As technology continues to reshape the business landscape, few voices carry as much weight as Dominic Jainy, a seasoned IT professional with deep expertise in artificial intelligence, machine learning, and blockchain. With a passion for applying emerging technologies across industries, Dominic has become a trusted guide for organizations navigating the complex challenges of AI adoption. In this insightful conversation with Maison Edwards, Dominic shares his perspective on the critical shortage of AI talent, the power of upskilling existing teams, and the practical steps companies can take to integrate AI into their current systems. From addressing wage disparities to leveraging familiar tools like SQL, this interview explores how businesses can turn their workforce into a competitive advantage in the age of AI.
How significant is the shortage of skilled professionals when it comes to adopting AI in businesses today, and what kind of hurdles does this create?
The shortage of skilled professionals is arguably the single biggest barrier to AI adoption right now. Companies are pouring money into cutting-edge tools, but without people who know how to apply them to specific business problems, those investments often fall flat. This gap creates a host of issues—projects stall, innovation slows, and there’s a real risk of “shadow AI,” where employees use tools without proper oversight, leading to errors or security risks. Compared to past tech waves like cloud computing or big data, the pace of AI’s rise has been much faster, and the skills needed are more specialized, which only widens the gap between demand and supply.
What’s driving the high salary premiums for AI-skilled workers, and do you think this trend will hold over time?
The salary premiums—sometimes as high as 56% more than comparable roles—are a direct reflection of scarcity. There’s a massive demand for AI expertise, but the pool of qualified workers is tiny. These premiums are fueled by competition among companies desperate to stay ahead, especially in tech-heavy industries. I don’t think they’re sustainable long-term, though. As more training programs emerge and companies focus on upskilling, the supply of talent will grow, and wages should stabilize. For now, though, smaller companies are hit hardest—they can’t match the offers from big players, which limits their ability to compete on AI innovation.
Why do you believe upskilling current employees often trumps hiring new AI specialists for many organizations?
Upskilling is a smarter play because your existing employees already understand your business—its quirks, its data, its goals. That domain knowledge is invaluable and something a new hire, no matter how skilled, can’t replicate overnight. Current staff can contextualize AI in ways that align with your specific needs, which speeds up implementation and reduces risk. Plus, upskilling boosts morale and retention; employees feel invested in when you give them new tools to grow. It’s a win-win compared to the costly and uncertain process of recruiting external talent.
What are some actionable steps companies can take to make AI literacy a standard skill for their tech teams?
First, companies need to treat AI literacy as a baseline, not a specialty. Start by embedding AI training into existing development programs, focusing on practical, hands-on learning rather than theory. Make sure these programs are accessible—offer beginner tracks for less tech-savvy staff and advanced modules for others. Partner with online platforms or internal mentors to provide ongoing support, and supply tools like sandboxes where employees can experiment safely. Finally, tie AI skills to real projects so learning has immediate impact. It’s about building confidence and capability across the board, not just creating a few experts.
How can companies use a structured approach to ensure their teams are ready to adopt AI effectively?
A structured approach starts with asking the right questions as a team. Everyone should understand what problems AI is solving—without that clarity, you’re just chasing trends. Next, focus on data readiness and guardrails; make sure your data is clean and you’ve got rules to prevent misuse or bias. Then, establish clear ways to evaluate AI outputs—how do you measure success? Lastly, plan for production—AI isn’t a lab experiment; it needs to run reliably in real-world settings. When all tech staff engage with these questions, you build a shared understanding that keeps projects grounded and effective.
Why is integrating AI into existing tech systems, like relational databases, so crucial for broader adoption within a company?
Integrating AI into familiar systems minimizes disruption and lets more of your team contribute. If your staff already knows SQL or works with certain databases, adding AI features—like vector search or embeddings—within those environments is a much lighter lift than forcing them to learn a whole new stack. This approach democratizes AI; it’s not just for data scientists but for analysts, engineers, anyone who touches the system. It lowers the barrier to entry and accelerates adoption because you’re building on what people already know rather than starting from scratch.
Can you share some examples of how existing skills, like SQL, can be adapted to support AI initiatives?
Absolutely. SQL is a great example because it’s so widely used—over 60% of developers rely on it. Teams can extend their SQL skills to handle AI by learning to work with embeddings or vector similarity searches directly in their databases. For instance, a data analyst could use SQL to query customer data and layer in AI-driven recommendations without leaving their familiar environment. It’s also about applying existing concepts like access controls or data lineage to AI workflows, ensuring they’re secure and traceable. This builds on strengths your team already has, making AI feel like a natural evolution rather than a foreign concept.
What is your forecast for the future of AI talent development in the coming years?
I’m optimistic about the trajectory of AI talent development. Over the next five to ten years, I expect we’ll see a massive push toward accessible education—think more certifications, boot camps, and employer-led training programs tailored to specific industries. The wage premiums will likely taper off as the talent pool grows, and AI literacy will become as fundamental as knowing how to use a spreadsheet today. Companies that invest in upskilling now will have a huge edge, not just in capability but in culture, as they’ll foster a workforce that’s adaptable to whatever tech comes next. The key will be maintaining a balance between specialization and broad-based skills to ensure flexibility in a fast-changing landscape.