RELIABLE

Score of 4 and above

Work moves fast from prompt to “ready to use,” with fewer revision loops and quicker turnaround.

INCONSISTENT

Score between 2 and 4

Speed is hit-or-miss, some tasks move quickly while others get stuck in re-prompting or polishing.

UNSTABLE

Score below 2

AI use creates drafts, but tasks still stall before completion, so turnaround time barely improves.

RELIABLE

Score of 4 and above

Outputs are consistently clean and usable, needing only light edits before sharing or using.

INCONSISTENT

Score between 2 and 4

Quality varies, some outputs land well, others need rework, clarification, or correction.

UNSTABLE

Score below 2

Outputs often miss the mark, requiring heavy rewriting, fact-checking, or manual rebuilding.

RELIABLE

Score of 4 and above

The team can apply AI across a wide range of work, from simple drafts to complex planning and structured outputs.

INCONSISTENT

Score between 2 and 4

AI works for a few familiar use cases, but the team struggles to apply it beyond basic tasks.

UNSTABLE

Score below 2

Usage is narrow and fragile, AI is only used for “easy wins,” and many tasks still feel out of reach.

RELIABLE

Score of 4 and above

People use AI confidently while staying within clear guardrails, with good judgment on what to share and when to verify.

INCONSISTENT

Score between 2 and 4

Some safe habits exist, but standards are inconsistent, people are unsure what is allowed, or verification is uneven.

UNSTABLE

Score below 2

Safety practices are unclear or unreliable, creating hesitation, risky behavior, or avoidable compliance exposure.

RELIABLE

Score of 4 and above

AI use is embedded into daily work, it shows up consistently across weeks, not just during peaks or experiments.

INCONSISTENT

Score between 2 and 4

Usage comes in bursts, the team uses AI sometimes, but it drops off without triggers or reminders.

UNSTABLE

Score below 2

AI use is sporadic or fading, there is no stable habit, so benefits do not compound over time.

RELIABLE

Score of 4 and above

The team is comfortable using AI, tries it early, and treats it as a normal tool for getting work done.

INCONSISTENT

Score between 2 and 4

Confidence is mixed, some people use AI readily, others hesitate, avoid, or only use it when told to.

UNSTABLE

Score below 2

There is strong friction or resistance, people avoid AI, distrust it, or feel unsure how to use it without getting it wrong.

RELIABLE

Score of 4 and above

AI usage scales through shared playbooks, templates, and repeatable workflows, so the team improves together, not just individually.

INCONSISTENT

Score between 2 and 4

A few individuals have good practices, but knowledge is not consistently shared or standardized across the team.

UNSTABLE

Score below 2

AI use is isolated and personal, there is little sharing, so capability stays fragmented and difficult to scale.

LOW SCORER INDICATORS

• Work that “used AI” still takes as long as before, sometimes longer.
• There are many rounds of back and forth, yet people still say “still refining” for days.
• Deadlines are not moving, even though tool usage is high.
• Managers feel that AI adds noise and extra drafts, not momentum.

HIGH SCORER INDICATORS

• First drafts arrive much faster, and the team spends more time deciding and less time typing.
• Turnaround time for common tasks drops, with no visible drop in quality.
• People arrive at meetings with clearer options or summaries prepared by AI.
• Managers notice that “things move” faster from idea to decision, not just from idea to document.

Velocity measures how quickly work moves from the first prompt to a usable outcome, with minimal drag, back-and-forth, or revision loops. It is not about typing faster or producing more drafts. It is about reducing friction so a task reaches “done enough to ship” sooner, whether that means a decision, a deliverable, or a next-step plan.

A low Velocity score usually means the team is using AI, but work still gets stuck. People generate ideas quickly, yet spend too long refining wording, re-prompting endlessly, or second-guessing outputs. AI becomes a brainstorming buddy, not a throughput engine. The telltale signs are: lots of drafts, slow approvals, repeated rework, and “we’ll finalize it later” energy.

A high Velocity score means AI consistently helps the team move from messy to clear, fast. People know how to frame prompts, give the right context, and choose the right output format (bullets, tables, email draft, slide outline, action plan) so they can progress without over-editing. Velocity shows up as faster turnaround times, fewer revision cycles, and quicker decision-making.

Shows how quickly your team can move from a prompt to a usable output with minimal back-and-forth.

Quality measures how consistently AI-assisted work meets your organization’s standards: clear, accurate, on-brand, and fit for purpose. It is not just “does AI sound good.” It is whether outputs are usable with minimal cleanup, and whether the team can maintain quality even when speed increases.

