Build or Outsource AI/ML Expertise in Your Company?

Building an in-house AI team is a high-risk strategy fraught with crippling financial costs, slow deployment timelines, and a volatile talent market; this article uses 2024 data to break down of the true costs and risks of building an AI team and why outsourcing AI development to a proven team is a more cost-effective and sound strategy for delivering business results.

High AI salaries are just the beginning - hidden costs compound fast

Executives indeed see the potential of AI to optimise their operations and value propositions. However, they also are aware of the complexity, unpredictability, and high costs with uncertain returns of building an in-house AI team to develop AI innovation strategies.

The sticker price of an AI engineer’s salary is just the tip of the iceberg. The total financial commitment is a series of compounding costs that can quickly spiral. In the US, it is reported that the annual median salary is about $145,080 – and, depending on experience and location, it can go as much as $180k+ to $200k+ which can still rise due to equity and bonuses. 

On top of that, the standard 15-30% fees for hiring technical recruiters will be included in this expense, and hidden costs like failed hires and internal time investments can significantly increase the true recruiting expenses. Before a single line of code delivers value, building in-house can burn through a seven-figure budget with no guaranteed return.

On-premise AI infrastructure costs surged to $47.4 billion globally in 1H24, driven by compute and storage. Outsourcing to an OpEx model on the other hand, offers scalability and eliminates high upfront costs, with 72% of AI server spending in cloud environments. Cloud solutions reduce maintenance and energy expenses which allow businesses to flexibly scale AI capabilities without the capital-intensive burden of on-premise setups, making outsourcing a cost-effective choice for mid-to-large companies. With the natural, modern choice of cloud adoption offering scalability, flexibility, and lower maintenance, the same logic applies to renting AI development and innovation teams - build lean, execute fast, and reduce expenses.

Logistics company outsourced AI, saved €10,000s instantly while cutting emissions and optimizing routes

The most significant cost of building an in-house team isn’t measured in dollars, but in forfeited market share and strategic momentum. Generally, it takes 6-12 months to have an AI development team; and while your team is still in the hiring phase, competitors are already shipping their AI solutions.

• Case: Consider the case of a mid-sized logistics company that struggles with reducing underutilised and empty truck journeys – a challenge that affects their 30-40% freight operations.

• Solution: Instead of spending a year building a team, they partnered with AI and Machine Learning deployment specialists. AAI Labs deployed a team of machine learning engineers who developed and integrated a long-term, built-to-scale advanced route optimisation system in a fraction of time.

• Results: The system delivered immediate cost reductions and efficiency gains by minimising empty backhauls and eventually improving environmental impact where it reduced fuel consumption and emissions. Additionally, since it’s built for scalability, the system positions it as a valuable solution in the long term, especially with the model’s capabilities to be enhanced in advanced real-time tracking features and other potential integrations.

Six-figure AI experts or 2-year turnover cycles - both sink project budgets

Even with a blank check, building a successful and stable AI team is one of the riskiest bets in modern business.

More than 80% of AI project failures can be broadly attributed to a few critical factors, including:

• While many managers desire successful AI outcomes, too many lack the understanding needed to translate this ambition into effective action and;

• Data quality is a major cause of AI failure which 80% of success depends on skilled data engineering to avoid corrupted algorithms.

Compounding the challenge, there’s a huge hyper-competitive market, reporting around ~750k open jobs in the US alone, and requires applicants to be adept in any AI-related skill thereby shortlisting limited qualified candidates. This talent gap drives intense competition among employers to attract and retain AI specialists, which is further supported by wage premiums and rapid job growth in AI roles. This problem is made worse by high turnover, the second-highest turnover rate among industries at 12.9; the median tenure for a machine learning professional is only 1.2 years, forcing companies into a constant hiring cycle.

Hiring an AI team is a spiralling cycle, a very costly and risky one – six-figure bets on talent that big players poach overnight, with no guarantees you’ll ever have enough support around them. For companies looking to bypass the hiring risk, the AAI Labs service provides immediate access to a cohesive, pre-vetted team of 50+ engineers with a track record of successful deployments.

Outsourcing enables long-term innovation with ongoing support, context retention, and flexible adjustments as needs evolve

A common and valid concern with outsourcing is knowledge retention. What happens when the project ends? Are you left with a black box you can’t maintain or improve?

This is where our post-services relationship comes in. This solution is not a hand-off but a continuous collaboration for as long as you like, and as you need. The end goal of this service is to facilitate capability transfer to minimise dependency while maximising internal control and expertise. Our post-engagement support includes:

1. Comprehensive documentation, code repositories, and training for your in-house technical staff.

2. Access to the core development team for troubleshooting and critical support on an as-needed basis.

3. Periodic updates to retrain the model with new data, enhancing its effectiveness over time.

4. A streamlined process to add new features, integrate new data sources, or scale the solution as business requirements grow.

The bottom line

Any business would understand what a huge investment it would be to stay ahead of the curve – from financial investments, timing of opportunity, and human capital. Building an in-house AI team can stall progress especially if you have a strategic decision on gaining competitive advantage. 

This is why outsourcing such a business process would be a viable strategy if you prioritise speed, cost-efficiency, and risk mitigation – with world-class delivery and expertise guaranteed and not compromising value.