The iPhone maker said on Monday that Ternus will take over on September 1, while Cook will become executive chairman.

The leadership change comes as Apple strengthens its focus on artificial intelligence, responding to competition from companies including Nvidia, Meta and Google.

Ternus joined Apple in 2001 and currently serves as senior vice-president of hardware engineering. He has worked on several of the company’s biggest products, including the Mac, iPad and AirPods.

He is also seen as an important figure in improving Mac sales in recent years, helping the product regain momentum against personal computer competitors.

Although he has kept a lower public profile than some Apple executives, the company has recently given him a more visible role.

Last year, Ternus presented the iPhone Air, a major redesign of Apple’s flagship device and one of the biggest changes to the product line in years.

At 50, he takes over at the same age Cook did when he succeeded Apple co-founder Steve Jobs in 2011.

Cook leaves the chief executive role after overseeing one of the most successful periods in Apple’s history. Since taking charge in August 2011, he has helped increase the company’s market value by about $3.6 trillion.

He was widely credited with expanding Apple’s global supply chain, especially through manufacturing partnerships in China, while also growing the company’s services and hardware businesses.

Cook also became the first Fortune 500 chief executive to publicly come out as gay in 2014 and often spoke on issues including workplace diversity and environmental policy.

Apple said Cook will remain involved in dealing with policymakers as executive chairman.

Ternus now inherits a company under pressure to show stronger progress in artificial intelligence.

Although Apple introduced Siri in 2011, it has struggled to match the pace of newer AI-focused companies.

Tech giants such as OpenAI and Anthropic have attracted millions of users with new chatbot products, while Nvidia has become the world’s most valuable listed company on the back of demand for AI chips.

In January, Apple reached an agreement with Google to use Gemini technology to improve Siri.

Ternus will also face competition in new devices. Meta Platforms has found success with smart glasses, while Apple’s Vision Pro headset has faced questions over its high price.

Alongside appointing John Ternus as CEO, Apple said Johny Srouji has been named chief hardware officer. He will continue leading the company’s custom chip and sensor teams.

The hardware engineering group previously led by Ternus will now be overseen by Tom Merieb.

In today’s fast-paced environment, effective leadership requires agility, foresight, and emotional intelligence.

When leveraged correctly, AI can significantly strengthen leadership abilities in five crucial areas: Strategy, Tactics, Relationships, Accountability, and Mindset. Embracing AI empowers leaders to navigate challenges with greater effectiveness and vision.

Strategy

In today’s fast-paced business landscape, it is essential for leaders to harness artificial intelligence to simulate complex decision-making scenarios ahead of implementation.

By tapping into the power of AI, leaders can obtain vital insights that allow them to anticipate and thoroughly understand potential people-related risks, extending well beyond traditional concerns like revenue fluctuations.

This proactive strategy equips leaders to identify and address issues before they escalate, ultimately cultivating a more resilient organization.

Automating status updates is a powerful strategy that frees up valuable time for leaders. Instead of spending hours on mundane data collection, they can shift their focus to more impactful activities like mentoring and coaching their team members.

This transformation is vital for elevating the quality of leadership engagement and fostering a motivated, high-performing workforce that drives exceptional business results.

By embracing AI in their management practices, leaders can create a dynamic and responsive team environment that consistently delivers outstanding outcomes.

Tactics

Training managers to effectively interpret feedback generated by AI systems is crucial for maintaining a balanced leadership approach. Managers must learn to leverage AI insights without becoming overly dependent on technology; human judgment must always lead decision-making processes.

Organizations must establish clear guidelines outlining the appropriate uses of AI to foster an environment that prioritizes human-centered values and ethical considerations in leadership. It’s imperative that these practices are put into place to ensure sound decision-making and effective management.

Conducting regular audits to evaluate the technology friction encountered by teams is not just beneficial, it is essential.

These assessments will pinpoint whether AI solutions are genuinely addressing challenges and delivering value or if they are complicating processes and adding unnecessary obstacles.

By actively monitoring the effectiveness of AI integration, leadership teams can make informed adjustments, ensuring that technology unequivocally enhances productivity and strengthens team dynamics, rather than detracting from them.

