Imagine this: You’re a CTO or Head of Product at a mid-size company that’s behind on innovation, and your CEO just said: “We need AI in our product. Now.”
The pressure to implement AI is intense. Competitors are touting smart AI-driven features, investors are questioning whether you’re innovating fast enough, and headlines scream about new machine learning breakthroughs daily.
Yet you face a stark reality: skilled ML engineers are hard to find, and your in-house team is already stretched too thin. In fact, AI spending was projected to hit $550B in 2024, but companies are staring at an AI talent gap of 50% [1]. With demand far outpacing supply, how can a resource-constrained team possibly keep up?
This is where the concept of a fractional AI team comes in.
What is a Fractional AI Team?
A fractional AI team is essentially an outsourced AI team of machine learning experts that works with your company on a flexible basis (fractionally) rather than as fully embedded hires. Think of it as having an on-demand crew of embedded AI engineers integrated into your projects. Instead of hiring a whole in-house data science department (which could take months of time and significant budget), you partner with a world-class AI team that seamlessly plugs into your organization.
These fractional teams are composed of seasoned machine learning experts (data scientists, ML engineers, AI architects) who have broad experience across many projects. They may work with several different companies a year, giving them a wide breadth of experience and a wizened perspective on what actually works, and what doesn’t. In fact,
working across multiple companies often gives these fractional AI experts an edge over single-company hires [2].
They bring their accumulated, proven knowledge directly to you. Most crucially, a fractional team doesn’t operate like an aloof outside one-size-fits-all platform handing off a black-box solution. Instead, they work with your internal team via regular close collaboration, hence, “embedded ML engineers.” They become an extension of your team for the duration of the project or beyond. You maintain the control and insight, and they contribute the expertise needed to build AI into your product.
Why Use a Fractional AI Team?
Why are companies now increasingly opting for these fractional AI teams (sometimes called “fractional AI experts” or “outsourced AI partners”) instead of hiring full-time? There are a few good reasons:
1. Bridge the Talent Gap Quickly
Given the severe shortage of AI talent, a fractional team will help you bypass those time-consuming hiring cycles. You get instant access to an AI engineer that’s already proven. No months-long recruiting; rather, they can start adding value in mere weeks, accelerating your AI implementation in record time.
2. Cost-Effective Expertise
Hiring a senior machine learning engineer or data scientist full-time is expensive, and there’s no guarantee you’ll find the right one. On the contrary, a fractional AI team can work on a fixed monthly retainer, allowing you access to principal-level-AI experts without the full-time salary, benefits, or overhead. This is ideal for companies who need to spend their budget wisely.
3. Focus and Flexibility
A fractional team can laser focus on your AI project. This bypasses endless internal meetings and months-long hiring cycles, and plugs seasoned machine learning strategists into your project with zero onboarding delays. These experts have executed AI rollouts time and again, so they know every shortcut, pitfall and hidden workaround to keep things moving fast.
4. Diverse Experiences & Innovation
As mentioned, these AI professionals often have experience across many various industries and problems. This breadth means they’ll bring fresh ideas and battle-tested solutions from other domains, as they’ve “seen around the corner” on so many AI projects already. That cross-pollination can accelerate innovation in your product that a homegrown team might not think of.
You’re not just getting some extra hands, you’re getting an extra, uber-valuable perspective.
5. Faster Time to Value
Because these experts have done this many times before, they can deliver results faster. Whether setting up data pipelines, training a model, integrating an AI service, a good fractional team will have the know-how to hit the ground running. The sooner you weave AI into your offerings, the sooner you create value (and avoid falling behind your competitors).
In short, using a fractional AI team allows you to augment (not replace!) your existing team with top-tier AI talent. It’s a strategic way to immediately level up your capabilities without the long-term commitment or risk of hiring in-house for skills you may only need temporarily. It’s no surprise that so many companies are adopting fractional AI teams as their secret weapons to accelerate AI innovation quicker than their competitors.
When Does a Fractional AI Team Make Sense?
