Should You Hire an AI Expert or Leverage Your Current Employees?

Most companies now recognize that AI is no longer a nice-to-have, but a necessity for continued competitiveness and growth ifor the foreseeable future.

The real question isn’t SHOULD we use AI — it’s HOW to utilize AI in a way that delivers the best results.

In practice, there are two dominant approaches. 1) Leverage and empower current employees to deeply adopt AI themselves, allowing each person or team to develop their own AI capabilities. 2) Hire a dedicated AI expert — either as an internal employee or an external consultant/agency — to design and build AI systems that teams utilize, as part of their daily work.

Believe it or not, hiring a dedicated AI expert or consultant is being established as the stronger approach for most companies, because it preserves specialization, shortcuts the steep AI learning curve, and turns scattered AI usage into scalable automation. 

TL;DR

• Best approach for most companies: centralize AI ownership under a dedicated expert (employee or consultant), and distribute usage across teams.

• Why: people perform best when focused on their core roles; the leap from “using AI” to building KPI-moving systems is steep; the biggest gains come from workflow-level automation.

Consultant vs internal hire: consultants often deliver faster impact and lower risk early on; internal hires make sense at scale and when AI becomes a long-term core capability.

Specialization Still Wins: Why AI Ownership Should Be Centralized

A sales rep creates the most value by selling. A marketer creates the most value by driving demand. An account manager creates the most value by retaining and expanding accounts.

When each of those roles is also expected to design AI systems, something breaks. Either their core performance suffers as time gets re-allocted to AI systems, or the AI initiatives developed pail in comparison to those built by a true expert. 

This isn’t a new idea. The foundation of modern capitalism is built on specialization and division of labor. Specifically, productivity increases when people focus on what they do best instead of trying to do everything themselves. Adam Smith’s classic “division of labour” argument is often taught as the starting point for this logic.

When AI ownership is spread across individuals and teams, the outcome is usually predictable:

• Multiple disconnected AI setups solving the same problem in different ways

• Personal prompt libraries and tools that don’t talk to each other

• Inconsistent quality, reliability, and decision logic

• Lack of automation results in manual-heavy AI usage

• No shared architecture, standards, or long-term plan

• No clear owner when something breaks or needs improvement

Centralizing AI ownership under a dedicated expert avoids this fragmentation. The expert builds better, shared systems that supercharge team/department capabilities, while everyone else stays focused on the work that drives the business forward.

The AI Learning Curve Problem: Why Expertise Matters More Than Access

Using AI tools is easy. Designing AI systems that reliably improve KPIs is not.

There is a significant gap between casually using AI and being able to design, build, and maintain AI workflows that materially impact revenue, efficiency, or customer experience. Closing that gap requires substantial time investment, repeated iteration, and exposure to real-world failure modes.

This isn’t just a theory problem — it’s a macro trend. Employers are explicitly forecasting large-scale skills disruption in the coming years, and “skills gaps” are widely described as a major barrier to business transformation. For example, the World Economic Forum’s Future of Jobs 2025 materials report that employers expect a meaningful share of core skills to change by 2030, and emphasize skills gaps as a major transformation barrier.

When AI adoption is pushed onto current employees, companies are effectively asking multiple people to climb a steep learning curve independently, while still performing their full-time roles. The result is slow progress, detracting from core activities, and uneven outcomes.

Some individuals get reasonably good, others struggle, and most never reach the level required to build reliable systems. Teams repeatedly relearn the same lessons about prompting, evaluation, data handling, edge cases, and governance — often the hard way.

Hiring a dedicated AI expert collapses the learning curve. Instead of waiting months or years for distributed competence to emerge, the company quickly gains the ability to design and deploy AI workflows that are stable, repeatable, and tied directly to measurable KPI improvement.

Speed matters. Companies that reach reliable AI implementation sooner compound their advantage.

Real AI Impact Comes From Automation, Not Individual Usage

Most internal AI adoption plateaus at manual usage, like:

• Drafting emails or content

• Manually prompting an LLM

• Generating ideas or outlines

These are useful, but the amount of human input needed severely reduces productivity gains. Instead, the largest AI gains come when it’s embedded into workflows and runs automatically, like when:

• Inbound leads are qualified, routed, and followed up with personalized messages, without manual intervention

• Support requests are categorized, answered, logged, and escalated intelligently

• CRM data is enriched, updated, and acted on in real time

• Insights trigger actions instead of sitting in dashboards

This isn’t just opinion. The consistent theme in AI adoption research and operator guidance is that meaningful value is captured when AI is operationalized inside workflows and paired with process redesign and governance — not when it’s used as a standalone tool for one-off tasks. McKinsey’s 2025 State of AI write-up points to workflow redesign and organizational changes as part of what separates “experimentation” from bottom-line impact, and Harvard Business Review similarly emphasizes AI-driven process redesign over bolt-on usage.

Building this level of automation requires system design skills that most employees should not be expected to develop on the side. It involves integration across tools, workflow logic, data structure, quality assurance, monitoring, and ongoing optimization.

Even highly capable employees tend to stop at “using AI.” A dedicated AI expert turns AI into infrastructure — something that works autonomously, continuously, and scales across the organization.

Employees Should Use AI — But They Shouldn’t Own the Architecture

Centralizing AI ownership does not mean limiting access.

A healthy model looks like this:

• AI systems are designed and built by a dedicated AI expert, but are informed by real departmental needs/insights

• Teams collaborate during implementation to refine workflows, edge cases, and usability

• Employees are responsible for utilizing AI systems effectively in their daily work - not creating them.

This approach ensures high-quality design without sacrificing adoption. AI becomes a shared capability rather than a shared burden.

Hiring an AI Consultant vs Hiring an Internal AI Employee

Once a company decides to centralize AI ownership, the next question is whether that role should be internal or external.

In practice, this decision usually comes down to company size, growth goals, and urgency.

Hiring an Internal AI Employee

Best suited for:

• Larger companies with ongoing AI needs

• Organizations with aggressive growth plans

• Environments where AI will be deeply embedded across many functions

Pros:

• Long-term ownership and continuity

• Deep institutional knowledge

• Full alignment with internal priorities

Cons:

• Slower time to impact

• Higher upfront commitment (salary, onboarding, ramp time)

• Harder to test before fully committing

Hiring an AI Consultant

Best suited for:

• Companies early in AI adoption

• Teams that want fast results

• Organizations that want to test initiatives before hiring internally

Pros:

• Significantly faster speed to impact

• Lower initial investment and risk

• Exposure to proven patterns from other companies

• Ability to test, validate, and refine use cases quickly

Cons:

• Less long-term continuity unless extended

• Requires internal ownership planning if scaling later

For many companies, consultants are not an alternative to hiring internally; they are an accelerator. Even organizations that plan to hire a full-time AI employee often benefit from starting with a consultant to establish systems, validate ROI, and shorten the ramp time for the eventual internal hire.

Conclusion - Should You Hire an AI Expert or Leverage Your Current Employees

AI is now a competitive necessity, but how responsibility is assigned determines whether it becomes a durable advantage or an ongoing distraction. Accordingly, deciding whether you should hire an AI expert or leverage your current employees is just as important as the AI systems you decide to utilize.

Trying to empower every employee to independently build AI solutions leads to fragmented systems, slow progress, and inconsistent quality. Centralizing AI ownership under a dedicated expert preserves specialization, bypasses the steep learning curve, and unlocks the real value of AI through automation. In short, AI should be everyone’s tool, not everyone’s job! To achieve the fastest, best, and most repeatable results with AI, hire a dedicated AI expert and let your employees focus on their own relative expertise.