Many organisations have already invested in AI, yet fewer are seeing the measurable business impact they expected.
Ana Anaya, People and Organisation Lead at Lancia Consult, shares her perspective on some of the most common challenges organisations face in turning AI experimentation into sustained value.
How do you define “AI value” at board level?
At board level, AI value should be defined in terms of measurable business outcomes, not tool usage. That means improvements in areas such as revenue growth, customer experience, operational efficiency, or risk management. Many organisations see AI activity increasing but struggle to connect that activity to meaningful outcomes. Board-level value comes when AI is embedded in key business processes and aligned with strategic priorities, so its impact can be measured against metrics leadership already cares about.
How do you secure leadership buy-in when AI is seen as just an IT initiative?
Leadership buy-in improves when AI is framed in business terms, not technical ones. Rather than focusing on tool deployment, connect AI use cases directly to outcomes that matter to that leader, such as revenue growth, customer impact, or operational efficiency. Demonstrating how AI solves a specific problem they care about is far more compelling than presenting abstract capability. Co-creating small, relevant use cases can build credibility and momentum, turning scepticism into sponsorship over time.
At what point should organisations stop experimenting and start scaling AI initiatives?
Experimentation is essential early on, but organisations should start thinking about scaling once a use case clearly demonstrates repeatable value. The key signal is when an AI application improves a real business process and can be applied consistently across teams or functions. At that point, the focus should shift from individual productivity gains to organisational impact, ensuring the use case is supported by appropriate governance, an adoption approach, and integration into existing workflows.
What typically blocks AI adoption: leadership hesitation or frontline resistance?
In most cases, the blocker is neither outright leadership hesitation nor frontline resistance, but a gap in structured adoption. Leaders often invest in tools but underestimate the effort required to embed them into daily work. At the same time, frontline teams may not fully understand how AI applies to their specific roles. Successful organisations address this by treating AI adoption as a change programme, with targeted training, role-based use cases, and clear leadership sponsorship.
Are there any practical steps you can suggest to help shift employee mindsets from fear of AI to confident, responsible use?
Shifting the mindset starts with how organisations introduce AI to their people. If AI is framed primarily as a cost-saving tool, employees naturally assume it threatens their roles. Instead, leaders should position AI as a capability that augments human work by removing repetitive tasks and enabling employees to focus on higher-value activities.
Practically, this means providing role-specific examples of how AI supports day-to-day work, investing in targeted training, and encouraging safe experimentation within clear governance boundaries. When people see AI improving how they do their job, confidence quickly replaces fear.
If you have a question about your AI adoption or you are interested in getting the most out of your AI investment, connect with the team at Lancia Consult here.