Relationships

Great leadership understands that emotional intelligence is an inherently human quality that can’t be handed off to algorithms or artificial intelligence.

While technology can certainly offer valuable insights and streamline processes, it should enhance and support the organization’s core mission, not overshadow it.

After all, it’s the human touch that truly drives connection and inspires teams to achieve greatness together.

Effective leaders know that authentic human connections are at the heart of great decision-making.

They recognize that technology should amplify our understanding, compassion, and empathy, not overshadow the essential interpersonal dynamics that fuel collaboration and uplift team spirit. When leaders embrace AI thoughtfully, they create a workplace where technology and human insight work hand in hand.

This approach paves the way for decisions that are not only informed but also deeply resonate with team members and stakeholders alike, fostering a more engaging and supportive environment.

Accountability

The integration of artificial intelligence in the workplace can sometimes lead to unintended consequences, such as eroding trust among team members and creating a state of chronic burnout that often goes unnoticed. While the adoption of AI may result in a noticeable increase in productivity, streamlining tasks and accelerating processes, there’s a significant risk that it can also diminish the development and engagement of individual employees.

When workflows become overly automated, the essential human touch may be sidelined, leading to a lack of personal interaction and oversight that is crucial for fostering a healthy work environment.

Furthermore, as responsibilities shift increasingly toward automated systems, employees may feel undervalued and disconnected, which can erode team cohesion and morale over time. Therefore, it is imperative that leaders actively monitor the impacts of AI on their teams. They must take responsibility for ensuring that the well-being of their workforce is prioritized, maintaining open lines of communication and providing support when needed. By doing so, leaders can help mitigate the risks associated with over-automation and cultivate a more resilient and engaged workforce.

Mindset

The leadership mindset has undergone a significant transformation in recent years, evolving from a narrow focus to a far more expansive and strategic approach. Initially, as businesses began to integrate artificial intelligence into their operations, leaders fixated on the question, “What can AI do for us?”

This focus demonstrated a strong interest in uncovering the functionalities, advantages, and potential applications of AI within their organizations.

Decision-makers recognized the need to thoroughly understand not only the technical capabilities of artificial intelligence but also its potential to enhance operational efficiency, improve decision-making, and drive innovation.

As leaders have developed a more sophisticated understanding of AI’s role, they are now actively engaging with a wider array of critical questions and implications. They recognize that AI will fundamentally reshape business models, influence company culture, and establish competitive advantages in an ever-evolving marketplace.

This shift underscores the acknowledgment of AI not just as a tool, but as an essential element of strategic planning and a key driver of long-term success.

As technology and decision-making continue to evolve, we find ourselves asking a more intricate question: “Which decisions truly deserve my insight, and which ones can AI handle with ease?”

This shift in focus invites us to explore the balance between human intuition and the power of artificial intelligence.

Effective leaders understand the undeniable importance of their unique judgment and expertise in the decision-making process.

They recognize that while AI excels at processing vast amounts of data and delivering valuable insights, certain decisions require human intuition, moral judgment, and creativity.

By clearly identifying these critical areas, leaders can leverage AI as a powerful supportive tool rather than allowing it to overshadow their decision-making authority.

A balanced partnership between human leadership and AI enhances strategic thinking and emotional intelligence.

By utilizing AI for data processing, predictive analytics, and routine decision-making, organizations can create a more effective and adaptive environment.

This collaboration enables leaders to focus on higher-level thinking while leveraging technology to streamline operations.

Conclusion

In today’s rapidly evolving digital landscape, artificial intelligence (AI) has emerged as a transformative tool that significantly enhances leadership capabilities. By utilizing AI, leaders can adopt a more strategic and tactical approach, enabling them to swiftly analyze vast amounts of data and make informed decisions that contribute to organizational success. Furthermore, AI supports the development of emotional intelligence, equipping leaders to better understand and respond to the needs and sentiments of their teams.

The Integration of AI brings a powerful sense of accountability to leadership. With data-driven insights at their fingertips, leaders can effectively monitor performance and outcomes, paving the way for a culture of transparency where decisions are backed by solid evidence.

