As organizations increasingly adopt artificial intelligence, it’s not surprising that technology leaders identified AI and machine learning as top priorities for the year, according to a survey for Robert Half’s Building Future-Forward Tech Teams report. But prioritization doesn’t always equal preparedness.
Many companies fall short of supporting AI initiatives appropriately due to talent shortages and other foundational challenges, such as technical debt. That’s why assessing AI readiness and creating a focused plan to address needs and obstacles can be critical first steps.
Understanding AI readiness and its Challenges
AI readiness is an organization’s ability to adopt, implement and scale AI and related technologies in a way that delivers meaningful business value. For technology leaders, building that readiness means tackling a set of daunting—but familiar—challenges:
- Skills gaps: More than three-quarters (76%) of tech and IT managers are grappling with skills gaps in their departments, according to Robert Half research. The most common gaps cited were AI, machine learning and data science.
- Talent scarcity: While the vast majority (94%) of technology leaders said they’re hiring for new or vacated permanent roles in the second half of 2025, 89% reported the skilled talent they need is hard to find.
- Technical debt: Fifty-five percent of leaders identified tech debt as a major hurdle to achieving their strategic priorities. Outdated systems and insufficient technology upgrades can easily lead to innovative projects taking a back seat.
Combined with budget constraints and economic uncertainty, these issues can easily derail AI initiatives before they even gain traction. A structured approach to AI readiness can help technology leaders and their teams meet these challenges head-on.
The AI readiness Checklist: Powered by People
AI readiness is about much more than infrastructure—it’s about building the workforce to support it. The following four steps can help tech leaders prepare their teams, and the broader business, for AI.
- 1. Use a talent lens when evaluating technical infrastructure.
You’ll need to assess data, security and other IT resources to ensure AI applications are reliable, secure and scalable, and identify opportunities to upgrade infrastructure and tackle technical debt.
Pay particular attention to these four areas—along with the talent required to support them:
- Computing power: AI-enabled tools need significant computing resources, especially cloud services and high-performing hardware. Skilled cloud architects and engineers are essential to design, maintain and optimize this infrastructure.
- Data infrastructure: Modernized data lakes and warehouses support reliable storage and governance for AI. You’ll need data engineers and administrators to build and maintain data pipelines, as well as data scientists and analysts to turn information into actionable insights.
- Integration capabilities: Is your IT infrastructure equipped to connect AI systems with existing apps and databases? You also need a skilled team to manage those integrations. This may include solutions architects and data engineers who can align AI systems with existing platforms and keep deployments running smoothly.
- Security: AI projects often require broader access to sensitive data, which makes them a potential target for threats. Your AI readiness plan should include aligning security specialists, proven tools and clear protocols to protect systems and data from the start.
2. Target skills gaps—including outside of IT.
AI initiatives require technical expertise, but they also depend on a broader set of skills to ensure they support business goals and operations. To close gaps, consider upskilling current employees or bringing in contract talent where needed.
Foundational roles include AI architects, AI/ML analysts and data scientists—professionals who support AI initiatives across the data life cycle. Skills in machine learning techniques such as deep learning and natural language processing, statistical analysis, and coding languages like Python, R, C++ and Java are especially valuable.
AI success also hinges on collaboration. Project management and communication skills help teams coordinate across departments and keep implementations moving ahead efficiently. Professional development in these areas can strengthen cross-functional execution.
Finally, data literacy is essential—and not only for data teams. IT, operations, business leaders and other nontechnical roles that interact with AI systems should be able to interpret data and apply these tools responsibly.
- 3. Prepare a change management strategy.
Integrating AI into your everyday workflows can significantly impact how teams collaborate and how employees carry out their roles. An effective change management strategy includes:
- Leadership buy-in: Active support from senior leadership who can underscore the strategic importance of AI initiatives.
- Transparent communication: A clear strategy that outlines your AI initiatives, their purpose and expected changes.
- Team preparation: Educational resources and support so employees understand how AI will be used and what guidelines apply.
Also, consider using a phased approach to AI implementation. Begin with pilot projects, then scale solutions so employees have the space to adapt with confidence.
- 4. Adopt a flexible talent model.
The final step in your AI readiness checklist may be the most critical for overcoming workforce challenges that can put projects at risk. If your initiatives need additional skills or support, augment your permanent team with contract professionals and consultants who can provide specialized AI expertise.
This staffing approach frees your core staff to focus on priority goals while interim professionals handle other tasks. Flexible staffing is also on the rise: In a Robert Half survey, 65% of technology leaders said they plan to increase their use of contract talent in the second half of 2025, citing access to specialized skills as a primary benefit.
Building a Foundation for AI Success
The path to becoming AI-ready isn’t simple or easy, but with a thoughtful, structured approach—addressing infrastructure, security, skills, change management and staffing—tech leaders can build a solid foundation for AI success. Just be sure to review and revise your AI readiness plan regularly, as skills needs are changing as fast as the technology itself.
GTIA Members: Download the Data & AI Guidebook: A Board-Level Blueprint for AI Success on the Member Portal.
Ryan Sutton is the executive director, technology, at Robert Half.
