The State of AI in 2025: Most Organizations Remain in Experimentation While High Performers Transform
Across North America, organizations are racing to hire AI specialists—yet few are realizing the transformation they expected. Three years after the introduction of generative AI tools triggered a new era of artificial intelligence, McKinsey’s latest State of AI 2025 report reveals a sobering reality: while 88% of organizations report regular AI use in at least one business function, nearly two-thirds have not yet begun scaling AI across the enterprise. Most remain stuck in the experimentation or piloting phases, with the transition from pilots to scaled impact remaining a work in progress.
The challenge isn’t limited to strategy or infrastructure—it’s the AI talent acquisition bottleneck. Most companies lack access to an AI-ready workforce with the mix of data, software, and DevOps expertise required to move from pilots to production.
Nearly one in five hiring managers now say AI fluency is more important than a degree from a top school, according to a Nexford University survey. More than a quarter (26%) say AI fluency is now a baseline requirement. Meanwhile, McKinsey data shows that AI data scientists, data engineers, and machine learning engineers are the most in-demand roles, with 29-30% of larger organizations actively hiring.
This creates intense competition for limited talent pools, particularly in organizations pursuing AI transformation strategy facing intense competition for limited talent pools—particularly in expensive North American markets where salaries for AI engineering recruitment have skyrocketed.
At DevEngine, we address this AI talent access challenge through local and nearshore staffing. Since 2019, we’ve specialized in sourcing, vetting, and managing technical teams across Latin America—connecting Canadian and U.S. companies with software developers, data engineers, cloud specialists, and DevOps professionals who have the skills needed for AI and data infrastructure projects.
This article explores why most AI transformation efforts stall, what truly AI-ready teams require, and how nearshore staffing can help North American organizations access the technical talent needed to move from experimentation to scaled deployment.
Why Most AI Implementation Strategies Fail to Deliver Real Business Impact
The Widening Gap Between Executive Vision and Employee Reality
The enthusiasm around AI has created a talent rush—but hiring AI specialists doesn’t automatically translate to organizational capability. McKinsey’s November 2025 research reveals that while reported cases of enterprise-wide EBIT impact are limited, only 39% of respondents report any level of EBIT impact from AI, with most indicating less than 5% of organizational EBIT is attributable to AI use.
Even more troubling is the perception gap between leadership and frontline employees. Recent research from BCG and Columbia Business School reveals a sobering disconnect: executive leaders are 51 percentage points more likely than individual contributors to think employees are well-informed about AI strategy (80% vs 29%). They’re 45 points more optimistic about employee enthusiasm (76% vs 31%). These massive perception gaps create real challenges—especially with AI adoption, where fear competes with opportunity.
The disconnect between AI hiring and operational impact happens at multiple levels:
Structural misalignment: AI professionals need access to clean, well-governed data pipelines. Many organizations lack the data engineering infrastructure required to support machine learning workflows, leaving AI specialists spending 80% of their time on data wrangling rather than model development.
Workflow incompatibility: Traditional software development processes weren’t designed for the experimental, iterative nature of AI work. Some organizations are adopting AI-assisted development approaches—using tools like GitHub Copilot or Cursor to accelerate prototyping.
While rapid AI-assisted coding (sometimes called “vibe coding“) can speed initial development, it comes with significant risks: Google’s 2024 DORA report found that a 25% increase in AI usage correlated with a 7.2% decrease in delivery stability, and GitClear identified an 8x increase in duplicated code blocks (Copy/Pasted) from AI tools. A 45% of the cases LLMs models introduce a detectable OWASP Top 10 security vulnerability rate into the code, and Forrester predicted in 2024 that 75% of technology decision-makers will see their technical debt rise to moderate or high levels of severity by 2026, driven by the rapid development of AI solutions adding complexity to IT landscapes. More recently, Forrester’s 2026 predictions warn that enterprises will defer 25% of planned AI spend to 2027 as financial rigor slows production deployments, forcing a market correction to align expectations with reality.
