The AI Rosetta Stone
68 business concepts you already know, each mapped to their AI equivalent. Start with what's familiar — the AI label is just a new name for something you already do.
Governance & Policy
18 conceptsAI governance directly parallels existing corporate governance. Every concept here maps to a discipline your organization already practices.
Enterprise Risk Management
You already manage risk across the enterprise — AI risk is the same discipline applied to AI-specific risks: bias, explainability, data quality, and model reliability.
Corporate Governance
Board oversight, accountability structures, and decision rights — applied to AI systems instead of (or in addition to) business units.
Regulatory Compliance
Filing requirements, examination readiness, and regulatory reporting — now including AI-specific regulations like the EU AI Act and state AI guidance.
Ethics & Conduct Standards
Your code of conduct and ethical guidelines — extended to cover how AI systems treat people, make decisions, and handle sensitive situations.
Audit & Internal Controls
The same audit discipline (test controls, verify compliance, document findings) applied to AI systems: are they doing what they're supposed to, accurately and fairly?
Vendor Risk Management
Your vendor assessment process — applied to AI vendors, APIs, and embedded AI in third-party software. What AI is in your supply chain?
Data Privacy & Protection
Your existing privacy program (HIPAA, GDPR, CCPA) — extended to cover how AI systems collect, process, store, and infer from personal data.
Change Management
The same organizational change discipline — applied to the workforce, process, and cultural shifts that AI adoption creates.
Business Continuity Planning
Your BCP/DR framework — extended to cover AI system failures, model degradation, and the operational impact when AI stops working.
Policy Development
Writing policies and standards — now including AI-specific policies: acceptable use, procurement, deployment, monitoring, and retirement.
Training & Certification
Your workforce development programs — expanded to include AI literacy for all employees, not just technologists.
Stakeholder Communication
How you communicate with stakeholders about decisions — extended to include when and how to disclose that AI is involved in a process or decision.
Performance Management
KPIs, dashboards, and performance reviews — applied to AI systems: accuracy, fairness, drift, and business impact measurement.
Intellectual Property Management
Your IP protection practices — extended to address questions about AI-generated content, model ownership, and training data rights.
Contract Management
Your contracting practices — updated to include AI-specific provisions: data rights, model ownership, performance guarantees, liability allocation.
Insurance & Liability
Your risk transfer practices — extended to cover AI-specific liability exposure and emerging AI insurance products.
Incident Management
Your incident response playbook — extended to cover AI-specific incidents: model failures, biased outputs, data breaches in AI systems.
Quality Assurance
Your QA processes — applied to AI outputs: testing for accuracy, consistency, fairness, and edge case handling before and after deployment.
Technical & Operational
25 conceptsEvery AI technical concept has a direct workplace analogy that makes it immediately intuitive to business professionals.
Statistical Modeling
Your existing statistical models (GLMs, regression, scorecards) — with more variables, non-linear relationships, and the ability to improve automatically as new data arrives.
Pattern Recognition
What your experienced professionals do intuitively (reading an X-ray, spotting fraud, hearing a bad bearing) — done at scale through layered neural networks that learn to recognize patterns from examples.
Document Processing
Your mailroom, data entry, and document review workflows — automated to read, classify, extract data from, and route documents without manual handling.
Language & Text Analysis
What your analysts do when they read reports, emails, contracts, and notes to extract meaning — done at scale across thousands of documents simultaneously.
Image & Video Analysis
What your inspectors, radiologists, adjusters, and quality engineers do when they look at images — done at scale by AI that can analyze thousands of images per minute.
Forecasting & Prediction
Your existing forecasting processes (budgets, demand planning, loss projections) — enhanced with ML models that consider more variables and update continuously.
Decision Rules & Workflows
Your business rules, decision trees, and workflow routing — enhanced with ML that handles the gray zone between clear yes and clear no.
Process Automation
Your existing automation (macros, scripts, RPA bots) — upgraded with AI that can handle exceptions, judgment calls, and unstructured inputs that rules-based automation can't.
Search & Information Retrieval
Your existing search tools — upgraded to understand meaning and context rather than just matching keywords, and to generate answers rather than just returning links.
Conversation & Communication
Your call centers, help desks, and customer portals — augmented with AI that handles routine inquiries through natural conversation, freeing humans for complex interactions.
Content Creation
Your content production workflows (reports, marketing, documentation) — augmented with AI that generates first drafts, adapts content for audiences, and handles high-volume content needs.
Recommendation & Matching
What your experienced people do when they match customers to products, candidates to jobs, or cases to specialists — systematized and scaled through ML.
Anomaly Detection
What your auditors, investigators, and quality inspectors do when they spot something that doesn't look right — applied to every transaction, every claim, every data point, continuously.
Optimization & Resource Allocation
Your operations research, scheduling optimization, and resource allocation decisions — solved with AI that tests millions of scenarios to find the best answer given your constraints.
Simulation & Scenario Planning
Your scenario planning and stress testing — enhanced with AI that runs millions of simulations faster and with more variables than traditional methods.
Data Integration & Matching
Your data integration, deduplication, and master data management challenges — addressed with AI that matches records, resolves entities, and maintains data quality automatically.
Testing & Validation
Your existing testing and QA processes — applied to AI models: validating that predictions are accurate, fair, stable, and performing as expected.
Monitoring & Alerting
Your existing system monitoring (dashboards, alerts, SLAs) — extended to cover AI-specific metrics: model accuracy, prediction drift, fairness metrics, and data quality.
