The most in-demand AI skills

Top AI skills CIOs need on their teams

The skills below are organized into five categories. Each entry covers what the skill is and why it matters, followed by its representative tools and frameworks.

1. Technical foundations

AI platform and cloud architecture

This skill involves designing secure, scalable compute and deployment for AI workloads, including GPU scheduling, multi-tenant controls and identity management.

Tools and frameworks: AWS, Azure, Google Cloud, Kubernetes, OpenShift, Nvidia GPU stack, identity access management (IAM), Microsoft Entra ID and HashiCorp Vault.

AI-ready data engineering

This skill involves curating and operating governed data pipelines for training and inference, including lineage, access control and drift-aware data to feed into production systems

Tools and frameworks: Databricks, Snowflake, Apache Airflow, Apache Kafka, open source data build tool and data observability tools.

Agentic RAG and Knowledge Retrieval Architecture

The original retrieval-augmented generation model -- convert query to vector, retrieve top chunks and inject into prompt -- is being superseded by advanced RAG patterns.

An agentic RAG turns the large language model (LLM) into a reasoning engine that decides its own retrieval strategy and iterates toward a reliable answer.

Frameworks like GraphRAG can augment retrieval with semantic knowledge graphs to produce traceable, auditable reasoning.

Tools and frameworks: Managed vector stores -- including Pinecone, Weaviate and open source pgvector -- embedding services, LlamaIndex, GraphRAG frameworks and evaluation harnesses.

API orchestration, integration architecture and agent protocols

The Model Context Protocol (MCP) connects LLMs and AI agents to enterprise systems. It reduces agent-to-system integration from bespoke API engineering to configuration.

Alongside MCP, the Agent2Agent (A2A) protocol is emerging as a standard for multi-agent coordination.

Overall, this skill involves designing and securing agentic architectures, managing permissions and entitlement risks, prompt injection and tool impersonation.

Tools and frameworks: MCP, A2A, MuleSoft, Google Cloud's Apigee, Kong, Camunda, Temporal and Apache Kafka.

2. AI development

Applied ML engineering

This skill involves implementing production machine learning (ML) systems with software engineering practices, including feature engineering, model training, inference integration and performance optimization. It also requires knowledge of LLM, natural language processing and deep learning frameworks.

Tools and frameworks: PyTorch, TensorFlow, the Hugging Face ecosystem, feature stores and model serving runtimes.

Agentic AI engineering and multi-agent orchestration

The engineering focus has shifted from prompt crafting to orchestration design -- or how agents hand off work, coordinate and escalate to human oversight. This is emerging as a key development skill.

Other skills involve building LLM-based systems with tools, memory, retrieval and policies, and designing agent workflows with controllable autonomy, tool calling and fail-safe semantics.

Tools and frameworks: LangGraph, LlamaIndex, Microsoft's Semantic Kernel, CrewAI, Microsoft's AutoGen, Salesforce agent platforms, Microsoft Copilot tooling, ServiceNow agent tooling and guardrails frameworks.

Context engineering

Context engineering addresses what information to give agents, not just what to ask. It is the structured design of information fed to agents across the full interaction lifecycle, including memory management, tool selection and dynamic context injection.

In multi-agent systems, context windows fill quickly with tool definitions, conversation history and data from multiple sources, also known as context rot. Context engineering has risen above prompt engineering as a differentiator in multi-agent systems.

Tools and frameworks: MCP server design, prompt versioning tools, memory management frameworks and token optimization libraries.

Fine-tuning and model adaptation

Most organizations expect to customize agents for their specific business needs, meaning out-of-the-box assembly is insufficient for most enterprise use cases.

This skills area involves adapting foundation models to enterprise domains using parameter-efficient fine-tuning, retrieval adaptation and evaluation-based iteration.

Tools and frameworks: Hugging Face PEFT/LoRA, managed fine-tuning services -- like Azure, AWS and Google -- and evaluation harnesses.

Prompt engineering

This skill involves designing prompts and interaction patterns that reliably produce desired behaviors and constrain failure modes.

