How Can Specialized External Talent Accelerate Your AI/ML Projects?
As enterprises increasingly invest in artificial intelligence (AI) and machine learning (ML), the challenge of sourcing the right talent has become a critical barrier to progress. In this interview, we speak with Alper Semerci, CTO at Proteams, who has more than a decade of experience in software development and team leadership. With a strong background in cloud and mobile technologies, Alper brings a practical perspective on bridging capability gaps, the strategic use of freelancers, and best practices for integrating external expertise into enterprise AI initiatives.
The Talent Gap in AI: A Persistent Challenge
Despite AI investments reaching $124.3B in 2024, 76% of companies still struggle to find enough AI-skilled workers. Only 16% of European executives feel they have sufficient tech talent internally, and the EU faces a projected gap of up to 3.9 million tech workers by 2027.
Alper confirms this reality: “When it comes to AI, it’s very hard to find talent. Even evaluating candidates is a challenge—you need AI experts to validate other AI experts.” For many MNEs, the difficulty isn’t just hiring, but also ensuring candidates have the right skills and experience.
Why External Talent Makes a Difference
For many large organizations, the traditional hiring process is slow and resource-intensive. “Onboarding through standard channels can take weeks or even months, especially for AI roles,” Alper explains. In contrast, leveraging specialized freelancers or external platforms can reduce onboarding times to days, allowing companies to move quickly from concept to execution.
This speed is especially valuable for project-based needs or when internal teams lack specific expertise. “If you don’t need AI experts long-term, but want to complete a project, freelancers are a very advantageous option,” he says. Companies also benefit from the proven track records and portfolios that experienced freelancers bring, reducing the risk of mismatched hires.
Recent data backs this up: 91% of organizations have maintained or increased freelancer use over the past three years, with 52% ramping up IT hiring, and project turnaround times averaging 30–40% faster than traditional models.
AI Success Stories from Pharma and Beyond
As CTO of Proteams, Alper is leading several AI initiatives for major clients in the pharma and consumer health industries:
Compliance Assistant Chatbot: Connects to document repositories, policy management tools, and regulatory databases to provide instant, verifiable compliance answers. “There’s no room for hallucination or misinformation in compliance,” Alper notes, emphasizing the need for expertise to ensure reliable AI outputs.
Knowledge Base and AI Search: Consolidates siloed R&D and medical data into a single structured knowledge graph, then applies retrieval-augmented generation (RAG) and semantic search to give scientists and clinicians fast, accurate access to technical documentation and trial results.
Competitive Intelligence Web Scraper: Offers a natural language interface for configuring crawlers that monitor competitor websites, filings, and publications in real time. Data is automatically normalized and visualized, reducing weeks of manual research to minutes.
Through these projects, Proteams helps organizations bridge technical gaps and make progress on evolving business priorities.
Best Practices for Integrating External AI Talent
Even with the benefits in mind, making freelance collaborations work effectively requires careful planning and alignment:
Define Clear Objectives: “Companies should define their targets very well—whether it’s research, solving a current problem, or improving a process,” Alper advises. Clear goals help ensure the right talent is brought in for the right reasons.
Prioritize Corporate Familiarity: External experts who understand corporate dynamics, compliance, and best practices are more effective partners. “Profiles familiar with corporate environments are a step ahead,” he notes, as they can navigate internal processes and collaborate seamlessly with in-house teams.
Invest in Oversight and Verification: With advanced AI models still prone to errors—OpenAI’s latest models have hallucination rates of 30–50%—leaders must invest in robust verification and validation processes, not just automation.
What’s Next?
The demand for AI expertise is only set to grow, but the nature of required skills is changing. Alper advises companies to distinguish between research-focused roles and those aimed at solving immediate business problems. New technologies make it easier to deploy AI without deep theoretical knowledge, but advanced expertise remains essential for complex or regulated applications.
For MNEs, the ability to quickly access specialized talent and implement flexible, responsive talent strategies will be both a key differentiator and a necessity for sustained success.