Does the “3.5% rule” apply to (AI-first) digital transformation?
It is difficult these days to find disagreement in incumbent businesses and industries — from health and finance, to manufacturing and retail — regarding the need to transform their businesses (and even their operating models) to be properly digital and benefit from technology in order to stay competitive. While plans for such transformations in many companies started years ago, the unfortunate reality is that to date, many such efforts have failed. According to a recent survey by McKinsey & Company, more than 70% of such initiatives across companies they studied have stalled. The same report suggests that in many of these organisations, slowdowns typically happen for reasons such as culture and goal alignment — things that are within such organisations’ control (see the figure below for more details).
In this post, I want to ask: Can organisational and cultural factors in digital transformation initiatives’ success allow us to draw parallels between this under-studied and relatively recent field, and fields such as social change (relatively well-established fields with decades of research)? According to Wikipedia, “social change involves alteration of the social order of a society; it may include changes in social institutions, social behaviours or social relations; it may refer to a paradigmatic change in the socio-economic structure; it may be driven through cultural, economic, scientific or technological forces”. Given the breadth of this field, I will pick a small subfield to illustrate this idea.
Erica Chenoweth (currently a professor of public policy at Harvard) and colleagues organised an international team of scholars aiming to identify all the major violent and nonviolent governmental change efforts of the twentieth century. They compared the data from over 200 violent revolutions and over 100 nonviolent campaigns. What grabbed my attention in their results was that every campaign that had active or sustained participation from at least 3.5% of the population succeeded (and by the way, no violent campaign achieved that threshold). In a recent paper, Chenoweth & Belgioioso built on those findings and proposed that the momentum of dissent is a product of participation (mass) and the number of protest events in a week (velocity); the famous p = mv (i.e., momentum equals mass times velocity) formula.
I want to build on these research findings, and draw parallels with company transformations and ask questions regarding a number of critical decisions that digital-transformation programs need to make.
Large ship vs. Speedboat: Many companies find it difficult to implement innovation in their main business, and therefore decide to set up a separate organisation to drive innovation. Many see this to be like you’re trying to turn a large ship by launching a speedboat; even if the speedboat takes off (which happens quite rarely), the ship doesn’t change unless the speedboat is tied tightly to the core of the business.
Q: Can the failure of speedboat approaches be explained by Chenoweth’s studies, in that such an approach is likely to be perceived by the mothership as a forceful uprising, hence highly unlikely to achieve the 3.5% sustained support (and hence the successful transformation)?
Sponsor focus vs. building mass: Everyone agrees that digital transformation cannot be achieved without CxO support; they provide such initiatives with budget, executive air cover, and many forms of supports needed until success. Unfortunately, however, many digital transformation teams and programs stay obsessed with such CxO relationships, instead of treating it like “smart investment” until they identify or build product-market fit.
Q: Can the 3.5% rule help define this balance: Is CxO support there to help digital transformation initiatives reach and sustain product-market fit for ~3.5% of employees? Can this help define how much sponsor support (and focus) is healthy and how much is too much or too little?
Not much vs. too much: You might be surprised to hear that even in tech companies such as Google, there was a high degree of skepticism for the use of AI towards business impact. This led Andrew Ng (who led Google Brain team in its early days) to start small, deliver a quick win and build momentum from there. Their first internal customer was Google’s speech recognition team; an important project, but nothing close to web search or advertising in terms of its importance and scale at Google. They continued from there to Maps, and eventually got enough support and faith to be let in the big league: advertising.
Q: Are “momentum of dissent” (as shown in the Figure earlier) and Google Brain stories telling the same thing? Should digital transformations achieve momentum (and organisational faith) by a number of small starting projects, as opposed to a big bet? Can the 3.5% rule be a good guiding principle for the definition of too small, too big, and OK for the size of such bets?
The 3.5% rule and AI-first digital transformation: I believe that AI-first digital transformation is the appropriate way of modernising incumbent companies and industries. Compared to pre-AI businesses, an AI-first business relies on R&D and other longer-term concepts. This is why AI-first digital-transformation teams’ ability to balance their long-term vision and building early support and faith is of utmost importance. I’ve mentioned the 3.5% rule, which should not be taken as a precise number (until future research finds the right number for digital transformations); it should rather be viewed as a directional guide towards gaining and accelerating tractions.
They say “culture eats strategy for breakfast”. A key driver of success for digital transformation initiatives is their ability to influence the incumbent culture; my main goal here was to highlight that sometimes, a small number of fans/users/partners can be all such initiatives need at first.
This is the fourth article in a series of posts that I’ve decided to write, about the development of AI-first products (and AI-first digital transformation) — challenges, opportunities, and more. I would love to hear your thoughts, and any learning and experiences you and your company might have in this space. Get in touch!
Of course, this is a personal opinion and does not necessarily reflect the viewpoints of AIG or the University of Oxford.
