Three Models of InnovationWe promote and practice constraint-based innovation in large enterprises. Here's why.
The 94% of global executives dissatisfied with their organizations’ innovation performance are generally presented with two models for addressing this: the innovation lab or the enterprise digital transformation. We suggest that there is an alternative: constraint-based innovation. It combines the best elements of the two most common approaches, and avoids some of their key weaknesses.
Our approach to constraint-based innovation was developed through a combination of our work with the Hertz Corporation in Europe and the entrepreneurial career of our Chairman Mike Adam. Below we review these three models of innovation and explain our belief in the constraint-based approach.
One executive response to the demand for more innovation is to create an innovation “lab.” This is, at face value, a logical response if the executive team concludes that the enterprise is so set in its ways that it cannot be changed from within. Instead entrepreneurial spirit is fostered in a dedicated innovation unit that operates in a hermetic bubble outside the enterprise.
Most innovation labs fail and the reasons why have been well summarised. They are too distant from the core business and its real problems. They cannot deliver value quickly enough. They focus too much on technology itself rather than what it is for. They cannot hire the right people and they have expectations that are set too high without a clear idea of how they might be met.
Taking all your most entrepreneurial employees and separating them from the rest of the business has its problems. The rest of the business is weakened and will resent the digital upstarts. Most importantly the lab itself is in quarantine. How can it produce anything of value to the core business when it has so little connection to it?
The problems with the lab approach have been revealed in both criticism and practice. Recent lab shutdowns include Nordstrom, Ogilvy, Microsoft, Coca-Cola, Disney, The New York Times, British Airways, and Adecco. By 2015, Capgemini had already spotted trouble ahead for the proliferating labs, showing that despite investment, results were limited. One expert they spoke to warned that the failure rate for labs was around 80-90%.
While isolating innovative teams from the rest of the business dooms what they produce to be irrelevant, resented, and unable to prove a return on investment, there are some strengths to the model. Small, interdisciplinary teams working in agile cycles are more efficient than the enterprise’s existing business units. Innovation labs do generate new ideas, build cool prototypes and get some things done. Their problem is that the things they get done are not leverageable or even welcomed by the mothership. This is not a new observation: in the 1980s, much of a new upstart called Microsoft was built on the wreckage of Xerox Labs.
Enterprise Digital Transformation
The most common innovation strategy currently (reportedly present in 62% of businesses) is the “enterprise digital transformation,” a well-funded cross functional project to change the enterprise by making it “more digital”.
While innovation labs take all the advantages of a start-up but fail to apply them to the context of a large enterprise, digital transformation projects apply themselves directly to the core business. They are often run by a Chief Digital Officer or Chief Innovation Officer, and sometimes even the CEO herself.
Enterprise digital transformations are exposed to all the pitfalls of transformational projects in large organisations. They encounter resistance to change that grows exponentially as more organisational lines are crossed and redrawn. Meanwhile the needs of individual business units continue to evolve at a pace that leaves the grand plan behind. This in turn feeds an endless spiral of complexity and delay. As progress grinds to halt, the project loses momentum, credibility and relevance. In many cases the project sponsor leaves or is fired. The only trace is often the increased cynicism and resistance to change of the employees. This is the sclerotic process that eventually brings large enterprises to a complete standstill and kills innovation entirely.
Although high profile digital transformation failures abound, they can succeed under exceptional leaders who mobilise under an immediate existential threat. Steve Jobs’s return to Apple in 1997 is a perfect example. The question is: how do you find a Steve Jobs? And, worse still, how bad does it need to get before innovation can be forced through by a charismatic leader?
An important article for Harvard Business Review reviewed the problems in digital transformation at GE, Lego, Nike, P&G, Burberry, Ford, Staples, Walmart, Sears, and Zynga. Despite their costly failures, the authors found much that was of long-term value to the business. They concluded that “instead of ramping up quickly, only to ramp down painfully, it would be much better if companies can make steady progress toward the right end state without making such costly mistakes.”
We agree. But how do you make steady progress? How do you avoid costly mistakes?
Combining the benefits of small, entrepreneurial lab teams and the strategic vision and business relevance of a top down transformation project seems like the solution to the innovation impasse. However it is not immediately obvious how this can be done. The weaknesses of each seem inherent in their strengths, and a compromise between the two even worse.