Organizations need engineers who can leverage AI tools for rapid prototyping while maintaining the architectural discipline and code quality standards required for production systems.
Integration gaps: AI models don’t exist in isolation—they require deployment pipelines, monitoring systems, and feedback loops. Without MLOps capabilities and DevOps integration, even excellent models fail to reach production or deliver business value.
Cultural resistance and trust deficits: Organizations hire AI experts but maintain decision-making processes that don’t account for probabilistic outcomes, model limitations, or the need for continuous retraining. When people don’t feel valued or understand the AI strategy, they won’t take the risks that AI adoption requires.
The pattern is consistent: pilot success followed by production failure. A proof-of-concept model performs well in controlled conditions, but scaling it across the organization reveals fundamental gaps in infrastructure, governance, and team composition. McKinsey’s research confirms this, showing that approximately one-third of organizations report beginning to scale their AI programs, with larger companies significantly more likely to have reached the scaling phase.
Building AI capabilities requires multi-disciplinary teams (data engineers, ML engineers, software engineers, DevOps specialists), but talent costs and scarcity make this financially challenging—especially in North American markets. Organizations compete for the same limited pool of AI specialists. Larger organizations are actively hiring for AI data scientists, data engineers, machine learning engineers, and software engineers.
Defining What It Means to Be “AI-Ready” in 2025
The Technical Teams Required for AI Transformation
Being AI-ready extends far beyond having data scientists on staff. McKinsey’s research reveals that 64% of respondents say AI is enabling their organization’s innovation, while nearly half report improvement in customer satisfaction and competitive differentiation. Yet these benefits remain concentrated among organizations that have built the complete technical teams to support AI work.
Data engineering as foundation: AI-ready organizations have teams that build and maintain data pipelines with clear ownership, documentation, and access protocols. Data engineers are among the most in-demand roles because without clean, reliable data infrastructure, AI initiatives can’t progress beyond experimentation. Organizations looking to hire Data Engineers in Latin America benefit from accessing technical talent with cloud-native data pipeline experience at competitive rates.
MLOps and DevOps for production deployment: AI-ready teams need professionals who can build deployment pipelines supporting model versioning, A/B testing, and continuous monitoring. They understand that machine learning models degrade over time and maintain rigorous processes for retraining and validation—capabilities that become even more critical with agentic AI systems. Yet the 2025 State of AI research shows only 23% of organizations are successfully scaling agentic AI, and Gartner predicts that more than 40% of agentic AI projects will be cancelled by the end of 2027. These statistics underscore a crucial reality: deployment expertise isn’t just about building systems faster—it’s about building them with the governance and monitoring required to sustain them. Business leaders must resist the temptation to deploy agentic AI indiscriminately and instead focus on use cases where robust MLOps capabilities can support measurable business value.
Software engineering for integration: AI capabilities don’t deliver value in isolation. Organizations need software engineers who can integrate AI/ML models into applications, build user interfaces, and connect AI systems to business processes in marketing and sales, strategy and corporate finance, and product/service development.
Cloud and infrastructure specialists: Modern AI work happens in cloud environments. Organizations need professionals skilled in AWS, Azure, GCP, and Snowflake to build the infrastructure that enables AI experimentation and deployment at scale.Cross-functional collaboration: AI-ready organizations structure teams with all these roles working together throughout the project lifecycle, but face intense competition and high costs in North American markets. Organizations looking to hire Data and Machine Learning Engineers must evaluate not just technical depth, but AI-native workflows and fluency.
The Human-Centric Reality
Technical excellence alone doesn’t drive AI success. Even with advanced data pipelines and MLOps frameworks, organizations fail when employees don’t trust or understand the purpose behind AI initiatives. Sustainable transformation comes from aligning technology with human experience—ensuring teams feel informed, empowered, and safe to innovate alongside intelligent systems.
Research from BCG and Columbia Business School reveals what actually drives AI adoption: employee centricity explains 36% of variance in AI maturity—significantly more than industry sector (14%), department (12%), or company size (5%). This finding fundamentally challenges the assumption that technical capabilities alone determine transformation success.
