Overcoming the AI Talent Conundrum: Build, Buy, or Borrow?

The numbers about artificial intelligence (AI) paint a vivid picture: 78 percent of organizations are using AI in at least one business function today. About 89 percent are advancing initiatives in generative AI (genAI). With a compounded annual growth rate (CAGR) of 36 percent, AI investments are rising across industries. AI has clearly become a critical strategic lever in key business functions, with leaders across sectors reporting AI-driven gains in profitability and growth. 

Enterprise constraints to this momentum are often less about models and infrastructure, and more about whether enterprises have the right human talent for AI-driven innovation. Organizations face a critical decision in acquiring AI talent to unlock business value. Leaders are learning that they must navigate the strategic balance between training their existing workforce for AI at the speed and depth required, versus trying to hire AI talent in an overheated market.  

The skills gap is huge: surveys consistently show AI adoption outpacing workforce readiness by a wide margin. For instance, research from McKinsey indicates that many leaders cite lack of in-house AI expertise as a top barrier. There are large shortfalls in AI project management and responsible AI roles, which are hard to fill, because industry demand significantly exceeds supply.​ 

This has become a dilemma that worries organizations: how to build an AI-skilled workforce without stalling transformation or over‑automating decisions? 

Talent is the critical enabler for accelerating and scaling AI adoption across the enterprise. The challenge is not just acquiring AI talent but scaling it responsibly and sustainably to deliver business value. This requires a strategic approach that aligns with evolving roles and a flexible operating model that supports speed, agility and long-term capability.  

Upskilling Alone, Just Not Enough 

Many enterprises rely on upskilling and reskilling, coupled with redesigning work, as their primary strategy to close AI skills gaps. However, many enterprises underfund training programs or offer fragmented courses to employees. For example, analyst data suggests that nearly half of the workers want formal training in genAI but fewer than a quarter feel supported, creating a structural readiness gap for scaling.​ 

Few enterprises have consistent curricula, hands-on projects or mature learning architectures — that measure skill progression, define role taxonomies or offer incentive systems — to make training stick.​ This makes training superficial or even unproductive. 

The reasons aren’t hard to find. For one, training takes time. Productivity dips temporarily. Learners need real project work for impactful outcomes. Some capabilities, such as senior ML engineering, model risk management or advanced machine learning operations (MLOps) are too deep or too urgent to build quickly from scratch, especially in regulated or critical domains. 

Add to the mix, the fact that generic courses could increase corporate risk: employees gain confidence without depth. This could lead to overreliance on AI outputs, poor prompt practices among employees and mounting of weak challenges to model recommendations.​ Enterprises end up with tool‑first deployments that outstrip human capability, thereby elevating operational, ethical and reputational risk.​  

Building talent in-house fosters long-term capability and custom solutions but requires significant investment and time. 

Acquisition Alone, Not a Panacea  

On the other side, many leaders default to buying AI talent because they worry that they cannot train fast enough. Yet, the talent market is capacity‑constrained. Time-to-fill is often measured in months, which can be a huge problem for AI program timelines. Competition for talent is intense, and traditional hiring channels are not enough in the search for top engineering and data talent. 

The demand for AI skills has been growing at double‑digit annual rates, which is far faster than supply, making lateral hiring expensive, slow and uncertain. This could leave many AI roles unfilled through the middle of the decade, especially in senior engineering and MLOps roles.​ The imbalance can also lead to cost overruns, stalled pilots and governance gaps, as the organization may have too few people who understand both AI and compliance.  

Buying experienced talent accelerates innovation and brings expertise, though at a high cost and with retention risks. Also, new hires bring technical depth but often lack the critical institutional and domain contexts needed to make high-stakes decisions for ethical and responsible AI. Besides, over‑reliance on a fragile layer of experts ends up creating brittle AI functions and ‘key‑person risk’: meaning, a small cadre of so-called AI heroes whose departure could materially disrupt programs and slow the diffusion of skills into the broader workforce. 

Talent Transformation Strategies 

The answer to the skills-vs-hiring conundrum starts with big picture thinking:  

• First, leaders must personally own AI capability, not just AI spend: make skills, ethics and human‑in‑the‑loop discipline board‑level priorities on par with revenue, cost and cyber risk. That translates to shifting from ad‑hoc training and slogans about responsible AI to funded, measured programs that deliberately build human capability alongside automation.​ 

• Second, build a portfolio of strategies: tackle talent transformation with a continuous build–buy–borrow playbook. Decide which AI capabilities must be grown internally over time, which roles need recruitment immediately and where partners can temporarily bridge gaps. Sequence the investments to make this happen over a multi‑year roadmap.​ Govern all the three levers so that ethics, human judgment and critical thinking are strengthened, rather than hollowed out by AI.  

Here are more ways to build a talent transformation framework: 

1. Make the skills gap an executive KPI:Measure AI workforce readiness like financial metrics. Require every AI initiative to include funded upskilling workstreams. Hire selectively for expert roles suchas, AI architects, senior ML engineers and model-risk leads. Use partners as force multipliers but insist on knowledge transfer and internal ownership. 

1. Build an enterprise AI learning system:Work with experts to create a structured curriculum that takes teams from basic literacy to advanced technical pathways, and ensure the curriculum is tailored forparticular roles. The system must embed learning on the job through micro-modules, sandboxes, labs and mentoring tied to live projects, rather than just e-learning or classroom sessions. This enables organizations to continuously upskill employees, democratize AI knowledge and embed AI capabilities into business processes.

1. Redefining Roles, Reshaping Strategy:The traditional roles are shifting and expanding. Emerging positions like AI strategists and business translators who bridge AI initiatives with business outcomes, AI governance and ethics specialists ensure responsible AI practices. These, along with several other evolving roles, demands hybrid skill sets—technical depth combined with domain and ethical understanding—are key to future-proofing talent

1. Build the ‘how’ of responsible AI:The “how” focuses on embedding fairness, transparency,accountability and ethics into every stage of the AI lifecycle. Many companies are establishing cross-functional AI councils with model-risk frameworks covering data quality, bias, explainability and monitoring. Where they can go further is in defining clear human-in-the-loop standards by illustrating when AI is advisory and when AI is making decisions. This may mean detailing the required checks in workflows, and in building escalation protocols with teams. 

1. Redesign work to preserve human judgment:Talent transformation teams can redesign current roles into AI-enabled roles that keep humans as the primary decision-makers. Make it an organizational mandate to pair AI with human review, explanation and challenge frameworks across sales, risk, operations and support. Set norms that question AI outputs, cross-check data and treat models as tools, not oracles. This will transform recruitment strategically, as well. 

Ultimately, the key is in striking just the right balance between upskilling and hiring talent. The right mix of these strategies directly impacts business value generation by enabling faster AI adoption, improving ROI and ensuring scalability. Success in the AI talent war lies in solving the strategic problems of overcoming training deficit, skills gaps and overreliance challenges.