How Tech Leaders Can Create an AI Readiness Plan: A Checklist for Success

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By Ryan M. Sutton, Executive Director, Technology, Robert Half

AI, machine learning and automation initiatives are among technology leaders’ top priorities in 2024, according to research conducted for Robert Half’s e-book, Building Future-Forward Tech Teams. Even so, that doesn’t mean their organizations are ready to fully support and derive the most value from AI. They face an array of challenges, primarily in two areas: talent and technology.

This post examines how tech leaders can help their organizations address those challenges head-on and build a foundation for AI success, using a five-step checklist as their guide to assess AI readiness. First, let’s take a closer look at the dynamics that typically hinder a business’s ability to make progress toward its AI goals — and achieve related tech and IT priorities — as quickly as it would like.

Research conducted for Robert Half’s e-book, Building Future-Forward Tech Teams, found that AI, machine learning and automation initiatives are among technology leaders’ top priorities in 2024. At the same time, 65% of tech leaders at organizations of all sizes are grappling with a skills gap in their department — and a top area where gaps are most evident is AI and machine learning.

Finding skilled tech and IT professionals available for hire is another challenge for today’s tech leaders. While nearly all technology managers (97%) surveyed for Robert Half’s Demand for Skilled Talent report said they are hiring for new or vacated roles, a nearly equal percentage (90%) are having difficulty locating candidates with the requisite skills.

Meanwhile, many organizations are facing a persistent obstacle to AI project success: technical debt. Research from global consulting firm Protiviti, a Robert Half subsidiary, found that this costly and complex issue is a drag on innovation for about seven in 10 companies.

The technical debt burden, combined with other IT budget constraints, ongoing economic uncertainty and the aforementioned shortage of skilled tech talent often spurs businesses to put future-forward initiatives on hold. That includes AI, machine learning and automation projects, which are often categorized by budget allocators in the business as “experimental” (and thus, expendable).

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 and relevant.

Business leaders are clearly concerned about their organizations getting left behind over the next decade, too. In Protiviti’s latest Top Risks survey, directors and executives cited the “rapid speed of disruptive innovations enabled by new and emerging technologies and/or other market forces” among their top five risks for 2034. They also pointed to talent challenges as the second-most significant risk for their business this year and looking out to 2034.

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 AI, let’s look at how tech leaders can help assess and bolster their organization’s AI readiness so more projects can get off the ground — and be successful.

There isn’t a quick or simple path to follow, but the five steps on this checklist can serve as a useful guide.

IT infrastructure forms the backbone of any AI initiative. To leverage AI effectively, tech leaders must help their organizations evaluate and, if needed, upgrade their existing IT infrastructure.

This isn’t just about implementing new infrastructure, though. It includes finding opportunities to let go of burdensome technical debt and maximizing the use of existing IT investments.

Among the many things you will want to consider as part of your evaluation are 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 (Graphics Processing Units), TPUs (Tensor Processing Units) and cloud computing services to achieve your AI goals.

Data storage and management capabilities: AI systems generate and process vast amounts of data and require modern, scalable storage solutions that make the data easy to access for processing and analysis. High-quality data is critical to any AI project, which is why robust data management practices are also a must.

Networking: High-speed, reliable network connections are essential for efficient data transfer between apps 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.

AI systems can introduce various security risks for businesses, 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 European Union’s General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA) in the U.S.

System security

AI systems should be safeguarded against cyber threats. Consider employing advanced security measures like encryption and access controls where needed and conducting regular security audits.

AI model security

AI models can be vulnerable to attacks like model inversion and adversarial machine learning. 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. Regularly test and update this plan to keep it effective.

In a Robert Half survey, 55% of technology leaders said they have shifted their focus to hiring different skills that are more in demand due to advancements in AI and automation. But don’t assume they’re just on the lookout for technical abilities. AI initiatives need to be supported by teams with diverse skill sets — technical and nontechnical, which are equally important.

In Building Future-Forward Tech Teams, Robert Half’s head of data science Danti Chen said that she seeks team members who can apply a holistic understanding of the business when preparing data for use in AI models.

“You can have a very solid programmer, but if that person doesn’t think about the real-world implications of data, they won’t likely do a good job of cleaning the data to remove the noise or doing the necessary gut checks to make sure the signals make sense,” she explained.

Cross-functional skills can also be incredibly valuable because AI projects 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

Those are only a few examples of skills and knowledge that can help your teams work effectively with AI. For a more extensive list, download our e-book, Building Future-Forward Tech Teams.

Implementing AI can significantly alter workflows and job roles, and not all employees — even in the IT department — will be keen to embrace the change that AI brings. 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 investments and underscore their strategic importance to the organization.

Promote transparency in communication: Develop a communication plan that explains the purpose of AI initiatives, changes to expect, and benefits to the business to help reduce resistance and anxiety within the workforce.

Introduce AI to your team: To help team members adapt to advanced tools and new processes — and build their comfort level in having AI as a coworker — make sure they are aware of any company guidelines related to AI use, and have access to continuous support and resources to understand AI technology.

Take a phased approach to AI implementation: If possible, start with pilot projects or small-scale deployments to test and refine AI applications before undertaking a broader rollout.

Finally, be sure to provide training that is both relevant and empowering. That’s a key message from James Johnson, executive vice president and chief technology officer at Robert Half, who explains in our e-book.

“Technology leaders who want to help move the business forward need to create opportunities for their people to learn new things constantly,” he said. “Provide them with training that is in the context of what you have asked them to do and then let them do it.”

The last step on this checklist is vital if you want to align the tech and IT talent needed to support AI, machine learning and automation projects and other tech priorities in today’s challenging labor market.

A scalable 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 specialized skills, 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 for the Demand for Skilled Talent Report found that 60% of technology managers plan to engage more contract professionals in the second half of 2024. And one of the top ways they plan to use this specialized talent is to support AI and machine learning initiatives.

Creating an AI readiness plan requires a multifaceted approach that addresses IT infrastructure, tech skills gaps, security risks, change management and scalable staffing strategies. With the above 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.

Robert Half is here to help. Contact us today to learn more about our technology staffing solutions, and tell us about your tech and IT talent needs.

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