A low Quality score usually means AI outputs are hit-or-miss. The team may get something that looks polished, but it contains vague claims, wrong details, inconsistent tone, or missing context. People spend time fixing errors, rewriting sections, or “humanizing” the output after the fact. The telltale signs are: frequent rework, inconsistent writing standards across people, and frustration like “AI makes it faster but I still have to redo it.”

A high Quality score means AI reliably produces strong first-pass outputs that need light refinement, not heavy rescue. People know how to give the right context, examples, constraints, and structure so the result is clear, relevant, and accurate. Quality shows up as fewer edits, fewer mistakes, and outputs that can be confidently shared with internal stakeholders or clients.

Capability measures whether your team has the practical skill to use AI well: prompting, iterating, evaluating, and choosing the right tool or method for the job. It is not about knowing AI trends. It is about being able to use AI effectively in real work without needing constant help.

A low Capability score usually means usage is shallow or inconsistent. People rely on generic prompts, get generic outputs, and do not know how to improve results when AI misses the mark. The telltale signs are: “AI is not that useful,” frequent copy-paste prompting, weak instructions, and dependence on one or two “AI champions” to make things work.

A high Capability score means the team can consistently get strong outcomes because they understand how to guide AI. People know how to provide context, specify constraints, request formats, and run quick iterations to improve quality. Capability shows up as confidence, faster problem-solving, and less reliance on a single power user to unlock value.

Safety measures how responsibly your team uses AI: protecting confidential data, avoiding compliance risks, and preventing errors from slipping into real work. It is not about being paranoid. It is about having practical guardrails so the team can move fast without stepping on landmines.

A low Safety score usually means risk is either unmanaged or unclear. Some people avoid AI entirely because they are unsure what is allowed, while others use it carelessly. The telltale signs are: inconsistent fact-checking, unclear rules on sensitive information, weak habits around citations or verification, and anxiety from leaders about what is being shared with AI tools.

A high Safety score means AI use is both confident and controlled. People know what not to share, when to verify, how to cross-check, and how to keep outputs compliant and accurate. Safety shows up as faster adoption because teams are not constantly second-guessing, and leaders feel safer scaling AI usage across functions.

Continuity measures whether AI usage is sustainable and repeatable, rather than dependent on individual effort, mood, or a few “power users.” It is about consistency over time: do workflows keep working next week, next month, and across different people?

A low Continuity score usually means AI adoption is fragmented. Some teams use AI a lot, others barely touch it. Good outputs depend on who is doing the work, and success does not transfer easily across the organization. The telltale signs are: no shared templates, no common ways of working, inconsistent practices, and AI usage dropping when people get busy.

A high Continuity score means AI is embedded into everyday work. There are shared workflows, prompt patterns, templates, and habits that make usage consistent across people and teams. Continuity shows up as stable productivity gains, smoother onboarding for new users, and fewer “start-stop” cycles in adoption.

Mentality measures the team’s mindset and behavior around AI: openness, confidence, and willingness to experiment while staying grounded. It is not hype. It is whether people treat AI as a practical teammate, or as something confusing, threatening, or unreliable.

A low Mentality score usually means AI creates emotional friction. People may feel intimidated, skeptical, or overwhelmed, so they either avoid AI or use it cautiously without building momentum. The telltale signs are: fear of getting it wrong, reluctance to share AI work publicly, “I’m not good at prompting,” or a belief that AI is only for certain roles or younger employees.

A high Mentality score means the team uses AI with a calm, practical confidence. People are willing to test, learn, and improve, and they do not treat early imperfect outputs as failure. Mentality shows up as experimentation, shared learning, and steady capability growth without anxiety or resistance dragging things down.

Scalability measures whether AI benefits can expand across teams, use cases, and time, without quality collapsing or risk increasing. It is not about one person being excellent with AI. It is about whether the organization can multiply impact across the workforce.

A low Scalability score usually means AI value is trapped in pockets. A few individuals get strong results, but there is no system to spread it. The telltale signs are: reliance on champions, lack of standardized workflows, inconsistent outputs between teams, and leaders hesitant to scale because quality or safety feels unpredictable.

A high Scalability score means AI usage can be rolled out broadly without chaos. The organization has repeatable workflows, training habits, governance, and reusable assets that help more people produce better work. Scalability shows up as compounding gains: more teams adopt AI, more use cases become viable, and impact grows without increasing rework or risk.