AI Talent Strategy: How to Build the Skills Your Organization Needs

As organizations move from experimenting with AI to applying it in everyday work, AI strategy is becoming a workforce strategy, too. 

For HR leaders, hiring managers, CIOs, and CTOs, the question is no longer simply which AI tools to adopt. It is also what people will need to do differently, which skills already exist within the organization, and where new expertise will be required. 

That makes AI talent strategy much broader than recruiting AI specialists. 

From my perspective working with technology leaders and hiring teams, the better place to start is with the business need. What are you trying to accomplish with AI? What capabilities will that require? Which already exist within your workforce, and what is the best way to close the gaps? 

I think of it as:

That workforce decision might involve developing existing employees, hiring permanent talent, bringing in specialized contract expertise, working with an outside technology partner, or combining several approaches. 

What AI Skills Does Your Organization Actually Need? 

It is easy to talk about “AI skills” as though they represent one category of expertise. In practice, the level of capability an organization needs depends on what it is trying to accomplish. 

An employee using AI to improve research or routine tasks does not need the same expertise as an engineer integrating AI into a customer-facing application. That engineer may not need the same depth as a machine learning specialist developing or evaluating models. 

For workforce planning, it can help to think about AI capability at three levels. 

AI Fluency 

Many employees will need enough understanding to use AI effectively within their existing roles. That can include knowing how to interact with AI tools, validate outputs, protect sensitive information, recognize limitations, and understand when human review is necessary. 

The goal is not to turn every employee into an AI expert. It is to make sure people using AI have the judgment to use it responsibly and effectively. 

Applied AI Capability 

Technical employees incorporating AI into products and workflows need deeper capabilities. Depending on the initiative, those might include model and API integration, AI-assisted software development, data preparation, workflow automation, testing, monitoring, and security. 

These employees are often responsible for connecting AI technology to an existing business or technical environment. 

Specialized AI Expertise 

Some initiatives require deeper expertise in areas such as machine learning, data science, data engineering, AI architecture, model evaluation, retrieval-augmented generation, or AI application development. 

Distinguishing among these levels can prevent employers from turning every AI-related need into a search for highly specialized talent. 

Where Are the AI Skills Gaps in Your Current Workforce? 

Before opening new positions, employers should understand what capabilities already exist internally. 

That matters because demand for AI skills is appearing across existing occupations, not only in newly created AI roles. Federal Reserve Bank of Atlanta research found that nearly 628,000 U.S. job postings in 2024 requested at least one AI skill, and the share of postings requiring AI skills has risen substantially since 2010. 

For workforce planning, the implication is important: AI talent strategy is not only about adding new positions. It is also about understanding how existing roles and skill requirements are changing. 

HR and technology leaders can start by asking: 

• Where are employees already using AI? 
• Which teams have relevant technical or data foundations? 
• Which capabilities can realistically be developed internally? 
• Where would a lack of specialized expertise slow an important initiative? 
• Which capabilities will be needed beyond the initial project? 

A skills gap matters in relation to something the business needs to accomplish. Connecting workforce analysis to specific AI priorities helps employers avoid hiring for skills they may not actually need. 

Should You Upskill Employees or Hire AI Talent? 

This is one of the most important AI workforce decisions, but it should not be treated as an either-or question. 

The World Economic Forum’s Future of Jobs Report 2025, based on a survey of more than 1,000 employers globally, found that two-thirds planned to hire talent with specific AI skills. The same research found significant expected demand for reskilling and upskilling as workplace skills change. 

For many organizations, the right AI talent strategy will combine internal development with external talent. 

Upskill When the Foundation Already Exists 

Existing employees may already understand your customers, systems, data, workflows, and industry. When they also have the right technical or functional foundation, developing their AI skills can extend expertise the organization already values. 

The practical question is whether the required capability can be developed to the necessary level within the timeframe the business needs. 

Hire When the Capability Is Genuinely Missing 

External hiring makes sense when the organization needs expertise that does not exist internally or would take too long to develop. This can be especially important for specialized or senior roles where practical implementation experience matters. 

