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Volume 1 | Issue 1

October 2024

The 20% Question You Must Assess For When Hiring for Transformation

Self-limiting beliefs
and self-imposed
mental barriers might
be holding you back

In my last issue I argued that dot-connecting — the ability to synthesise across disciplines and identify what matters — is the defining leadership capability for this moment. Today I want to get specific about one dimension of that in the context of hiring for AI transformation.

Here’s the problem I keep coming back to. Most organisations embarking on AI transformation launch a wide portfolio of initiatives, spread resources across them, and measure success by activity. The research says this is precisely where they go wrong.

The Pareto Principle — the well-established pattern, first applied to management by Joseph Juran, that roughly 80% of outcomes are produced by 20% of causes — shows up consistently in AI transformation data. PwC’s 2026 AI Predictions report identifies the spread-thin approach as the primary failure mode: organisations that place “small sporadic bets” produce projects that “almost never lead to transformation.” The prescription from PwC’s decade of client research is unambiguous — real results require “precision in picking a few spots where AI can deliver wholesale transformation.”

BCG’s 2024 study of 1,000 senior executives across 59 countries puts hard numbers to this. Only 5% of companies are generating AI value at scale, while roughly 60% report little or no material benefit despite significant investment. The differentiator isn’t resources or ambition — it’s focus. AI leaders pursue about half as many initiatives as their less successful peers, yet achieve more than twice the ROI and successfully scale more than twice as many AI products. Fewer bets, placed with greater precision, compound into dramatically better outcomes.

McKinsey adds a detail I find particularly striking. A significant share of transformation value is lost not during execution, but at the target-setting phase — before the real work even begins. Many transformations are compromised before they start, simply by choosing the wrong things to focus on.

The pattern is consistent: in AI transformation, roughly 20% of initiatives drive the majority of outcomes. The organisations that win aren’t doing more. They’re doing the right things.

The pattern is consistent: in AI transformation, roughly 20% of initiatives drive the majority of outcomes. The organisations that win aren't doing more. They're doing the right things.

What this means for hiring

his creates an uncomfortable implication for hiring. The ability to identify that vital 20% cannot be assessed through credentials, résumé pattern-matching, or standard competency frameworks. It requires judgment — and judgment is one of the hardest things to hire for.

When I think about what separates transformation leaders who deliver from those who don’t, I keep coming back to this one capability. Not whether someone can execute a transformation — most strong candidates can — but whether they will execute the right transformation, in your specific organisation, with your specific constraints and starting point.

The standard criteria — track record, comfort with digital and technology, stakeholder influence, change management experience — all matter and are the price of entry for any serious candidate. But none of them test for this. Here’s how I think about assessing it.

Look backwards first. Ask candidates to identify the two or three decisions in a past transformation that were most consequential for the outcome — and why. Can they clearly distinguish what was genuinely load-bearing from what just felt important at the time? Leaders who attribute success to everything they did, or who struggle to isolate the vital few, are displaying exactly the pattern you want to screen against.

Then look forwards. Present a realistic scenario — a set of AI initiatives currently underway — and ask which two or three are most likely to drive the majority of outcomes. Ask which ones they’d slow down. Listen for whether they reason from business outcomes backward, or evaluate activities in isolation. A willingness to make a definitive call, and to articulate what evidence would change their mind, is a strong signal.

Test them under ambiguity. BCG’s research shows that 70% of AI transformation failures are people-and-process failures, not technical ones — yet most organisations spend disproportionate time on algorithms and tools. A candidate who defaults to technical prioritisation when the picture is unclear, rather than reasoning about organisational dynamics and behavioural levers, will likely replicate that same misallocation in the role.

Ask references the right question. Most reference conversations stay too general. Ask specifically: how did this person decide where to focus when everything felt urgent? How often did they get that call right? What happened when they got it wrong?

The ability to identify that vital 20% cannot be assessed through credentials, résumé pattern-matching, or standard competency frameworks. It requires judgment — and judgment is one of the hardest things to hire for.

The broader question of how to evaluate transformation leaders — across the full range of criteria — is something I’m developing into a more complete framework. I’ll share that in a future issue. But in my view, this judgment dimension deserves to sit near the top of that framework, because without it, everything else is just well-executed activity.

Sources:

  1. Pareto, V. (1896). Cours d’économie politique. University of Lausanne.
  2. Juran, J.M. (1951). Quality Control Handbook. McGraw-Hill.
  3. PwC (2026). 2026 AI Business Predictions. pwc.com/us/en/tech-effect/ai-analytics/ai-predictions.html
  4. BCG (2024). Where’s the Value in AI? — Survey of 1,000 CxO executives, 59 countries.
  5. BCG (2025). From Potential to Profit: Closing the AI Impact Gap.
  6. McKinsey & Company (2018). Unlocking Success in Digital Transformations.
  7. McKinsey & Company (2021). Losing from Day One: Why Even Successful Transformations Fall Short.

Anu D’Souza is the CEO of Bricoleur Consulting — insight-led leadership recruitment and transformation. She has spent her career at the intersection of business growth strategy, brands and leadership, working with and within companies including Unilever, Ogilvy and BBDO across multiple markets and cultures. Bricoleur works with senior leadership teams across APAC who are navigating AI and digital transformation — from readiness assessment through to placing the permanent and fractional leaders who make it stick. Anu is also the author of Aligned: Why CEOs Need Company Brand Alignment in the Age of a Questioning Workforce.

Connect with Anu:

insight@bricoleurconsulting.com · calendly.com/bricoleurconsulting/30min