7 Ways 2024 Was a Year of AI Experimentation and Learning

2024 was a year of both excitement and caution regarding artificial intelligence in the workplace. While generative AI (GenAI) tools became more widely available, organizations spent much of the year carefully exploring their potential while grappling with practical implementation challenges. The story that emerged was less about revolutionary change and more about methodical learning and adaptation. 

Below, I summarize my take and share some of my favorite news articles, research, and reports that were published last year across the media landscape, grouped by seven themes.

1. Early Patterns in AI Adoption

Research suggested increasing experimentation with AI tools across organizations, though actual integration into core business processes remained limited. While tools such as GitHub Copilot and various AI assistants showed promise in specific use cases, most organizations were still in the early stages of understanding how to effectively deploy these technologies.

The experience varied significantly across sectors and businesses. Some organizations reported early productivity gains in targeted areas such as data analysis and content creation, while others encountered challenges with implementation and employee adoption. This diversity of outcomes suggested that AI’s impact depends heavily on the organization’s context and implementation approach.

• New Research From Google Workspace and The Harris Poll Shows Rising Leaders Are Embracing AI to Drive Impact at Work (Google Cloud)
• Generative AI and the Workforce: 10 Big Trends We’re Seeing Right Now (World Economic Forum)
• Nearly Two-Thirds of Professionals Are Overwhelmed by Workplace Change (LinkedIn)
• The Rapid Adoption of Generative AI (National Bureau of Economic Research)

2. Learning About Human-AI Collaboration

Early experiments with AI tools revealed both opportunities and limitations in human-AI collaboration. Microsoft’s research with users of its Copilot tool suggested potential productivity benefits, though the long-term implications remain unclear. Some workers reported saving time on routine tasks, while others noted challenges in effectively incorporating AI tools into their workflows.

A key insight from 2024 was that successful AI implementation appears to require significant organizational learning and adaptation. Early adopters found that simply deploying AI tools wasn’t enough—organizations need to rethink processes and carefully consider how to integrate AI capabilities with human work.

• AI Thinks Differently Than People Do. Here’s Why That Matters (Harvard Business Review)
• How the Next Generation of Managers Is Using GenAI  (Harvard Business Review)
• How CEOs Are Using GenAI for Strategic Planning (Harvard Business Review)

3. Leadership Challenges and Trust

News throughout 2024 revealed significant uncertainty around AI leadership and governance. Many businesses struggled with basic questions about how to responsibly deploy AI while maintaining employee trust and engagement. Common concerns included:

• Data privacy and security.
• Transparency in AI decision-making.
• Fair and ethical use of AI tools.
• Appropriate balance of automation and human judgment.

These challenges highlighted the importance of thoughtful change management and clear communication in AI initiatives.

• Work, Workforce, Workers Reinvented in the Age of Generative AI (Accenture)
• From Challenges to Opportunities: Navigating the Human Response to Automated Agents in the Workplace (Humanitites and Social Sciences Communications)

4. The Role of Middle Management

Questions emerged about how AI might affect management roles. While some observers speculated about AI replacing certain management functions, early evidence suggested a more nuanced reality in which managers need to learn to effectively incorporate AI tools while maintaining their essential human leadership responsibilities. The exact nature of this evolution remains to be seen.

• Generative AI, the American Worker, and the Future of Work (Brookings Institution)
• AI in Organizations: Some Tactics (Ethan Mollick)
• Workplace Skills Survey (Deloitte)

5. Early Lessons in Implementation

Organizations experimenting with AI in 2024 began identifying some preliminary patterns for successful implementation:

• Start small with clearly defined use cases.
• Focus on augmenting rather than replacing human capabilities.
• Invest in training and support.
• Maintain strong feedback loops to learn from early experiences.
• Keep expectations realistic.

However, these patterns are still emerging and will likely evolve as organizations gain more experience.

• How to Create Value Systematically with GenAI (Harvard Business Review)
• Companies Had Fun Experimenting With AI. Now They Have to Show the Returns (The Wall Street Journal)
• Work Innovators: A Playbook for Innovation in an Uncertain World (Upwork Research Institute)

6. The Technology Landscape

While 2024 saw continued advancement in AI capabilities, particularly in areas such as large language models and coding assistance, many promising technologies remained in early stages. The concept of AI agents—AI systems designed to execute tasks with varying degrees of autonomy—generated significant discussion, though practical applications were limited. The gap between potential and current reality remained significant in many areas.

• Technology Will Shape Workplace Productivity in 2025, But Some Warn of AI Overload (WorkLife)
• Generative AI: A Source of ‘Costly Mistakes’ for Enterprise Tech Buyers (TechRepublic)

7. The Emerging Question of AI Agents

As we move further into 2025, one of the most intriguing yet uncertain developments is the evolution of AI agents. While 2024 saw early experiments with agent technology from companies including Microsoft, OpenAI, and Salesforce, most implementations remained relatively basic. The practical reality of agent deployment proved more complex than initial announcements suggested, requiring significant training, oversight, and refinement to be useful in production environments.

• Marc Benioff Says AI’s Future Is All About Agents, Not Chatbots (Quartz)
• OpenAI’s Next ‘Giant Breakthrough’ Tipped to Land Soon and Control Your Computer (TechRadar)

Looking Ahead

Several key questions remain unanswered for 2025:

• How will AI tools evolve to better complement human work?
• What new skills and capabilities will workers need to develop?
• How can organizations effectively balance automation with human judgment?
• What governance frameworks will emerge for responsible AI use?

These articles offer some potential answers:

• A Tumultuous Year Behind: A Challenging, Important 2025 (Josh Bersin)
• The New Future of Work (Microsoft)
• How AI Could Break the Career Ladder (Bloomberg)

Continued Experimentation

The year 2024 might best be characterized as a year of learning and early experimentation with AI in the workplace. While the technology showed promise in specific applications, broader transformation remains a work in progress. Organizations are still in the early stages of understanding how to effectively integrate AI while maintaining focus on human needs and capabilities.

• Don’t Fear AI: Used Well, it Can Empower Us All (The Times)
• Reflections (Sam Altman)

The path forward will likely require continued experimentation, learning, and adaptation. Rather than dramatic transformation, the AI journey appears to be one of gradual evolution as organizations learn to effectively combine human and machine capabilities. Success will likely depend not just on the technology itself, but on thoughtful approaches to implementation that prioritize human needs and organizational learning.