As artificial intelligence continues to reshape industries, a new role is emerging at the intersection of technology, strategy, and innovation: the AI Product Manager. This isn’t just a passing trend—it’s a reflection of how integral AI is becoming in the development and optimization of modern products.

To succeed in this evolving role, AI product managers must do more than understand traditional product lifecycles. They’ll need to navigate complex AI and machine learning (ML) systems, evaluate performance metrics, and ensure responsible, ethical deployment of technology. That requires a unique blend of technical acumen, data fluency, and cross-functional leadership.

Core Competencies of Future-Ready AI Product Managers

To lead in this space, product managers should develop proficiency in the following key areas:

• AI-Specific Technical Competence – Understanding how models are built, trained, tested, and deployed.

• Data Science Knowledge – Ability to interpret data, partner with data teams, and drive data-informed decisions.

• Model Performance Evaluation – Knowing how to measure, optimize, and communicate model performance.

• Ethics, Bias, and Regulation – Staying informed about legal and societal implications of AI systems.

• Education and Influence Management – Evangelizing AI within the organization and aligning diverse stakeholders around AI initiatives.

Why Every Product Manager Needs AI Skills

Just as “internet product managers” were once a niche, only to evolve into the standard model of digital product management, AI is on track to become a core element of every product manager’s toolkit.

According to Forrester, AI will become so embedded in product development that PMs who lack foundational AI knowledge may find themselves at a disadvantage. Generalist product managers won’t need to be AI engineers, but they will need to understand how to integrate AI into product features, make informed trade-offs, and iterate based on user feedback and AI performance.

How Product Leaders Can Prepare Their Teams

Leadership plays a crucial role in preparing product teams for the AI-powered future. That means more than just encouraging learning—it means building a culture that values experimentation, continuous education, and hands-on practice.

Here’s how leaders can start:

• Offer AI literacy programs tailored for non-technical professionals.

• Create hands-on experiences through internal projects, hackathons, or partnerships with AI teams.

• Provide access to online, interactive courses and workshops that blend theory with application.

• Recognize and reward team members who take the initiative to upskill.

Conclusion

AI isn’t just a buzzword—it’s rapidly becoming a foundational element of modern product strategy. As such, the AI product manager role is not only growing but evolving into a key pillar of the future workforce.

Product leaders who invest in upskilling today will set their teams up for long-term success, ensuring they’re not only keeping up with the market but helping to define it.

FAQs

Q: What skills do AI product managers need?
A: They should develop AI-specific technical knowledge, data science fluency, the ability to evaluate AI performance, a strong understanding of ethics and regulation, and the ability to educate and influence across teams.

Q: Why is AI knowledge becoming essential for all product managers?
A: AI is becoming a standard part of digital products. PMs will need to understand how to apply AI responsibly and effectively to remain competitive and meet evolving customer expectations.

Q: How can leaders support their teams’ AI/ML development?
A: Provide access to literacy courses, create hands-on learning opportunities, encourage cross-functional collaboration, and foster a culture of curiosity and continuous learning.

Data and analytics are no longer just about crunching numbers and generating reports. They are now a key driver of business success, helping organizations to optimize operations, improve decision-making, and stay competitive in a rapidly changing world.

Why Data and Analytics Matter

Data and analytics help organizations to:

• • Gain insights into customer behavior and preferences

• • Identify areas for improvement and optimize operations

• • Make data-driven decisions, rather than relying on intuition or anecdotal evidence

• • Stay ahead of the competition by being more agile and responsive to changing market conditions

The Challenges of Data and Analytics

While the benefits of data and analytics are clear, many organizations struggle to implement effective solutions. This can be due to a range of factors, including:

• • Limited resources, including budget and personnel

• • Complexity and technical difficulties in implementing and maintaining data analytics solutions

• • Lack of expertise and knowledge in data analysis and interpretation

• • Resistance to change and cultural barriers to adopting new technologies and processes

Overcoming the Challenges of Data and Analytics

While the challenges of data and analytics are real, there are many ways to overcome them. This can include:

• • Seeking expert guidance and support to help implement and maintain data analytics solutions

• • Investing in employee training and development to build in-house expertise

• • Starting small and gradually building up capabilities and expertise

• • Building a strong business case and demonstrating the value of data and analytics to stakeholders

Conclusion

Data and analytics are no longer optional, but a crucial part of any digital transformation strategy. By gaining insights into customer behavior, identifying areas for improvement, and making data-driven decisions, organizations can stay ahead of the competition and achieve their goals.

The Rise of Converged Threats

From ransomware campaigns that shut down building access systems, to IoT vulnerabilities that bridge physical and digital entry points, organizations are dealing with the reality that treating physical and cyber security as separate disciplines leaves dangerous gaps. That’s a core theme threading through ISC West 2025’s expanded educational tracks, keynote lineups, and panel sessions.

Identity as the New Security Perimeter

A major theme emerging from ISC West is the redefinition of "perimeter." In a world where physical and digital access points are increasingly interwoven, identity becomes the critical link. Gill believes the convergence of physical and cybersecurity is being driven by the need to secure identity, context, and intent—not just assets.

Cultural Hurdles and Strategic Shifts

But while the technology is advancing rapidly, many organizations still struggle with cultural inertia.

ISC West: The Industry’s New Convergence Hub

To support this vision, ISC West has expanded its cybersecurity and connected IoT pavilion, introduced a cyber hub sponsored by ZeroFox, and integrated RSA-curated educational tracks. Shaughnessy told me the SIA New Products and Solutions Showcase will highlight over 100 debut technologies—many blurring the line between physical and cyber applications.

Conclusion

As the attack surface grows more complex, ISC West is rising to meet the moment by reshaping how the industry thinks about defense. Threats don’t respect borders, so security can’t either. The future is converged—and it’s already underway.

FAQs

Q: What is the convergence of physical and cyber security?
A: The convergence of physical and cyber security refers to the merging of the two previously separate worlds of physical security (managing gates, guards, and surveillance systems) and cybersecurity (handling firewalls, malware, and identity threats).

Q: Why is convergence important?
A: Convergence is important because it allows organizations to address the increasingly interconnected threats that were previously handled separately.

Q: What are some key takeaways from ISC West 2025?
A: Some key takeaways from ISC West 2025 include the need for a unified approach to security, the importance of identity in securing access points, and the need for AI and machine learning to optimize security strategies.