From Hype to Habit: Embedding Gen AI Before You’re Left Behind

Embedding AI into an organisation starts with visible, aligned leadership, but it’s not about the specific Gen AI tools you choose. The real priority is ensuring employees using the Gen AI have the foundational knowledge, confidence, and curiosity to explore how it can enhance their work. While 77.3% of business owners believe leadership supports AI, only 37.8% of entry-level employees agree. This gap highlights why leaders must do more than endorse AI in board meetings. They need to actively guide the organisation through the mindset shift AI demands. For many, this guidance begins while the business is in the Manual or Basic stages of our VML AI Maturity Framework so leaders should be explicit about the first step on the curve and the horizon that follows.

That means clearly communicating a vision for AI that connects to organisational goals and answers the “what’s in it for me?” for every role. One way to achieve this is through an AI Vision Communication Campaign: an ongoing, structured programme that ensures employees understand why AI is being adopted, how it will be used, and what it means for them. This is not a one-off announcement or a set of slides; it’s sustained, tailored communication that speaks directly to the needs, priorities, and concerns of different audiences, from the C-suite to frontline staff.  

For senior leaders, the focus might be on AI’s role in driving competitive advantage, innovation, and long-term growth. For operational teams, it could highlight practical, role-specific benefits such as reducing repetitive tasks, improving work quality, or freeing up time for higher-value activities. Crucially, the campaign should be multichannel, using town halls, team meetings, internal system updates, videos, and peer-led sessions. It should also be interactive, with opportunities for employees to ask questions, share feedback, and see real examples of AI in action. As parts of the business approach Intelligent, the same channels should explain guardrails for agents and how human oversight will work; where units are Agentic, focus communication on ongoing assurance and how freed capacity is being reinvested. 

By shifting the conversation from “what tool are we using?” to “how can we all use AI effectively?”, and by making AI feel relevant, supported, and achievable for every role, leaders can turn strategic intent into sustained cultural change.  

To move from surface-level adoption to deep integration, organisations must first look inwards. While our ‘AI at Work’ report shows that 58.8% of workers believe their business is ready for AI, this optimism masks a critical readiness gap. A closer look reveals that while 80% of business owners feel prepared for the transition, only 42.2% of entry-level employees share that confidence. This gap between the strategic vision at the top and the operational reality on the ground is where many AI initiatives falter. Closing it requires moving beyond assumptions and conducting a structured, honest assessment of organisational impact. Practically, that means first understanding where each function sits on the maturity stages and what phase it is in (manual to agentic/Discovery to Scale).  

A successful assessment must therefore be holistic, looking beyond the immediate impacts on technology and process to understand the full ripple effect across the business. While numerous frameworks exist to guide this analysis, the key is to choose one that forces a comprehensive, 360degree view. A classic example is the McKinsey 7S model, which prompts leaders to analyse how AI will impact every interdependent element of the organisation. It pushes the analysis beyond the tangible elements of core Strategy, reporting Structure, and daily Systems (where 54.3% of employees already report feeling the impact in our ‘AI at work’ report) to also scrutinise the crucial people-centric dimensions: the Skills and capabilities of Staff, the prevailing leadership Style, and the Shared Values that define the organisation's culture, again emphasising that it’s not the tools being implemented that is important, but rather those who are using them.  

Ultimately, conducting a comprehensive analysis provides the essential blueprint for the change. It allows leaders to craft a targeted strategy that is based on evidence, tailored to the specific cultural and operational impacts, and designed to close the critical readiness gap between the C-suite and the frontline.  

Now that your vision has been effectively communicated and you understand the impact of your Gen AI tooling, appointing AI Champions across all levels can bridge that divide between strategic intent and day-to-day reality. Our ‘AI at Work’ report highlights a clear hierarchy divide, with senior leaders far more optimistic and engaged with AI than entry-level employees, and champions help close this gap by acting as trusted, relatable advocates within their teams. By giving them early access to the tools, targeted training, and clear expectations, they can model desired behaviours, answer questions, share success stories, and provide support to peers that builds confidence and reduces resistance. In the Discover phase, these champions help teams move from Manual or Basic to Augmented by demonstrating safe, everyday use and collecting local insights. 

Establishing a formal change network moves this from an informal group of enthusiasts to a strategic asset for the transformation. The network creates a dedicated, two-way communication channel, ensuring that feedback from the front line reaches the project team and that key messages are translated accurately to the teams. These champions become a trusted focus group for testing new ideas and a source of expert users who can provide practical, on-the-ground support for new processes, significantly reducing the burden on any central project/transformation team. This is the engine room of the Transform phase and readies teams for the next step toward Intelligent.

 The most effective champions are not always the most senior; they are respected, credible individuals within their peer groups who have the capacity to dedicate time to the role. Once recruited, they must be formally inducted and equipped. This goes beyond just training them on the AI tools; it means investing in their change management capabilities. Providing them with a basic understanding of change models, active listening techniques, and how to handle resistance elevates their role from simple advocates to skilled facilitators of change. As agent-supported work emerges, many champions evolve into “agent owners” who steward local adoption and controls, a pattern that becomes essential as you look to move into the Scale phase. 

Ultimately, this network provides the essential human interface for a technological revolution. It ensures the change is led by trusted peers who can translate strategic vision into practical reality, making the transformation something that is done with the organisation, not to it. Seen through the maturity lens, the network grows with you: champion communities in the Discover phase, enablement hubs during Transform phase, and a federated centre of enablement to sustain Agentic operations as an organisation looks to drive forward through the Scale phase.

The real measure of AI success isn’t tied to a single launch date. For many organisations, there may never be one. Some will start by giving everyone access to enterprise tools like Microsoft Copilot; others will begin with education and exploration, helping colleagues understand what AI is, how it works, and where it can add value. In every case, the goal is the same: to build a workforce that is informed, confident, and curious enough to identify opportunities and integrate AI into everyday work. The emphasis will vary by stage: in Manual or Basic stage, you are primarily building literacy and confidence, while Augmented and Intelligent teams may be in the Transform phase, integrating AI into habitual workflows. Truly Agentic areas and workflows are likely to only be realised during the Scale phase. 

This is where building foundational competence becomes critical. Our ‘AI at Work’ report reveals a stark skills gap: while 74% of business owners believe their company provides adequate AI training, only 27.4% of entry-level employees agree. Closing this gap means going beyond theory to deliver practical, role-specific learning, equipping people to experiment, adapt, and apply AI in ways that are relevant to their work. It also means addressing the inevitable “learning dip” (the temporary slowdown as people adapt to new ways of working), with patience, support, and reinforcement. 

 At the same time, leaders must recognise and address one of the most significant human barriers to AI adoption: the fear that “AI will take my job.” Left unaddressed, this anxiety can stall progress and undermine trust. Applying proven change management methodologies such as transparent communication, active listening, and involving employees in shaping AI use cases can help reframe the conversation. By showing how AI can augment roles, remove repetitive tasks, and create opportunities for higher-value work, leaders can shift the narrative from threat to empowerment. This becomes even more important as discrete tasks progress to Intelligent agents and some processes trend toward Agentic autonomy. 

Beyond today’s skills, market leaders will be those who foster a culture of continuous adaptation. This requires creating “psychological safety” (Edmondson, 2014: an environment where employees feel safe to experiment, share learnings, and even fail without fear of blame). As new AI capabilities emerge, organisations with this culture can explore them in “safe-to-fail” environments, quickly testing and scaling what works. As your maturity advances, the same safety to experiment should exist at each phase. Introductory sandboxes in Discover, production-adjacent pilots in Transform, and monitored optimisation loops in Scale.