By Ryan M. Sutton, Executive Director, Technology, Robert Half
AI, machine learning and automation initiatives are among technology leaders’ top priorities this year, according to research for Robert Half’s 2025 Building Future-Forward Tech Teams report. Even so, that doesn’t mean their organizations are ready to fully support and derive the most value from these emerging technologies. They face many hurdles, including the need to address critical skills gaps.
In this article, we’ll examine how tech leaders can help their organizations address those challenges head-on and build a foundation for AI success using a five-step AI readiness checklist as their guide. First, let’s take a closer look at the concept of becoming “AI ready” and the general steps to get there.
What does AI readiness mean?
AI readiness refers to an organization’s ability to successfully adopt, implement and scale artificial intelligence technologies in a way that delivers meaningful business value. But it’s not just about having the right tools or platforms in place. True AI readiness depends on a strong technical foundation, the right mix of skills and talent, robust data management practices, and a clear plan for change.
AI readiness also depends on strong alignment between leadership priorities and the organization’s culture. To build momentum, companies must adapt workflows, invest in employee training and foster trust in AI-driven processes. With these foundations in place, companies can move beyond experimentation and start delivering real business value with AI.
What is the AI readiness process?
The AI readiness process is the structured approach an organization takes to prepare effectively for AI adoption. The general steps in this journey include assessing IT infrastructure, prioritizing security, identifying skills gaps, planning for change management and building a flexible talent strategy. Taken together, these five steps can help tech leaders align people, processes and technology so the organization can move from pilot projects to real problem solving and innovation with AI.
Potential AI readiness blockers: Skills gaps, a talent shortage and technical debt
In a survey conducted for our Building Future-Forward Tech Teams report, 76% of tech leaders at organizations of all sizes said they are grappling with a skills gap in their department—and the area where gaps are most evident is AI, machine learning and data science. Nearly half (45%) said they have already seen staffing challenges negatively impact their projects.
Finding skilled tech and IT professionals available for hire is another challenge for today’s tech leaders. Most technology managers (94%) surveyed for Robert Half’s latest Demand for Skilled Talent report said they are hiring for new or vacated permanent roles in the second half of 2025. However, 89% said they are having difficulty locating candidates with the requisite skills in the current labor market.
Meanwhile, many organizations are struggling with another complex and costly issue working against their efforts to adopt AI successfully: technical debt. According to Building Future-Forward Tech Teams, more than half of tech leaders (55%) see technical debt as a major obstacle to achieving their strategic priorities this year.
The technical debt burden, combined with other IT budget constraints, ongoing economic uncertainty and the shortage of skilled tech talent often spurs businesses to put innovative projects, like AI, machine learning and automation projects, on hold. But given how quickly AI is advancing and becoming a part of everyday workflows, pushing these initiatives to the back burner isn’t sustainable for companies that want to stay competitive in a landscape shaped by emerging technologies.
See our quick guide to choosing the right resources to tackle technical debt.
Understanding your current state: An AI readiness checklist to guide your next steps
Now that we’ve covered, at a high level, some of the challenges and concerns businesses face as they seek to step up their use of artificial intelligence, machine learning and other emerging technologies, let’s examine how tech leaders can help assess and bolster their organization’s AI readiness so more projects can get off the ground—and deliver the expected ROI.
The path to becoming AI ready is not simple or straightforward, but this practical AI readiness checklist can help guide your efforts.
1. Evaluate the state of technical infrastructure
IT infrastructure forms the backbone of any AI initiative. To leverage AI effectively, tech leaders must help their organizations evaluate and upgrade their existing IT infrastructure, including finding opportunities to let go of burdensome technical debt and maximize the use of existing IT investments.
Among the many things you’ll want to consider is the status of your organization’s:
Computing power
AI applications, particularly those that involve deep learning, require substantial computing resources. You may need access to an ample supply of high-performance GPUs and TPUs to achieve your AI goals. Effective and scalable cloud computing services are also very important, as well as cloud architects and cloud engineers to design, manage and optimize cloud platforms.
Data storage and management capabilities
AI systems depend on modern, scalable data infrastructure—including data lakes, warehouses, data catalogs and integration tools—that make information accessible for processing. High-quality, well-labeled data is critical to AI performance, which is why data modernization and strong data governance practices are essential.
To support effective data management and governance, organizations need skilled professionals in place, such as data engineers to build and maintain data pipelines and data analysts to extract meaningful insights from raw information.
Networking
High-speed, reliable network connections are essential for efficient data transfer between applications and servers and for real-time processing. Evaluate your network’s capacity to handle increased traffic generated by data-intensive AI apps.
