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Foundational Concepts
Core AI concepts that shape how organizations think about adoption.
Agentic AI
Definition
Agentic AI is artificial intelligence that takes independent action toward a goal, making decisions and executing tasks across multiple steps without requiring human input at each stage.
Why it matters for Leaders and AI Decision Makers
Most AI tools your team uses today wait for instructions. Agentic AI doesn't. As more vendors add agentic features to existing tools (your CRM, your email platform, your project management software), your team will start delegating decisions to AI without realizing it. Knowing where this is happening is the first step to governing it.
Real-world example
A sales team installs an AI assistant that drafts follow-up emails, schedules the meetings, updates the CRM, and re-prioritizes the rep's calendar based on deal stage, all without anyone clicking approve.
In your assessment
Section C: How Work Gets Done (Workflow Clarity) questions surface where agentic AI use is already showing up in your team's workflows.
In your report
Your Agentic Governance score reflects how prepared your team is to manage AI that acts on its own.
Related terms
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Convergence
Definition
Convergence is the moment when multiple emerging technologies, regulatory shifts, and market forces collide in a way that fundamentally changes how an industry operates.
Why it matters for Leaders and AI Decision Makers
AI isn't arriving alone. It's arriving alongside agentic systems, new regulations, shifting workforce expectations, and rapidly evolving customer behavior. The leaders who recognize convergence early adapt. The ones who treat each shift as a separate problem fall behind.
Real-world example
A regional healthcare clinic faces three converging pressures at once: agentic AI scheduling tools entering the market, new HIPAA guidance on AI-generated clinical notes, and patients increasingly expecting same-day digital responses. Treating these as one connected shift produces a different strategy than treating them as three separate IT projects.
In your assessment
Your industry and overall response patterns inform the Convergence section of your report.
In your report
The Convergence section identifies which forces are most likely to affect your business in the next 12-24 months.
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Human-in-the-Loop
Definition
Human-in-the-Loop is a design pattern where AI systems require human review, approval, or intervention at defined points in a workflow before continuing.
Why it matters for Leaders and AI Decision Makers
Human-in-the-Loop is the most reliable safeguard against AI errors, hallucinations, and decisions that look right but cause harm. The question isn't whether to use it. The question is where to place the human checkpoints so they catch real risk without slowing the team down.
Real-world example
A finance team uses AI to draft monthly client reports, but the analyst always reviews the AI's data interpretation before the report goes out. When the AI misreads a chart in March and reverses two numbers, the analyst catches it before the client ever sees it.
In your assessment
Section E: Oversight and Decision Control questions evaluate where Human-in-the-Loop checkpoints exist in your current AI workflows.
In your report
Your Human-in-the-Loop maturity appears in the Agentic Governance and Workflow Integration scores.
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Governance and Risk
Frameworks and indicators for managing AI responsibly across the business.
Agentic Governance
Definition
Agentic Governance is the framework an organization uses to define what autonomous AI is allowed to do, what requires human approval, and what is off-limits entirely.
Why it matters for Leaders and AI Decision Makers
When AI starts taking action on its own, the question shifts from "what can it do" to "what should it be allowed to do." Agentic Governance is how you answer that question before something goes wrong. Most organizations don't have one yet.
Real-world example
A marketing director defines that the AI campaign tool can launch A/B tests under $500 in spend on its own, but anything above that threshold requires her sign-off before going live.
In your assessment
Section E: Oversight and Decision Control questions surface gaps in Agentic Governance, which appears as a flagged Emerging Risk Indicator in your report when policies are missing.
In your report
Agentic Governance appears as one of three Emerging Risk Indicators on page two of your report, marked Covered, Needs Attention, or High Risk based on your responses.
Related terms
Agentic AI, AI Governance, Oversight Transparency, Human-in-the-Loop
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AI Governance
Definition
AI Governance is the system of policies, processes, and accountability structures an organization uses to manage AI tools across the business.
Why it matters for Leaders and AI Decision Makers
AI Governance isn't a document you write once and file away. It's how you decide which tools to adopt, who gets to use them, what data they touch, and what happens when something breaks. Without it, every team makes its own rules and risk piles up invisibly.
Real-world example
An operations director publishes a one-page policy stating that any AI tool touching customer data must be approved by IT and Legal before purchase, with quarterly reviews to confirm tools in use still meet the bar.