Yet, it’s essential to remember that AI shouldn’t overshadow the essential qualities that make leadership truly effective. At its core, authentic leadership is all about inspiring, motivating, and forging deep connections with people on a human level. Balancing data with that human touch is where the real magic happens!

As we venture into the future, the most impactful leaders will be those who skillfully blend AI tools with their unique leadership style, all while preserving the vital human touch in decision-making.

This harmonious blend will not only equip leaders to tackle intricate challenges but also cultivate a thriving, collaborative atmosphere where technology and humanity can flourish side by side.

Despite proposing some of the most lucrative packages in Silicon Valley history, the project is finding it difficult to hold on to its star hires.

The Superintelligence lab, launched in April 2025 with the aim of leapfrogging Meta competitors in artificial general intelligence (AGI), has been hit by high-profile departures.

Rishabh Agarwal, recruited from Google DeepMind earlier this year on a reported $1 million salary, announced in late August that he would be leaving after barely five months. 

“It was a tough decision not to continue with the new Superintelligence TBD lab, especially given the talent and compute density. But after 7.5 years across Google Brain, DeepMind, and Meta, I felt the pull to take on a different kind of risk,” Agarwal wrote in a farewell post on X.

He also repeated Zuckerberg’s own words back at him: “In a world that’s changing so fast, the biggest risk you can take is not taking any risk.” The quote has since been widely interpreted as researchers using the Meta chief’s mantra to justify walking away.

Avi Verma and Ethan Knight, both previously with OpenAI, have returned to their former employer after brief stints at MSL. In a further blow, longtime Meta executive Chaya Nayak has also left, joining OpenAI to work on special initiatives.

These issues have led to uncomfortable questions for Meta. If billion-dollar offers cannot retain talent, what can? Insiders point to structural problems: frequent reorganisations, shifting goals, and reports of micromanagement at the top. 

The company recently split its AI staff into four separate groups, creating suspense inside a lab already tasked with one of the most ambitious projects in tech.

Experts say money is not the ultimate driver for the best minds in the field. DeepMind cofounder Demis Hassabis once said frontier researchers want to “help influence how AGI plays out and steward the technology safely into the world” rather than simply chase paycheques. 

Similarly, Anthropic’s cofounder Benjamin Mann said: “My best case at Anthropic is we affect the future of humanity. My best case at Meta is we make money.”

Meanwhile, rivals are capitalising. OpenAI has not only regained former staff but strengthened its bench at a time when it publicly criticised Meta’s aggressive poaching tactics. 

Elon Musk’s xAI is also pulling engineers away from Zuckerberg’s company, with reports noting at least 14 defections this year alone. Unlike Meta’s cash-heavy approach, Musk promotes a performance-driven culture anchored in equity and speed.

Meta has invested heavily in leadership, hiring Scale AI founder Alexandr Wang and former GitHub CEO Nat Friedman to run its AI efforts. But reports of disagreements between Zuckerberg and Wang over timelines for superintelligence highlight deeper tensions. 

Meta’s resources can buy time and talent, but not loyalty or mission alignment.

This year alone, Microsoft has sunk $64 billion into infrastructure, much of it driving the AI growth through data centres powering services like Copilot in Microsoft 365. 

While most tech firms are cautiously navigating unstable markets, Microsoft’s share price has surged over 30%, a sign investors are betting on the company’s aggressive AI focus.

Behind the scenes, Microsoft appears to be recalibrating its alliance with OpenAI. Though the two remain close, with Microsoft still a strategic backer, OpenAI has been given leeway to partner with others, including Oracle, on the massive Stargate data centre project in Texas.

What’s happening is Microsoft is gradually placing itself as a neutral technology provider, what some are calling an “arms dealer” in the AI wars, rather than locking into exclusive alliances. This neutrality allows it to offer AI tools across industries without being boxed in by one partner’s limitations or priorities.

Meanwhile, demand for AI services in Azure, Microsoft’s cloud platform, is climbing. CEO Satya Nadella has suggested that AI costs can be drastically reduced through algorithmic efficiency. “Once it settles on an algorithm and begins to optimise it, Microsoft can obtain 10 times better performance for the same computing costs,” he said. That’s the kind of return that could redefine tech margins.

