The disconnect between intention and reality
While AI adoption is increasing, there’s a growing gap between ambition and capability. Most organisations are encouraging AI use, and many employees are already experimenting with it. But underneath that momentum lies uncertainty and misalignment.
Leadership worries about cost, unclear value, and security risks. Meanwhile, employees often feel unsupported - lacking the tools, training, or confidence to navigate AI’s growing role in their work. There’s a collision happening: hype-driven change is smashing into legacy management systems.
To navigate this landscape, Jo urged organisations to go back to first principles. Ask: what problem are we solving? What are our options? How will we deliver? These aren’t just AI questions - they’re the basics of good innovation and strategic management.
Retrofitting won’t work
One of Jo’s key takeaways was clear: trying to bolt AI onto outdated systems and structures won’t work. Traditional approaches, built for control and task management, don’t align with how AI operates. AI amplifies the cracks rather than smoothing them over.
Organisations don’t need to rebuild from scratch, but they do need to move away from treating AI as a side project. Instead, AI must be integrated into how the organisation thinks, works, and delivers outcomes.
Culture: the invisible architecture
Jo made the case that culture is the first place to start when planning for AI maturity. No technology or strategy will succeed in a culture that resists it.
Culture, as Jo described, is the invisible architecture of how work gets done - shaped by what people believe, what leaders reward, and what customers expect. Left unchecked, it reinforces the status quo. But when designed with intention, culture becomes a powerful lever for change.
AI-ready cultures support ambiguity, learning, and cross-functional collaboration. They empower employees to take ownership, encourage safe experimentation, and reward outcomes over control. Importantly, cultural maturity also requires alignment across investors, leaders, employees, and customers. Without that alignment, AI efforts can stall or face active resistance.
Operating models: making strategy real
If culture is what we believe, the operating model is how we make it real.
Jo explained that AI requires a very different kind of operating model - one that allows flexibility, shared decision-making, and integrated delivery. It’s not just about team structures or governance charts, but about how people collaborate, how funding flows, and how decisions are made.
Leading organisations are already making bold changes. Moderna, for instance, merged HR and IT at the executive level, acknowledging that human and technical capability must now move together. Others are building cross-functional teams and “model offices” where real-world users help shape and test AI solutions from day one.
A good AI operating model includes:
Cross functional teams: product owners, data scientists, risk specialists, operational leads, developers.
Co-creating, testing and scaling solutions: proofs of concept are built and tested with the people who will use them.
Model offices: real-world users can test, adapt the model and build confidence.
Incremental funding: funding cycles are aligned to delivery and money is tied to delivery outcomes.
Clear governance: a transparent cross-functional group oversees AI adoption and delivery.
Preparing for the hybrid workforce
As AI becomes more embedded in work, the boundaries between human and machine are blurring. Jo described this as the rise of the “hybrid workforce” where some work is fully automated, some fully human, and much increasingly shared.
This shift requires a major rethink of job roles, decision rights, and leadership approaches. Organisations must:
Map work domains: understand where work is routine and can be automated, and where it is complex, creative or requires empathy.
Design hybrid roles: people will supervise algorithms, escalate edge cases, and tune algorithms. Others will use AI to uncover insights and make better decisions faster.
Rethink job design: AI isn’t just replacing inexperienced roles; it’s shifting what all roles look like, including how we build and manage teams. Leaders will need to understand how to line manage a hybrid workforce.
It’s not just entry-level roles that will change. Every part of the organisation - from operations to leadership – will be impacted and needs to adapt to this new shape of work.
Building capability from within
To truly thrive with AI, organisations must invest in capability building across all levels.
Jo outlined five critical enablers for an AI-ready workforce:
Shared objectives and role clarity – teams need to be aligned around common outcomes, not siloed KPIs.
T-shaped skillsets – people should be deep in their expertise but have enough breadth to collaborate across boundaries.
AI literacy across the organisation – not everyone needs to be an expert, but everyone should be able to engage meaningfully.
Embedded learning loops – peer learning, shadowing and communities of practice are essential.
AI champions – identify and support those who can bridge the gap between tech and business.
Capability-building needs a mix of hiring, training, partnering and experimentation. It’s not a one-off programme but an ongoing strategy to support people in real-world roles, with real-world impact.
Bringing it all together
As Jo wrapped up the series, she left participants with three powerful messages:
Culture must support learning, risk-taking and cross-functional collaboration.
Operating models must reflect that culture - embedding shared decision-making and agile delivery into everyday practice.
Capabilities must be built deliberately, so people can thrive in an AI-enabled world.
AI adoption isn’t just a technical deployment. It’s an organisational transformation. One that only works when people are kept at the centre - from design through to delivery.
Wherever you are in your AI journey, the key to sustainable success lies not just in the systems you build, but in the people who bring them to life.
Know where you stand
Understanding your organisation’s AI maturity is the first step to scaling it effectively and responsibly. Our AI Maturity self-assessment helps you do just that, giving you a clear view of your current strengths and areas to improve, and providing a tailored roadmap to guide your next steps.
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