DORA AI Capabilities Model (Team Profiles & Capabilities)

DORA's 2025 empirical taxonomy for AI-era software delivery — seven data-derived team-profile clusters paired with seven capabilities (AI stance, data ecosystems, version control, small batches, user focus, quality platforms) correlated with better AI-adoption outcomes. Reframes AI ROI as an organizational-system property rather than a tooling property.

Also known as: team profiles, AI capabilities model, seven team profiles, AI Capabilities Model report, capabilities model

Part of: DORA & Engineering Metrics

How authors use it

Oldest first (extraction date when no publication date is known).

Documents (5)

• AI-Assisted Software Development: Team Profiles and Capabilities for Putting Research into Action Ben Linders · 2026-07-30 source ↗ Reader 🖥️
  DORA's 2025 research reframes AI adoption in software development as an organizational-system problem rather than a tooling problem: AI amplifies whatever team dynamics already exist, and the return on AI investment comes from developing identifiable, data-derived capabilities (from a clear AI stance to quality internal platforms) matched to your team's actual profile, not from adopting the technology itself.
• The Acceleration Whiplash Faros · 2026-04-09 Reader 🖥️
  AI coding tools have crossed, without any deliberate organizational decision, from assistant to primary author of code, and two years of telemetry from 22,000 developers show that this shift is producing a widening gap between throughput (up) and production quality (down) that holds regardless of engineering maturity -- so organizations must raise code quality at the point of AI authoring rather than add more downstream review.
• Gene Kim on Vibe Coding - Part II | Episode 411 | Troubleshooting Agile Douglas Squirrel · 2025-12-18 source ↗ Reader 🖥️
  Vibe coding changes software teams' organizational unit of work — toward smaller, option-rich teams and even individual business-stakeholder-plus-developer pairs rather than net headcount reduction — and because trust in these tools is a learned skill that takes about a year to build, leaders should start adopting now rather than wait for the technology to mature further.
• State Of AI-Assisted Software Development DORA · 2025-09-23 Reader 🖥️
  AI's effect on software delivery is not a property of the tool but of the organizational system it is embedded in: across nearly 5,000 developers, AI consistently amplified whatever team dynamics already existed — toward 'harmonious high-achievement' or toward a 'legacy bottleneck' — and organizations only convert individual AI-driven effectiveness gains into durable team, product, and organizational performance by deliberately building a cluster of systemic capabilities (AI-policy clarity, data health, version-control discipline, small batches, user-centricity, and platform quality) and treating AI adoption as a whole-system transformation rather than a tooling rollout.
• 2024_final_dora_report DORA · 2024-10-25 Reader 🖥️
  Technology investments — generative AI adoption and internal developer platforms alike — reliably raise individual, team, and organizational performance only when paired with the underlying sociotechnical fundamentals (small-batch delivery discipline, developer independence, transformational leadership, a genuine user-centric and documentation culture, and stable priorities); absent those fundamentals, the same investments degrade software delivery throughput and stability and can increase burnout, so AI and platforms amplify whatever organizational system already exists rather than fixing it.

Propositions touching this concept (2)

By publication month, oldest first (extraction month when no publication date is known).

2026-04

• explicitThe attackers built an elaborate fake corporate identity — including a cloned Slack workspace with branded channels, LinkedIn activity, fake team profiles, and other OSS maintainer personas — to establish credibility.
  The Axios supply chain attack used individually targeted social engineering · p2

2026-07

• explicitdevelopedDORA's 2025 survey and qualitative research identified seven distinct team profiles that differ significantly on team performance, product performance, individual effectiveness, friction, and burnout.
  AI-Assisted Software Development: Team Profiles and Capabilities for Putting Research into Action · p2

Open gaps (5)

• incompleteThe DORA AI Capabilities Model's seven capabilities — the report's headline, survey-validated deliverable — include no apprenticeship or skill-transmission capability, even though Matt Beane's own essay in the same report argues default AI use measurably erodes the junior-to-senior pipeline across many occupations and (early signs suggest) in software engineering too. The model and the warning sit side by side without being reconciled.
  State Of AI-Assisted Software Development — read next: Track whether DORA's next annual survey operationalizes a skill-development or apprenticeship-health measure as an eighth capability; read Matt Beane's ongoing SkillBench research directly for software-specific findings.
• illogicalThe report's statistical methodology explicitly disclaims causal interpretation (its own footnotes describe the regressions as comparisons, not effects), yet its practical-advice sections phrase every one of the seven capabilities as something leaders should causally 'invest in' or 'build' to amplify AI's benefit. This leaves open whether building a capability causes the amplification, or whether already well-run organizations simply both have the capability and separately extract more value from AI.
  State Of AI-Assisted Software Development — read next: Look for a longitudinal/panel-data follow-up DORA report, or independent experimental/quasi-experimental evidence isolating any one of the seven capabilities from general organizational maturity.
• incompleteThe report's two major empirical halves — the seven team archetypes (cluster analysis) and the sociocognitive impact findings (pride, ownership, skill reprioritization) — are never cross-tabulated against each other, so it's unknown whether, say, the 'Legacy bottleneck' cluster shows different ownership or pride effects from AI than the 'Harmonious high-achiever' cluster.
  State Of AI-Assisted Software Development — read next: A follow-up analysis joining cluster membership with the sociocognitive constructs in DORA's underlying dataset.
• incompleteThe report repeatedly argues a causal story (AI adoption drives quality decline) from correlational, cross-company telemetry (Spearman's rho at the team-quarter level) -- the same methodological gap this corpus's extraction of the DORA AI Capabilities Model report (extract-dora-ai-team-profiles-and-capabilities#g2) flags as unaddressed there. Confounds such as simultaneous process changes, hiring waves, or deadline pressure occurring in the same high-adoption quarters are not ruled out.
  The Acceleration Whiplash — read next: A controlled or quasi-experimental design (staggered rollout comparison, matched teams) isolating AI adoption from co-occurring organizational changes.
• incompleteNo discussion of scale or organizational-profile effects on the prescription. Does a $50M-token-spend enterprise need materially different governance from a five-person team's monthly token bill? 'Team as cost unit + explicit causal model' is presented as a universal recipe, but DORA's AI Capabilities Model research suggests AI-adoption outcomes cluster into distinct organizational-profile clusters -- implying that one governance recipe may not transfer cleanly across very different scales or team maturities.
  TBM 437: Tokens, Hours, Points, and Other Curious Proxies — read next: DORA's 2025 AI Capabilities Model report on the seven data-derived team-profile clusters and their differing capability needs.