Security & Access Control
Your cybersecurity program — extended to cover AI-specific threats: adversarial attacks on models, data poisoning, model theft, and prompt injection.
API & System Integration
Your existing integration architecture — extended to manage AI model endpoints, LLM APIs, and the growing number of AI-powered services in your technology stack.
Version Control & Release Management
Your existing CI/CD and release management — adapted for ML models: versioning trained models, managing training data, automating retraining, and rolling back bad model versions.
A/B Testing & Experimentation
Your existing experimentation practices — applied to AI: testing which model version performs better, which features improve predictions, and whether a new approach beats the current one.
Data Warehouse & Reporting
Your data warehouse, BI tools, and reporting infrastructure — enhanced to support AI workloads: feature stores, training data management, and real-time prediction serving.
User Experience Design
Your UX design practices — extended to design interactions where humans and AI work together: when to show AI confidence levels, how to handle AI uncertainty, and when to escalate to humans.
Vendor & Platform Evaluation
Your existing vendor evaluation process — with AI-specific criteria: model transparency, data handling practices, bias testing, performance guarantees, and lock-in risk.
Data & Infrastructure
12 conceptsAI infrastructure extends the data platform your organization already runs.
Data Governance
Your existing data governance program — extended to cover training data quality, data provenance for models, consent management for AI data usage, and bias in data.
Data Quality Management
Your existing data quality practices — applied to the data that trains AI models, where quality issues don't just produce bad reports, they produce bad predictions at scale.
Database Management
Your existing data storage — extended with specialized stores for AI: vector databases that enable semantic search, and feature stores that manage the variables ML models consume.
Data Architecture
Your data architecture — extended to support AI workloads: data pipelines for model training, real-time data feeds for prediction serving, and feedback loops for model improvement.
Master Data Management
Your MDM challenges (duplicate records, inconsistent data across systems) — addressed with AI that matches entities, maps relationships, and maintains a unified view.
Data Lineage & Cataloging
Your data lineage documentation — extended to track which data trained which model version, how training data was filtered, and the provenance chain from raw data to model output.
Data Security & Encryption
Your data security controls — extended to protect training data, model weights, prediction outputs, and the inference pipeline from adversarial attacks and unauthorized access.
Data Retention & Archival
Your data retention policies — extended to cover model retirement, training data archival, and the lifecycle management of AI assets that are fundamentally different from traditional data assets.
Cloud Infrastructure
Your cloud strategy — extended to include GPU/TPU compute for model training, model serving infrastructure, and the specialized cloud services (SageMaker, Vertex AI, Azure ML) that support AI workloads.
Edge Computing
Your distributed computing strategy — extended to include AI models running on local devices (factory sensors, mobile phones, medical devices) rather than in the cloud, for real-time decisions without network dependency.
Data Sharing & Partnerships
Your data sharing agreements and challenges — addressed with techniques that enable collaborative AI without sharing raw data: the model travels to the data, not the data to the model.
Synthetic Test Data
Your test data management practices — enhanced with AI that generates realistic but artificial data for testing, development, and scenarios where real data is too sensitive, too scarce, or too regulated to use.
Strategy & Business Model
13 conceptsAI strategy follows the same strategic planning and investment analysis frameworks leaders already use.
Digital Transformation Strategy
Your digital transformation roadmap — with AI as a specific capability layer: where to apply it, how to prioritize, what infrastructure it requires, and how to measure value.
Innovation & R&D
Your innovation process (POCs, pilots, R&D) — applied to AI: evaluating AI vendors, running pilots, measuring results, and scaling what works.
Operating Model Design
Your operating model (how work flows through your organization) — redesigned to incorporate AI: which decisions AI makes, which it augments, and how human roles evolve.
Workforce Planning
Your workforce planning — updated to account for AI's impact: which roles change, which new skills are needed, and how to manage the transition without losing institutional knowledge.
Customer Experience Strategy
Your CX strategy — enhanced with AI touchpoints: personalization, predictive service, intelligent self-service, and proactive outreach based on behavioral signals.
Product Strategy
Your product roadmap — with AI as both a feature enabler (intelligence embedded in your product) and a product category (AI-powered products and services you sell).
Competitive Strategy
Your competitive analysis — updated to assess how AI changes your competitive landscape: who's deploying it, what advantages it creates, and where it threatens your current differentiation.
Partnership & Ecosystem Strategy
Your partnership strategy — extended to include AI vendors, data providers, model providers, and the ecosystem of AI-adjacent services that support your AI capabilities.
ROI & Business Case Development
Your business case methodology — adapted for AI: measuring value that's harder to quantify (decision quality improvement, risk reduction, time saved on judgment work), with longer payback periods and higher uncertainty than traditional IT investments.
Organizational Design
Your org structure — evolved to support AI: where does the AI team report, how do business units access AI capabilities, and how do you avoid both the ivory tower and the fragmented approach?
Culture & Change Leadership
Your culture management — with specific attention to AI adoption: building trust in AI-augmented decisions, managing fear of replacement, and creating a culture of human-AI collaboration.
Talent Acquisition & Retention
Your talent strategy — expanded to include AI-specific roles (data scientists, ML engineers, AI ethicists) and AI-augmented roles (every role that now works alongside AI tools).
Metrics & KPI Framework
Your KPI framework — updated to include AI-specific metrics (model accuracy, bias scores, automation rates) and AI-enhanced traditional metrics (predictive vs. retrospective measurement).
Ready to Go Deeper?
These 68 concepts are the foundation. See how they come alive in specific department workflows across 8 industries.