What does it actually mean for an organisation to be AI-ready, beyond having the right tools and data

“Being AI-ready is fundamentally about openness to learning and the ability to react quickly. While having the right tools and well-managed data is essential, true readiness is defined by an organisation’s capacity to operate, monitor, and measure the effectiveness of AI solutions.

We often see organisations invest heavily in implementation and tooling, only to realise that no one is prepared to take responsibility for running, monitoring, and improving AI systems.

AI-savvy organisations design solutions differently depending on the type of work, operational versus knowledge work, and, for knowledge work, focus on measuring effectiveness rather than just productivity.”

Where do most companies go wrong when trying to embed AI into their operations?

“Many companies treat AI solutions like traditional IT projects, using user acceptance as a checkpoint between development and handover to IT operations. This approach often fails before it even begins.

AI performs tasks that typically require human intelligence, perception, reasoning, and decision-making. While AI can execute these tasks with far greater precision and consistency than humans, someone within the organisation remains ultimately accountable for the results.

The most common misstep is underestimating the need to provide users with the right level of oversight and control so they can accept accountability for AI-driven decisions.

For example, explaining how AI decisions are made and demonstrating that they are ethical and fair depends not only on transparency and traceability but also on maintaining control and proper training data records.”

How can leaders prevent transformation fatigue during AI-driven change initiatives?

“Change is inevitable, so responding to it is part of effective leadership. AI will transform how businesses operate, but transformation fatigue arises when people feel constantly subject to change rather than in control of it.

Deliberate planning and thoughtful communication help, but the most effective approach is to empower people to feel more in control. This often involves organising teams around value streams that cut across business, technology, and operations.

Leaders can ensure teams have the skills and information necessary to take ownership of outcomes and make adjustments based on real results. This is especially important with AI solutions, which should be structured to provide continuous feedback, allowing teams to monitor performance, improve models, and refine processes based on learning.”

What kind of mindset and cultural shift is required for AI to deliver long-term value?

“Delivering long-term value from AI requires a shift from control to collaboration, and from predictability to adaptability. Organisations focused on individual targets and siloed accountability often struggle to realise AI’s full potential.

Value emerges when teams adopt a collective mindset, defining success by shared outcomes, whether customer experience, business impact, or strategic growth. Individual productivity only matters when it benefits the whole system.

Another critical shift is embracing uncertainty. Traditional corporate cultures often reward certainty and fixed plans. Cultures that support experimentation, feedback loops, and incremental change are more likely to see lasting benefits from AI.

This cultural evolution isn’t just about tools; it’s about how work is structured, how teams interact, and how decisions are made. Empowering teams to act fast, learn fast, and improve fast is central to sustaining AI-driven value.”

How can organisations balance AI experimentation with maintaining trust, transparency, and alignment with business goals?

“Each AI initiative should be evaluated based on the type of work and value it aims to deliver, whether efficiency, experience, or innovation. Different goals require different levels of oversight and distinct success metrics, making a portfolio approach to investment essential. Maintaining alignment with business goals means focusing on outcomes rather than outputs.

This requires systems where feedback, transparency, and learning are built in from the start, allowing initiatives to fail gracefully. Trust begins with a clear governance framework, as AI, like any transformative technology, can have unintended consequences. Transparency is not just audit trails; it’s about inviting dialogue, sharing lessons learned, and adapting as standards and regulations evolve.

Experimentation and learning go hand in hand. Delivering incremental value early builds credibility and transparency, helping teams understand what works and what doesn’t. Ultimately, AI is only valuable to the extent that it drives the business toward its strategic goals.”

How do organisations deal with some of the risks associated with AI – hallucinations, privacy issues, etc. – and how do they go about both securing essential data and overcoming employee resistance to the technology?

“Treating AI adoption as an iterative, feedback-driven process is key to managing risks. Success is less about getting everything perfect from the start and more about structuring work to minimise unintended consequences and adapt quickly.

“Hallucinations” is a misleading term. Today’s AI doesn’t imagine things; it follows programmed rules based on probabilities and patterns. Like any software, AI carries risks of errors or mismanaged data.

What is new is how AI uses data, to train models that imitate human decision-making. Without careful management, models can produce biased or unethical outcomes. Technology does not remove employee accountability. Recognising this allows organisations to design AI solutions with lower risk.

Designing solutions with humans in the loop is critical. It promotes transparency and explainability and is the most effective way to overcome resistance while maintaining control over outcomes.”

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