REINVENTION READINESS INDEX™ · 2026 BENCHMARK

Who's actually ready to reinvent — and what separates them from everyone else.

The pace of change has outrun the playbooks most organizations use to manage it. This is an interactive walk through the research behind the 2026 RRI Benchmark Report, developed by Leadogo in partnership with the Smith School of Business at Queen's University.

66
AVERAGE RRI SCORE, OUT OF 100
58
ORGANIZATIONS · CANADA & U.S.
9/10
SIGNIFICANT CORRELATIONS, p<.05
40+
PEER-REVIEWED SOURCES CITED

Every generation of leaders has had to manage disruption. What's different for this one is speed: the interval between noticing a change and being made obsolete by it has compressed from years to quarters — and most of the machinery organizations built to manage change was designed for the slower version of this problem. The Reinvention Readiness Index measures five capabilities that, together, predict how ready an organization actually is to adapt, as opposed to how ready it feels.

Two capabilities move together almost as one; two others are statistically unrelated — a pattern with direct implications for how leaders should sequence investment.
  • The RRI measures five capabilities that, together, predict how ready an organization is to adapt. Each grounded in established management research, not a homegrown framework.
  • Across 57 organizations in Canada and the US, average readiness sits at 66 out of 100. Two capabilities move together almost as one; two others are statistically unrelated.
  • Ecosystem Strength is the connective hub of reinvention. It correlates significantly with all four other dimensions (r=0.52–0.71) — more than any other single capability measured.
  • AI exposure is the clearest measured driver of execution speed, not of leadership maturity. A mature leadership team does not, on its own, make an organization fast.
01
FRAMEWORK

Five capabilities, not one

Reinvention rarely fails for lack of a single missing piece. It fails because organizations invest in the pieces that are easiest to see, while the harder, less visible capabilities go unmeasured and, eventually, unbuilt.

An organization first has to sense that something is changing and adjust direction under real uncertainty — the territory of Adaptive Leadership. It then needs people throughout the organization who will act on that sensing without waiting to be told — Entrepreneurial Ownership. Because no organization reinvents itself alone, it needs the external partnerships and networks that extend what it can sense, learn, and build — Ecosystem Strength. All of that has to translate into actually moving faster on real opportunities and threats — Acceleration Readiness. And because reinvention for its own sake burns people out, it has to stay tethered to a purpose that matters to the people doing the work — Impact Alignment.

Adaptive Leadership
Sensing change & adjusting direction under uncertainty
Entrepreneurial Ownership
Individual initiative without being directed
Ecosystem Strength
External partnerships & collaboration networks
Acceleration Readiness
Execution speed on new opportunities or threats
Impact Alignment
Connection to social & environmental outcomes
02
WHAT WE FOUND

Reinvention is uneven, but predictable

With the framework established, the first question is simple: where do the 57 organizations in this study actually land, and which capabilities move together?

Average readiness across the sample sits at 66 out of 100. Hover any bar, tier, or cell below for the detail behind it.

Exhibit 1
Dimension Scorecard
Adaptive Leadership
4.12
Strong
Entrepreneurial Ownership
3.55
Solid
Ecosystem Strength
3.57
Solid
Acceleration Readiness
3.37
Developing
Impact Alignment
3.68
Solid
Dashed marker = benchmark average for context
Average score across all five RRI dimensions (0–5 scale), n=57
Exhibit 2
Organizations by Readiness Tier
68%Ready + Leading
  • Leading10 orgs · avg 87.7
  • Ready29 orgs · avg 69.8
  • Developing15 orgs · avg 52.2
  • Emerging3 orgs · avg 35.4
Organizations by overall RRI tier, n=57

Knowing where organizations stand is only half the picture. The more useful question for a leader sequencing investment is which capabilities move together and which are genuinely independent. Ecosystem Strength turns out to be the connective hub — it correlates significantly with all four other dimensions, more than any other single capability measured.

Exhibit 3
How the Five Dimensions Interact
ALAL—0.51***0.52***0.22ns0.37**EOEO0.51***—0.71***0.56***0.52***ESES0.52***0.71***—0.56***0.57***ARAR0.22ns0.56***0.56***—0.48***IAIA0.37**0.52***0.57***0.48***—
Cross-dimension Pearson correlation matrix, n=57. *** p<.001, ** p<.01, * p<.05, ns = not significant
Ecosystem Strength is the connective tissue of reinvention — it correlates significantly with all four other dimensions (r=0.52–0.71, all p<.001). If leadership teams fund one capability, this is the one with the widest blast radius.

Four patterns in particular carry the strongest statistical support — and the most direct implications for how a leadership team should sequence its people-and-partnerships strategy.

01

Stop treating culture and partnerships as separate line items.

Entrepreneurial Ownership and Ecosystem Strength rise and fall together in nearly every organization studied (r=0.71***). Fund them as one integrated initiative.

02

If you can only make one investment, make it ecosystem strength.

It is the single dimension most connected to every other capability measured — lifting leadership, ownership, execution, and impact simultaneously.

03

A strong leadership bench will not fix a slow organization.

Leadership maturity and execution speed (r=0.22, ns) are the one pairing that does not move together.

04

AI exposure is your clearest lever on speed.

Organizations with deeper AI exposure execute meaningfully faster, consistent with a growing body of research linking AI capability to faster decisions.

Averages can hide as much as they reveal. The RRI dataset spans a real spread of industries, company sizes, growth stages, and AI exposure levels — select a lens below to see how average readiness actually varies, benchmarked against the full-sample average.