In your assessment
Section G: Rules and Responsibility questions evaluate your current AI Governance posture across tool selection, data handling, and accountability.
In your report
Your overall AI Governance maturity informs your Readiness Persona and shapes your top recommendations.
Related terms
Agentic Governance, Oversight Transparency, Workflow Integration
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Emerging Risk Indicators
Definition
Emerging Risk Indicators are governance and awareness gaps surfaced by your assessment responses that require direct attention before expanding AI use, separate from your readiness scores.
Why it matters for Leaders and AI Decision Makers
Maturity Scores tell you how prepared you are. Emerging Risk Indicators tell you where the trapdoors are. Even a high overall readiness score can mask a single risk indicator that, left unaddressed, creates operational, regulatory, or trust failures down the line.
Real-world example
A company scores 4.1 overall on AI Readiness but receives a Needs Attention flag on Personal AI Dependency Awareness. The flag tells them their employees may be using AI tools for personal guidance without policy or guardrails, a quiet exposure their strong overall score would otherwise hide.
In your assessment
Specific responses across multiple sections trigger risk indicator evaluation, separate from the dimension scoring logic.
In your report
Emerging Risk Indicators appear on page two as a dedicated section, with each indicator marked Covered, Needs Attention, or High Risk.
Related terms
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Oversight Transparency
Definition
Oversight Transparency is the degree to which an organization can see, audit, and explain how AI tools are being used across the business.
Why it matters for Leaders and AI Decision Makers
You can't govern what you can't see. Oversight Transparency is the foundation of every other AI governance practice. Teams with low Oversight Transparency often discover months later that critical decisions were being made by AI tools nobody approved.
Real-world example
A department head asks his five direct reports to list every AI tool they use weekly. The list comes back with 23 tools, only 4 of which IT had approved or even knew existed.
In your assessment
Section E: Oversight and Decision Control questions surface gaps in Oversight Transparency, which appears as a flagged Emerging Risk Indicator in your report.
In your report
Oversight Transparency appears as one of three Emerging Risk Indicators on page two of your report, marked Covered, Needs Attention, or High Risk based on your responses. It is also reflected in your Oversight and Decision Control dimension score.
Related terms
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Personal AI Dependency Awareness
Definition
Personal AI Dependency Awareness is an organization's understanding of and policy response to employees using AI tools for personal guidance, emotional support, or decision-making outside of work-specific tasks.
Why it matters for Leaders and AI Decision Makers
Most AI policies focus on work tasks. Personal AI Dependency Awareness recognizes that employees increasingly use AI for personal questions, mental health prompts, relationship advice, and life decisions, often using the same accounts, devices, and platforms they use for work. Without awareness and guidance, organizations carry hidden risk around data exposure, employee wellbeing, and the blurred line between professional and personal AI use.
Real-world example
A team member uses the company's enterprise AI account to talk through a difficult personal decision after work hours. The conversation is logged in the company's AI usage data, creating an unintended privacy exposure and a question about whether the company should have policies addressing personal use of work AI tools.
In your assessment
Section E and Section G questions surface gaps in Personal AI Dependency Awareness, which appears as a flagged Emerging Risk Indicator in your report.
In your report
Personal AI Dependency Awareness appears as one of three Emerging Risk Indicators on page two of your report, marked Covered, Needs Attention, or High Risk based on your responses.
Related terms
Emerging Risk Indicators, AI Governance, Human Connection Risk
Human-Centered Concepts
What gets lost or protected when AI replaces human-to-human interaction.
Emotional Outsourcing
Definition
Emotional Outsourcing is the practice of using AI to handle interactions that historically required human empathy, judgment, or relational skill.
Why it matters for Leaders and AI Decision Makers
When AI writes the apology email, mediates the team conflict, or coaches the underperforming employee, something gets lost even when the output is competent. Emotional Outsourcing isn't always wrong, but it's almost always invisible until customer trust or team cohesion starts eroding.
Real-world example
A customer success manager starts using AI to draft all responses to upset clients. The replies are well-written and on-brand, but renewal rates begin slipping six months later because long-time customers can sense the change in voice and feel less personally connected.
In your assessment
Section B: Human Connection and Customer Trust questions surface where Emotional Outsourcing may already be happening in your team's communications and customer interactions.
