New CEOs who focus on these essential questions will lay the foundation for stronger, more resilient companies.
A growing trend of corporate regret is unfolding as companies like Ford and IBM begin rehiring employees after cutting staff due to AI-driven layoffs and flawed technology rollouts. This shift highlights the challenges and reconsiderations in replacing human talent with automation prematurely.
Team members who enhance your group's strength often differ from those who simply blend in.
Most discussions around AI-ready leadership emphasize the technology—what to rebuild and which tools to adopt. However, the CEO's true role lies beyond technology; it is centered on people transformation, starting with their own readiness to lead this change. At Star, we call this process “organizational recomposition,” which involves taking existing talent and skills, breaking them down, and reassembling the company into a more capable organization without necessarily growing in size. This is done by upskilling employees, shifting roles into new areas made possible by AI, and fostering new ways of working.
Importantly, it also involves identifying and removing those unwilling to embrace the AI transformation. It’s not simply a technology project—it’s a people-focused decision that rests on the CEO’s shoulders.
Two primary challenges arise: company culture and the pace of change. Resistance often stems from fear of altering the company’s core culture, which is the foundation of its identity for customers, stakeholders, and employees. Successful transformation uses the existing culture as a vehicle for change, evolving rather than replacing it.
Speed is another critical factor. The journey isn’t about rushing AI adoption blindly but pacing it strategically, learning from past transformations like cloud computing. Leaders must balance moving fast enough to remain competitive with avoiding costly mistakes and runaway expenses, such as uncontrolled AI usage costs.
Ultimately, the CEO must make the challenging decisions regarding culture and strategic direction. AI transformation is a marathon, not a sprint, and success means becoming a more capable company with the people you already have, even if the company’s size remains the same. This is the real win worth striving for.
Michael Schreibmann is CEO and cofounder of Star.
Is AI capable of managing a successful business and directing human employees? A recent experiment challenges this question by giving an AI system its own storefront, three employees, and a $100,000 budget to test its abilities in real-world conditions.
Compensation decisions go beyond mere data figures. When employees bring AI-generated salary benchmarks to the table, leaders must carefully balance these insights with human factors to maintain trust, nurture a positive culture, and retain talent.
You don't have to increase salaries to inspire and engage your team. There are powerful, cost-effective methods to boost motivation and productivity that don’t require a bigger payroll.
James Yang/theispot.com
The Research
This article draws from two key research streams: a qualitative study on AI-assisted discovery highlighting four pathways AI uses to generate insightful analytical breakthroughs, and an examination of organizations as algorithmic assemblages—shaped by data accessibility, configured capabilities, and agency distribution. The core value of professionals lies in generating insight—a fresh perspective or connection that reshapes understanding. Expert knowledge, while powerful, can limit discovery by framing what is seen and overlooked. AI, particularly agentic AI, moves beyond simple prompting to proactive, configuration-based analysis that sustains deep, systemic insight.
Two Ways of Working With AI
Most users engage AI conversationally—asking, refining, and interpreting responses. Agentic AI requires configuring context, capabilities, and orientation to enable persistent, autonomous analysis. This allows professionals not just to prompt but to direct, orchestrating multiple agents that analyze the same data from different perspectives, producing richer insights.
Four Discovery Approaches
- Use Multiple Lenses: Apply competing frameworks simultaneously to reveal contradictions and new questions.
- Surface Silences: Identify vital organizational issues not named in official discourse by comparing data and conversations.
- Bridge Levels: Trace problems from symptoms to root causes across organizational layers.
- Stress-Test Categories: Compare formal classifications with operational reality to uncover hidden problems.
Skillful Directing
Effective AI direction requires designing systems for discovery, valuing unexpected findings, evaluating patterns as proposals, and tracking analytical decisions. Agentic AI enhances but does not replace professional judgment; the best outcomes come from framing problems to reveal new insights and leveraging AI as a powerful tool for discovery.
AI and product management are converging in two key ways, presenting new opportunities and challenges for companies. To stay competitive, businesses must integrate expertise in both areas effectively.
More companies are allocating portions of their marketing budgets to empower store employees and micro-creators as brand ambassadors, seeing better results than with traditional mega-influencers.
