Solving the Challenge of Data & AI Talent Acquisition

Challenges Hiring for Data & AI

1. Lack of Key Skills and Expertise

   Practical AI applications, such as predictive analytics, pattern recognition, and process automation, are still relatively new. The talent pool is both small and specialised. Many professionals haven’t been trained in AI or advanced data science, and emerging talent is often spread thin across industries. Upskilling internal teams can take time and isn’t always viable mid-transformation. There is also often a disconnect between what companies want (such as hands-on AI implementation experience) and what’s available.

2. Growing Demand for Generalist AI Skillsets

   Once AI-led transformations are complete, your business needs professionals who can not only interpret and act on AI-driven insights, but also continuously optimise these systems. This requires a hybrid skillset that includes digital fluency, industry acumen, and strategic thinking. Forward-thinking businesses are moving from niche technical hires to embedding AI awareness across the business , redefining traditional roles to include data literacy.

3. High Demand and Fierce Competition

   Data and AI talent is in high demand across all sectors. Many candidates have multiple offers on the table, and even passive talent is being approached regularly. As a result, standard compensation packages often aren’t enough. Since speed to hire is critical in data and AI talent acquisition, your employer value proposition (EVP) must stand out and feel authentic.

4. Difficulty Assessing Skills

   Many hiring managers aren’t equipped to confidently assess AI competencies. This can lead to an over-reliance on CVs and missed red flags in interviews, ultimately resulting in underwhelming or poorly aligned hires. Additionally, using AI tools to assess skills without properly implementing them can increase bias and hinder diversity in hiring.

5. Retention Risk

   The talent you do hire must be retained, which can be challenging in such a competitive market. Data & AI professionals tend to seek innovation-focused cultures that offer access to interesting, high-impact projects as well as ongoing professional development. If your environment lacks these elements, expect high turnover and rising costs per hire.

The Consequences of Not Being Able to Hire Data & AI Talent

Data and AI talent acquisition is essential to future-proofing your business. Failing to meet your data and AI talent acquisition goals slows progress and creates systemic risk. Here’s how:

• Slower transformation: Critical AI and automation initiatives stall, delaying cost savings and process efficiencies.
• Inefficient processes: Without the right AI tools or expertise to deploy them, teams spend more time on manual, error-prone work.
• Team burnout: Existing staff pick up the slack, leading to disengagement and higher attrition.
• Loss of competitive edge: Rivals leveraging AI effectively gain faster insights, better risk control, and more informed strategic decisions.

Data & AI Talent Acquisition Strategies That Work

Solving the challenge starts with reframing your approach. Here’s what Global Networks’ consultants advise:

1. Build Long-Term Talent Pipelines

   Don't wait until you have a role to fill. Proactively engaging with data & AI professionals across sectors means:

   • Faster turnarounds on urgent briefs
   • Access to passive talent not visible on job boards
   • Deeper insight into cultural and technical fit

   Start by mapping the critical AI and data roles your business may need over the next 6–18 months. Identify potential candidates early and engage them through informal conversations, thought leadership, or project-based work. Maintain regular touchpoints, share relevant updates, and build trust over time.

2. Focus on Potential, Not Just Pedigree

   Rethink your criteria. Some of the best data and AI professionals didn’t come from “AI backgrounds.” They’re problem solvers, self-taught, and experienced in applying models to real-world challenges. Instead of filtering by tools, look for:

   • Evidence of impactful projects
   • Cross-functional collaboration experience
   • Strong commercial understanding

3. Upskill Internally with Purpose

   Not every hire needs to be external. Some of the most successful data and AI teams combine new external talent with trained internal leaders who understand the business deeply. Offer:

   • Structured learning paths in AI, data science, and tools like Power BI or Python
   • Pilot projects to test new capabilities
   • Mentorship from external AI specialists

4. Create an Environment AI Talent Wants to Join

   Think beyond salary. If your EVP isn’t compelling, your competitors will win, no matter how strong your offer is. Talented professionals look for:

   • Access to innovation
   • Leadership buy-in for AI initiatives
   • Autonomy and progression
   • Inclusive, forward-thinking cultures

   Start by showcasing real examples of innovation, successful automation, or AI-led improvements. Ensure your leadership team actively champions data and AI, not just in strategy decks but in how budgets and resources are allocated. Build career paths that give AI professionals room to grow, lead, and influence, and foster a culture where ideas are welcomed, risks are supported, and different perspectives are genuinely valued.

5. Partner with the Right Recruiters

   Data and AI talent acquisition requires sector knowledge and a strong network. When evaluating a recruitment partner, look for depth, not just reach. Ask how they stay connected with the data and AI talent market, and how well they understand both the technical landscape and the commercial needs of a finance function.

   A strong partner should demonstrate proactive engagement with candidates, clear understanding of transformation pressures, and a team-based delivery model (not a one-person desk). Most importantly, they should ask the right questions about your business, challenge assumptions, and bring forward candidates you wouldn’t find on your own.

Moving beyond reactive hiring models and taking a strategic, relationship-led approach to data and AI talent acquisition will be critical as demand for expertise continues to outstrip supply. At Global Networks, we help you cut through the noise. We bring the full weight of our team, experience and network to every role, delivering the people who can turn your AI strategy into measurable impact.

Whether you’re scaling a data team, hiring a transformation lead, or embedding AI capability across finance, we can build the right team, together.

Contact us today to discuss your data and AI talent needs.