Description
Deploying and Scaling AI is the executive capability to move AI from promising prototypes to dependable, enterprise-wide operation. It equips leaders to recognize what different AI systems — models, assistants, agents — can and cannot do, choose between building, buying, partnering or acquiring, and shepherd solutions through a lifecycle that is probabilistic, data-hungry and never truly finished. In practice it shows up as sourcing decisions defended with cost and value evidence, lifecycle and model documentation that satisfies regulators, monitoring that catches drift before users do, governance that scales across business units, and deliberate choices between edge and cloud deployment. The capability matures through repeated decisions, exposure to real constraints and honest review of what worked, deepening from basic fluency toward setting enterprise-wide standards.
Expected Behaviors
✎
LEVEL 1
Fundamental Awareness
Participates in early AI strategy discussions and vendor briefings, naming the system type, learning method and business function behind a proposed use case. Explains where today's models fall short on reliability, cost per token and autonomy, and what agent components, connection protocols and control gates are involved. Outlines the build, buy, partner and M&A routes, common reasons prototypes stall, and the sponsor, ROI horizon and metric questions that frame an AI investment.
🌱
LEVEL 2
Novice
Supports AI investment and planning work by weighing delivery routes on time-to-market, control, cost, risk and integration, and positioning candidate opportunities against business-model and audience dimensions. Scores organizational readiness and governance maturity, builds value maps with risk-adjusted benefit estimates, and drafts project plans that respect data-driven, probabilistic lifecycles. Compares deployment options, splits provider responsibilities, interprets drift signals and explainability outputs, and sets KPIs.
🌍
LEVEL 3
Intermediate
Owns delivery and scaling of AI initiatives across business units. Produces lifecycle and model documentation aligned to EU AI Act, GDPR and CCPA, plans drift monitoring and retraining, and justifies deployment choices through cost-benefit and achievability testing against human baselines. Diagnoses stalled initiatives, maps unit adoption maturity, designs federated and hybrid-cloud training approaches, stands up councils, governance controls, ROI gates and dashboards, and runs adoption and communication plans.
⭐
LEVEL 4
Advanced
Directs the AI operating model and its risk posture at enterprise scale. Shifts pipelines to ML-aware operations, selects the right DataOps, MLOps, ModelOps or LLMOps practice per workload, staffs GenAI operations, and reviews monitoring, guardrail and orchestration designs for completeness. Leads AIOps rollouts under delivery deadlines, resolves data bottlenecks, audits lineage and bias, rules on synthetic data, EU AI Act risk tiers, vendor due diligence, cross-border transfers and pilot go/no-go.
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LEVEL 5
Expert
Sets enterprise-wide standards that govern how AI is deployed and scaled. Defines data pipeline practice, model card requirements for high-risk systems, the data strategy playbook, vendor protection requirements and joint accountability across data, security and compliance. Establishes the edge-versus-cloud position, supported deployment types, latency targets, hardware, processor and compression standards, total cost and FinOps policy, and edge security posture, while advising leadership on regulatory and architectural trade-offs.
Micro Skills
✎
LEVEL 1
Fundamental Awareness
Distinguish narrow, general and superintelligence (ANI, AGI, ASI) and identify which describes today's enterprise systems
Describe the main learning methods (supervised, unsupervised, reinforcement, self-supervised, few-shot, contrastive, diffusion, transformer-based) with a business example of each
Explain the epistemic limits of LLMs (hallucination, poor confidence calibration, context-window limits) and why outputs need verification
Compare small and large language models on cost, latency, accuracy, energy use and fine-tuning effort
Distinguish bots, AI assistants, AI agents and agentic AI by autonomy, complexity and learning
Identify the function (automation, NLP, predictive analytics, computer vision, recommendation, decision support) and learning method behind an AI use case
Describe the components of an AI agent (planning, memory, tools, data sources, model) and how they work together in a business workflow
Explain how MCP connects models to tools and data and how A2A enables agent-to-agent collaboration
Describe the agent typology from reactive to autonomous agents
Identify the AI system type, learning method and function described in a published AI case
Explain which AI architectures (SLM, LLM, hybrid, RAG, agentic) suit which kinds of business applications
Explain how optimization techniques (RAG, LoRA fine-tuning, distillation, quantization, caching) change a workload's cost, speed and accuracy
Explain how per-token pricing drives model running costs
Identify the main risks of deploying agents (coordination at scale, memory drift, reward hacking, tool privileges, hallucinated actions, adversarial use) and the mitigation pattern for each
