How Nearshore Teams Provide the Sustained Capacity Production AI Actually Requires

Most AI projects fail not because the models don’t work, but because nobody can keep them running after launch.

Gartner estimates that only 48% of AI projects make it to production, and those that do take an average of eight months to get there.

The challenge comes down to sustained engineering capacity, with underlying structural issues.

Data pipelines need constant maintenance, monitoring requires round-the-clock attention, and MLOps automation demands specialized expertise that most teams simply don’t have. At the same time, US-based MLOps engineers command premium salaries that quickly strain budgets.

This is where nearshore engineering teams from Latin America step in, offering real-time collaboration, proven technical depth, and 40–60% cost savings compared to domestic hiring.

In this article, you’ll see which production practices nearshore teams accelerate most, how they integrate seamlessly into existing workflows, and what a resilient, production-grade AI lifecycle actually looks like.

Why Nearly Half of AI Projects Never Make It to Production

Building a model is one phase of the work. Keeping it running in production is another.

Many teams are well equipped for experimentation, but far less prepared for the engineering required to operate systems over time.

Recent research paints a sobering picture. 95% of generative AI pilots deliver zero measurable return, and 42% of companies abandoned most AI initiatives in 2025, up from just 17% the year before.

The barriers are consistent across industries. Informatica’s 2025 CDO Insights survey identifies data quality and readiness as the top obstacle (43%), followed by lack of technical maturity (43%) and data literacy (35%).

RAND reports similar outcomes. Its research shows that around 80% of AI projects fail, roughly twice the failure rate of traditional IT initiatives.

Source: RAND

What these surveys describe, in different ways, is the same pattern.

Most failures begin much earlier, in how problems are defined and in the data organizations have available, and in how systems are designed around technology rather than user problems.

Those gaps often remain hidden during pilots. They surface later, when systems have to be deployed, integrated, and maintained inside real organizations.

Under scale, infrastructure becomes a constraint. Platforms built for current needs struggle as traffic grows and data volumes increase, while the small pool of engineers who know how to deploy, monitor, and maintain these systems is stretched thin.

This is why production success depends less on the model and more on the operational work around it.

Six Practices Where Nearshore Teams Create Outsized Value

Nearshore teams from Latin America are particularly good at the operational work AI systems require. Here are the key practices they accelerate most:

Data Pipelines That Stay Reliable Under Pressure

Bad data quality kills more AI projects than anything else, yet keeping pipelines healthy takes constant work. Because data flows from multiple sources with inconsistent formats, this means values could change meaning over time, and upstream systems can modify their output without warning.

Keeping this reliable requires ongoing engineering effort, and good data engineers are expensive and hard to hire in the US.

Fortunately, nearshore teams from Latin America bring that same data engineering expertise at a significantly lower cost base, working in overlapping time zones that enable real-time collaboration when pipeline issues surface.

AI Validation That Proves Value Before You Scale

Most AI failures happen because teams move too quickly from idea to full production without first validating whether a solution actually works for their specific use case. Without early evidence, organizations struggle to estimate ROI, making it difficult to secure budgets and justify continued investment.

This is where disciplined validation makes the difference. Nearshore teams help test AI solutions with real data in controlled environments before committing to full-scale deployment.

For example, through Validate:AI, GAP runs structured proof-of-concept projects that verify technical feasibility, measure performance under real conditions, and quantify business impact using your actual data. This surfaces compatibility issues and establishes ROI when changes are still easy to make.

Monitoring That Catches Problems Before They Escalate

Effective monitoring requires sustained attention that most internal teams simply don’t have bandwidth to provide.

Models degrade gradually, which makes problems difficult to detect until performance has already deteriorated significantly. By the time business metrics reflect the issue, considerable value has often been lost.

This gap is usually a monitoring problem. Nearshore teams establish comprehensive monitoring and configure alert systems that trigger investigation as soon as key thresholds are crossed, preventing small issues from becoming production incidents.

Deployment Automation That Moves at the Speed You Need

When deployment is manual, every update becomes a project. Teams have to coordinate across functions, spend days testing by hand, and move cautiously to avoid breaking production. Over time, this turns routine improvements into a bottleneck.

Finding MLOps engineers who can build proper CI/CD pipelines is tough. However, nearshore teams bring engineers who know how to automate testing, set up deployment systems, and put safeguards in place so updates can move quickly without sacrificing reliability.

Building Infrastructure That Scales Without Constant Rebuilds

Infrastructure decisions made during initial development often shape everything that follows. Teams optimize for getting prototypes working, not for the growth those systems will face in production.

The cost of those early choices shows up later. Systems that were easy to launch become hard to extend, and scaling starts to require redesign instead of configuration.