In your report
Your Emotional Outsourcing risk score appears in the Human Connection section.
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Human Connection Risk
Definition
Human Connection Risk is the measurable degradation of relational trust, team cohesion, or customer loyalty that occurs when AI replaces human-to-human interaction in contexts where the human element was load-bearing.
Why it matters for Leaders and AI Decision Makers
Your competitive advantage often lives in your relationships. Human Connection Risk tracks where AI adoption is quietly eroding that advantage, often in places leadership doesn't notice until a customer leaves or a key employee disengages.
Real-world example
A sales team automates first-touch outreach to all prospects with AI-personalized emails. Conversion rates improve in the short term, but long-tenured prospects who used to convert through founder-led conversations stop responding entirely.
In your assessment
Section B: Human Connection and Customer Trust questions measure your exposure to Human Connection Risk across customer-facing and internal interactions.
In your report
Your Human Connection Risk score is paired with specific recommendations for protecting the relationships that drive your business.
Related terms
Emotional Outsourcing, Human-in-the-Loop, Oversight Transparency
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Assessment and Measurement
How Brave Concept AI measures organizational readiness across dimensions.
AI Readiness
Definition
AI Readiness is an organization's combined capacity to adopt, govern, and benefit from AI without sacrificing human judgment or operational stability.
Why it matters for Leaders and AI Decision Makers
AI Readiness isn't about how many tools you've bought or how technical your team is. It's about whether your people, processes, and culture are positioned to use AI well. A team with low readiness adopting powerful tools creates more risk than a team with high readiness using basic tools.
Real-world example
A 40-person professional services firm with clear AI policies, trained team members, and defined escalation paths gets more value from a basic AI writing tool than a 200-person firm with no governance and five overlapping AI subscriptions across departments.
In your assessment
Every section contributes to your overall AI Readiness score.
In your report
Your AI Readiness score is the headline number on page one and anchors your Readiness Persona.
Related terms
Maturity Score
Definition
A Maturity Score is a numerical rating from 0 to 5 that summarizes an organization's current capability across one specific dimension of AI adoption.
Why it matters for Leaders and AI Decision Makers
Maturity Scores let you see exactly where your team is strong and where you're exposed. They turn fuzzy questions like "are we ready for AI" into specific, addressable answers. Each score falls into one of four tiers: Early Stage (0.0 to 1.9), Developing (2.0 to 2.9), Progressing (3.0 to 3.9), and Advancing (4.0 and above).
Real-world example
An operations director sees her team scored 4.0 on Human Connection and Customer Trust but only 3.3 on How Work Gets Done. The gap tells her exactly where to focus the next quarter: not adopting more tools, but tightening the workflows underneath them.
In your assessment
Each section produces its own Maturity Score across the six dimensions: Human Connection and Customer Trust, How Work Gets Done, Team Comfort and Learning, Oversight and Decision Control, Tools and Information, and Rules and Responsibility.
In your report
Maturity Scores appear in the Readiness Profile section as both individual dimension scores and as the components of your overall AI Readiness score.
Related terms
Readiness Persona
Definition
A Readiness Persona is a named profile that describes an organization's overall posture toward AI adoption based on combined Maturity Scores across all assessment dimensions.
Why it matters for Leaders and AI Decision Makers
Readiness Personas turn a stack of numbers into a recognizable pattern. They tell you not just where you stand, but what kind of leader your current state reflects and what your most likely next move should be. Brave Concept AI uses five Readiness Personas arranged on a progression: Workflow Firefighter, Tool-Curious Explorer, Cautious Optimizer, Human-First Builder, and AI-Ready Leader.
Real-world example
Two companies score the same overall AI Readiness of 3.4, but one is classified as a Tool-Curious Explorer (high tool adoption, low governance) and the other as a Cautious Optimizer (moderate everything). Their next steps look completely different despite the identical score.
In your assessment
Your responses across all sections combine to determine your Readiness Persona.
In your report
Your Readiness Persona appears on page one alongside your AI Readiness score and is also shown on a five-stage progression spectrum that identifies your current persona and your next level.
Related terms
Workflow Integration
Definition
Workflow Integration is the extent to which AI tools are woven into an organization's daily operations in a coordinated, governed way rather than adopted as disconnected point solutions.
