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Build AI expertise in-house when the capability will be used repeatedly, depends on your organization’s context, or needs sustained internal ownership. Train employees for broad AI literacy and role-specific skills; hire when you need a continuing specialist capability; and use outside experts for a defined gap your team cannot currently cover. These routes can work together. There is no evidence-based universal rule that one is cheapest or fastest. Decide by identifying the work, the capability it requires, and the balance of urgency, accountability, continuity, and dependency that fits your organization.
Start with the capability gap—not the choice of vendor or course
“AI expertise” can mean very different things. An employee who needs to use an AI tool responsibly in an existing role does not need the same preparation as someone building, integrating, evaluating, or maintaining AI systems. Treating every gap as a need for a specialist risks overspending on narrow expertise; treating every gap as a training problem can leave critical work without an owner.
Separate the need into three levels:
- General AI literacy: people need to understand appropriate uses, limitations, and risks well enough to make informed decisions in their jobs.
- Applied role skill: employees need practice using AI in particular tasks and workflows, with guidance suited to their work.
- Specialist capability: the organization needs people able to develop, integrate, evaluate, govern, or maintain AI systems and related processes.
The OECD’s 2024 labor-market analysis distinguishes workers exposed to AI from specialist AI workers: many people who use AI are unlikely to need specialist AI skills—or even detailed knowledge of how AI systems function. OECD’s AI and the labour market analysis supports matching the level of expertise to the actual work, rather than assuming that exposure creates a specialist vacancy.
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How to choose between training, hiring, and outside expertise
Use these questions to make the decision. They are a practical framework, not a validated scoring model or cost formula.
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- What capability is missing? Is the need general literacy, a job-specific application, or specialist development and maintenance?
- How enduring is it? Will the work recur, and does it rely on institutional knowledge, sensitive context, or ongoing operational ownership?
- How urgent is the gap? Do you have people with a realistic foundation to learn, or is the work blocked until a specific skill is available?
- What accountability and control are required? Who will approve decisions, manage risks, and remain responsible for outcomes?
- Can knowledge be retained internally? If an outside party does the work, will your staff understand and be able to manage what is delivered?
- What can your organization support? Compare local compensation, provider costs, employee workload, and the capacity to recruit or train; the available evidence does not establish a generally cheapest or fastest route.
When training employees is the right route
Train current employees when the need is broad literacy or an applied skill that people can develop alongside their existing roles. This is especially appropriate when many teams need a shared baseline or when AI changes tasks within established jobs rather than creating a need for a dedicated specialist.
Make training match the work
Choose learning around actual tasks and provide opportunities to practice. A general introduction may help build awareness, but it does not by itself establish that staff can use AI appropriately in a particular workflow. Identify what employees should be able to do afterward, and assess learning against those tasks.
The U.S. Department of Labor’s AI Literacy Framework offers a flexible resource for workforce and education program designers. It identifies five foundational content areas and seven delivery principles, and allows adaptation to sectors, roles, and context. Those are framework components—not survey findings or a requirement for every employer. Read the Department of Labor AI Literacy Framework.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11In August 2025, the Department of Labor encouraged state and local workforce systems to use Workforce Innovation and Opportunity Act (WIOA) funding for AI skill development. This is U.S. public-workforce guidance, not a general employer mandate. See DOL’s Training and Employment Guidance Letter No. 05-25.
Know what training cannot establish on its own
A course is not proof that an organization has the expertise, capacity, or governance needed to build and maintain AI systems. The OECD has noted that current training supply may not be sufficient to meet growing needs for general AI literacy. That is a reason to assess whether your chosen learning path is adequate—not to assume that any one course will close the gap. OECD’s analysis of bridging the AI skills gap discusses both general literacy and advanced expertise.
Available sources do not quantify the return on investment for training at a particular employer or guarantee that training alone will meet a specific need. Check whether employees can practice, whether the skills transfer to their roles, and whether specialist responsibilities still require a separate owner.
When to hire an AI specialist
Hire when a defined specialist capability is continuing, important to the organization, and feasible to source. A permanent role can make sense when the work needs sustained internal ownership—for example, when staff must repeatedly develop, evaluate, integrate, or maintain systems in ways that depend on organizational context.
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Hiring is not simply a matter of writing a job description with “AI” in the title. The OECD’s guidance on public-sector AI capabilities points to targeted recruitment, skills-based hiring, competitive compensation, career paths, and workplace flexibility as relevant to recruitment and retention. OECD’s guidance on governing with artificial intelligence discusses these workforce levers. Apply them to a clearly defined capability: assess the skills the work requires and offer a credible path for the person to do it and grow.
Do not infer that every AI-exposed role requires an AI specialist. The OECD’s 2025 policy brief reports that one in three job vacancies in OECD economies had high AI exposure. That measure describes exposure, not the share of jobs requiring specialist AI skills. The OECD labour-market analysis provides the relevant distinction.
When to use an outside AI specialist or partner
Engage an outside specialist when a capability gap needs support your organization cannot currently provide, particularly when the work is bounded or the required expertise is not available internally. External help can add capacity and specialist knowledge without assuming that a long-term internal role is always necessary.
Before work begins, agree on who is responsible for decisions and outcomes, what the provider will deliver, how your organization’s data and governance requirements will be handled, and how staff will receive enough knowledge to manage the result. Without those arrangements, a short-term capacity solution can become a lasting dependence—or leave the organization unable to oversee work for which it remains accountable.
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OECD discussion of public institutions treats outsourcing as a common capability lever while emphasizing the need to balance it with accountability. It also notes that in-house capacity can help tailor systems to institutional needs, reduce information asymmetries during procurement, and avoid provider dependencies. These are trade-offs, not proof that external procurement is inherently harmful. OECD’s discussion of AI in the public sector explains the public-institution context behind those considerations.
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Why a combined approach can work
Training, hiring, and external support are not mutually exclusive. An organization can use an outside specialist for a defined need while training employees and developing internal ownership. For example, outside expertise may help with an initial project, while internal staff learn to oversee the work and handle ongoing responsibilities. This combination is a practical application of the three capability routes, not a prescribed formula.
Make the boundaries explicit: decide which work the provider owns, which decisions remain with your organization, what staff need to learn, and what capabilities must exist internally after the engagement. The right mix depends on how enduring and institution-specific the work is, how quickly capacity is needed, and how much control and accountability the organization must retain.
What workforce figures do—and do not—tell you
Two U.S.-specific survey findings reported by The Conference Board on July 28, 2026, offer context about training and use; they are descriptive results, not universal workforce census values or proof of what your organization should do:
- 55.1% of surveyed workers said they use generative AI or AI agents daily or weekly.
- 33.3% said they had used organization-provided AI training during the past six months, while 28.3% said their organization did not provide AI training at all.
These findings can help frame questions about access to training, but they do not establish that training is effective, that a particular role needs specialist skills, or that any route has a better return. Read The Conference Board’s July 28, 2026 release for its reported survey results.
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