Is It Better to Hire an AI Specialist or Train Someone Internally?

As businesses rush to integrate AI tools like ChatGPT and Copilot into daily operations, a pressing question emerges for SMEs: should you hire vs train when it comes to AI expertise? Articles in SME News and insights from Southern Enterprise Awards 2026 underline a stark reality — most small and medium-sized enterprises experiment with AI tools but struggle to redesign processes around them.

Understanding this AI skills gap is essential before leaping into recruitment or internal upskilling. This blog post will walk https://bizzmarkblog.com/whats-the-difference-between-an-ai-user-and-an-ai-project-lead/ through the key considerations for both routes and why effective project leadership for AI and automation often trumps a search for elusive ‘AI unicorns’.

SMEs and the AI Experimentation Landscape

Recent reports from AI Global Media highlight that many SMEs are already deploying tools like ChatGPT to automate customer queries, draft reports, or aid coding tasks with Copilot. The speed at which these tools have been adopted is impressive.

Yet the gap between AI usage and genuine process redesign remains wide. Many companies use AI as a “bolt-on” to existing workflows without rethinking handoffs, templates, or approvals. This often means missing out on productivity gains and risking chaotic, uncontrolled processes.

Common Workflow Challenges

• Tasks still done manually: Despite AI availability, frequent tasks such as status reporting, document approvals, and data validations often remain manual due to lack of redesign.
• Ownership gaps: AI initiatives may lack clear responsibility — who leads what, who trains whom?
• Tool-first thinking: The focus is on plugging in tools rather than changing workflows to exploit AI strengths.

Addressing these requires both AI competencies and deep operational knowledge.

Hire vs Train: What Changed in the Workflow?

Before deciding whether to hire an AI specialist or train internal staff, always start by Microsoft Copilot for SMEs asking, “What changed in the workflow?” If the introduction of AI tools means new processes, handoffs, and decision points, someone needs to lead that transformation, not just insert technology.

This is where many SMEs stumble. They hire AI experts who may understand algorithms brilliantly but lack knowledge of the existing operational model. Alternatively, they train staff adept in current workflows but lacking AI fluency, leading to misapplication or underuse of the technology.

Effective AI deployment depends on blending both competences. Let’s explore each approach in detail.

Training Existing Staff: Pros and Cons

Training internal employees to use and manage AI tools offers several advantages. These staff know existing processes intimately and understand where automation or AI can deliver immediate benefits.

Pros

• Contextual understanding: Internal staff grasp the nuances of reporting cycles, approval thresholds, and customer interaction protocols that AI needs to respect.
• Faster adoption: Since these employees already handle related workflows, retraining them can be quicker than onboarding new hires.
• Improved morale: Upskilling motivates employees and reduces fear around automation replacing jobs.
• Cost-effective: Training often costs less than hiring specialist roles, especially considering recruitment and onboarding expenses.

Cons

• AI skills gap: AI technologies evolve fast. Internal staff may require continuous support to stay current, constraining project timelines.
• Lack of deep technical expertise: Complex AI model tuning, data governance, and architecture design might be beyond the existing capability.
• Risk of fragmented efforts: Without dedicated project leadership, training can be ad-hoc and fail to catalyse broader process redesign.

Hiring AI Specialists: Pros and Cons

Recruiting a dedicated AI specialist or automation expert brings focused technical expertise but comes with trade-offs.

Pros

• Deep technical knowledge: Ability to customize models, integrate AI tools, and implement robust automation pipelines.
• Project leadership potential: Can drive end-to-end AI transformation projects with clear ownership.
• Faster scaling to advanced use cases: Specialists keep pace with rapid AI innovations and emerging best practices.

Cons

• Operational disconnect: New hires often struggle to understand SME’s current processes, leading to solutions that do not fit daily reality.
• Higher costs: Specialist salaries are significantly higher, plus recruitment cycles may be lengthy.
• Potential culture clash: New roles can introduce friction if the organisation is not prepared for AI-driven change management.

Practical Recommendations for SMEs

Based on experience and industry reports, here are some practical steps SMEs can take to close the AI skills gap without compromising delivery.

1. Start with process mapping: Identify what tasks are currently manual and could benefit most from AI (e.g., report generation, customer query triage, approvals).
2. Appoint a project lead with hybrid skills: Look for someone who understands both operational workflows and AI tools like ChatGPT and Copilot, whether internal or external.
3. Invest in targeted training: Upskill staff on specific AI capabilities relevant to their roles rather than broad theoretical courses.
4. Engage AI specialists strategically: Use external experts as consultants or mentors rather than full-time hires, focusing on knowledge transfer.
5. Implement governance: Define data ownership, approval steps, and quality controls to embed AI responsibly into workflows.
6. Measure impact: Track process efficiency, error rates, and employee satisfaction to guide further adjustments.

Case Study: AI Leadership in SMEs Recognised at Southern Enterprise Awards 2026

At this year’s Southern Enterprise Awards 2026, several SMEs were lauded for AI project leadership that combined internal training with strategic hiring. One winning company appointed a process-savvy manager as AI project lead, complemented by periodic consultancy from AI Global Media’s recommended experts. Together, they redesigned customer complaint workflows using Copilot-powered automation, doubling case resolution speed while boosting customer satisfaction.

This hybrid approach harnessed workflow knowledge and technical expertise effectively, illustrating that the question isn’t simply hire vs train but how to blend these according to your SME’s unique needs.

Conclusion: Tailoring Your AI Skills Strategy

SMEs face a critical choice in building AI capabilities: recruit specialist talent or empower internal teams through training. The sharper question to ask is, “What aspects of our workflows are changing, and who best combines domain and technical knowledge to lead that change?”

Jumping on the AI bandwagon with tool-first enthusiasm often leads to patchy results. Instead, focus on project leadership that bridges existing operations and AI expertise. Upskilling current staff has clear benefits but needs guidance and governance. Hiring AI specialists can accelerate progress but risks disconnect if isolated from SME realities.

Ultimately, a blend—leveraging internal knowledge with external AI fluency—often wins. As reported by SME News and seen in award-winning SMEs at the Southern Enterprise Awards 2026, success with AI means smarter workflows, clear ownership, and ongoing training—not just chasing tools or titles.

For SMEs ready to take the next step, ask yourself:

• Which tasks are still manual today that AI could improve?
• Who understands these workflows best inside your team?
• Can you appoint a project lead who combines process and AI tool knowledge?
• How will you measure success and iterate learning?

Answering these leads to a balanced, sustainable AI skills strategy that delivers true transformation.