Prompt engineering has value in structured prompting and evaluation, but it is becoming table stakes rapidly. It is not a substitute for retrieval accuracy, integration or monitoring. Broad upskilling is more appropriate than specialist hiring for most organizations.

Tools and frameworks: Prompt/version management tools, structured prompting frameworks, prompt evaluation suites and safety filters.

3. AI-native software engineering

AI-assisted and agentic software development

AI-generated code is a growing part of coding. The engineering role is shifting from writing code to orchestrating and validating.

However, AI co-authored code can contain more issues than human-written code. Organizations that deploy AI coding tools without structured review processes can accumulate technical debt and security vulnerabilities.

This skill area involves systems architecture thinking over syntax; specification and goal decomposition for coding agents; AI output review and validation; and context engineering for code agents -- injecting design patterns, security constraints and architectural guidelines into the AI's working context.

Tools and frameworks: Claude Code, GitHub Copilot, OpenAI Codex, Cursor, Qodo, CodeRabbit AI code review and open source, spec-driven development frameworks, like SpecKit or OpenSpec.

4. Operational and risk-related skills

LLMOps and MLOps

These skill areas involve continuous integration/continuous delivery for models and prompts, reproducibility, deployment automation, rollbacks and governance gates for LLM and agent releases. These practices bridge development and reliable production operation.

Tools and frameworks: MLflow, Kubeflow, managed ML platforms -- like Amazon's SageMaker, Google's Vertex AI and Microsoft's Azure ML -- model registries, canary and A/B deployment infrastructure.

AI observability, AIOps and cost management

These skill areas involve monitoring model outputs and system behavior across quality, drift, toxicity, latency, cost per inference and tool-call errors. Token budgeting -- which means managing aggregate token spend across an agent portfolio -- and LLM routing, or selecting the lowest-cost model that meets quality thresholds per request type, map directly to CFO-level scrutiny of AI investment returns.

Tools and frameworks: OpenTelemetry, Datadog, Dynatrace, New Relic, AI-specific observability platforms, LLM cost dashboards and LLM routing frameworks.

AI output validation, testing and artifact QA

This skill area involves extending software QA to probabilistic systems with golden test sets, scenario-based coverage, regression tests for prompts and retrieval pipelines and acceptance criteria tied to business KPIs.

Tools and frameworks: LLM evaluation harnesses -- like TruLens and open source Ragas, golden set management tools, adversarial prompt suites, contract test frameworks and synthetic test generation.

AI security engineering

Privacy and security are the top-cited enterprise AI risks.

This skill involves securing AI systems across data, model and integration surfaces, including prompt injection, jailbreaks, data exfiltration using retrieval, MCP over-permissioning and privilege controls for agents.

Tools and frameworks: AI threat modeling frameworks, secure retrieval patterns, data loss prevention tools, secrets management -- including HashiCorp Vault and AWS Secrets Manager -- runtime policy enforcement and adversarial testing pipelines.

AI governance and compliance operations

Regulatory requirements are now being operationalized across U.S., EU and UK markets. Governance maturity strongly influences AI scaling.

This skill area involves establishing policy, accountability, inventory, lifecycle controls and audit-ready documentation for AI assets, including agents.

Tools and frameworks: AI inventory and policy-as-code tools, AI governance platforms, governance, risk, and compliance integration, approval workflows and the EU AI Act compliance tooling.

Model risk management, validation and explainability

This skill area is critical in regulated sectors but is also expanding across industries as regulatory requirements increase and boards demand evidence-based oversight of AI systems.

It involves the independent validation of model behavior, bias, thoroughness and compliance with documented risk appetite to produce defensible explainability artifacts for audit and regulatory sign-off.

Tools and frameworks: Model validation frameworks, bias testing tools, explainability toolkits -- like SHAP and LIME -- documentation automation and model cards.

5. Business and strategic skills

Human plus AI workflow design and change leadership

The operating model is shifting from humans alone to humans plus AI, where human experts steer, validate and own outcomes, while AI handles execution and generation.

The change management skills required to implement this at scale are typically underestimated. They include redesigning processes so AI shifts task allocation without collapsing accountability and managing the adoption, training and role redesign that follows.

