Should businesses hire AI specialists or train existing teams? In this article you will find global adoption trends, costs, and bottom line gains for organisations. TL:DR - jump to a summary which details all you need to know here.
AI adoption is accelerating but are businesses investing in the right areas?
Artificial intelligence is reshaping industries at an unprecedented pace, influencing everything from business development and lead generation to automated scheduling and data management.
Yet, as AI advances, companies face a critical dilemma:
Do they hire external AI specialists to drive innovation?
Or should they upskill their existing workforce to build AI capabilities in-house?
The right choice is not just about cost, it is about strategy, speed, and long-term sustainability. The decision is also heavily influenced by global AI adoption trends, economic factors like tariffs, and the reality that AI talent is in short supply.
So, what is the best approach?
Global AI adoption: Where do different markets stand?
AI adoption varies significantly across regions, with some nations aggressively investing in AI-driven automation while others take a more cautious approach.
United States – AI investment is at an all-time high, with large-scale funding in AI-driven infrastructure. However, rising tariffs on technology imports from China, Canada, and Europe could lead to higher costs for AI-driven automation.
United Kingdom – AI adoption is outpacing many global markets, with 42% of CEOs already integrating generative AI, compared to just 32% globally.
Europe (Germany, Romania) – Lagging behind, with European businesses trailing US firms by 45–70% in AI implementation. Companies are slower to invest due to regulatory complexities and economic uncertainty.
UAE (Dubai, Abu Dhabi) – Aiming to become an AI powerhouse by 2031, with government-backed AI initiatives and a strong focus on automation. Businesses here are actively investing in AI talent and AI-led efficiencies.
These adoption rates influence talent availability and investment strategies, making the hire vs upskill debate even more complex.
To hire AI talent: Immediate expertise at a premium
Many organisations turn to external hiring when they need immediate AI expertise to stay competitive. But is this the right move?
Advantages of hiring AI specialists:
Faster implementation – New hires bring expertise that allows businesses to execute AI projects quickly.
Specialised knowledge – AI engineers, data scientists, and machine learning experts provide insights that an internal team may lack.
Competitive advantage – Businesses that bring in AI specialists can leverage AI more effectively than their competitors.
Challenges of hiring AI talent:
Expensive and in high demand – AI talent comes at a significant premium, with salaries for AI engineers increasing by 10–20% year over year.
Talent shortage – AI skills are in high demand, meaning many businesses struggle to recruit top talent quickly.
Integration challenges – New hires need time to understand company culture, processes, and existing infrastructure.
Many companies that want to hire AI specialists should be prepared for high costs and fierce competition.
How might recruitment benefit from AI?
In this video, hear Louisa Plint, CEO discussing the use case for AI in recruitment (where it should not be involved) and thinking about the areas that AI can almost certainly support.
Upskilling internal teams: A sustainable but slower approach
Many organisations opt to develop AI skills internally, investing in employee training and AI certification programmes. But does this pay off?
Advantages of upskilling:
Cost-effective in the long run – Avoids the high salary costs of hiring AI specialists externally.
Boosts employee retention – Investing in AI training builds a more engaged workforce and fosters loyalty.
Leverages existing knowledge – Employees already understand internal processes, making AI easier to integrate into business operations.
Challenges of upskilling:
Slow adoption – AI training can take months or even years before employees are proficient enough to drive impact.
Skills gaps still exist – Some technical AI skills require advanced expertise that upskilling alone cannot bridge.
Not every employee is suited for AI roles – Retraining requires significant internal investment and may not be feasible for all industries.
While upskilling is more sustainable, it takes time. Businesses that need AI expertise now may struggle to keep up with competitors.
Why small AI wins matter more than big projects
One of the biggest mistakes businesses make with AI is assuming that large, complex projects will yield the most significant returns. In reality, the most valuable AI applications often come from automating small, repetitive tasks that free up time.
If a process takes three hours per month, saving one hour feels like a win.
But if a process takes two minutes and happens 100 times a week, reducing it to 15 seconds per task saves exponentially more time.
The tariff factor: Why companies are turning to AI
Trade tensions and tariffs introduced under Trump’s policies are forcing businesses to rethink their AI strategies.
Tariffs on China, Canada, and Europe – Many companies that rely on global supply chains are facing higher costs due to increased tariffs on technology imports.
AI as a cost-saving tool – Instead of absorbing rising production and labour costs, some companies are investing in AI-driven automation to cut expenses in the long run.
AI talent is becoming more expensive – As demand for AI expertise grows, AI salaries and technology solutions are being priced at a premium, meaning businesses need to make careful AI investment decisions now.
Companies looking to AI as a cost-cutting solution must understand that AI investment comes with its own price tag.
A hybrid approach is the smartest play
The most effective strategy is often a hybrid approach, where businesses:
Hire external AI specialists to accelerate AI adoption.
Invest in internal AI training to develop a sustainable long-term AI workforce.
Focus on small AI efficiency gains to drive immediate impact.
Whether businesses hire externally or upskill internally, one thing is clear, AI is not just a competitive advantage, it is a business necessity.
The real question is not whether to invest in AI—it is how to invest in AI wisely.
Frequently Asked Questions (FAQs)
1. Should businesses hire AI talent or upskill existing employees?
This depends on a company’s needs, budget, and long-term strategy. Hiring AI talent offers immediate expertise but comes at a higher cost, while upskilling employees is more cost-effective but requires time. Many businesses combine both approaches, hiring external AI specialists to drive immediate impact while training internal teams for long-term sustainability.
2. What are the biggest challenges of hiring AI talent?
High salary costs - AI engineers and data scientists command significant premiums, with salaries increasing 10–20% year over year.
Talent shortages - Demand for AI professionals outstrips supply, making recruitment highly competitive.
Integration issues - New AI hires need time to adapt to company culture, processes, and existing tech stacks.
3. What are the advantages of upskilling employees in AI?
Upskilling employees in AI offers long-term benefits:
Cost-effective - Avoids the need to pay premium salaries for external hires.
Better integration - Employees already understand company processes.
Improves retention - Investing in AI training can increase employee engagement and loyalty.
4. Which countries are leading in AI adoption?
United States: The global leader in AI investment and infrastructure.
United Kingdom: Rapid AI adoption, with 42% of CEOs implementing generative AI, outpacing global averages.
Europe (Germany, Romania): Lagging behind the US in AI integration due to regulatory and economic challenges.
UAE (Dubai, Abu Dhabi): Aiming to become a global AI powerhouse by 2031, backed by government-driven AI initiatives.
5. How can AI automation deliver efficiency gains?
The biggest efficiency gains come from automating small, repetitive tasks, rather than focusing only on large AI-driven projects.
For example:
A task that takes 3 hours per month, saving 1 hour is useful.
A task that takes 2 minutes and happens 100 times a week, reducing it to 15 seconds per task saves exponentially more time.
Businesses that start with small automation wins often see faster returns on AI investment.
6. How do tariffs impact AI investment decisions?
New trade tariffs on China, Canada, and Europe are increasing technology import costs, making AI-driven automation a priority for many companies. Instead of absorbing higher costs from global supply chain disruptions, businesses are investing in AI to cut operational expenses and remain competitive.
7. What is the best AI workforce strategy for businesses?
The most effective approach is a hybrid strategy that includes:✅ Hiring external AI specialists for immediate impact.✅ Investing in internal AI training for long-term scalability.✅ Focusing on small automation efficiencies to drive measurable ROI.
By combining external expertise with internal development, companies can leverage AI effectively while managing costs and future-proofing their workforce.
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