Nearshore teams bring production engineering experience that informs infrastructure decisions from the start. They implement cloud-native architectures that scale horizontally, design data systems that handle volume growth gracefully, and configure monitoring that provides visibility as systems expand.

They stay engaged as systems mature, continuously optimizing infrastructure to maintain performance as usage grows. This prevents the technical debt that accumulates when teams move on to new projects immediately after launch.

Model Retraining That Prevents Silent Performance Decay

Models don’t fail suddenly. They degrade gradually as real-world conditions drift away from training data. Customer behavior shifts, market dynamics evolve, regulatory requirements change. Each of these forces chips away at accuracy until predictions become unreliable.

Without proactive retraining, organizations only discover drift after it’s already damaged business outcomes. Studies show that 91% of machine learning models degrade over time if nobody’s actively managing them.

Nearshore teams implement automated retraining pipelines that respond as soon as drift detection systems signal degradation. They define clear performance thresholds, trigger retraining when accuracy drops, and automate the process of collecting fresh data, validating improvements, and deploying updated models. This continuous learning approach keeps AI systems relevant as conditions evolve, rather than letting them become disconnected from production reality.

How Nearshore Teams Integrate to Support Continuous Innovation

The question for most engineering leaders comes down to integration: will adding nearshore teams help your platform evolve or just create more coordination overhead?

Latin American engineers work in US time zones or within 1–3 hours, which means urgent issues get addressed immediately. This turns nearshore teams into actual teammates who can participate in rapid iteration instead of external contractors you need to manage.

When you compare this to traditional offshore development, the difference becomes obvious. Offshore teams operating 12 hours ahead create coordination delays that slow everything down.

The best nearshore partnerships assign dedicated engineers who join your team’s tools, processes, and communication channels. They participate in sprint planning, contribute to architectural decisions, and take ownership of specific systems instead of just taking orders.

This means they understand context, anticipate what you need, and make decisions that align with your broader strategy. The advantages of choosing nearshore over offshore or onshore become clear when teams work this closely together.

Good nearshore engagements also establish clear boundaries so everyone knows who owns what. These teams take full ownership of specific areas like data pipeline maintenance, monitoring infrastructure, and deployment automation while maintaining clear interfaces with your internal teams. This reduces coordination overhead and lets both groups move faster.

Self-Assessment: How Close Are You to Production-Grade AI?

Use these questions to figure out where you stand:

• Can your infrastructure handle 10x growth? If traffic or data volume jumped by an order of magnitude tomorrow, would your systems keep running or would everything break?
• Are your pipelines automated with quality checks? Do your data pipelines validate inputs automatically, flag problems before they reach models, and maintain consistent transformations across training and production?
• Do you catch model degradation within hours or days? When models start drifting, do you get alerted immediately or do you only find out after business metrics take a hit?
• Can you deploy updates quickly? How long does it actually take to move a model improvement from development all the way through to production?
• Is testing automated for AI-specific issues? Do your tests validate data quality, check for drift, monitor prediction patterns, and verify model performance before deployment?
• Are you preventing problems or reacting to them? Do you have systems that catch issues before they affect production, or does your team spend most of their time firefighting?

If you answered “no” or “unsure” to more than two of these, your AI systems probably need more production engineering capacity.

You can also try out our AI readiness assessment calculator for a better rounded evaluation.

How GAP Builds Production-Grade AI With Nearshore Teams

At GAP, we’ve spent over 18 years building nearshore teams that integrate with how our clients work. Our engineers across Latin America work on the operational problems that keep AI systems running after launch, from data engineering to machine learning operations to infrastructure work.

Our teams bring expertise in building robust data pipelines, designing ML algorithms, and creating systems that give your products a competitive advantage.

Our teams work in your time zone and collaborate directly with your internal teams. This means stand-ups happen together, communication flows naturally, and you get the cost benefits of nearshore hiring without the coordination delays that come with offshore teams.

The companies we work with see the impact over time, when their models are still performing well, their pipelines are still running, and they’re not scrambling to fix problems that could have been prevented. You can see examples in our portfolio.

Scale Your AI With Nearshore Engineering

Most AI projects fail because teams can’t sustain the work needed to keep them running. Data pipelines break, models drift, infrastructure doesn’t scale, and monitoring falls through the cracks.

The companies succeeding with AI aren’t necessarily the ones with the best models, but the ones who figured out how to maintain and evolve their systems over time.

Nearshore teams from Latin America give you the capacity to do that work without breaking your budget. They bring the expertise in data engineering and MLOps that’s hard to find domestically, they work in your time zone so collaboration actually happens, and they cost significantly less than US hiring.

If your AI systems are stuck in pilot mode or degrading after launch, the problem usually isn’t the technology. It’s capacity. And that’s exactly what nearshore teams solve.

Contact GAP to discuss how nearshore engineering teams can help keep your AI systems working and delivering value long after launch.