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Alp Sezginsoy is the founder of Expertera, a platform helping companies access vetted independent experts and build smarter ways of working with external talent.
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Key takeaways
- The biggest barrier to working with freelancers is not talent supply, but company mindset. Many companies are still more willing to experiment with AI than with new workforce models.
- Independent experts should not be hired like full-time employees. Long interview cycles and slow procurement processes destroy the speed advantage of external talent.
- The companies that win will combine AI with independent human expertise. AI can accelerate analysis and execution, but external experts bring the judgment, context, and last-mile value that turns work into business outcomes.
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The real blocker is not talent. It is mindset.
When companies talk about the future of work, the conversation often starts with technology. AI, automation, digital transformation, new tools, new systems.
But when Alp talks about the future of work, he points to something more human: mindset.
Companies are usually very curious about new technology. They test AI tools, run innovation pilots, and talk about transformation. But when it comes to changing how work itself gets done, many organizations still default to the old model: hire full-time, build internally, control everything.
That creates a strange contradiction. Businesses want speed, flexibility, innovation, and access to rare expertise. But they still try to solve these needs with processes designed for a slower world.
As Alp put it in the conversation, companies are often more open to experimenting with technology than with new workforce models. And that is where the real gap begins.
Independent experts are already available. Platforms already exist. The talent is there. The question is whether companies are ready to work with it properly.
Why freelance collaboration gets stuck inside companies
One of the most interesting parts of the conversation was the difference between a company that can onboard an expert platform in two weeks and one that takes eight months.
Same market. Same type of talent. Completely different speed.
According to Alp, the difference is usually not the size of the company alone. It is whether the business unit actually needs the solution and pushes it internally.
When the need is urgent, the business side becomes the internal champion. They work with procurement, legal, compliance, and leadership to make the collaboration happen. But when the request gets sent to procurement without business urgency, it can disappear into the system.
And this is where many companies lose the advantage of independent talent.
Freelancers, contractors, and external experts are supposed to help companies move faster. But if the internal process takes months, the company is no longer buying expertise. It is blocking access to it.
Alp made a sharp point here: many procurement systems still treat external experts like any other vendor category. But hiring a high-level independent expert is not the same as buying detergent, office supplies, or raw materials.
You are not buying a commodity. You are accessing judgment, experience, and problem-solving capacity.
That requires a different process.
Stop hiring independent experts like full-time employees
A major lesson from the episode is that companies often use the wrong hiring logic for freelancers.
When you hire someone full-time, you are making a long-term investment. You need to think about culture fit, development, leadership potential, onboarding, and long-term career growth.
But when you work with an independent expert, the logic is different.
You are usually hiring for a specific problem, project, bottleneck, or opportunity. You are not necessarily trying to build a future leader inside the company. You are trying to bring in the right expertise at the right time.
That means the process should be faster, more outcome-driven, and easier to test.
Alp described this as a shift from trying to create perfect certainty before the work starts to learning quickly once the collaboration begins. With the right platform, screening, references, past performance data, and service layer, companies can start faster and see very quickly whether the fit is right.
In many cases, you do not need six interview rounds. You need a clear brief, a trusted matching process, and a small first step.
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The human layer still matters
One of the strongest ideas in the conversation was that talent platforms are not only about algorithms.
Yes, matching systems matter. Data matters. Previous reviews, ratings, experience, and availability all help.
But Alp also described the importance of the service layer: the human team that understands both the client and the expert.
That human layer acts almost like a translator between the company’s needs and the platform’s capabilities. It helps clarify the brief, understand the context, improve the match, and make the collaboration smoother for both sides.
In an AI-driven world, this becomes even more important.
Alp used a helpful analogy: using a platform with a strong service layer is like having a prompt engineer next to you while you use AI. The system can do a lot, but the human layer helps you get better results because it understands the tool and understands you.
That is also a useful way to think about the future of work more broadly.
The best results will not come from technology alone. They will come from combining technology with human expertise, judgment, and context.
The last mile of work is still human
AI is changing the way work gets done. Many analytical, operational, and execution-heavy tasks are already being automated or accelerated.
But Alp pointed to something companies should not ignore: last-mile expertise.
AI can produce analysis. It can summarize information. It can accelerate research. It can support execution.
But someone still needs to understand the market, check the quality, apply judgment, make the decision, and turn the output into something useful for the business.
That final layer is where independent experts become even more valuable.
The future may not require more traditional headcount for every new challenge. Instead, companies may increasingly combine internal teams, AI tools, and external independent experts who bring specific judgment exactly when needed.
This is a powerful shift.
The companies that win will not necessarily be the ones that own all the talent. They will be the ones that can access the right independent expertise quickly, combine it with internal leadership, and use AI as leverage.
Access may matter more than ownership
Perhaps the biggest idea from the whole conversation is this: the future of work is not only about who you employ. It is about what expertise you can access.
For decades, companies built advantage by hiring and keeping talent internally. That still matters. But it is no longer enough.
Markets change too quickly. Skills become outdated too fast. New problems appear before job descriptions can be approved.
Independent experts give companies a way to respond faster. They bring outside perspective, deep specialization, and immediate experience from different industries, companies, and contexts.
But to use them well, companies need to change how they think.
They need to stop seeing freelancers as a last resort. They need to stop pushing independent experts through full-time hiring systems. They need to stop treating expert knowledge like a commodity.
And most importantly, they need to start small.
As Alp said near the end of the conversation, the advice for companies is simple: pick the right partner, pick a task, start fast, and start small.
That may be the easiest way to begin building a more flexible, intelligent, and future-ready workforce.
Tips for Success
- Start with one urgent business problem.
Do not try to redesign your whole workforce model at once. Pick one bottleneck where external expertise could create immediate value. - Create a faster path for independent experts.
Avoid using the same process you use for full-time hiring. Define a lighter process for project-based experts, with clear scope, fast approval, and simple onboarding. - Test the collaboration quickly.
Start with a small project, short sprint, or first milestone. Evaluate the result based on output, communication, and business impact.
