AI Capability Roadmap

AI Capability Roadmap

Purpose

Builds the organization's AI capability map and a scheduled roadmap from the current state to the target state. Answers the question "WHEN does each prioritized AI opportunity get implemented," complementing ../ai-opportunity-portfolio/SKILL.md, which answers "WHAT is worth doing and WHAT TYPE of change is it."

Anchored in research

• Market research — roadmaps and business capability maps
• Research digest "Methods, Frameworks, and Competencies for Identifying AI Opportunities and Capacity in Business" (2026) — the three-horizon Strategic AI Roadmap and the AI Target Operating Model (ATOM) / Readiness Scorecard concept, part of the deliverable material of large consulting firms' discovery engagements.

Method

1. Take the prioritized portfolio as input from ../ai-opportunity-portfolio/SKILL.md: the selected items with their scores, Value Play classification (if transformative), and Deploy/Reshape/Invent class.
2. Place every item into one of three horizons:
   • Horizon 1 — Efficiency (0–6 months). Fast, low-risk items. Typically corresponds to the "Quick Wins" category in the ai-opportunity-portfolio skill's 2x2 matrix and often the "Deploy" category in the Deploy-Reshape-Invent taxonomy (rolling out ready-made tools).
   • Horizon 2 — Transformation (6–18 months). Redesign of core processes. Often corresponds to the "Reshape" category — requires organizational change, not just tool adoption.
   • Horizon 3 — New business (18–36 months). Transformative items that create new revenue. Often corresponds to the "Invent" category and a "Strategic Bets" position in the 2x2 matrix. Note: the horizon and the Deploy/Reshape/Invent class CORRELATE but aren't the same thing — the same Reshape-level opportunity can land in Horizon 1 OR 2 depending on resources and dependencies. Don't automatically copy the class as the horizon; assess timing separately (dependencies, resources, the organization's capacity for change relative to other projects already underway).
3. Build the AI Target Operating Model (ATOM) / Readiness Scorecard. Describe for every horizon or key capability area:
   • The human-AI division of labor — which roles/tasks are Automate/Augment/Human-Only (see ../task-level-decomposition-and-automation-fit/SKILL.md) AT this stage of the roadmap, and how the division of labor changes as the organization moves from one horizon to the next.
   • The organization's readiness level by capability area (e.g. data architecture, governance model, staff AI literacy, change- management capacity) — a rough scale (low/medium/high) is enough at this stage, a precise maturity model isn't needed.
   • Critical missing capabilities that need to be built BEFORE the next horizon's items can start (e.g. Horizon 2 often requires a data pipeline that Horizon 1 didn't yet need).
4. Identify the dependencies between horizons explicitly. Horizon 2 and 3 items often rest on infrastructure or organizational learning built in Horizon 1 — mark these dependencies on the roadmap, don't treat the horizons as independent of each other.
5. Produce a structured output: a horizoned roadmap (item → horizon → dependencies → ATOM division-of-labor description) (see ../../references/ once added).
6. Validate the result with stakeholders or your own experience-based checklist. Make sure in particular that Horizon 1 implementations don't require more resources than the organization can supply alongside Horizon 2 planning.

What this skill does NOT do

• Doesn't make the final decision for you — it produces a structured draft to support a human decision.
• Doesn't confirm figures, market data, or competitor data from memory — it uses the inputs you provide, or marks an assumption clearly ([assumption — verify]).
• Doesn't commit budget or resources — it produces a roadmap draft for approval.
• Doesn't do the prioritization itself — it uses the already- prioritized list produced by ../ai-opportunity-portfolio/SKILL.md as input, it doesn't re-assess the value of opportunities.
• Doesn't build a full organizational AI maturity model — the ATOM/ Readiness Scorecard here is a rough, roadmap-supporting description, not a separate maturity audit.

Refinement notes

Areas to keep deepening with real practice:

• your own rules of thumb and heuristics for this technique — e.g. how many Horizon 1 items an organization can typically run in parallel
• concrete templates (into ../../references/, e.g. an ATOM/Readiness Scorecard template)
• reference cases / your own examples
• what this skill deliberately does not do (guardrails, common mistakes) — add to the list above

Once this section is filled in and validated in practice, update the maturity field in skills_index.json to draft, validated, or canonical (see ../../../meta/maturity_levels.md). Don't add new fields to the frontmatter — name and description are the only ones allowed (see ../../../meta/frontmatter_schema.md).

Continue from here

• Preceding skill in this pack: ../ai-opportunity-portfolio/SKILL.md — produces the prioritized list that this skill schedules.
• Once this step is done, move to ../../../change-and-communication/skills/stakeholder-communication-plan/SKILL.md
• Related skill in this pack: ../ai-discovery-engagement-design/SKILL.md — if the roadmap is produced as part of a larger discovery engagement, this skill corresponds to the engagement's Phase 4.
• A ready-made skill chain for this situation: see ../../../playbooks/
• This pack's shared guardrails: ../../CLAUDE.md

References

• ../../references/ — the pack's shared background material
• ../../CLAUDE.md — the pack's shared guardrails