The company is also being tactical about how it handles computational surges. Instead of building more expensive data centres, Microsoft is leaning on “neocloud” providers like CoreWeave. These firms specialise in delivering Nvidia-powered AI infrastructure on demand. It’s a leaner, faster, more flexible approach to scaling.

Cantor Fitzgerald analyst Thomas Blakey said: “If they have to flex up in some way, they’ve been consistently saying that they’re going to shift away from buying more data centres and dirt and cement and they’re going to leave that to the neoclouds.”

The Microsoft Build 2025 conference, running until May 22, is not just a developer gathering this year. It’s a moment of clarity about Microsoft’s vision to take over the AI stack, monetise it, and use every tool, from GitHub to Azure, to keep developers building inside its ecosystem.

In the rush to appear innovative, many teams are rushing to integrate AI features into their products simply to keep pace with global trends and competition.

However, the real differentiator is knowing when to use it and the best way to do so. The winners in the AI era will be those who adopt it with purpose and intention, not just those who are the fastest to do so.

Every plan is a balancing act between wanting to do a lot and staying focused, and AI makes that even tougher. An effective AI strategy is not just about chasing trends but aligning AI plans with clear product goals, sustainable competitive advantage and long-term customer value.

This is where good leadership comes in as a steady hand that guides the team on deciding when AI is the right answer.

Damilola Ojo, a senior product manager who’s guided many complicated, fast-growing products, sees AI as a tool that has to justify its spot in the plan, not just a cool thing to add.

For Damilola, a good AI strategy starts with knowing the business problem you’re trying to solve. Often, teams start with a model and then search for a problem to solve with it. She does the opposite. By finding the problems that hurt customer value and make you different from the competition, she makes sure AI investments pay off in real ways.

This keeps you from using “AI for the sake of AI” and puts resources where they give you a lasting edge. For example, in a digital health platform that she managed, rather than adding AI symptom checkers just because ‘every other app had them’, she first identified the core point of friction for the user: users struggled to access timely health guidance while trying to navigate complex appointment systems.

AI was specifically applied to triage incoming queries, reducing response times by over 60% while improving patient satisfaction scores. AI wasn’t a gimmick but a direct solution to a problem with measurable business impact.

Another key thing is figuring out if you’re ready. Even a good AI idea can fail if you don’t have the right data, infrastructure, or clear governance. Damilola says teams should take it slow, test their ideas, and grow responsibly.

This means trying out AI features in small pilots, gathering user feedback from these pilots and using them to improve the features,. This avoids the trap of promising too much too soon in early roadmaps, which can produce weak, low-quality or unsustainable solutions.

She also thinks ethics and trust are very important, and sees trust as a competitive advantage in the AI era. AI products that ignore transparency, fairness and privacy will quickly lose users’ trust.

Damilola includes ethical principles from the onset, getting teams to agree on rules for using data, mitigating bias in models, and making AI outputs easy to understand via user-friendly language. This helps avoid bad press and promotes transparency, which eventually makes the product a trusted option.

Making AI part of the roadmap is where the planning turns into action. Damilola makes sure AI projects aren’t just seen as separate entities but as part of how the products improve.

This means talking to engineering about what’s possible, design about how it affects users, and leadership about the costs and benefits. She believes that AI features should help the product, not fight with what it already does well.

Finally, you have to think about long-term growth. AI models and the systems that power them need to change as the market changes, new regulations are implemented, and users’ needs evolve.

Damilola’s method includes regular monitoring, scheduling retraining, and having flexible systems that can swap out or upgrade models without diminishing the user experience. This keeps products relevant, competitive and strong over time.

In a market that is oversaturated with AI trends, being able to create an AI strategy that is realistic, ethical, and able to scale is a key mark of a senior product leader. Damilola Ojo’s work shows how making thoughtful decisions can cut through the confusion, turning AI from something trendy into a lasting edge over the competition.

For companies trying to ride the AI wave without losing their way, this level of clear-thinking isn’t just a nice extra, it’s what separates something that’s here today gone tomorrow, from having a real and lasting impact.