Securing manager approval for innovative ideas is often challenging. Research reveals that employees who have transitioned from working-class backgrounds into white-collar roles tend to communicate in ways that make their ideas more likely to be heard. These 'upward transitioners' are more prone to acknowledge potential gaps in their ideas, invite managers' input, and remain open to revising their proposals. Surveys and behavioral experiments involving over a thousand participants, including a multinational engineering firm's employees in India, indicate this communication style resonates particularly well with powerful managers. The openness reduces defensiveness, shifting the focus from who is right to what solutions might work, and encourages managers to take ownership of the ideas. This approach contrasts with the confident, firm pitches often used by employees with consistently higher-class backgrounds, which may be less effective with those in positions of power.

Agentic AI is shedding light on leadership deficiencies that have long hindered business growth, offering fewer excuses and more accountability for effective management.

HR's role is evolving beyond simply managing tasks; it is now actively reshaping how work is designed and carried out.

For the past two years, the prevailing belief in enterprise AI was that greater autonomy meant better performance. The idea was to build agents that could independently plan, decide, and act across multi-step workflows with minimal restrictions. However, in real-world production settings, this approach is proving problematic. Successful companies are now focusing on creating AI agents with clearly defined responsibilities that operate within strict rules.

Recent forecasts reveal that over 40% of agentic AI projects may fail by 2028, not due to AI capability but because of rising costs, unclear business value, and insufficient risk controls. Governance maturity in responsible AI remains low, with only about 30% of organizations achieving advanced control measures. This gap between AI capability and governance is reshaping the competitive landscape: the priority has shifted from deploying the most autonomous agent to building trustworthy systems that satisfy risk, compliance, and legal teams.

Full autonomy often breaks down in production because autonomous decisions are hard to trace and audit, especially in regulated environments like finance or healthcare. Integration complexity arises when legacy workflows must be rebuilt to accommodate AI agents acting without human input. Enterprises that dive into this without a comprehensive governance strategy tend to stall or cancel projects.

Leading enterprises adopt four key governance patterns: narrow-scope agents instead of broad ones, human checkpoints before critical decisions, built-in decision traceability, and active data sovereignty to limit risk exposure. This approach balances autonomy with accountability and reduces the risk of costly errors or compliance breaches.

A practical framework for evaluating AI agents involves asking whether action decisions can be reconstructed, agents have bounded responsibilities, checkpoints exist before decisions execute, and data access is properly contained. This framework helps businesses scale AI with calibrated control rather than unchecked autonomy.

Ultimately, the companies that win with agentic AI by 2027 will be those that earn the trust of legal and compliance teams through disciplined governance embedded from the start. It’s not about maximum autonomy but smart orchestration of agents with clear oversight and accountability.

Algorithm-driven tools hold the promise of democratizing knowledge access and boosting creativity. However, research shows a hidden downside: these tools can restrict creative potential by undervaluing expertise. The root cause lies in their design. Algorithms typically prioritize popular and relevant information, reinforcing users' existing knowledge rather than encouraging new exploration. This creates 'ideation bubbles'—clusters of similar ideas leading to homogeneity in thinking.

Our research introduced an alternative approach, modifying traditional algorithms to emphasize diversity and uncommon ideas. In experiments involving sustainability challenges, users of this exploration-focused algorithm produced notably more creative solutions. Experts, in particular, benefited, outperforming novices by leveraging diverse insights and combining knowledge across fields—a process we call recombinant innovation.

The organizational impact is significant: exploration-based algorithms help experts break free from conventional thinking, generating a broader range of novel ideas. Businesses should treat algorithm design as a strategic choice, matching exploration tools to innovation tasks while maintaining exploitation tools for efficiency. Encouraging experts to critically engage with algorithm-generated content and continuously auditing idea diversity can further combat ideation bubbles. Ultimately, expertise remains essential, but its value transforms with AI, shifting toward the ability to synthesize and innovate from diverse information pools.

This shift calls for investing in expertise and configuring AI tools to unlock creative potential, positioning organizations to harness AI-driven innovation effectively.

I recently had a discussion with a cybersecurity company's founder who mentioned that the board only thinks about a potential M&A process when they’re "in the mood," signaling that selling is often treated as a backup plan when growth slows or liquidity pressures arise. However, the best moment to consider selling usually comes when things are going exceptionally well—when revenue is growing, customer retention is strong, and market momentum is high. This is when strategic buyers tend to offer the best valuations since they prefer acquiring winning businesses.