Constraint-based innovation offers a third approach that we have proven can foster innovation relevant to the core challenges of a business, while delivering measurable, early returns on investment.
The counterintuitive insight at the heart of this approach is that innovation is fostered by necessity and limited resources, not by opportunity and abundance. It turns out that necessity is indeed the mother of invention. How does an enterprise create necessity and nurture invention?
The first step is to choose a single business unit, identify its long-standing problems and give the unit the mandate to address these within strictly defined constraints. These constraints fall into a number of categories:
Each additional organisational boundary crossed exponentially increases both time to delivery and the risk of failure.
Innovative projects need to constrain themselves to small teams, minimising the number of organisational boundaries that they cross. The team constraint encourages small teams to work with open minds on the core business challenges that they have the most intimate knowledge of.
Each additional organisational dependency exponentially increases both time to delivery and the risk of failure.
When the project is forced to cross boundaries, teams must ensure that dependencies are only inbound. The project should flex as required to react to changes in connected areas. If dependencies are outbound the project imposes requirements on other teams to change their working practices. This is what slows down delivery, provokes defensive responses and increases the risk of failure.
The role of technology should be to minimise dependencies, mitigate risk and to accelerate delivery.
Teams must select technology according to whether it can be configured to work with the existing infrastructure, not on whether it is theoretically optimal. This prevents technology becoming an end in itself.
Slow progress discourages creativity and increases risk.
Innovators should impose on themselves the shortest possible time to deliver. This reduces risk by ensuring the lowest initial investment of time before results are seen. If projects are given too long they lose momentum and will not complete at a high enough rate for iteration to pay dividends. Tight deadlines also force teams to look for creative solutions rather than settle for conventional ones.
A big budget amplifies the risk of failure, which is an obstacle to innovation.
The value of setting a tight budget is that it will encourage experimental “string and glue” solutions. Iterating through experiments is necessary to identify bad ideas early and hit on transformative ones, even the ones that initially seemed a little ambitious or strange.
Clear evidence of the project’s success or failure is essential.
The experiment’s business impact must be measured. This means embracing a constraint in what constitutes a measure of success. “Vanity metrics” like click-throughs and so on may be useful in analysis but should not be used to cloud the picture of a failed project.
If the scope of the project is too broad, evidence of success will be insufficiently clear.
The only way to prove success in a constrained project is to target the boldest possible outcome in response to a precise goal. If a project is too broad in scope it will not produce clear evidence of its success or failure quickly enough. There will always be reason for doubting its successes and for hoping despite its failures.
Aiming to reach too large an audience at the outset increases the risk of damage to the brand if the experiment fails.
Projects should be focussed on the smallest subset of possible customers required to provide definitive proof of success (or failure). Limiting the operational disruption and brand risk of a project helps ensure delivery, support, and keeps it manageable within a small team.
Any new project incurs risks. It may go over budget, miss deadlines, cause serious operational problems, or damage the brand. This is why leaning into constraints is such a powerful approach. Demanding that projects accept constraints in how long they can take, how much they can spend, and how many other business units they can involve radically shrinks the risk of trying out new ideas.
Constraint-based innovation also helps teams avoid the optimisation trap (when a team only has the freedom to make minor tweaks to something that is ultimately failing, making it the best it can be only to prolong its life unnecessarily). Innovative teams must not be afraid to kill off failing projects, systems, or customer experiences.
What an innovative enterprise actually looks like
Managing constraint-based innovation is challenging. The urge to make grand plans must be avoided. The template for constraint-based innovation is rule-based and not plan-based. Executive leadership plays four key roles:
- To identify and communicate core goals – this creates necessity
- To agree constraints – this stimulates invention
- To welcome fast failure – this encourages risk taking
- To reward and promote success – this propagates innovation
A team that actively takes up the challenge and accepts constraints will iterate more quickly through experiments and will tackle bigger issues.
Innovation then spreads organically through evolutionary natural selection. Individuals who do not embrace the process naturally fade in influence. Meanwhile new leaders emerge from successful projects and make their way up through the enterprise, spreading best practice. This is a meritocratic and entirely natural process. The innovative enterprise grows naturally from within.
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