Why it matters for Leaders and AI Decision Makers
Most organizations end up with five or ten AI tools that don't talk to each other, each owned by a different team, each with its own data and rules. Workflow Integration measures whether AI is making your business more coherent or more fragmented.
Real-world example
A 60-person firm has marketing using one AI writing tool, sales using a different one, and operations using a third. Each team's outputs sound different, brand voice fragments, and the operations team ends up rewriting half of what marketing produces because the tools don't share context.
In your assessment
Section C: How Work Gets Done (Workflow Clarity) questions evaluate how AI tools are currently integrated across your team's workflows.
In your report
Your Workflow Integration score appears in the Operations section with specific recommendations for consolidation or coordination.
Related terms
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The Gap That Nobody Owns, Convergence Is Coming for Your Industry
Common Questions About AI Terminology
What's the difference between Agentic AI and AI Agents?
The terms are often used interchangeably, but Agentic AI describes the underlying capability (AI that takes autonomous, multi-step action toward a goal) while AI Agents typically refers to the specific tools or implementations that use that capability. An AI agent is a product. Agentic AI is the design pattern that product is built on. For governance purposes, the more important question isn't what to call them, but where they're operating in your business.
Human-in-the-Loop sounds the same as human oversight. What's actually different?
Human oversight is the broad principle that humans should maintain meaningful authority over AI decisions. Human-in-the-Loop is a specific design pattern where humans must approve, review, or intervene at defined checkpoints in an AI workflow before it continues. Oversight is the why. Human-in-the-Loop is one of the hows. A team can have strong oversight without using formal Human-in-the-Loop checkpoints, and a team can implement Human-in-the-Loop without having strong oversight culture. The strongest AI governance practices use both.
What's the difference between a Maturity Score and an Emerging Risk Indicator?
Maturity Scores measure how prepared your organization is across six dimensions of AI readiness. Emerging Risk Indicators flag specific governance and awareness gaps that need direct attention regardless of overall readiness. A high maturity score tells you you're generally ready. A flagged risk indicator tells you there's a specific trapdoor to address before expanding AI use. The two work together: scores map your overall position, indicators surface the specific risks scores might mask.
Where do these definitions come from?
Every term in this glossary is grounded in the Brave Concept AI Human-AI Readiness and Risk Assessment, which we developed based on twenty years of UX research, governance work in regulated industries, and direct experience advising leaders through AI adoption decisions. We define these terms the way our clients use them, not the way vendor pitch decks define them. When industry standards exist, we align with them. When they don't, we name the thing in plain language that makes it easier for leaders to make decisions.
Why five Readiness Personas instead of more or fewer?
Five personas captures the meaningful range of organizational AI readiness without splitting hairs. Workflow Firefighter is for organizations still putting out daily operational fires. Tool-Curious Explorer captures teams experimenting with AI but without governance. Cautious Optimizer represents teams adopting AI thoughtfully but slowly. Human-First Builder describes organizations leading with human-centered design. AI-Ready Leader is for organizations operating at the highest maturity tier. Each persona has different priorities and different next steps, and most leaders recognize their organization in one of these patterns within minutes of seeing the framework.
How do I see my organization's scores across these terms?
Take the free Human-AI Readiness and Risk Assessment. The assessment takes about fifteen minutes and produces a personalized report covering all six dimensions, your Readiness Persona, your Maturity Score, and any flagged Emerging Risk Indicators. Every term in this glossary appears somewhere in your report, with your specific scores or status across each.
Why does my report show some high scores and some low ones? Is that normal?
Yes, and it's actually the most useful pattern to surface. Most organizations are uneven across the six dimensions. A team might score 4.2 on Tools and Information but 2.1 on Rules and Responsibility. The strength on tools doesn't compensate for the gap in governance. The mixed score pattern tells you exactly where to focus next, and it's far more actionable than a single overall number.
Will this glossary keep growing as the field evolves?
Yes. The AI field is moving faster than any glossary can keep up with, but we update this one whenever a term enters the conversation that leaders need to navigate. New terms get added when they show up repeatedly in client conversations, board discussions, or public discourse where the lack of shared definition is causing confusion. If you want to be notified when new terms are added or existing definitions are updated, you can subscribe to the Brave Concept AI newsletter at the bottom of any page on this site.
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