From curiosity to tradition: Advocating for AI
Companies have poured significant investment into AI tools, yet many employees remain hesitant. Concerns about job security and uncertainty about how AI fits into their daily work persist — even as Gartner predicts that AI will ultimately create more jobs than it displaces.
According to Ivanti, over a third (36%) of workers keep AI to themselves for a perceived edge, while 30% stay quiet out of fear their roles could be at risk. That hesitation is limiting productivity gains and slowing enterprise transformation.
Leaders must remove structural blockers, including model biases, and establish cultural standards that make AI safe, visible and useful. ROI should be measured not only in speed, but also in collaboration, quality, and insight.
Create a safe space for experimentation & practical training
Advocating for AI adoption starts by meeting employees in an open forum then leading by example. Across industries, interest in AI is high: nearly 90% of companies surveyed in our expect to increase AI budgets this year, and most expect measurable transformation within two years. But budget alone won’t deliver value.
To capture that potential, companies must take an employee-first approach to AI investment. Define a clear strategy across teams, enable safe experimentation, provide practical training and make tools accessible for building AI agents. Leaders should demonstrate use cases, share both wins and failures, and create clear pathways for staff to gain AI skills.
Providing engaging AI upskilling materials to create AI buy-in is particularly essential here. This can include mandatory AI foundations courses, technical explainer series, and compact practical modules. These all work to tackle AI hesitancy, building confidence and buy-in.
Lead the change: for employee development and business productivity
AI adoption isn’t a volume game. Identify where AI delivers the most ground-level impact, pilot those high-impact workflows, measure outcomes, and scale what works. A strong mantra: take risks, fail fast, get stuff done.
Through this testing process with AI, organizations can guide employees to create mutual benefits. Employees acclimatize to new AI tools, increasing career value, productivity, and job satisfaction in the process.
Additionally, teams work faster and make better decisions; companies gain resilience, competitive advantage, and sustainable growth. Leadership matters – not just in mandate, but in modeling responsible and productive AI use.
Tackle the ROI question
Across industries at large, there is debate about whether AI is delivering value today. MIT’s State of AI in Business study shows only about 5% of integrated pilots deliver significant value.
That statistic doesn’t indict AI itself; rather, it points to strategic failures. We see that AI can deliver efficient work – improving onboarding, assisting coding workflows, automating sales admin, processing invoices, forecasting demand, and optimizing inventory. We found leading firms using AI for process automation report productivity gains of about 37%, far outstripping peers.
The challenge around ROI isn’t in its ability to deliver results, it’s strategy. Where strategy fails, so does AI. Organizations need AI advocates to drive this forward – advisors and architects – that can catalyze its value. Real value won’t come from isolated pilots, but from embedding AI across core business processes.
The companies that succeed start with practical, high-impact improvements, then scale toward broader transformation, all grounded in trust, transparency and a human-centric approach.
Build systems with scalable trust
As a leader, if you’re serious about building a solid foundation of success from AI, you need to scale your new AI tools or workflows at speed. But where is AI budget investment being channeled? It goes back to strategic investment from the start. Once you have that system in place, you will reap increasing benefits over time.
There is no denying that AI implementation demands a huge process shift and reviewing how you organize workflows around trust. Trust is the foundation of every successful AI strategy and requires buy-in from customers, partners and employees.
Scalable trust requires enterprise-grade security, clear data compliance, and transparent model behavior so customers, partners, and State of AI in the Enterprise report employees are confident innovation doesn’t come at the cost of privacy or governance.
This is why you should embed governance and visibility into all aspects of AI workflows, with controls that let organizations manage access, audit decisions, and maintain compliance as they scale.
Advocating for the right kind of change
As we closely scrutinise the way AI delivers value, it is apparent that the human-machine relationship is a core piece of the ROI puzzle. You need changemakers within your organisation to advocate for AI – doing so with efficiency and ethics. AI is a type of technology that doesn’t work without a guiding hand. From bias and hallucinations to model breakdown, AI is susceptible to failure and this risk element needs to be managed closely through human oversight.
This is why it’s vital to champion trust-driven and training-backed AI tools. When employees understand how AI arrives at results, they’re more likely to use it confidently and creatively. Over time, this trust compounds: teams share insights more freely, innovation accelerates, and the enterprise becomes more adaptable.
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