“AI doesn’t replace people. It amplifies what people can do.”
— Fei-Fei Li

The AI as a Service market is expected to grow faster, like the number of prompts, from $20.26 billion in 2025 to $91.20 billion by 2030. That’s a 35% annual growth rate. Sounds impressive, doesn`t it? And here’s the other truth: 92% of the world’s top 2000 companies already use outsourcing to fuel innovation.

But some companies aren’t ready to change anything. They keep waiting for the “perfect moment” to start. The perfect team. The perfect data. The perfect plan.

While they are waiting — someone faster, braver, smaller — has already launched, tested, failed twice, and found what works.

As a CEO, I’ve seen both sides. Companies are burning millions trying to build AI teams from scratch. Others outsource their crown jewels and regret it overnight.

That’s why outsourcing should be a lifeline, not a shortcut. Because AI is not about hype, it’s about motion.

When It’s Time to Outsource Your AI Project

AI project outsourcing isn't a weakness. It's a strategic resource allocation. And at the same time, most companies should be using AI, not building AI teams. There's a difference.

As the CEO of Smartexe, I recommend saying yes to an external AI team if:

1. "You’re Bleeding Money". AI talent is expensive. Let’s talk about numbers. A mid-level ML engineer costs $150K-$200K. Senior ones? That’s $250-$400k annually. And that's just salary—add benefits, equity, recruiting fees, training, tools, and infrastructure. 

   Now multiply that by the size of the team you actually need. You need data engineers, ML engineers, MLOps specialists, DevOps, and maybe a research scientist. That's a lot of money in the budget before you get the first results, and we do not talk about the budget for hiring. Oh, and retention?

   By contrast, outsourcing an AI project typically costs between $50-$300K, depending on scope, and you get a working product, not a hiring headache.

2. "It isn't your Superpower". You're great at logistics. Or healthcare. Or e-commerce service. Or whatever your actual business is. That's your superpower. That's what you should be doubling down on. 
   AI requires a different DNA: experimentation, iteration, and deep technical specialization. If that's not your core competency, trying to build it from scratch is expensive, so outsourcing is an excellent solution, especially for specific tasks.

   Outsourcing allows you to work with a skilled team that already knows the pitfalls and design data pipelines, train models, and deploy production systems without slowing down your core business.

3. The speed to market is everything. The perfection doesn't matter if you're late to the party. Your competitor just launched a new feature. Your biggest client is evaluating solutions next quarter. That conference is in 8 weeks, and you promised a demo? Sounds familiar?
   Building an internal AI team can take from one to six months. Then there's onboarding. Then there's the learning curve with your specific data and systems. Finally, you are looking at 9-12 months deadline before you see real output.

   Outsourcing is a shortcut. An organic team needs 8-16 weeks to implement a specific feature for most projects.
   Speed isn't just a nice-to-have. It's the difference between winning and becoming irrelevant. As the first version that works will always outperform the perfect version that’s too late.

4. Risk Feels Too Heavy. AI isn’t just code — it’s a minefield. Data privacy violations. Algorithmic bias. Compliance nightmares. Security breaches in your training data. Regulatory audits you're not prepared for. 

   Any one of these can ruin your reputation.
   Experienced outsourcing partners already know:

• data anonymization pipelines,
• ethical AI reviews,
• model monitoring dashboards,
• explainability frameworks,

• fallback systems.

  The remote team knows the regulations that apply to your industry. They've handled GDPR, CCPA, and HIPAA compliance. They've debugged bias in production.

5. Your Team Is Burning Out. Your developers are already working on the features. Adding AI integration on top of their plate can be destructive. In six months, the in-house team will be exhausted, resentful, and producing mediocre work on both main tasks and the AI project.

   AI projects require experimentation. Failed experiments. Multiple iterations. Late nights debugging why the model accuracy suddenly dropped. That's a full-time job, not a side project. An additional load onto already-maxed-out teams is a recipe for disaster.

   Outsource AI teams can handle the experimental load while your internal team focuses on core tasks. And when the AI solution is ready? Your internal team integrates and maintains it—but they're not burned out from building it.
   It's not about whether your team can do it. It's about whether they should have to.

6. Can’t Scale Fast Enough. If your AI solution works, everyone will want a piece of it — marketing, HR, ops, customer service. Suddenly, your small AI team is the bottleneck for the whole company.
   You can't hire fast enough. Each new initiative gets delayed. Priorities clash. Your AI team will be overwhelmed, and quality suffers. The project deadline will be missed as the team can’t handle the load.
   Outsourcing brings your project back on track. You’ll be able to scale teams up or down, bring new expertise mid-project, or parallelize multiple tasks without waiting for recruitment cycles.
   You're not locked into a fixed capacity. You scale with demand, not with recruitment cycles.
   And here's the kicker: as these projects mature, you can selectively bring maintenance in-house while keeping the outsourcing partner for new experimental work. You get the best of both worlds—internal stability and external agility.

Why Outsourcing AI project Works 

Alright, so you're convinced you should outsource. But why does it work when so many other business strategies fail?
It is as simple as that, outsourcing helps you:

• skip the trial-and-error phase;
• don’t drown in technical terms;
• be flexible without long-term commitment;
• get map of the AI around.

When your partner tells you, "We tried that approach on three other projects and here's why it didn't work," that's worth its weight in gold. You're buying their scar tissue, not just their expertise.

The right team brings not just technical skills but frameworks:

• Data readiness audits that uncover gaps before you start.
• MLOps pipelines that make your model maintainable.
• Governance checklists that ensure compliance with evolving AI regulations.

Do you want to start your first AI project without causing roadmap issues?

We help you to integrate organic AI teams seamlessly into your workflow. Let’s chat and see what works for you.