Another sign to start thinking about sale options is when the founder begins to lose energy or shifts focus, though this doesn’t necessarily mean a sale—sometimes a leadership transition or a partial liquidity event suits better. Also, when multiple buyers show interest, it’s valuable intel that the company may be strategically well-positioned, even if formal selling isn’t immediately planned.

Typically, boards only seriously consider selling when the company faces challenges like slowing growth or cash constraints, but at this point, valuations often reflect struggles, and shareholders may receive less favorable offers. Instead, these moments might be better for a strategic reboot like pivoting or leadership changes to regain momentum.

Boards should actively avoid inertia by continuously evaluating whether selling, scaling, pivoting, or remaining independent will best create shareholder value. Ideally, these conversations happen proactively—not out of urgency or crisis.

Itay Sagie advises tech companies and boards on strategy and M&A, emphasizing the importance of timing and strategy in maximizing company value.

Many boards have made strides in increasing diversity with more women and people of color serving as directors. However, diversity alone is insufficient without true inclusivity, which ensures all members’ voices are heard and valued. Our interviews with board chairs and members revealed a gap: chairs generally believe their boards are inclusive, but many directors disagree. Inclusion means every board member feels respected and empowered to contribute, with diverse perspectives actively sought and thoughtfully considered. We identified five often-overlooked behaviors that chairs can adopt to foster inclusion: 1) Using pre-meeting calls to understand, not control, perspectives; 2) Framing agenda items for discussion rather than presentation, encouraging open dialogue; 3) Demonstrating true hearing by acknowledging and integrating input beyond just active listening cues; 4) Managing seating arrangements to disrupt power clusters and ensure equal participation; and 5) Providing equal access to information and executives to all members consistently.

Additional inclusion boosters include explicitly measuring board inclusiveness through surveys or one-on-one conversations, and offering training to close expertise gaps on complex topics like AI and sustainability. Intentions alone don’t create inclusion—it requires deliberate, consistent chair behaviors. Chairs who embrace these practices can transform diverse boards into inclusive environments where smarter, more innovative decisions thrive.

In recent years, lawsuits have surged over the use of AI in hiring decisions, spotlighting concerns about fairness and transparency. Erin Kistler, a seasoned product manager, applied to thousands of roles at big companies like PayPal, Microsoft, and Netflix, yet never landed an interview. Now, she is leading a class-action lawsuit against Eightfold AI, a Silicon Valley firm whose software screens applicants. The case argues that the software acts as a secretive evaluation tool, ranking candidates without allowing them to review or dispute their scores. This legal fight raises critical questions about how companies use automated tools to hire and fire, potentially impacting countless job seekers.

Many companies believe they truly understand their customers through research, behavior tracking, and feedback collection. However, products often fail to resonate with real life because initial insights get diluted during handoffs between teams. Insights are gathered in one place, interpreted elsewhere, then adjusted under different constraints before reaching production, losing their original nuance. This gap is especially evident in how products are described versus designed. For example, furniture made to support real human use often gets simplified into broad, less meaningful descriptions like "stylish and functional." A common issue is translating specific insights into generalized language to ensure clarity and alignment, but in doing so, the true value of the design is lost.

A better approach is to lead with the design story itself and then reveal who it serves, offering clarity and trust without reducing the insight. Companies that protect original insights by involving designers at every product development stage—from problem definition to manufacturing—maintain the essence of the product. Small features that may seem minor, like a built-in grab bar or storage shelf, are crucial and survive only when those who understand their importance influence tradeoffs. The details that remove friction and bring delight are what create lasting loyalty to products. These qualities thrive when the original insight remains central throughout development.

Ben Wintner, CEO of Michael Graves Design, emphasizes that the products which build loyalty over time are those that stay true to genuine insight and real user needs.

The fierce competition for AI talent reveals the challenges companies face in keeping their best employees. Despite high stock-based compensation and retention bonuses, companies like OpenAI have seen key employees leave for rivals offering even more lucrative deals. This intense battle underscores a wider problem across industries: valuable employees are highly mobile, and simple pay raises often don’t suffice to keep them. Retention requires understanding the various employee mobility barriers—ranging from individual preferences and job satisfaction to organizational culture and societal constraints. These barriers differ in how much control employers have over them and can be managed through proactive or reactive strategies, either centralized or delegated. Effective talent retention combines personalized approaches with companywide initiatives, such as career development programs and meaningful job design. Importantly, retention strategies must be dynamic and context-specific, focusing on unique barriers that competitors cannot easily replicate, like culture and autonomy. By comprehensively addressing these factors, companies can better retain critical talent and adapt to evolving workforce challenges in the AI-driven landscape.