A publishing rhythm of one substantive long-form piece per quarter and two field notes per month, supplemented by a curated reading list of the research from McKinsey, Bain, BCG / MIT Sloan, Stanford HAI, and the SHREK firms whose data underwrites our thesis.
The launch sequence. Each piece is a 1,800–2,400 word essay, drawn from current engagement work, footnoted to primary research. Subscribe via LinkedIn for publication notifications.
Why most enterprise AI strategies are detached from enterprise strategy — and what the leadership architecture looks like that closes the gap. The taxonomy of leadership archetypes for the AI era — Architect, Optimizer, Transformer, Translator — and why the absence of a shared specification language is the principal source of executive misfire across both corporate and PE-backed AI mandates.
Publishing · Q3 2026How sophisticated boards and sponsors are restructuring operating models around AI — and the four leadership archetypes the situation now requires at each stage. The macro thesis from primary sources translated into the leadership specification both audiences can underwrite.
Publishing · Q4 2026The pattern beneath the seventy-percent digital-transformation failure rate — and the specific leadership-architecture choices that distinguish the survivors. A diagnostic for boards, CEOs, and Operating Partners alike: what the AI-stalled enterprise looks like from the outside, what its leadership team is missing on the inside, and which archetype the eighteen-month rescue actually requires.
Publishing · Q1 2027Excerpts are reproduced as brief attributed editorial commentary. Read the full pieces at the source. The selections below are the primary research underwriting our thesis on AI-era leadership across both corporate and private-capital audiences.
"With less leverage and lack of multiple growth, these expensive deals only pencil out if you assume much larger increases in EBITDA — something closer to 10%–12% to generate that 2.5x return over five years."
The clearest single articulation of the macro reframe. Bain's "12 is the new 5" is the data point on which the Aretas thesis rests: in an environment where multiple expansion has left, EBITDA growth becomes the sole lever — and AI maturity becomes its dominant source. Required reading for any sponsor or Operating Partner whose 2026 vintage was underwritten on prior-cycle assumptions.
Read at bain.com →"The conditions that once amplified returns — declining interest rates, expanding multiples, and abundant leverage — have passed. Outcomes will increasingly be shaped by deliberate choices: how investors exert sufficient discipline on asset selection and entry multiple; how early and consistently players create operational value; how successfully participants navigate and leverage AI; how rigorously they develop leadership."
McKinsey's framing pairs with Bain's: alpha must be made, not assumed. The four levers identified — entry discipline, operational value, AI leverage, leadership development — are the constituent elements of the Aretas Alignment Audit. Operational value creation, the report argues, must do more of the work; leadership is its operating instrument.
Read at mckinsey.com →"A startling 41% of private equity executives say senior portfolio company leadership — its quality, retention, and ability to execute — is a significant challenge for the year ahead."
Ten years of longitudinal data on the single variable that differentiates PE returns: leadership quality in the portfolio. AlixPartners' 45-point gap between how CEOs rate their own teams versus how their PE backers rate them is the most concrete data point in circulation for the Phase 01 diagnostic question — is the existing C-suite architectured to execute the value-creation plan, or is the firm assuming it away? The survey also documents that 86% of CEO turnover at portfolio companies is driven by the PE firm, not the company itself. That is the search mandate the Audit is designed to frame before it is urgent.
Read at alixpartners.com →"Most organizations are still in the experimentation or piloting phase: nearly two-thirds of respondents say their organizations have not yet begun scaling AI across the enterprise … just 39 percent report EBIT impact at the enterprise level."
The "pilot purgatory" diagnostic, in McKinsey's own data. Adoption is universal; value capture is not. The reasons cited — insufficient workflow redesign, weak senior leadership engagement, governance immaturity — are leadership specification failures. They are precisely what the Phase 02 Maturity Diagnostic and Phase 03 Archetype Selection in the Audit are designed to surface before the search begins.
Read at mckinsey.com →"Only 30% of companies fully meet their timeline, budget, and scope expectations in large-scale tech program implementations … more than half of organizations miss one or more delivery targets, exposing persistent execution gaps that undermine strategic intent."
BCG's analysis of over 850 companies confirms what Phase 02 of the Audit surfaces at the individual company level: the failure rate is not a technology problem. It is an operating model and leadership alignment problem. The 70% failure rate has remained consistent across a decade of BCG research — which means the organizations that beat it are doing something structurally different. That difference, in every studied case, begins with how the leadership architecture was specified relative to the transformation mandate. This is the search problem Aretas was built to solve before it becomes an outcome problem.
Read at bcgplatinion.com →"In the new era we are entering, the performance that winning firms need to deliver will rely on their ability to rapidly generate strong EBITDA growth, full stop." — Rebecca Burack, head of global PE practice, Bain & Company
Burack's "full stop" is the macro premise distilled to seven words. EBITDA growth is the lever. Everything else — multiple expansion, leverage arbitrage, pricing tailwinds — was prior-cycle. The implication for retained search at the C-suite and operating-partner level is that every leadership specification must be defensible against the EBITDA-growth question. The Audit is how that defensibility is constructed.
Read the press release →"AI capabilities are advancing faster than the institutional capacity to govern them — widening the gap between the AI strategy a board can approve and the leadership architecture an enterprise can deploy."
Stanford’s AI Index is the canonical macro reference the corporate-side reader trusts. It is the source we cite when a Board Chair asks for the data underneath the strategic premise. The report’s through-line over four annual editions is the same one the Audit operationalizes at the company level: the binding constraint is no longer technology supply, it is the organizational capacity — including leadership specification — to absorb the supply that exists.
Read at hai.stanford.edu →"The way to win in the age of AI is not to build a better operating model on top of an existing business; it is to build the business around the AI-native operating model from the outset — and to specify leadership accordingly."
The corporate-side equivalent of the Bain Global PE Report. Iansiti and Lakhani name the underlying mechanic for which the macro data is the symptom: AI-native firms compete on different operating dynamics than incumbents. The implication for leadership architecture is structural — the incumbent does not compete by adding AI to the existing C-suite; it competes by re-specifying the C-suite around AI-native dynamics. This is the situation the Audit’s Phase 02 (Operating Model Compatibility) is designed to surface.
Read at hbr.org →AI maturity is now the dominant source of enterprise value creation. For the boards and CEOs of public and private companies, and for the sponsors and operating partners who own them. Aretas Partners architects the leadership systems — the C‑suite, the operating‑partner bench, the board — that convert AI ambition into measurable enterprise value, on the timeline that capital and competition demand. We are retained when the next eighteen months will decide the decade.
One long-form essay per quarter. Two field notes per month. Nothing else.