Generative AI in Action: Cross-Industry Use Cases

• Finance: AI copilots summarize financial reports, assist in code generation, and streamline claims processing, improving quote accuracy and speed.
• Manufacturing: GenAI helps predict equipment failure, optimize supply chains, and even generate design prototypes.
• Healthcare: Pharma firms like Pfizer use GenAI to cut R&D timelines by months through document automation and trial design.
• Retail: Personalized marketing content is generated at scale, while AI chatbots handle service interactions and upselling.
• Professional Services: McKinsey and others use GenAI for internal knowledge tagging, legal drafting, and audit report generation.

Business Benefits: Speed, Productivity, Innovation

• Faster time-to-market: AI accelerates product development, quoting, and decision cycles.
• Productivity gains: Knowledge workers using GenAI save up to 8 hours per week, freeing them for higher-value tasks.
• Cost savings: Automated content generation, call center support, and HR workflows reduce operational costs.
• Innovation: GenAI enables new product ideas, business models, and hyper-personalised services that were previously unattainable.

• Companies fully integrating AI see 2.5x higher revenue growth, according to Accenture.
• 74% of GenAI investors report that outcomes have met or exceeded expectations.
• Only 17% of firms have realized GenAI-driven EBIT growth so far—but that number is rising.

Strategies for Scalable GenAI

1. Link to Strategy: Prioritize use cases tied to business goals—growth, customer experience, innovation.
2. Build Data Foundations: Invest in clean, governed, and accessible proprietary data. RAG techniques (grounding AI in company data) are becoming standard.
3. Empower Talent: Upskill staff to work effectively with GenAI. Redesign workflows for AI-human collaboration.
4. Start Small, Scale Smart: Prove value with pilots, but plan for enterprise-wide deployment. Address the "last mile" of integration.
5. Strengthen Governance: Develop clear AI policies, review protocols, and privacy safeguards. Stay ahead of regulation (e.g. EU AI Act).
6. Foster Collaboration: Form cross-functional teams and leverage ecosystem partnerships—from cloud AI platforms to academic alliances.

• Accuracy risks: Use RAG and human review to counter hallucinations.
• Data privacy: Secure sensitive data, especially in cloud or partner-hosted AI.
• Compliance: Monitor regulations, especially in high-risk sectors like finance and health.
• Governance gaps: Standardize GenAI policies across the organization.
• Workforce disruption: Invest in change management and workforce reskilling.