what’s possible and how to make the world better.”

It’s important to understand what the competition is doing but the point is a good one – following competitors obsessively can mean that you are limiting your potential to what is currently possible rather than imagining what might become possible over time. Far too few companies look beyond their own sector to understand what they can learn from how companies in completely unrelated industries have solved their challenges or are innovating in ground-breaking ways. My favourite example of this is when Great Ormond Street hospital brought the Ferrari pit crew in to advise on handover processes from the operating table to ICU, and they were able to make changes which reduced the number of technical errors made by hospital staff by a half.

As businesses face particularly pressing challenges they can often become very inwards-facing, with an increasing proportion of time spent on internal priorities, company politics and managing upwards. In my first book I talk about the idea of the ‘networked organisation’ and what I mean by that is that people within the business are connected to interesting and challenging sources of inspiration and input. In the diagram above the box that represents the company is a dotted line, referencing the fact that the organisation itself needs to be outwardly-facing, and ‘porous’ to allow a continuous flow of inputs from multiple sources. This idea of the ‘porous enterprise’ (and the inspiration behind the idea of stocks and flows of knowledge) is talked about by John Hagel, John Seely Brown and Lang Davison and their book ‘The Power of Pull’, in which they argue for the value that can be derived from the flow of knowledge into a business.

When it comes to output rate, most company’s rate of experimentation is simply way too low. Testing and learning should be continuous, and part of a culture of evidence based decision-making and development, but this should be combined with creative thinking in the framing of challenges and the application of new technologies to solve problems better. The other mistake that can be made here is a lack of focus around experimentation. Running unfocused experiments can mean that a company is trying to move in all directions at once, which can dilute the potential for shared learning and the ability to build momentum towards the delivery of a company strategy.

STOCK:

As well as focusing on how inflows of ideas and perspectives can build the stock of knowledge within a business, leaders should concentrate on how that knowledge flows within the business. This goes way beyond setting up corporate information resources that staff can draw on. It’s about how teams and individuals that have unique perspectives or interesting ideas or contextually useful knowledge can effectively share that know-how or thinking with other teams and individuals in the company. In the diagram above this is represented by the many arrows showing the internal flow of information and ideas. Communities of Practice (I’ve always liked Emily Webber’s work in this area) can be excellent ways to distribute and build knowledge aligned to disciplines, skills areas or particular challenges. One of my favourite examples of this is the engineering business Arup who have created a whole series of ‘skills networks’ designed to share knowledge around highly specialised engineering disciplines. When Arup were asked to build the Hong Kong equestrian centre for the 2008 Beijing Olympics, the engineers working on the project couldn’t accurately work out how much animal waste would be produced by hundreds of highly strung horses that were arriving from around the world. They could have spent countless hours interviewing expert veterinarians and pouring over spreadsheets but instead they posted the problem onto the relevant skills network and within a few hours had an answer from another Arup engineer half way round the world who happened to have worked on the design for the New York Jockey club. Access to knowledge sharing spurs innovation. When Andrew Carnegie enabled almost 1,700 libraries to be built in towns and cities across America, the rate of innovation in those cities in the following 20 years (as measured by numbers of patents) increased by between 7-11%.

AI will of-course make all this a lot easier (many companies, for example, already have internal chatbots that are designed to answer employee questions) but this is also about deliberately bringing different perspectives together. In all of my books I argue for the value in cross-functional working in innovation and problem-solving. Small, multidisciplinary teams can be an engine for change and transformation, and enable teams to spark off one-another, work concurrently to solve challenges, and combine very different perspectives easily and efficiently. Innovation is often combinatorial in nature (bringing together ideas that had not previously been considered in conjunction with one another) and so the more organisations work to break down silos the more they can benefit from not only the alleviation of inefficient handovers but also the bringing together of different perspectives. In his book ‘How Breakthroughs Happen: The Surprising Truth About How Companies Innovate‘, Professor Andrew Hardagon describes what he calls ‘technology brokering’, and how the connections between people and teams involved in innovation can help apply existing ideas and new technologies in new ways by bridging the gaps between what might have been disparate groups of people.

Creating these networks can vastly improve knowledge and idea sharing across the organisation, and when done well this means that businesses can scale capability rapidly and build momentum around capability building by creating a ‘flywheel’ approach to organisational learning. Amazon and Jeff Bezos have popularised the ‘flywheel’ approach (which apparently originated out of a conversation Bezos had with Jim Collins) which became the basis for Amazon’s business model and arguably it’s success. Just as an industrial flywheel builds momentum which enables the wheel to turn and build further momentum in a self-reinforcing loop, so businesses can build positive reinforcing loops that can scale capability fast. As an example, take the current trend du jour, Generative AI. With so many potential applications within so many workflows and processes, it can be hard to know where to start and what will work. A company can build learning around this new technology rapidly by enabling an inflow of knowledge and understanding around examples and application, and support easier knowledge sharing around effective application between teams in the business, which leads to more capabilities which teams can draw on, which encourages more experimentation with the new technology, which leads to wider knowledge sharing, which generates more capabilities, and so on. 

As Jim Collins originally pointed out, flywheels need to be focused in order to work, but applying positive inputs at any stage of the wheel helps build the momentum even further. 

The catalyst to successfully building innovation capability and rapid growth in organisational learning is for the organisation to be focused on both the flow AND stocks of knowledge.