Integration capabilities
AI systems often need to integrate with existing business applications and databases. Make sure that your IT infrastructure supports seamless integration through APIs and other connectivity solutions.
2. Make security a priority, not an afterthought
AI systems can introduce various security risks and addressing them proactively is crucial to protecting data, users and the business. Some critical areas for your organization to consider as it implements or expands its use of AI technology include:
Data privacy: Implement robust data governance policies to protect sensitive information. Confirm that the business is compliant with relevant data protection regulations, such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA).
System security: Safeguard AI systems against cyberthreats. Consider advanced security measures like encryption and access controls and conduct regular security audits.
AI model security: AI models can be vulnerable to attacks like model inversion (an attempt to reverse-engineer or reconstruct input data) and adversarial machine learning (techniques used to intentionally deceive or manipulate AI systems). Implement strategies to help protect your AI models from exploitation.
Incident response: Develop a comprehensive incident response plan to address potential security breaches involving AI systems. But don’t “set and forget it” this plan—you’ll need to test and update it regularly.
3. Identify technical and nontechnical skills gaps
AI initiatives need to be supported by teams with diverse skill sets, both technical and nontechnical. For example, as AI becomes more embedded in business operations, data literacy is emerging as a core capability—not just for data teams, but across functions. IT, operations and business leaders all benefit from a working understanding of how data is collected, cleaned, governed and applied in AI workflows.
Cross-functional skills can be incredibly valuable for AI projects, which often require collaboration among different departments. Encouraging your team members to develop skills in project management, communication and cross-disciplinary collaboration can help facilitate smoother AI implementations.
As for technical abilities, look for professionals who have:
Proficiency in coding languages foundational to AI development such as Python, R, Java and C++
The ability to perform statistical analysis and derive insights to assess AI model features, conduct hypothesis testing, and interpret model results using tools like NumPy and statistical modeling techniques
Experience in designing and applying algorithms that allow computers to learn and make predictions from data, and knowledge of techniques such as regression, classification, clustering, deep learning, reinforcement learning, and natural language processing (NLP) and generation
Another tip: When you’re addressing critical skills gaps in your organization, be sure to prioritize roles that support AI initiatives across the data life cycle, including:
AI architects, who design, deploy and maintain secure and stable AI solutions using leading AI technology frameworks
AI/ML analysts, who develop, implement and manage models that address complex business problems and enhance decision-making processes
Data scientists, who develop and refine AI models, apply advanced analytics and help improve performance of information systems and databases
See Robert Half’s latest Salary Guide for compensation trends related to these and many other in-demand technology roles.
4. Create a comprehensive change management strategy
Implementing AI can significantly alter workflows and job roles. Not all employees—even in the IT department—will be keen to embrace the change that AI brings. That’s why effective change management strategies are essential to help allow for a smoother transition. You’ll want to:
Gain buy-in from senior leadership: Company leaders need to actively champion AI initiatives and underscore their strategic importance.
Promote transparency in communication: Develop a communication plan that explains the purpose of AI projects, changes to expect and benefits to the business.
Introduce AI to your team: Ensure team members are aware of AI usage guidelines and have access to support and learning resources.
Take a phased approach: Start with pilot projects before scaling across the business.
Project managers and business analysts with change management skills and certifications are in high demand for digital transformation projects. Learn more in Building Future-Forward Tech Teams.
5. Embrace an adaptive and flexible talent model for tech and IT
The last step on this AI readiness checklist can be crucial to aligning the tech and IT talent—from data scientists to machine learning engineers—your business needs to support its AI, machine learning and automation projects and other technology priorities this year.
An adaptive and flexible talent model involves supplementing your permanent staff with contract professionals and consultants and tapping third-party resources for support and expertise for as long as your business needs them. With this approach, you can access in-demand skill sets, keep projects moving forward and offload work from core employees so they can stay focused on what they do best.
This model has become a go-to staffing strategy for many leading employers. Robert Half’s research found that 65% of technology leaders at organizations across industries are increasing their use of interim or contract professionals in the second half of 2025. They cited access to specialized skills or experience as one of the top benefits of engaging this talent.
Creating an AI readiness plan requires a multifaceted approach built on thoughtful strategies that address IT infrastructure, tech skills gaps, security risks, change management and adaptive, flexible staffing models. With the above AI readiness checklist in hand, you can help your organization build a solid foundation for AI adoption and move more confidently toward a future of data-driven and AI-enabled innovation and growth.
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