The initial hype surrounding artificial intelligence (AI) is beginning to fade. Concerns about global financial markets and the threat of investors U-turning on the prospects for AI, coupled with the news that as many as 95% of AI projects fail, are now prompting organizations to pull back from the rapid and somewhat impulsive approach to its adoption.

A reset is a welcome opportunity for learning leaders to look past the hype and identify the operational outcomes that will provide tangible value. Critically, it should herald a commitment to understanding the implications of AI technologies for staff, skills and future career progression.

AI, or any other innovation, can and will deliver business benefits with the right approach, but this will demand new operational models built on different technology skills and a shift in management mindset. Not to mention it will significantly alter the roles and experiences of millions of employees worldwide.

It is imperative, therefore, that training and learning must be embedded at every level of the business. Learning must be integrated into everyday work and clearly aligned to both organizational goals and individual growth. After all, this is the foundation for continual improvement.

The pace of AI adoption has amplified the decades long underinvestment in the people, skills and training required to successfully innovate. Organizations that act now — by shifting their focus towards the skills required to deliver business outcomes — will lead the way in achieving long-term AI success.

The Cost of a Technology-First Mindset

Despite the estimated $30–40 billion spent by businesses on generative AI, pilot failure has become the norm. A 95% failure rate is high, but not unprecedented. Enterprise resource planning (ERP), customer relationship management (CRM) and other large-scale digital transformation initiatives have followed similar patterns for years. The extraordinary pace of AI awareness and adoption has simply amplified a longstanding issue.

Many organizations lack the right approach, capabilities and culture to successfully adopt new technologies. While the potential for long-term impact is significant — McKinsey estimates the long-term AI opportunity could be worth $4.4 trillion in added productivity — realizing this value requires more than experimentation.

Executive teams often lack clarity regarding the strategic direction of AI investment and, as a result, fail to make the rapid changes to workforce skillsets, engagement and experience required for successful change. From defining the necessary additional skill sets for project delivery to assessing the implications for existing roles, many organizations are simply not committing to the essential workforce rebalancing and training required.

Begin With Outcomes, Not Tools

Rather than looking at how to use a large language model (LLM) or automate a task, organizations need to start by defining the outcomes they want to achieve:

• What business problem is being solved?
• What skills will be required to reach those goals?
• What are the implications for existing job roles?

Answering these questions helps learning leaders identify where reskilling or redeployment is possible and what employees need to learn to succeed in new or evolving roles.

Skills gaps are a primary contributor to project failure. The result of layering AI over the top of the existing lack of knowledge and expertise is inevitable: faster failure. Without the right foundation, including project management, communication and leadership skills, failure will continue. The starting point is to close the skills gap, and that requires not only a far stronger commitment to training and learning, but one predicated on clearly defined business and personal outcomes.

Linking Learning to Measurable Results

Using a learning management system (LMS) that supports every facet of workplace learning, from creating and delivering training to tracking progress and reporting on results, allows organizations to focus on measurable outcomes.

By connecting learning activities to business priorities, organizations gain visibility into progress and performance. Employees, in turn, gain a clearer view of how their development aligns with the organization’s goals, helping them take ownership of their learning journey.

For learning leaders, this connection is critical. It shifts conversations with executives from course completion to capability building, and from activity metrics to business results.

Continuous Skills Development

Organizations that recognize the value of training directly tie learning and development to specific outcomes, building a model of continual iteration and improvement. This helps create a culture where any innovation is considered on the basis of specific goals rather than generic promises. As such, it supports organizations in prioritizing projects, robustly assessing technologies and, as a result, defining the next generation of skills, learning and retraining required.

This approach, however, is at odds with the current model. Rather than investing in skills, organizations are using AI as an excuse for job cuts, underlining the attitude that has prevailed for decades, where investment in training and development has been continually sidelined by businesses. The result has not only been inevitable skills shortages that continue to undermine productivity and performance at both business and national level but expensive and disruptive employee turnover: 94% of workers said development opportunities would keep them in a role.

Preparing for a Transformed Workforce

The pressure of AI-led innovation is now placing a spotlight on this failure. According to the World Economic Forum, 1.1 billion jobs are likely to be radically transformed by technology in the next decade. Aneesh Raman, LinkedIn’s chief economic opportunity officer, maintains that by 2030, 70% of the skills required for the average job will have changed.

Without a deliberate strategy for building these capabilities, organizations risk continued AI project failure. As a result, AI will not replace human jobs at the scale implied by many of the companies making cuts, and millions of highly intelligent and experienced individuals will have lost opportunities and been sidelined for no reason.

Ultimately, AI success hinges less on the technology itself and far more on whether organizations develop the capabilities needed to use it effectively. By focusing on measurable outcomes, not technology, and ensuring the right skills are being continuously developed to achieve those outcomes, organizations can realize AI’s potential while bringing their workforce along on the journey.