Describe why agentic AI is still early for mainstream adoption (evaluation gaps, autonomy boundaries, integration complexity)
Explain why high-risk agent actions need autonomy limits and human approval gates
Describe the four delivery choices (build, buy, partner, M&A) and a reference case for each
Explain the two faces of GenAI value: workforce productivity tools versus solutions that change processes and offerings
Identify the AI deployment models in use (embedded, built, bring-your-own AI)
Describe why prototypes fail to reach production (poor scoping, no agreed success criteria, low-impact problems)
Identify the controls an AI agent needs before broad sharing (misuse and jailbreak, logging, oversharing, bias, tool misuse)
Explain why most organizations take a hybrid sourcing approach to AI
Describe the questions agent makers, CIOs and CISOs each ask when balancing governance and innovation
Explain the decisions that frame an AI investment: ROI horizon, executive sponsor and success metrics
🌱
LEVEL 2
Novice
Compare build, buy, partner and M&A on time-to-market, control, cost, scalability, advantage, risk, talent and integration
Position AI opportunities on the opportunity matrix (improve vs. create business models; internal vs. external recipient)
Assess AI readiness across the five value drivers (business strategy, technology & data, AI strategy & experience, organization & culture, governance & security)
Apply structured criteria (strategic alignment, cost, value, scalability) to evaluate an AI investment choice
Complete a use-case value map linking goals, tactics, KPIs and benefit formulas
Calculate use-case benefits (revenue uplift, time savings, fewer false positives) with risk adjustment from given assumptions
Apply a build, buy, partner or M&A decision framework to a real company's AI adoption
Place the organization on the five-stage AI governance and security roadmap
Apply the differences between the AI lifecycle and the software lifecycle (data-driven, probabilistic, continuous retraining) when planning an AI project
Map provider and organization responsibilities for each GenAI deployment option (SaaS, API, PaaS, IaaS, self-hosted)
Plan an AI project through the GenAI lifecycle stages (plan, data preparation, model development, experimentation and evaluation, validation and deployment, monitoring and feedback)
Diagnose data drift versus concept drift from performance changes in a deployed model
Use SHAP and LIME outputs to explain individual and overall model decisions
Compare traditional ML, GenAI and multimodal GenAI on data, modeling, evaluation, scalability and security
Compare deployment options on time to deploy, control, customization, privacy, cost and skills required
Define functional and non-functional success requirements and KPIs for an AI project
🌍
LEVEL 3
Intermediate
Create lifecycle documentation for an AI project covering each stage's activities and decisions
Maintain model documentation (overview, training data records, decision logic, explainability, audit trails)
Plan drift prevention (monitoring, statistical tests, retraining schedules, incremental learning)
Run a cost-benefit analysis balancing target accuracy against compute, compliance and human-oversight costs
Plan integration, adoption and change management for an AI tool in existing workflows
Test whether proposed success measures are achievable against human baselines and cost
Align model documentation and governance with regulation across the full lifecycle (EU AI Act, GDPR, CCPA)
Monitor model effectiveness and business alignment after deployment
Select a deployment model (SaaS through self-hosted) for a GenAI workload and justify the trade-offs
Assess where each business unit sits on the five-stage AI adoption curve (learning, experimenting, standardizing, innovating, leading)
Diagnose why an AI initiative stalls at scale (infrastructure complexity, inference costs, data silos, governance drag)
Design a federated learning and hybrid-cloud approach for cross-unit model training without moving raw data
Select a global AI governance model (centralized, federated, hybrid, ecosystem) for the organization's context
Track a use case's progression across adoption stages with KPIs and ROI per stage
Apply the governance frameworks that fit the organization (NIST AI RMF, ISO/IEC 42001, EU Trustworthy AI, OECD)
Diagnose communication barriers to adoption (fear of displacement, misaligned expectations, trust gaps, skills gaps, silos, vendor over-reliance)
Create an AI scaling roadmap covering governance, infrastructure and cross-functional collaboration
Implement governance controls (model monitoring, shadow-AI discovery, FactSheets, approval workflows, cross-border playbooks)
Run an AI adoption communication playbook (narrative, role-based upskilling, champions, KPI dashboards, recognition)
Establish phase ROI gates and shared OKR/KPI dashboards from proof to scale
Set up a cross-business AI council for prioritization and execution
Operate a federated governance structure (central guilds, domain leads, model register, value–guardrail matrix)
Apply an AI expansion strategy to a real organization and align it with business-unit goals
Draft the enterprise AI governance policy stack (ethics, risk, explainability, localization) for multi-business scale
Track enterprise AI KPIs against ROI, risk exposure and compliance performance
