Five strategies to maximize productivity gains from AI automation
Experts reveal how professionals can leverage AI-driven time savings to enhance productivity and create business value beyond routine tasks.
As artificial intelligence (AI) automates mundane tasks, professionals must strategically reinvest their saved time. Five industry leaders share transformative approaches:
1. Drive Operational Transformation
Gabriela Vogel of Gartner emphasizes that merely using AI for task automation isn't enough. "The crucial parts are the operational changes or business model transformations" needed to create ROI. Professionals should focus on large-scale optimizations, new products, or business model adjustments.
2. Reinvent Roles and Departments
Adobe CIO Cindy Stoddard reveals how AI automation has eliminated up to 95% of some roles at Adobe. The solution? "Reinvent my role or department" - with affected employees transitioning to new value-added positions within the company.
3. Prioritize Strategic Work
LinkedIn's Tomer Cohen advises professionals to "go back to why you chose this job" and focus on core value-creation activities. For marketers, this means strategy and creativity; for developers, customer-centric ideation.
4. Explore New Business Frontiers
EY's Joe Depa demonstrates how tax professionals use AI agents to shift from compliance to strategic modeling, including tariff impact simulations across supply chains and workforces.
5. Invest in Self-Development
Carruthers and Jackson CEO Caroline Carruthers advocates using AI-gained time for "learning a new skill, being creative" and personal well-being, while maintaining human oversight on critical decisions.
Key Takeaway: As generative AI handles routine work, professionals must proactively redirect their efforts toward innovation, strategic thinking, and continuous learning to remain indispensable in the AI-powered workplace.
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About the Author

Dr. Sarah Chen
AI Research Expert
A seasoned AI expert with 15 years of research experience, formerly worked at Stanford AI Lab for 8 years, specializing in machine learning and natural language processing. Currently serves as technical advisor for multiple AI companies and regularly contributes AI technology analysis articles to authoritative media like MIT Technology Review.