Strategies for Expanding GenAI Use Across Multiple Business Functions
Article
With GenAI now in use by over three-quarters of survey respondents, businesses are seeking ways to scale AI capabilities—often across multiple functions simultaneously. According to McKinsey’s latest survey, well-structured workflow redesign, top-level governance, and adaptive risk management systems are pivotal for ensuring that larger AI initiatives maintain momentum without compromising on quality.
1. Defining Clear Roadmaps for Growth
Phased Implementation: High-performing organisations often expand AI usage in stages, beginning with pilot projects before moving to full-scale deployment.
Priority Alignment: By focusing on the most critical business functions first—such as operations, sales, or customer service—companies can demonstrate quick wins, build internal support, and refine processes for subsequent rollouts.
Continuous Metrics Tracking: AI success hinges on ongoing analysis of performance data, from cost savings and revenue improvements to user satisfaction levels.
2. Addressing Data and Infrastructure Challenges
Unified Data Strategy: Consolidating data across departments prevents duplications and inconsistencies. A single source of truth enhances machine learning efficiency and reliability.
Scalable Tech Stacks: As AI usage grows, so does the demand for compute power and storage. Organisations frequently invest in cloud-based platforms or on-premise infrastructures that can handle large-scale workloads.
Ongoing Security and Compliance: With expansive deployments, the risks of data breaches and regulatory violations multiply. Maintaining robust data governance is non-negotiable.
3. Reinforcing Governance and Oversight
Senior Leadership Engagement: Active CEO or board-level involvement correlates with higher EBIT impact, particularly at larger organisations. This top-down model fosters accountability and swift decision-making.
Hybrid Governance Models: While core compliance and risk management are centralised, some organisations distribute everyday AI oversight to departments for faster adaptation to local conditions.
Human Review Protocols: Despite AI’s growing sophistication, human checks remain invaluable. Some industries, especially professional services, demand near-universal review of AI outputs to avoid reputational risks.
Key Takeaway:
Scaling GenAI is not a simple matter of ramping up compute resources or rolling out more AI-powered applications. It demands holistic planning, involving a clear growth roadmap, refined data infrastructure, and robust governance to ensure quality, efficiency, and trustworthiness—particularly as complexity mounts.
Author’s Bio
“ Authored by Vineet Baveja, Founder of Conceptualise, a strategic authority in digital transformation. Drawing on 20 years of experience, Vineet specialises in guiding organisations through multi-phased AI rollouts that balance ambition with risk-managed growth. Find out more at conceptualise.in.”