⭐
LEVEL 4
Advanced
Lead the shift of delivery pipelines from DevOps to ML-aware operations (data, train, validate, observe, remediate)
Select the operations practice (DataOps, MLOps, ModelOps, LLMOps) that fits each AI workload
Review the AIOps capabilities (telemetry, correlation, anomaly detection, root-cause analysis, remediation) in place for AI services
Lead the adaptation of operations for GenAI workloads (data needs, lifecycle, metrics, infrastructure) versus traditional ML
Review GenAI monitoring metrics (factuality, prompt sensitivity, cost per token, bias scans) alongside standard ML metrics for completeness
Staff the GenAI operations team (data, ML and platform engineers, prompt engineers, testers, risk and compliance)
Lead an AIOps prototype for automated model monitoring
Review model monitoring workflows deployed with cloud-based tools
Review GenAI pipeline designs for prompt testing, guardrails, prompt A/B tests, RAG index refresh and agent orchestration
Lead delivery of a working AI pipeline within a fixed delivery window
Lead an AIOps rollout for a business scenario (real-time risk scoring, edge predictive maintenance)
Organize the xOps practices (data engineering, model engineering, deployment services) behind the AI operating model
Establish policy-driven automation with human approval for high-risk remediation
Drive adoption of scalable AI operations that reduce technical debt through reuse, automation and modularity
Evaluate whether data quality and availability can support GenAI at scale
Review AI initiatives for GDPR compliance (principles, data subject rights, penalty exposure)
Review AI systems against the EU AI Act timeline and risk tiers (prohibited, general-purpose, high-risk)
Review model cards for completeness against high-risk system requirements
Select a synthetic data technique (GANs/VAEs, agent-based simulation, differential privacy) to meet privacy requirements
Approve synthetic data for use based on fidelity, utility and privacy risk
Resolve data bottlenecks in AI pipelines
Audit data lineage from ingestion to model output and assess data quality and bias
Assess whether synthetic data or federated learning can replace raw data sharing in a proposed project
Review a proposed GenAI fine-tuning against purpose-limitation, data-minimization and consent requirements
Evaluate a third-party GenAI partnership and make a go/no-go decision on a sandboxed pilot
Identify vendor red flags (undisclosed training data, data pooling, weak legal basis, hidden subprocessors, no lineage, retained embeddings, model co-ownership) and explain their compliance impact
Determine whether a system is high-risk under the EU AI Act and the deployer obligations that follow
Specify pre-launch technical, contractual and compliance conditions and the metrics for scaling a pilot
Lead role-based executive due diligence (CAIO, CDO, CISO, CPO, CRO) using role-specific vendor questions
Assign risk mitigations to executive owners (DPIA, model cards, EU-only processing, co-development clauses, plain-language explanations)
Structure cross-border data transfers with SCCs, transfer impact assessments and EU processing safeguards
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LEVEL 5
Expert
Set practices for managing data pipelines that support large-scale AI
Set the organization's model card standard for high-risk AI systems
Develop the organization's data strategy playbook covering governance, lineage and bias mitigation
Define joint accountability across data, security and compliance teams for GenAI partnerships
Set data protection requirements for AI vendors (encryption, access model, NIS2, exfiltration monitoring, incident response)
Advise leadership on how privacy regulation trades off against innovation and consumer outcomes
Set the organization's position on edge versus cloud AI by latency, scalability, privacy and connectivity requirements
Define which edge deployment types (on-device, gateway, edge server, hybrid edge-cloud) the organization supports and for which use cases
Choose the edge deployment approaches (on-device, local edge, cloud-trained edge, federated, cloud SaaS) for the enterprise portfolio based on training capacity, privacy, outage impact and latency
Set response-time targets for latency-sensitive AI use cases
Set hardware selection criteria for edge AI (TOPS, throughput, latency, memory bandwidth, TOPS/watt)
Set model compression standards (quantization, pruning, distillation, small open models) for edge deployments
Own the edge-versus-cloud total cost model (compute, egress, power and cooling, scale effects) for AI workloads
Set the cost optimization policy for edge fleets (hybrid workload placement, right-sizing, FinOps tracking, idle power-down)
Define the heterogeneous processor strategy (NPU, GPU, DSP, CPU) for edge AI workloads
Advise leadership on edge-versus-cloud trade-offs and the recommended deployment architecture
Own the edge AI risk posture for healthcare and critical infrastructure
Set edge security standards (hardened devices, zero trust, model versioning and rollback, EDR/SIEM monitoring, red-team exercises)
Decide when decentralized computing benefits the business across a global use-case portfolio
Set the enterprise architecture balancing cloud capacity and local processing for scale and resilience