AI adoption for forward-thinking organisations

Market trends

Organisations are gravitating toward AI tools that promise productivity gains and efficiency improvements. However, many businesses are deploying AI solutions without a clear roadmap, leading to poor usage and minimal impact. Staff often receive little training or contextual guidance, meaning they aren’t sure how to apply the tools effectively to their day-to-day work.

Meanwhile, the market is becoming increasingly saturated with new tools launching at breakneck speed. For those yet to begin their AI journey, this rapid pace can be paralysing. For others, AI adoption becomes a knee-jerk response to competitor pressure, rather than a strategic move.

AI adoption challenges

Organisations face a host of practical and cultural barriers that can undermine AI’s success. There is fear around job security and a general resistance to change. One of the most immediate challenges is a lack of trust in the technology itself and its outputs. From a technical perspective, many organisations encounter integration challenges, especially when trying to align AI tools with legacy systems or existing workflows.

Lack of leadership buy-in is another critical issue. People also need to be realistic about the benefits that can be achieved to avoid frustration. Lastly, organisations frequently lack good quality data and KPIs to measure success, which makes it difficult to track progress or justify further investment.

Industry-specific considerations for AI adoption

In the public sector, AI has the power to improve service delivery, streamline internal processes, and enhance citizen engagement. However, this comes with high expectations around transparency, data protection, and ethical governance. Leaders need to weigh the benefits of efficiency and responsiveness against the risks of bias, misinformation, or public mistrust.

Speed and scalability are key advantages of AI in journalism and publishing, with tools supporting content generation, summarisation, and editorial research. Yet these gains must be balanced against ethical challenges. Ensuring factual accuracy, preserving human editorial judgement, and maintaining audience trust remain paramount. Publishers must also consider how to differentiate their voice in a landscape where AI-generated content is on the rise.

Beyond journalism, broader media organisations are exploring AI to personalise content, automate metadata tagging, and accelerate production workflows. While operational gains are significant, these tools must be deployed with care to preserve brand identity, avoid over-reliance on automation, and support rather than replace creative talent.

Legal practices are identifying clear use cases, from contract drafting to legal research, where AI can reduce workload and increase speed. However, challenges around confidentiality, accuracy, and regulatory compliance mean firms must proceed with caution. Successful AI adoption depends on a thoughtful mix of legal expertise, human oversight, and careful evaluation.

People-specific considerations for AI adoption 

Successful AI adoption must account for the distinct priorities and concerns of different leadership roles within an organisation.

• Chief Technology Officers (CTOs) focus on adoption from a technical standpoint. Their priority is ensuring the tools are scalable, secure, and integrated with existing systems, while supporting user adoption through smart technical enablement.
• Chief Information Officers (CIOs) are concerned with data quality, governance, and interoperability. They want to see AI link meaningfully back into the business, improving information flow, not fragmenting it.
• HR Leaders see the employee experience (EX) as central. They are focused on how AI can reduce friction in daily workflows, improve wellbeing, and support upskilling, while addressing change anxiety and fears about job security.
• Heads of Strategy are looking at AI through a growth and competitiveness lens. They want to align adoption with longer-term value creation and innovation.
• Chief Operating Officers (COOs) are focused on efficiency and cost reduction, and whether AI is truly driving productivity improvements and delivering measurable impact on the bottom line.
• Heads of Change are responsible for embedding tools in a way that sticks. They focus on the critical enablers of sustained adoption: communication, stakeholder engagement, training, and behavioural change.