Well over half of organizations use some form of AI in their recruitment process, and for good reason. AI has the potential to revolutionize industries by enhancing decision-making, improving customer experiences, and optimizing operations. In fact, 75% of HR professionals also report AI as their top technology investment priority.
Without experienced professionals leading AI initiatives, companies struggle to unlock the full potential of AI and achieve ROI, often encountering issues with model accuracy, data integration, and scalability.
Why organizations struggle to unlock the full potential of AI
AI promises tremendous value. McKinsey research sizes the long-term AI opportunity at $4.4 trillion in added productivity growth potential from corporate use cases, but unlocking that growth hinges on recruiting skilled professionals who can design, implement, and scale effective AI solutions.
AI-critical skill shortages also remain widespread, with 94% of leaders affected and one-third experiencing gaps exceeding 40%. While some improvement is anticipated, nearly half of leaders expect persistent shortfalls of 20–40% in key roles through 2028.
Another challenge comes from the lack of specialized expertise and the fragmentation of AI teams across different business functions.
In many organizations, AI initiatives are siloed, data science teams work separately from IT, product development, and business strategy teams. This disconnect leads to:
- Poor alignment between AI models and business objectives
- Inaccurate predictions and models due to insufficient data or poor-quality inputs
- Scalability issues when trying to deploy AI models at scale
- Integration failures between AI models and existing systems
- Low user adoption and lack of trust in AI-powered decisions
- Missed business opportunities due to insufficient insights from AI models
When AI projects aren’t guided by experienced talent with a holistic view of both the technology and the business, they fail to drive the anticipated value and return on investment.
Implementation gaps lead to fragmentation and missed ROI
Many AI challenges begin at the hiring stage. Organizations often hire AI talent without aligning the right expertise to their strategic goals. Data scientists might be great at building models, but may lack a deep understanding of the business processes that need to be improved. Similarly, AI engineers may be proficient in coding but not skilled at scaling AI models across enterprise environments.
This fragmentation worsens over time. Poorly aligned AI projects lead to:
- Inefficient workflows and disconnected efforts
- Increased time spent on manual interventions due to poor model performance
- Higher operational costs as a result of slow deployment and testing cycles
- Decreased trust and confidence in AI-based decisions across the business
To avoid these pitfalls, companies must align their AI talent with their broader business goals and technological needs.
What it takes to successfully implement AI across your organization
For AI to perform at scale, it requires the right people with the right expertise and the right structure. Companies must focus on hiring AI talent who not only understand the technical side but can also align AI initiatives with business strategy. Below, we explore the 4 hiring strategies that determine AI ROI.
1. Establish centralized AI governance
Create a dedicated AI governance function that oversees all AI initiatives across departments. This team should manage project lifecycles, ensure ethical and responsible AI usage, track performance metrics, and coordinate cross-functional collaboration.
2. Align teams by AI specializations
Structure your AI team based on technical expertise, such as data science, machine learning engineering, AI ethics, and deployment, rather than by traditional departments. This approach ensures seamless handoffs, consistency in the application of AI models, and quicker resolution of issues.
3. Cross-train for system-wide awareness
Cross-train your AI professionals so they understand how their work impacts the larger business ecosystem. For example, a data scientist should know how data flows through different systems and how their model will be used by other departments. This reduces friction and ensures better collaboration.
4. Hire certified, experienced AI professionals
Hire AI professionals with real-world experience in delivering AI solutions, not just general technology expertise. Look for candidates with a proven track record in implementing AI at scale, certified in relevant AI platforms, and skilled in specific areas such as deep learning, NLP, or reinforcement learning.
Turning AI into a competitive asset for your business
The value of AI depends on how well it’s implemented, managed, and aligned with business needs. Without the right talent, even the most advanced AI technology won’t deliver the results your business needs.
To get results, organizations must move beyond fragmented AI efforts and short-term solutions. By hiring the right AI experts, fostering centralized governance, and aligning teams to work collaboratively, companies can turn AI into a unified, high-performance tool that drives real business value.
Procom provides certified AI consultants, project leads, and specialists who can help you bring AI into your organization effectively, ensuring that AI projects are aligned with your strategic goals, deployed efficiently, and scalable for future growth.
