How Organizations Can Effectively Upskill for an AI-Driven Future

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What are the table stakes in a world increasingly driven by artificial intelligence (AI)?

Some might argue that the foundation lies in data mastery—access to high-quality, well-structured, and diverse datasets. Others would point to a robust technological infrastructure, seamless workflow integration, or a strong cybersecurity framework to protect sensitive information.

Yet, the most critical factor remains the skilled workforce—individuals who understand AI’s complexities and navigate its ethical, strategic, and operational challenges. Without human expertise to interpret, refine, and align AI with business objectives, even the most advanced systems risk failing to achieve their full potential.

According to the World Economic Forum’s Future of Jobs Report 2025, 44% of workers’ core skills will be disrupted in the next five years, with roles in data analysis, AI, and cybersecurity experiencing the highest demand. Simultaneously, traditional job functions are being reshaped, requiring workers to adapt to new digital tools and AI-driven decision-making processes. However, the skills gap remains a pressing challenge, with many organizations struggling to find talent equipped with the necessary AI and tech capabilities.

The question is no longer whether companies should train their employees in AI; it is how to do it effectively. 

Traditional corporate training methods, often generalized and disconnected from real-world business needs, are insufficient. The demand for AI-driven capability academies—targeted, role-specific learning ecosystems  seamlessly integrating AI skills into employees’ workflows—is rapidly growing.

Beyond One-Size-Fits-All: The Case for Adaptive Learning

One of the biggest barriers to AI upskilling is relevance. Employees, particularly those outside technical roles, often perceive AI training as too broad or too complex. A blanket approach—where every employee takes the same course—fails to address how AI  reshapes different job functions.

Adaptive learning is emerging as a solution. It uses AI to assess each learner’s baseline skills and tailor training accordingly. Personalized learning paths ensure that employees focus on what’s most relevant to their roles, making the process more efficient and impactful. 

“Organizations should begin by identifying the AI competencies essential for various roles and designing scalable training programs aligned with business objectives,” says Ritesh Malhotra, Chief Business Officer at Great Learning. 

The training programs should be developed  with industry experts and leading faculty, integrating hands-on learning, real-world case studies, and interactive labs to ensure immediate applicability. Moreover, these academies should be structured for scale, enabling continuous upskilling across multiple employee cohorts.

Use Case: AI Upskilling in Action

One of the most successful engagements at Great Learning, Malhotra says, is its GenAI academy. The initiative is designed to equip employees across multiple skill levels and roles with the knowledge and expertise to integrate Gen AI into everyday tasks.

The academy offers a GenAI 101 workshop, providing foundational AI skills to over 15,000 employees from diverse teams. The broad exposure ensures that AI fluency is not limited to technical teams but becomes an organization-wide competency. Employees engage in hands-on, prompt engineering techniques, applying their learning to real-world business challenges.

For technical teams, the program moves beyond the basics. Advanced practitioner skills are taught through short, role-specific workshops tailored to software engineers, AI/ML engineers, solution architects, and data scientists. Approximately 800 mid-to senior-level professionals have participated, many of whom were already proficient in GenAI and sought to advance their expertise in areas like agentic AI.

The training is provided through tailored content, cutting-edge technology, and a service-driven approach. The customized curriculum is developed with industry experts and global faculty from the Massachusetts Institute of Technology, Texas McCombs, Johns Hopkins, and Northwestern, aligning learning with real-world business needs. To enhance hands-on learning, practical lab exercises and sector-specific case studies are incorporated, enabling learners to apply AI concepts in real scenarios.

AI Mentor and AI Teacher, Great Learning’s proprietary AI-powered tools provide real-time guidance and support. Beyond content and technology, the program is supported by a structured governance model. Regular reports with learner- and cohort-level insights, weekly governance calls, and a responsive program office ensure seamless execution and continuous optimization. Client feedback  is crucial  in refining the curriculum and ensuring it evolves in alignment with business needs.

Overcoming Resistance to AI Training

Despite the clear advantages of AI upskilling, resistance remains a challenge. Employees fear job displacement, while organizations struggle with engagement. To counter this, companies need to reposition AI training from a top-down mandate to an opportunity for professional growth.

Malhotra recommends the following strategies that can help organizations drive AI adoption:

• Clear Communication:Communicate how AI training will enhance their roles, streamline tasks, and contribute to personal growth to ensure that employees understand the value of it.
• Leadership Support: Encourage leaders to actively endorse and participate in AI learning initiatives, showing employees that these programs are a priority and beneficial for long-term success.
• Personalized Learning: Offer personalized, role-specific training that aligns with employees’ current skill levels, reducing the perceived difficulty of learning AI and demonstrating its immediate relevance.
• Hands-On Experience: Incorporate practical exercises, case studies, and real-world applications to help employees see how AI can be directly integrated into their daily tasks, fostering confidence and engagement.
• Continuous Feedback and Support: Provide ongoing mentorship and AI-driven tools to assist learners, ensuring they feel supported throughout the learning journey.
• Inclusive Culture: Build a learning culture where upskilling is seen as a collective goal, promoting collaboration and shared learning experiences rather than competition or fear of job displacement.

A well-trained workforce—fluent in AI’s applications, potential, and implications—will be a true competitive differentiator. By embedding AI learning into workflows, personalizing training, and fostering a culture of continuous upskilling, companies can move beyond skill-building to true capability-building—where employees don’t just use AI but actively innovate with it.