ecosystem26
Built for CAIOs, CIOs, and technology leaders designing the AI function as priorities, structure, and scope are still forming.
A recurring pattern across mid-market and enterprise organizations looks approximately the same: a board approves an AI plan, delegates execution to the CIO, CAIO, or CHRO, and requisitions open against a generic role taxonomy. Time-to-fill stretches past forty-four days, candidate abandonment runs above sixty percent, and most hires that land do not stay eighteen months. The instinct is to treat the problem as a labor-market issue or a JD problem. The cause sits upstream of both, because role design is an output of work specification rather than an input to it.
The AI function is structured around a small set of archetypes that serve four distinct organizational purposes. Each archetype carries its own compensation band, reporting line, skill signature, and sequencing logic, and together they constitute the twelve-role taxonomy published inside the toolkit. The preview below shows the shape of the taxonomy; in the reference guide, every role is named, positioned, and specified in full.
Three previews follow, each partially redacted for this public page. Inside the toolkit, all three appear in full and in context.
| Archetype | Strat | ML | MLOps | Data | Gov | Prod |
|---|---|---|---|---|---|---|
| Chief AI Officer | E | F | F | P | E | P |
| Head of AI Strategy | ||||||
| AI Product Manager | ||||||
| ML Engineer | ||||||
| + 8 more archetypes |
E = EXPERT . P = PROFICIENT . F = FOUNDATIONAL
Eleven more cards inside. Comp bands and skill signatures unredacted in the toolkit.
Because role design is downstream of work specification, six organizational dimensions must be scored before any archetype is ranked. The toolkit scores each dimension on a five-point scale, feeds the results into a priority scorecard, and produces a hiring sequence that reflects your actual organizational signals rather than the market's defaults.
Each input's definition, scoring rubric, and consequence chain lives in Chapter 2 of the reference guide.
The three composites below, drawn from recurring patterns across RBD. client engagements in insurance, financial services, manufacturing, and professional services, illustrate the same toolkit producing three distinct hiring plans. The divergence is expected behavior: the six input specifications score differently in each organization, and the resulting sequence tracks those scores.
Composite organizations, not specific clients. Patterns consolidated from multiple RBD. engagements.
The toolkit is delivered as two instruments designed to be used together. The reference guide specifies the framework and publishes the full archetype taxonomy. The working Google Sheet turns your organization's inputs into a live priority scorecard and a quarter-by-quarter hiring sequence, both of which recompute as you adjust the signals.
A five-chapter publication in the RBD. format containing the full framework, every archetype, every input specification, every decision tool, and three applied cases at three organizational scales.
Copy the master Sheet into your Drive in a single click, fill the six dropdowns on the Input Specs tab, and watch the priority scorecard recompute live as the hiring sequence builds itself against your organizational signals.
The Toolkit tier delivers the working instrument on its own. The Bundle tier adds the Workforce 2030 research brief as the strategic context layer around it. Both tiers share a single access key and open immediately after purchase.
For context. An executive search for a Chief AI Officer typically runs $50K to $150K in retainer fees and ninety-plus days of calendar time, and a misfire at day forty-four costs measurably more than both. The toolkit sits upstream of that process, specifying the work before requisitions open. The $47 tier is the entry point; the $95 tier adds the strategic context layer that most boardroom AI conversations require.
ecosystem26, which opens the reference guide immediately after purchase.ecosystem26, which opens both the toolkit and the brief.Four forces are reshaping enterprise talent strategy simultaneously, and each one compounds the others. The toolkit provides the operational response. Workforce 2030 provides the strategic narrative for the pressure the four forces together generate. Used in combination, the two instruments function as a single decision framework rather than a tool paired with a reading.
Both. The reference guide specifies the framework, and the companion Google Sheet operationalizes it: you fill six dropdowns on the Input Specs tab, the Priority Scorecard ranks archetypes against your organizational signals, and the Hiring Sequence pulls the top four into a Q1 through Q4 plan automatically. A senior leader can produce a defensible hiring plan in a single sitting. The $95 bundle adds the strategic narrative required for the board conversation that typically follows.
Generic taxonomies list archetypes; this toolkit sequences them. The ten-skill matrix surfaces which roles are too narrow or too broad for your context, and the six input specifications determine which archetype comes first for your organization rather than for the market as a whole. The decision tree also catches the single most common hiring error we see in post-mortems of failed AI programs: the Bridge cluster is routinely skipped, and the diagnosis almost always points back to that omission. No JD library contains that pattern, because the pattern is organizational rather than role-level.
Yes, and the framework sequences regulated organizations differently by design. Under the EU AI Act and comparable U.S. regimes, the AI Ethics and Governance Lead functions as a named regulatory contact rather than a ceremonial reviewer. Composite Two in the applied cases is modeled on a $12B-AUM financial services firm where the resulting sequence hires governance before delivery. The $95 bundle covers the broader compliance dimension inside the Workforce 2030 brief.
Yes. Composite Three in the applied cases is modeled on an enterprise SaaS organization with a CAIO already seated, and the sequence that follows identifies Product Manager, MLOps, and Data Product Owner as the next three hires. The toolkit functions as the decision framework for the second, third, and fourth hire rather than the first. The ten-skill matrix also serves a secondary diagnostic purpose, because it surfaces whether the CAIO in seat is operating as a CAIO or as a relabeled CIO, which is a question worth running explicitly.
The $47 tier delivers the toolkit itself, meaning the reference guide and the working Sheet, and answers the operational question of which roles in which sequence. The $95 tier adds the Workforce 2030 research brief, which answers the strategic question of why the labor market is producing these specific dysfunctions now, and why the design window for proactive adaptation closes within twenty-four months. If you expect to present AI hiring decisions to a board, a CEO, or a full executive team, the bundle is the appropriate tier; if your work sits one layer below that, the toolkit alone is sufficient.
Built to be used inside a single leadership conversation. The natural venues are the next board meeting, the next search firm briefing, or an executive team session where AI hiring sits on the agenda.