At the same time, employers should avoid turning that need into an unnecessarily long list of AI tools and platforms. AI technology is evolving quickly, so judgment, technical foundations, and adaptability may be more durable indicators than familiarity with every current tool. 

Consider Flexible Talent for Specialized or Changing Needs 

Not every capability gap requires a permanent position. 

Contract talent or an outside technology partner can be useful when specialized expertise is needed for a defined initiative, the long-term staffing requirement is unclear, or an internal team needs experienced support to move from planning to implementation. 

The capability need should drive the workforce model, not the other way around. 

How Should Employers Evaluate AI Talent? 

Evaluating AI talent creates a familiar hiring challenge in a fast-moving environment: distinguishing familiarity with current tools from the ability to solve meaningful problems. 

A candidate’s experience with a particular model, platform, or framework provides useful context, but it should not become the entire evaluation. 

Depending on the role, hiring teams should look for evidence that someone can: 

• Identify when AI is appropriate for a problem 
• Explain why they chose a particular approach 
• Evaluate limitations and validate AI-generated output 
• Work effectively with data 
• Consider security, privacy, reliability, and maintainability 
• Connect technical decisions to business outcomes 
• Adapt as tools and models change 

This emphasis on judgment is particularly important as AI becomes more integrated into software development. Stack Overflow’s 2025 Developer Survey found that 84% of respondents were using or planning to use AI tools in their development process, yet more developers distrusted the accuracy of AI output than trusted it. 

The strongest signal is not necessarily who has experimented with the most tools. It is whether someone has the underlying knowledge to determine when AI can create value, evaluate what it produces, and recognize when human judgment is needed. 

How Is AI Changing Candidate Behavior and the Hiring Process? 

AI is not only changing the skills employers need. It is also changing how candidates approach the hiring process. 

Candidates can now use AI to research employers, tailor application materials, prepare for interviews, and communicate their experience more effectively. As these tools become more common, resumes and other application materials may become more polished, but that polish does not necessarily tell employers more about a candidate’s underlying capabilities. 

For hiring teams, the practical implication is that application materials should be treated as a starting point rather than proof of capability. 

Employers can respond by: 

• Using structured interviews tied to the capabilities required for the role 
• Asking candidates to explain the experiences, decisions, and outcomes described in their applications 
• Designing assessments around realistic work and problem-solving 
• Looking for consistency across application materials, interviews, and assessments 
• Establishing clear expectations for when AI use is appropriate during the hiring process 

The goal is not to determine whether a candidate used AI to prepare. AI is becoming a normal part of how people work, and using it does not automatically make an application less authentic. 

What matters is whether the candidate can substantiate their experience, explain their reasoning, and demonstrate the capabilities required for the role. As AI makes it easier to create polished content, hiring processes need to become better at uncovering the knowledge and judgment behind it. 

How Should AI Change Workforce Planning? 

One shift I’ve noticed is that AI conversations often begin with technology selection and reach workforce implications later. Organizations may be better served when those conversations happen together. 

AI can affect which capabilities teams need, how existing roles are performed, where specialist expertise is necessary, and which skills should be developed internally. The World Economic Forum identifies AI and big data among the fastest-growing skills through 2030, while also highlighting analytical thinking, resilience, leadership, and other human capabilities as important workforce skills. 

For HR leaders, that is an important distinction. AI workforce planning should not focus exclusively on technical expertise. 

Technology leaders can help define where AI could create value and what implementation requires. HR leaders bring perspective on workforce design, recruiting, development, role definition, and talent models. 

Together, they can determine how AI changes the work before deciding how it changes the workforce. 

A Practical Framework for AI Talent Strategy 

Organizations do not need to predict every AI skill they will need several years from now. They do need a repeatable way to make workforce decisions as priorities evolve. 

