© Redbow Consulting Group 2026, All rights reserved “There is a bit of FOMO created by stories of CEOs using multiple AI systems in every meeting.” O ne firm’s CIO leads a sophisticated AI initiative, including deploying an in - house [proprietary Gen AI] and a team of PhD experts, underscoring a hands - on, technologically grounded leadership style : “The CIO just lives and breathes this stuff.” Other organisations have baked accountability into senior roles: “[AI Implementation] is now a corporate key objective , driven by a senior executive accountable for it.” It is important to note that even with enthusiastic executive backing, workforce concerns remain a challenge. Disparate AI literacy levels and uncertainty about AI outputs contribute to wariness among employees, making transparent communication and targeted training even more crucial. Leaders who neglect these elements risk fostering disengagement or superficial compliance. The overarching lesson is clear: leadership is not merely about setting strategy but about cultivating an empathetic, empowering culture that accelerates genuine transformation. This includes careful pacing of AI initiatives to avoid burnout or reluctance, combined with cultivating forums where employee feedback on adoption barriers can be surfaced early and addressed constructively. External Reference The report’s finding that sustained executive sponsorship , especially from CEOs and CIOs , underpins effective AI adoption — resonates strongly with external themes advocating for CEO - led, cross - functional squads orchestrating enterprise - wide agentic AI transformations. Bain & Company notably emphasises that early adopters realise EBITDA uplifts of up to 25% by embedding AI agents into workflows, but crucially, this leap comes with a demand for rigorous leadership commitment to shift from fragmented pilots to aligned, scalable systems. “CEOs must champion moving beyond pilots to enterprise - wide agentic AI transformation...” (Bain & Co., 2025). This mirrors the shift from discrete AI experiments to cohesive roadmaps, reflecting the idea that leadership styles heavily influenc e cultural embrace and operational pacing. Importantly, the external literature introduces the “autonomy ladder” maturity model, encouraging pharma leaders to plan AI evolution along phased stages — from assistive tools to self - evolving agent meshes — thus underscoring the significance of the report’s recommendations about pacing and engagement. Recommendations: • Sustain prominent and steady executive sponsorship, with senior leaders actively championing AI as a means to augment performance and enhance decision - making processes. • Embed governance frameworks within leadership teams to carefully calibrate the tempo of AI rollout, ensuring innovation is pursued boldly but with due diligence to manage potential risks and employee engagement. • Develop and implement comprehensive, role - tailored AI literacy programmes that go beyond basic technical training, focusing on practical AI application aligned with specific job functions. • Foster open communication channels that encourage employee feedback, allowing organisations to promptly detect and address worries, misconceptions, or resistance . • Customize the AI adoption roadmap to reflect organisational size, culture, and maturity level, striking a balance between providing structured guidance and granting teams enough autonomy to innovate independently.