Monkeys and Pedestals: Why Should We Care about Implementation Science?

This post relates Astro Teller’s mental model of monkeys and pedestals to implementation of evidence-based practices.
Implementation Science
Author

Nien Xiang Tou

Published

December 21, 2025

Research has generated substantial robust evidence demonstrating the effectiveness of many health interventions. However, many of these evidence-based interventions fail to improve population health because they are inadequately implemented in practice. This blog post shares my thoughts on using an innovative company’s mental model to think about bridging this gap between evidence and practice.

Image generated using artificial intelligence via Google Gemini.

Gap between Evidence and Practice

Scurvy was a common and deadly disease that plagued many seafarers during long ocean voyages during in earlier centuries. It was claimed that more than two and a half centuries elapsed from the first experiments showing that lemon juice could cure the vitamin C deficiency disease until citrus use was mandated in the British merchant marine. This history illustrates how slowly research evidence can be translated into actual practice. While the speed of translation is expectedly faster in today’s Internet age, it is still widely reported that evidence-based health interventions take on average seventeen years to be incorporated into routine healthcare practice (Morris, Wooding, and Grant 2011).

Many health interventions are created with the premise to improve health outcomes and achieve their intended impact. Logically, these interventions can benefit populations only if they are delivered as intended to the appropriate recipients. Even the most effective interventions would make little difference if they are poorly implemented. Therefore, achieving strong implementation outcomes is an essential precondition for realising the health impact on population (Proctor et al. 2011).

Monkeys and Pedestals

Imagine that you are tasked with training a monkey to juggle flaming torches while standing on a pedestal. Which aspect will you first work on? Training the monkey or building the pedestal? This hypothetical question is the basis of the ‘monkeys and pedestals’ mental model.

I first read about this interesting parable in Annie Duke’s book, ‘Quit(Duke 2022). It is a problem solving approach that originated from Astro Teller, the CEO of X, an innovation lab within Google that invents and launches moonshot technologies. In this hypothetical scenario, training the monkey is clearly more challenging than building the pedestal, yet it is the most critical component of the performing act. If the monkey cannot be trained to juggle the flaming torches, even the best-designed pedestal is meaningless. Thus, the key lesson is to recognise that to solve a problem, we need to tackle the hardest part of it first.

Many tasks we face at work and in life consist of multiple components, some of which are more challenging than others. It is easy to convince ourselves that we are making progress by completing easier but less important tasks. Astro Teller argues that we should always “tackle the monkey first”. Resources and efforts should therefore be directed first toward addressing the hardest part of the problem, because if that fails, progress on the easier tasks ultimately becomes futile.

Implementation: The Monkey or the Pedestal?

Three decades ago, healthcare shifted towards the paradigm of evidence-based medicine with clinical practices based on quality research evidence instead of reliance on dogma or authority alone (Sackett et al. 1996). While this evidence movement is clearly beneficial in improving quality of care, it might have also fostered a misconstrued mindset of “if you build it, they will come(Beidas et al. 2022). The misassumption that once an intervention is proven efficacious or effective, it will naturally find its way into routine clinical practice.

The very existence of implementation science as a field of study highlights that research evidence alone does not guarantee adoption in real-world practice. Many implementation research studies have demonstrated that implementation of evidence-based practices is an incredibly complex and challenging task due to a myriad of organisational and individual barriers. Effective implementation does not just naturally happen but requires intentional efforts to do so.

The most efficacious interventions will make little difference to improve population health if they are not adopted or poorly implemented. Beyond failing to achieve their intended impact, the resources invested in developing these interventions also contribute to research waste. In other words, it is pointless to have an effective intervention if we are unable to implement it at the end of the day.

From this perspective, implementation should arguably be understood as the monkey, and not the pedestal. Deliberate, careful, and thoughtful efforts are required from the outset to consider how efficacious interventions can be adopted, scaled, and sustained in real-world settings. Context plays a critical role in implementation, and appropriate implementation strategies are needed in addressing potential key determinants to improve the likelihood of implementation success.

Implementation science aims to bridge the gap between evidence and practice. The premise of this field of study is that there is a science (and perhaps art) behind effective implementation. Effective implementation rarely happens naturally or simply because an intervention seems like a good idea or is endorsed by authority figures. Instead, implementation science seeks to generate empirical evidence on how to plan, design, and evaluate implementation efforts systematically, so that effective interventions can be successfully translated into real-world settings.

Effective interventions must, of course, be first grounded in robust evidence of efficacy. However, it is important to note that such evidence alone is not adequate. Equal attention and effort should be given to how these interventions can be implemented successfully.

When we recognise implementation as the hardest and most essential part of the work, we shift our focus to ensure that our research interventions actually work in the real world.

If we want to realise meaningful and equitable improvements in population health, we must tackle the monkey first. Implementation science gives us the tools to do exactly that.

References

Beidas, Rinad S., Shannon Dorsey, Cara C. Lewis, Aaron R. Lyon, Byron J. Powell, Jonathan Purtle, Lisa Saldana, Rachel C. Shelton, Shannon Wiltsey Stirman, and Meghan B. Lane-Fall. 2022. “Promises and Pitfalls in Implementation Science from the Perspective of US-Based Researchers: Learning from a Pre-Mortem.” Implementation Science 17 (1): 55. https://doi.org/10.1186/s13012-022-01226-3.
Duke, Annie. 2022. Quit: the power of knowing when to walk away. London: Ebury Edge.
Morris, Zoë Slote, Steven Wooding, and Jonathan Grant. 2011. “The Answer Is 17 Years, What Is the Question: Understanding Time Lags in Translational Research.” Journal of the Royal Society of Medicine 104 (12): 510–20. https://doi.org/10.1258/jrsm.2011.110180.
Proctor, Enola, Hiie Silmere, Ramesh Raghavan, Peter Hovmand, Greg Aarons, Alicia Bunger, Richard Griffey, and Melissa Hensley. 2011. “Outcomes for Implementation Research: Conceptual Distinctions, Measurement Challenges, and Research Agenda.” Administration and Policy in Mental Health and Mental Health Services Research 38 (2): 65–76. https://doi.org/10.1007/s10488-010-0319-7.
Sackett, D. L, W. M C Rosenberg, J A M. Gray, R B. Haynes, and W S. Richardson. 1996. “Evidence Based Medicine: What It Is and What It Isn’t.” BMJ 312 (7023): 71–72. https://doi.org/10.1136/bmj.312.7023.71.