The Hybrid Approach to AI: Build and Partner
AI is making waves in every industry. From boardrooms to brainstorming sessions, from customer support to code generation, everyone is buzzing about the possibilities of AI. I have the same conversation with every company I talk to, whether it’s a startup building its first product or a digital-native enterprise: What is the best way to tap into AI capabilities?
Most of the people I talk to want to know which side to pick. Am I Team Build, they ask, or Team Buy? They are trying to choose between two options, either to build up their in-house capabilities or to bring in a partner to run the AI project for them.
But while I’ve seen both approaches succeed and fail, there is a third option that often goes overlooked – the hybrid model. A hybrid approach blends in-house capability and external support, tailored to your business maturity, use case, and speed of execution.
The Benefits of Building In-House
There’s a lot of value in growing AI expertise within internal teams. Nurturing AI talent in-house means retaining ownership over all data, models, and IP. It limits the risks of third-party data breaches and ensures that you stay in control of AI usage.
Building in-house AI talent is also a smart long-term move. As the team gains deep knowledge over time, they’ll make better decisions about which AI solutions to develop and which tools can deliver the most value. Embedding AI thinking into the culture and growing a team that knows how to move fast and experiment with AI capabilities is essential.
But there’s also a downside: building in-house AI talent is hard. We’re not just talking about hiring some data scientists. Such AI capabilities require MLOps engineers, cloud architects, product managers who understand AI, and domain experts. Existing tech employees, including developers and QA personnel, need to become familiar with AI methodologies and development tools, which involve new concepts that they aren’t used to.
What’s more, you’ll have to invest in the resources and infrastructure they need for success. That includes building an AI tech stack, creating and implementing governance policies, and learning about the relevant AI regulations so as to ensure compliance. Even large enterprises can struggle with this challenge. For scale-ups and mid-sized companies, it can be overwhelming and slow.
The Partner Advantage
What about buying talent by working with a partner? The right AI partner can be a force multiplier, bringing a new synergy that drives success. A good AI service provider has a strong team of certified AI/ML architects who already have plenty of experience. Because they’ve worked on many use cases across numerous industries, they can suggest the best solutions for each challenge and the best methodologies and tools to get there.
Working with a partner offers a shortcut to success. You can power up your infrastructure, cut costs, and bring your solution to market faster because you won’t need to spend time on training before you get started. Their experience strengthens security for AI projects because they already know the best policies and protocols to implement, and helps companies scale by running multiple projects at the same time.
What’s more, some of the best AI partners have AWS Generative AI Competency which is a global recognition for AWS Partners that demonstrate strong technical expertise and a proven track record in delivering generative AI solutions on AWS.They know how to maximize the potential of AWS infrastructure, their alignment helps to reduce costs, and they make sure to keep up to date with all the new services that AWS rolls out.
Of course, there is also a downside to buying AI talent. They get projects up and running quickly, but in the long run the company remains dependent on external skills. Sometimes communication fails and the final product doesn’t match your vision. Over time, paying partners to complete AI projects can run more expensive than keeping it in-house. Companies that outsource all AI also risk falling behind their rivals when it comes to AI knowledge, which can affect their decision-making and strategic edge.
What a Hybrid Model Looks Like
There is a third option: a hybrid model. When it comes to time-to-value and accelerating the first AI use cases, working with a partner is the way to go. But building up a strong internal AI practice at the same time, step by step, means that you won’t be stuck with long-term dependencies.
This hybrid model is ideal for companies that have strong engineering capabilities but lack AI specialization. It allows them to move fast, without a six-month lag to hire new talent and train existing employees. Companies can build capability internally and take advantage of expertise and experience that’s been honed over time, without reinventing the wheel.
Hybrid AI Best Practices
A successful hybrid AI project requires choosing an AI partner that works well with your organization and has the knowledge and experience to build the right architecture according to best practices. The partner should empower your team rather than replacing them, to ensure that the internal team can take over and scale. Certifications are a good signal of knowledge and skills, but a portfolio of past use cases and customer testimonials is even better.
The AI partner should accelerate the first two or three use cases to get everything off the ground, while also running structured enablement through workshops and on-the-job training. There are plenty of AI certification programs that offer rich resources and dedicated guidance for AI training. A nascent in-house AI team can shadow external AI partners and gain hands-on experience by working on their own smaller projects.
Keep an eye on the long game. As you begin to build your AI capabilities, think about what you’ll need a bit further down the road. A robust infrastructure that can scale to accommodate larger projects as AI requirements grow is the foundation for long-term success. Choose resources that can scale for bigger teams and tools that can flex to support bigger AI solutions.
The Best of Both Worlds
It’s important to remember that AI isn’t just another tool, it’s a capability. Like any capability, it takes time, the right mindset, and a smart combination of internal and external resources to develop it. Thankfully, you don’t have to decide between going it alone or outsourcing forever. The companies winning in 2025 are the ones that know when to build and when to bring in help.