The impact is measurable. Organizations with high employee centricity see teams that are 70% more likely to feel enthusiastic about AI adoption, 92% more likely to feel well-informed about AI strategy, and 57% more likely to rate their organization’s technology adoption speed as faster than competitors. This translates to a specific design principle: the organizations succeeding at AI build human-in-the-loop systems where AI augments rather than replaces decision-making. They invest equally in technology and people—providing clear communication about AI strategy, creating psychological safety for experimentation, and building trust through transparency about how AI will change, not eliminate, roles.
The Missing Link: Accessing Technical Talent for AI-Ready Teams
Research confirms that both technical excellence and employee-centric culture determine AI maturity. Yet most North American organizations struggle to assemble complete, AI-ready teams—not because of vision or budget, but because AI engineering recruitment in Canada and the U.S. has become prohibitively competitive.
Why Talent Access Determines AI Transformation Success
Building AI capabilities requires multi-disciplinary teams working together: data engineers who build reliable infrastructure, ML engineers who develop and refine models, software engineers who integrate AI into applications, DevOps specialists who deploy and monitor systems, and cloud architects who enable experimentation at scale.
In Vancouver, Toronto, and major U.S. tech hubs, competition for these specialized roles has made it financially prohibitive for mid-sized organizations to build complete teams. The result is a predictable pattern: organizations hire one or two AI specialists, but without the surrounding infrastructure roles, AI initiatives stall in the pilot phase. McKinsey’s data confirms this, showing that nearly two-thirds of organizations remain stuck in experimentation despite reporting regular AI use.
This talent access gap isn’t just about technical skills—it’s about building teams where engineers integrate well, feel supported, and can collaborate effectively. Cultural fit, communication capabilities, and shared working norms matter as much as technical depth when creating the psychological safety that drives AI adoption.
How Nearshore Staffing Enables Complete Team Composition
Nearshore AI staffing bridges this talent gap by connecting North American companies with equally skilled professionals across Latin America. With competitive compensation structures and strong English proficiency, nearshore AI development teams deliver cost efficiency and cultural alignment—without sacrificing quality or collaboration. This model delivers three strategic advantages:
Cost efficiency without quality compromise: Organizations achieve 25-35% cost reductions—not by lowering standards, but by accessing talent in markets where compensation expectations differ. This financial advantage enables mid-sized companies to build the complete, multi-disciplinary teams that AI projects require, rather than attempting transformation with incomplete capabilities.
Time zone alignment for real-time collaboration: Latin American professionals work in time zones (UTC-3 to UTC-6) that overlap significantly with North American business hours. This enables real-time standups, collaborative debugging sessions, and synchronous communication that offshore models cannot match—critical when teams need to iterate quickly on AI experiments or troubleshoot production issues together.
Cultural proximity that reduces integration friction: Latin American engineering talent shares business norms with North American teams. English proficiency is standard, agile methodologies are well-established, and professionals understand North American project expectations and communication styles. This cultural compatibility eliminates the common offshore challenges around stakeholder engagement and project management approaches.
DevEngine’s Approach to Building AI-Ready Nearshore Teams
As AI implementation partners across North America, DevEngine has specialized in connecting Canadian and U.S. organizations with technical talent across Latin America—primarily Costa Rica, Argentina, Brazil, Mexico, Colombia, and Panama since 2019. These regions combine strong STEM education systems with growing AI-native technical expertise and professionals actively developing machine learning capabilities.
Our model focuses on core elements that address the specific challenges North American organizations face when building distributed AI teams providing a long long-term team stability rather than transactional placements.
Take the Next Step in Your AI Transformation
Your AI initiatives shouldn’t stall because of talent access challenges in expensive North American markets. DevEngine specializes in AI staff augmentation and nearshore AI development teams that enable Canadian and U.S. organizations to build complete technical capabilities at 25-35% cost savings.
Schedule a consultation to discuss your specific AI transformation requirements and how nearshore talent can accelerate your path from experimentation to scaled deployment.