Workforce & Engineering Talent
AI adoption is moving quickly. Building the teams needed to support it is not.
Enterprises are investing in generative AI, data platforms, cloud modernization and AI-powered applications. But behind every successful AI initiative is a team of engineers who can prepare the data, connect systems, build applications, manage infrastructure and take the solution into production.
That is where many organizations are facing a growing challenge: the AI talent gap.
McKinsey found that 47% of surveyed C-suite leaders believe their organizations are developing and releasing generative AI tools too slowly. Among those leaders, talent skill gaps were the most frequently cited reason for the delay.
The problem is no longer simply finding developers.
It is finding people with the right combination of engineering, data, cloud and AI skills and bringing those skills together quickly.
What Is the AI Talent Gap?
The AI talent gap is the shortage of professionals with the skills needed to build, deploy and scale AI-enabled technology. And it goes beyond hiring AI specialists.
An enterprise implementing AI may need:
- AI and machine learning engineers
- Data engineers and architects
- Cloud engineers
- Software developers
- DevOps professionals
- QA and automation engineers
- Cybersecurity specialists
- AI integration specialists
The challenge is that these skills rarely sit within a single role or team. A company may have strong software developers but limited AI expertise. Another may have data scientists but not enough cloud or application engineering capacity.
This is why simply adding an “AI engineer” to the team does not automatically make an organization AI-ready.
AI Projects Need More Than AI Engineers
Consider a bank building an AI assistant for relationship managers. The objective sounds straightforward: help employees find customer information, summarize interactions and surface relevant insights.
But the AI assistant is only the visible part of the project.
Behind it, the organization may need engineers to connect CRM, core banking and data warehouse systems. Data teams need to make information reliable and accessible. Cloud engineers need to create the right infrastructure. Security teams need to control access to sensitive customer data. Application developers need to integrate the AI capability into existing workflows.
So, the real project looks more like:
Data → Integration → Application → AI → Security → Testing → Deployment
This is why enterprises often struggle to move an AI initiative from a successful pilot into everyday business operations.
The Shift from Hiring Roles to Building Teams
Traditional recruitment asks:
“Which position do we need to fill?”
AI-driven transformation requires a different question:
“What team do we need to deliver this outcome?”
For example, a company modernizing a legacy application may need a combination of:
- Cloud architects
- Application engineers
- API developers
- DevOps engineers
- QA specialists
- Data engineers
Hiring each person separately can take time. It can also leave the organization responsible for coordinating several new resources before the project even gets moving. A flexible engineering workforce can help bring these capabilities together around a specific project or business objective.
A Real-World Example: Manufacturing AI
Take predictive maintenance in manufacturing. A manufacturer may use AI to predict when equipment is likely to fail. The data scientist develops the predictive model using historical machine and sensor data.
But that model does not create value sitting in a data science environment.
Someone still needs to:
- Connect machine and operational data
- Build reliable data pipelines
- Integrate the model with existing systems
- Deploy the solution to the appropriate cloud or edge environment
- Automate testing and deployment
- Monitor performance
- Maintain the application
The lesson is simple:
AI may start the project, but engineering makes it usable at scale.
The Skills Gap Is Changing, Too
The workforce challenge is not only about the number of people available. The skills themselves are changing.
A software engineer may now need to work with AI APIs and AI-assisted development tools. A data engineer may need to build pipelines designed for AI workloads. A cloud engineer may need to understand the infrastructure requirements of generative AI applications.
Even QA teams are changing as AI-enabled applications introduce new testing challenges. McKinsey’s 2025 research found that demand for AI fluency increased nearly sevenfold between 2023 and 2025 across occupations where AI-related skills appeared in job postings.
That does not mean every employee needs to become an AI engineer. It means more technology roles need to become AI-capable.
Upskilling Is Part of the Answer
Enterprises should not look outside the organization for every new skill. Existing employees already understand the company’s products, customers, processes and technology environment. That knowledge is difficult to replace.
Upskilling these teams can therefore be just as important as hiring new specialists.
The World Economic Forum’s Future of Jobs Report 2025 found that 77% of employers expect to pursue workforce upskilling or reskilling in response to AI disruption through 2030.
A practical approach is to combine both.
Upskill the people who already understand the business.
Bring in specialists where expertise is difficult to build quickly.
This creates a workforce that can adapt without losing valuable institutional knowledge.
When Should Enterprises Bring in External Engineering Talent?
Not every skill needs to become a permanent internal capability.
External engineering talent can make sense when an organization:
- Needs specialized skills for a transformation project
- Has a temporary shortage of engineering capacity?
- Needs to accelerate application modernization
- Is moving workloads to the cloud
- Wants to launch an AI or data initiative faster
- Needs additional QA or DevOps capacity
- Has difficulty finding niche technology skills locally
For example, an enterprise may have a strong internal product team but lack enough cloud engineers for a six-month modernization program.
Rather than delaying the project until permanent hiring is complete, it can add specialized engineering capacity for the transformation and allow the internal team to retain ownership of the product and business roadmap.
What Does an AI-Ready Engineering Workforce Look Like?
An AI-ready workforce is not simply a large technology team.
It is a team with the right combination of capabilities.
AI + Data
Engineers who can prepare data, build AI solutions and connect intelligent capabilities to business processes.
Cloud + Infrastructure
Specialists who can create scalable and secure environments for modern applications and AI workloads.
Application Modernization
Engineers who can connect legacy systems with APIs, cloud platforms and modern applications.
DevOps + Quality Engineering
Teams that can automate testing, deployment and monitoring so new technology can move into production reliably.
Business + Technology Understanding
Technical professionals who understand not only how to build the solution but also why the business needs it. That last capability is often overlooked. Technology transformation is rarely successful when engineering teams work in isolation from business objectives.
The Future of Enterprise Engineering Is More Flexible
Technology requirements are changing faster than traditional workforce planning cycles. A company may need one set of skills for a data modernization project today and a completely different combination for an AI implementation six months later.
That makes workforce flexibility increasingly important. Instead of building every capability permanently in-house, enterprises can combine:
Internal teams + specialist talent + workforce upskilling + flexible engineering capacity
This gives organizations access to skills when they need them while keeping strategic knowledge and decision-making within the business.
Closing the AI Talent Gap Starts with the Right Workforce Strategy
The AI talent gap is not going away simply because enterprises hire more people. The bigger opportunity is to rethink how engineering teams are built.
Organizations need a workforce strategy that can bring together AI, data, cloud, application and engineering expertise around specific business outcomes. The companies that move fastest will not necessarily have the biggest engineering teams.
They will be the ones that can access the right skills, build the right teams and scale those teams when business priorities change.
AI may be changing what enterprises build. But people still determine how quickly those ideas become reality.
Build the Engineering Capacity Behind Your AI Strategy
NAM Info helps enterprises access specialized workforce and engineering talent across AI, data, cloud, application modernization, DevOps and enterprise technology.
Whether you need to strengthen an existing team, support a transformation initiative or build engineering capacity for a new technology program, the right talent can help you move from planning to execution faster.
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