While fractional AI teams can be a strategic and innovative choice for many, it’s not a silver bullet for every situation. Here are scenarios where this approach makes the most sense:
Exploring AI Without Full Commitment
Maybe you’re AI-curious. Perhaps you suspect AI could add value in your product or platform, but you’re not 100% sure where or how. A fractional AI team can function as your exploratory strike force. They can do an AI opportunity assessment, iterate quickly, and identify what will move the needle. This lets you dip your toes in AI waters with low risk. If the experiments show promise, then great, double down! If not, you’ve saved time and money compared to hiring a whole team that might have been underutilized.
Spikes in AI Needs
Sometimes you have a one-off project, like analyzing a big dataset for insights, or a 3-month prototype for a client, that requires data science expertise temporarily. Hiring a full-time team for a short stint doesn’t make sense. Fractional AI teams thrive in these scenarios.
Need for Cross-Disciplinary Skills
Implementing AI isn’t simply about data science, but often requires a mix of skills (data strategy, change management, prompt engineering, AI/ML expertise, pitching buy-in to executives, etc). It’s difficult to hire one or two people to tick all those boxes; but a fractional AI team can collectively provide that skillset. As a result, you get a well-rounded solution from day one.
In these situations and other similar ones, a fractional AI team offers speed and agility. You can instant AI horsepower exactly when and where you need it, avoiding the delay of hiring and commitment of long-term payroll. It’s about aligning AI expertise to your timeline and your needs, rather than the other way around.
Accelerate Your AI Journey with the Right Team
Every ambitious tech leader faces a similar problem nowadays: How do we inject AI into our business quickly, effectively, and affordably? For many, fractional AI teams are emerging as the missing piece to that puzzle. They offer a way to move fast on AI innovation despite talent shortages.
If your organization is feeling the pressure to deliver AI-driven features but is slowed down by resource gaps, consider tapping into a fractional AI team. It might just be the accelerator you need to leap forward.
At Xyonix, we specialize in providing AI expertise to companies looking to innovate quickly. Our approach blends seamlessly with your team. We know time is critical, and our services like our AI Innovation Accelerator are designed to infuse AI into your products in record time.
Ready to supercharge your AI initiatives?
Learn how our Innovation Accelerator can embed top-tier AI engineers into your team and jumpstart your machine learning projects on a free exploratory call.
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Sources:
Roscoe, J. (2024, December 15). “I received a first, but it felt tainted and undeserved”: Inside the university AI cheating crisis. The Guardian. Retrieved from https://www.theguardian.com/technology/2024/dec/15/i-received-a-first-but-it-felt-tainted-and-undeserved-inside-the-university-ai-cheating-crisis
Lewis, C. (2024). National ChatGPT survey: Teachers accepting AI into classrooms, workflow even more than students. The 74 Million. Retrieved from https://www.the74million.org/article/national-chatgpt-survey-teachers-accepting-ai-into-classrooms-workflow-even-more-than-students/
Darling-Hammond, L., & Furger, R. (2024). Study: How districts are responding to AI—and what it means for the new school year. CRPE. Retrieved from https://crpe.org/study-how-districts-are-responding-to-ai-and-what-it-means-for-the-new-school-year/
Strauss, V. (2024, April). New data reveal how many students are using AI to cheat. Education Week. Retrieved from https://www.edweek.org/technology/new-data-reveal-how-many-students-are-using-ai-to-cheat/2024/04
Barnett, S. (2023). ChatGPT and academic integrity. Medium. Retrieved from https://medium.com/@malin114/chatgpt-and-academic-integrity-1aa73122dbed
Microsoft. (n.d.). Is using AI the same as plagiarism?. Microsoft 365 Life Hacks. Retrieved from https://www.microsoft.com/en-us/microsoft-365-life-hacks/writing/is-using-ai-the-same-as-plagiarism
NDTV. (2024). US professor uses ChatGPT to check papers, fails most students. NDTV. Retrieved from https://www.ndtv.com/feature/us-professor-uses-chatgpt-to-check-papers-fails-most-students-4046843
Hern, A. (2024, August 4). OpenAI’s ChatGPT text watermark “cheat detector” tool heads to public tests. The Verge. Retrieved from https://www.theverge.com/2024/8/4/24213268/openai-chatgpt-text-watermark-cheat-detection-tool
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