AI now seems to appear around every corner of organisational life. From machine learning algorithms that forecast trends to autonomous agents that orchestrate routine tasks and chatbots that field customer queries, AI is rapidly becoming embedded in how organisations operate. The possibilities seemingly stretch as far as our imagination can take us. Have a problem? Why not consult your AI agent of choice?

The case for AI within organisations is compelling, with some of the most commonly cited promises being reduced costs, increased productivity, and enhanced efficiency. Where AI can absorb time-intensive and repetitive tasks, staff hypothetically gain time for “high-value” tasks, such as more creative or relationship-driven work. The research suggests these promises are not far-fetched, with studies lending support to the notion that AI can help people work at a quicker pace and unlock new skillsets. With the aim of realising AI’s potential, organisations are funnelling their resources into AI infrastructure; estimated AI spending worldwide is forecasted to tip over 902 billion U.S. dollars by 2029, up from 334 billion U.S. dollars in 2025.

Yet, according to a preliminary research report from MIT, relatively few organisations are seeing measurable impact from AI in practice. AI experimentation is widespread across industries, with organisations in sectors such as professional services, healthcare, and financial services piloting generative AI (i.e., GenAI; AI that responds to prompts with new content, such as text and images) solutions. However, the findings suggest that only 5% of integrated AI pilots are delivering measurable organisational returns. Coined as the “GenAI Divide”, the figure reflects a wide gap between organisations that translate AI adoption into tangible outcomes and those where investment has yet to create meaningful organisational impact.

Recent headlines reflect this disconnect between AI’s promise and its everyday reality for staff. A recent Financial Times article, for instance, notes that incentives designed to encourage AI adoption among employees can backfire. In some cases, staff were rewarded for using AI through leaderboards or direct links to their performance reviews. Rather than encouraging meaningful engagement with AI, staff reported that these practices undermined their sense of job security and agency.

If AI is becoming more capable, why are so many organisations struggling to harness its potential?

Alongside technical and implementation challenges, the answer lies in how people understand, trust and engage with the technology. The rise of AI is not only introducing new tools in the workplace, AI is reshaping how people think and work within organisational systems. On account of this systemic change to organisational life, the accountability frameworks, governance structures, roles and responsibilities that underpin organisations will also need to evolve.

The following article series explores how organisational psychology research can help leaders navigate this shift. By examining what we know so far, we will identify the competencies, behaviours, and judgment that leaders need to navigate uncertainty, harness the potential of AI, and ensure that people remain at the centre of organisational life.

Beyond organisational performance, there is a growing responsibility to understand how AI impacts people in the workplace. If AI is changing how we think, collaborate, and lead, then understanding and responding to those changes is as much a human challenge as a technological one. The question then becomes, not only how can organisations benefit from AI, but how they can harness its potential while safeguarding the conditions that enable people to exercise judgment, uphold autonomy, and flourish at work.

Follow the series on LinkedIn as we explore what AI means for leadership and organisational life. If your organisation is navigating the ways AI is reshaping how people work and lead, you can get in touch with us here.

References

Bankins, S., Ocampo, A. C., Marrone, M., Restubog, S. L. D., & Woo, S. E. (2024). A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice. Journal of Organizational Behavior, 45(2), 159–182. https://doi.org/10.1002/job.2735

Brynjolfsson, E., Li, D., & Raymond, L. (2025). Generative AI at work. The Quarterly Journal of Economics, 140(2), 889–942. https://doi.org/10.1093/qje/qjae044

Hill, A., & Jacobs, E. (2026, July 13). Employers pushed staff to use AI more. That has backfired. Financial Times. https://www.ft.com/content/cc03adda-7b56-4392-8f9a-e83a71a25ea1?syn-25a6b1a6=1 

Nanda, M., Challapally, A., Pease, C., Raskar, R., & Chari, P. (2025). The GenAI divide: State of AI in business 2025. Massachusetts Institute of Technology. https://www.artificialintelligence-news.com/wp-content/uploads/2025/08/ai_report_2025.pdf

Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192. https://doi.org/10.1126/science.adh2586

Shaw, S.D. & Nave, G. (2026). Thinking—Fast, Slow, and Artificial: How AI is Reshaping Human Reasoning and the Rise of Cognitive Surrender. SSRN. http://dx.doi.org/10.2139/ssrn.6097646

SHRM. (n.d.). AI in the workplace: Common use cases and practical application. https://www.shrm.org/topics-tools/workplace/ai-workplace-common-use-cases-practical-applications

Taylor, P. (2026, May 6). Forecast artificial intelligence (AI) infrastructure spending worldwide in 2025 and 2029. Statista. https://www.statista.com/statistics/1659696/global-ai-infrastructure-spending/