Tools and frameworks: Process mining tools, workflow automation platforms, change metrics dashboard and training platforms.

AI product management and portfolio prioritization

This skill area involves translating business problems into AI products with clear KPIs, operating constraints and adoption plans. It also involves governing a portfolio to prevent fragmented point tools and shadow AI activity.

Tools and frameworks: Product analytics platforms, experimentation and A/B testing tools, portfolio governance workflows and value tracking dashboards.

Where to invest in people vs. outsourcing

CIOs can base the hire vs. upskill vs. outsource decision on the following points:

• Strategic: Does this skill create differentiation, or failure that leads to regulatory exposure?
• Integration: Is this capability embedded across many workflows?
• Risk: Does failure create material harm?

Skills that score high on all three -- such as agentic RAG, LLMOps, AI output validation, governance and security -- should be priority areas for in-house expertise.

The table below applies this logic across different skills.

Overall, context shapes the final decision. For example, a financial institution building proprietary risk models must internalize model validation, evaluation and governance because audit evidence and risk appetite alignment are core business requirements. In this case, outsourcing without strong internal control is a risky proposition.

On the other hand, a retail firm using vendor LLM APIs for customer service can outsource more engineering, but must keep retrieval governance, output validation, monitoring and QA internal to prevent data leakage and compliance failures.

How to develop and retain AI skills

Organizations want repeatable delivery, measurable reliability and assured compliance across the enterprise AI portfolio. The key foundation for that is retained talent.

The following steps can help IT leaders build a better culture of retention in their organizations.

1. Conduct a skills inventory

Start with a role-based inventory mapped to delivery stages across the AI stack: data, retrieval, orchestration, operations, assurance and governance. Map current staff competencies to each stage to identify gaps, adjacencies and realistic upskilling pathways.

2. Map skills to the AI roadmap

Connect the inventory to the enterprise AI delivery pipeline. For most enterprises, the capabilities constraining the next wave of deployment are agentic RAG quality, multi-agent orchestration, AI output validation and governance operationalization.

3. Launch targeted upskilling programs

Internal mobility is simultaneously a productivity lever and a retention mechanism. Focus on adjacent skill transitions with a clear path, including the following:

• DevOps/SRE engineers into LLMOps and AIOps.
• Software QA into AI output validation and test engineering.
• Application security into AI red-teaming and MCP security.
• Audit and compliance staff into AI governance operations.
• Software engineers into agentic coding workflows and AI output review.

4. Partner with vendors strategically

Use vendors for accelerators, platforms and initial setup, but specify internal control points in any engagement. These include the following:

• Evaluation artifacts.
• Audit evidence documentation.
• Incident response responsibilities.
• Data usage mapping.

Regulations coming into force across the U.S., EU and UK markets make audit-ready evidence a compliance requirement. Where vendor dependency exists, internal staff must be able to evaluate, challenge and override vendor outputs.

5. Track operating metrics closely

IT leaders should track metrics and KPIs related to delivery, cost and risk. These metrics highlight any gaps between AI goals and available skills and inform skilling investment decisions and priorities.

These include the following:

• Delivery velocity: Time-to-deploy and time from prototype to production.
• Reliability and cost: Model reliability rate, latency SLOs, cost per inference, token budget adherence, tool-call failure rates and LLM routing efficiency.
• Risk and compliance: Evaluation pass rates, red-team findings closure time, AI-generated code vulnerability rates, compliance incidents and audit evidence completeness.

To retain AI talent, establish clear AI career tracks, such as AI platform engineering, agentic application engineering, AI assurance -- like evaluation, QA or red-teaming -- AI governance and AI security. Talent retention follows visible skill progression and meaningful work.

Also, set AI proficiency expectations across all engineering roles, not just dedicated AI teams. Organizations that build AI as a broad workforce capability can compound their talent advantage over time.

Kashyap Kompella, founder of RPA2AI Research, is an AI industry analyst and advisor to leading companies across the U.S., Europe and the Asia-Pacific region. Kashyap is the co-author of three books, Practical Artificial Intelligence, Artificial Intelligence for Lawyers and AI Governance and Regulation.