For example, an organization planning to automate a document-review workflow may need employees with AI fluency, engineers who can integrate an AI service, and specialized expertise to evaluate security, accuracy, and data requirements. The organization can then determine which capabilities already exist and whether the remaining gaps should be addressed through training, hiring, contract talent, or an outside partner. 

1. Start With the Business Need

Identify the outcome first. Are you trying to automate a workflow, improve decision-making, increase developer productivity, or build an AI-enabled product? 

Starting with the problem prevents AI itself from becoming the strategy. 

2. Define the Required Capabilities

Determine what people need to be able to do to achieve that outcome. Be specific about the depth of AI expertise required rather than defaulting to broad AI credentials or job titles. 

3. Assess What You Already Have

Look for relevant technical foundations, domain knowledge, existing AI experience, and employees who could realistically develop the required skills. 

4. Decide How to Close the Gaps

For each meaningful gap, determine whether the best response is to upskill, hire, use flexible talent, work with an outside partner, or combine approaches. 

5. Revisit the Plan

AI workforce planning cannot be a one-time exercise. Technologies, available skills, and business priorities will continue to change, so the talent strategy needs enough flexibility to change with them. 

Building an AI-Ready Workforce 

If there is one idea to carry into your next AI workforce discussion, it is this: do not begin by asking which AI roles you need to hire. Begin by asking what the organization needs to accomplish. 

From there, define the capabilities required, determine what already exists internally, identify the meaningful gaps, and choose the most practical way to close them. That may mean developing existing employees, hiring permanent talent, using flexible expertise, working with an outside partner, or combining approaches. 

The goal is not to build the largest AI team or hire for every emerging skill. It is to build the right combination of capabilities for the organization’s priorities. 

From my perspective, that requires technology strategy and talent strategy to develop together. When HR, hiring managers, and technology leaders start with the business need rather than a predetermined job title or workforce model, they are better positioned to make thoughtful decisions about where and how to invest in talent. 

At SOLTECH, we work with organizations as they define technology initiatives and build the teams needed to support them. Whatever the workforce model, getting clear about the capabilities required for success is the best place to begin. 

FAQs 

Which roles should be involved in developing an AI talent strategy? 

AI talent strategy should involve both technology and people leaders, with participation shaped by the initiative. CIOs, CTOs, engineering or data leaders can define technical needs, while HR and talent leaders can bring workforce planning, recruiting, development, and organizational perspectives. Business leaders may also need to participate when AI will significantly change a function or workflow. 

How often should companies reassess their AI skills needs? 

AI skills needs should be reviewed regularly rather than treated as part of an annual planning exercise alone. A reassessment may be especially useful when the organization introduces a new AI initiative, changes technology platforms, moves from experimentation to production, or discovers that existing roles are changing more quickly than expected. 

Do companies need dedicated AI roles to adopt AI successfully? 

Not necessarily. Some organizations will need specialized AI engineers, machine learning professionals, or data experts, while others may be able to incorporate AI into existing technology and business roles. The right structure depends on the complexity of the initiative, the capabilities already available internally, and how central AI will be to the organization’s products or operations. 

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Linda Wiesen

Director of Client Success

Linda Wiesen is the Director of Client Success at SOLTECH and has more than nine years of experience delivering successful client outcomes, leading complex technology initiatives, and building strong client partnerships. Since joining SOLTECH in 2017, Linda has held leadership roles in project delivery and client success, helping organizations achieve their business goals through strategic technology solutions. She holds an MBA and a Bachelor of Science in Business Administration from Robert Morris University.

As Director of Client Success, Linda leads SOLTECH’s Talent Acquisition and Account Management teams while continuing to foster strong client relationships. She works closely with clients and candidates to identify, evaluate, and place highly skilled technology professionals for both client engagements and internal opportunities. Linda is passionate about creating positive experiences for candidates and clients alike while helping organizations build high-performing teams.

Drawing on her experience leading client success initiatives and working closely with technology professionals, Linda shares insights on tech hiring trends, career development, interview preparation, candidate best practices, and what it takes to succeed in today’s competitive technology job market.