Segment Explorer
How Different Organizations Compare
Tech / SaaS / Digital
73
n=16
Other Industry
68
n=16
Professional Services
66
n=14
Social Impact / Nonprofit
64
n=5
Manufacturing / Traditional
55
n=3
E-Commerce / Retail
43
n=2
SMB / Local Business
48
n=1
↑ Full-sample average: 66
Exhibit — Average RRI score by industry sector, n=57. Sectors with n<5 (Manufacturing, E-Commerce, SMB) are directional, not statistically confirmed on their own.
03
WHAT PREDICTS READINESS

AI exposure is the clearest lever on speed

Segment averages raise a natural question: what actually explains the differences? We tested industry, company size, company stage, and AI exposure against each readiness score, isolating each factor's effect from the others.

Three individual results reached statistical significance out of 42 tests run — shown below with the caveats each one carries.

Exhibit 4
Acceleration Readiness by AI Exposure
Low (n=7)
Accel. Readiness
2.43
Overall RRI (0–5)
3.34
Moderate (n=32)
Accel. Readiness
3.37
Overall RRI (0–5)
3.62
High (n=18)
Accel. Readiness
3.72
Overall RRI (0–5)
3.85
Acceleration Readiness by AI exposure tier, n=57
Exhibit 5
Readiness Tier by AI Exposure
Low (n=7)
Moderate (n=32)
High (n=18)
Leading
Ready
Developing
Emerging
% of organizations in each tier, by AI exposure

Each step up the AI exposure scale is associated with a real, statistically reliable gain in execution speed — the only relationship in the entire regression suite where both the individual predictor and the overall model hold up. Two other findings are directionally strong but carry more caution, shown honestly below rather than folded into the headline.

AI Exposure → Acceleration Readiness

Confirmed

Each step up the AI exposure scale (Low → Moderate → High) is associated with a 0.38-point increase in predicted Acceleration Readiness, holding industry, size, and stage constant. This is the only model in the full regression suite where both the individual predictor and the overall model are statistically reliable.

β+0.38
p-value0.039
R²0.341
Model F-testp=0.003

Smaller Industries → Ecosystem Strength

Suggestive, Not Confirmed

The four smaller sector categories score 0.79 points lower on Ecosystem Strength than the baseline industry group — the single strongest individual p-value anywhere in this analysis. But the overall model isn't statistically significant, so this is a strong, suggestive signal rather than a fully confirmed finding.

β−0.79
p-value0.007
R²0.200
Model F-testp=0.120 (ns)

Smaller Industries → Overall RRI

Suggestive, Not Confirmed

The same sector effect shows up on Overall RRI, just under the p=0.05 threshold — but with the model overall not reaching significance, this should be read as directionally consistent with the Ecosystem Strength finding above, not as independent confirmation.

β−0.46
p-value0.048
R²0.194
Model F-testp=0.134 (ns)
GET THE FULL REPORT

This is the highlight reel. The full benchmark report has all of it.

32 pages: the complete methodology, every exhibit, the full regression and correlation detail, sector and demographic breakdowns, and the references behind every finding. Enter your email and it's yours.

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This is a pilot study — a strong starting point for a longer-running Index, not a final verdict. That is worth saying plainly, and worth expanding on for anyone deciding how much weight to put on these findings.

N=58 organizations completed the survey by the analysis cutoff date; one respondent gave identical answers to all 29 items (a data-quality exclusion, not a legitimate low score), leaving a clean analysis sample of n=57. 91% Canada, 9% United States. The sample skews toward knowledge-economy organizations — Tech/SaaS, Professional Services, and "Other" account for the majority of respondents — so findings are best read as a benchmark for that segment, not a general claim about every industry.
This shows correlation, not causation — two capabilities moving together doesn't tell us which drives the other. It's a snapshot, not a trend: one point in time, not proof of future causation. And four measured factors (industry, size, stage, AI exposure) don't explain everything — leadership style, culture, and other unmeasured factors matter too.
Both Pearson and Spearman correlation methods confirm the same pattern throughout the dataset — the relationships reported are not an artifact of outliers or non-normal distributions. All five hypothesized dimension-to-readiness pathways (H1–H5) are statistically significant (all p<.01). Every predictor in the regression models was checked for multicollinearity (VIF); all came in well under the standard concern threshold.
This report is the first wave of an ongoing research program, not a one-time study. Future waves will grow the sample, extend sector coverage, and move from correlational snapshots toward longitudinal evidence that can speak more directly to cause and effect — including whether a higher RRI score actually predicts outcomes like growth or resilience over time.

The Reinvention Readiness Index™ was developed by Leadogo in partnership with the Smith School of Business at Queen's University, Centre for Entrepreneurship, Innovation and Social Impact. Research led by Dr. Nagina Kanwal, with a research team from Queen's University. It reflects the combined work of the following contributors.

Lead Contributors
Nagina Kanwal, PhD
Nathan Veiga
Queen's University
Toyin Oladele
Queen's University
Vithushan Umaputhiran
Queen's University
Areeba Khan
Queen's University
Anjola Aderinto
Queen's University
Also Contributed
Taiwo Ayenleye
Queen's University
Naitik Trivedi
Queen's University
Patty Nayel
Queen's University
Matt Hawksley
Leadogo
Yiwei Li
Queen's University
Paluck Kohli
Leadogo