Top 20 AI Leaders Driving Industry Transformation in 2026

Artificial intelligence has moved beyond experimentation to become a defining force in how organizations build, scale, and compete. In 2026, AI leadership is no longer limited to research labs or data teams; it spans CEOs shaping enterprise vision, CTOs and CPOs operationalizing intelligence, and engineering leaders building the infrastructure that powers AI at scale. From generative AI and AI-driven security to data platforms, governance, and responsible innovation, today’s AI leaders are turning ambition into execution.

The Top AI Leaders of 2026 spotlight recognizes the executives, architects, and innovators driving this shift across industries. These leaders are advancing AI through real-world deployment, modernizing platforms, embedding intelligence into products, strengthening trust and governance, and translating complex models into measurable business impact.

As AI becomes core to enterprise strategy and mission-critical operations, the leaders featured here represent the individuals shaping how intelligence is built, adopted, and scaled. Their vision, technical depth, and execution are defining the next phase of AI-powered transformation.

Why AI Leadership Matters More Than Ever

Nearly every organization is increasing its investment in artificial intelligence over the next few years. Yet, despite this momentum, only a small fraction of enterprises consider themselves truly AI-mature. This disconnect highlights a critical reality: technology alone does not deliver transformation; leadership does. Organizations need leaders who understand AI not just as a tool, but as a strategic capability that reshapes decision-making, operations, and culture.

Today’s leaders must develop an AI-first mindset, one that balances innovation with responsibility. This means fostering teams that are comfortable experimenting with advanced technologies, addressing ethical and governance challenges head-on, and building practical frameworks that turn AI ambition into real business outcomes.

Also Read: Top 10 Women IT Leaders Who Redefined Innovation and Digital Transformation in 2025

Meet the Top AI Leaders of 2026

Journey of AI Leaders Shaping Enterprise Intelligence

1.Aravind Srinivas, Co-Founder and CEO of Perplexity

Aravind Srinivas is the Co-Founder and CEO of Perplexity, an AI-powered search platform focused on delivering trustworthy, cited answers. Founded in 2022, Perplexity has scaled briskly to more than 1,200 employees and around US$148 million in revenue, reflecting strong investor confidence in AI search beyond traditional chatbots. Under Aravind’s leadership, the platform stands out for its conversational search experience that prioritizes verifiability, directly addressing concerns around AI hallucinations and misinformation.

2.Anuj Jain, MD/Partner – Data and AI at Accenture

Anuj Jain is a Managing Director/Partner – Data and AI at Accenture, where he plays a key role in driving enterprise-scale AI transformation for global organizations. With deep expertise across artificial intelligence, data platforms, and advanced analytics, Anuj helps businesses move from experimentation to real-world AI impact, embedding intelligence into core operations, products, and decision-making processes. His work focuses on translating emerging AI capabilities, including generative AI and intelligent automation, into scalable, secure, and responsible solutions aligned with business outcomes.

3.Dario Amodei, CEO of Anthropic

Dario Amodei is the CEO of Anthropic, one of the most prominent AI companies shaping the future of responsible large-scale AI. Founded in 2021 following his departure from OpenAI, Anthropic has emerged as a credible alternative to ChatGPT, combining rapid commercial growth, approximately US$4 billion in revenue and over 1,000 employees. Dario’s leadership has attracted US$27.3 billion in funding, proving that safety-first AI development can achieve premium valuations at scale.

4.Mustafa Suleyman, CEO of Microsoft AI

Mustafa Suleyman is the CEO of Microsoft AI, where he leads the company’s efforts to embed artificial intelligence into everyday products at global scale. His move from DeepMind to Microsoft underscores the intensifying talent wars as tech giants compete for top AI leadership. At Microsoft, Mustafa champions an “AI for people” vision through platforms like Microsoft Copilot, reflecting the industry’s shift from pure research to mass-market, productivity-driven applications.

5.Andrew Ng, Founder of LandingAI

Andrew Ng is the Founder of LandingAI and one of the most influential figures in modern artificial intelligence. While Dan Maloney serves as CEO, Andrew continues to shape the company’s long-term vision and technical direction. His career bridges academia and industry, from co-founding Google Brain to serving as Chief Scientist at Baidu, where he helped scale AI adoption in real-world systems. At LandingAI, he focuses on making AI practical and accessible for enterprises, particularly in manufacturing and industrial automation, driving innovation in computer vision and machine learning systems that solve tangible business problems.

6.Alan A. Director of AI Infrastructure at Databricks

Alan A. is an experienced technology leader with over 20 years of expertise in AI infrastructure, machine learning platforms, and distributed systems. At Databricks and NVIDIA, he has led teams building large-scale model training and deployment pipelines, cloud-scale compute environments, and GPU-accelerated solutions. Alan is adept at aligning engineering, research, and product teams to deliver high-performance, reliable AI systems that drive enterprise-scale impact.

7.Joe Mayberry, Head of AI at SailPoint

Joe Mayberry is the Head of Artificial Intelligence at SailPoint, where he leads the strategy and execution of AI initiatives that enhance identity security and intelligent automation. With deep experience in building and scaling AI capabilities, Joe oversees the development of machine learning models and intelligent systems that strengthen access governance, risk detection, and anomaly response. His strategic leadership has been instrumental in positioning SailPoint as a pioneer in AI-driven identity management, enabling organizations to proactively mitigate security risks while optimizing user access.

8.Saptarshi Ghose, Senior Director, AI Engineering at Salesforce

Saptarshi Ghose is the Senior Director of AI Engineering at Salesforce, where he leads the development and scaling of AI‑driven capabilities embedded across the company’s CRM and enterprise platforms. With a strong foundation in AI research and engineering, Saptarshi drives innovation in machine learning, natural language processing, and predictive intelligence to enhance customer experience, automation, and actionable insights for global users. He plays a pivotal role in building scalable AI systems that power Salesforce’s intelligent services, enabling organizations to leverage data more effectively and transform how businesses engage with customers.

9.Dong Yu, CEO of Tencent

Dong Yu is the Chief Executive Officer of Tencent, where he leads one of the world’s largest and most influential technology companies in expanding its AI and digital transformation initiatives. Under his leadership, Tencent has accelerated the development and deployment of AI-driven products and services across gaming, cloud computing, social platforms, enterprise solutions, and healthcare innovation, reinforcing its position at the forefront of intelligent innovation.

10.Grant Rawstron, Senior Director, AI GPU Engineering Programs at Oracle

Grant Rawstron is the Senior Director of AI GPU Engineering Programs at Oracle, where he leads initiatives to accelerate AI performance through optimized GPU architectures and systems. At Oracle, Grant drives the design and scaling of GPU‑enabled infrastructure that powers large‑scale AI training and inference workloads across cloud environments. With deep expertise in high‑performance computing and hardware‑software integration, he focuses on delivering robust, efficient, and enterprise‑ready AI platforms that support demand‑intensive applications.

11.Sumedh Thakar, President & CEO at Qualys

Sumedh Thakar is the President and CEO of Qualys. He leads the company’s AI-driven initiatives to enhance cybersecurity, automation, and enterprise risk management. With a background as an engineer and product innovator, Sumedh has been instrumental in integrating AI and intelligent automation into Qualys’ cloud security and vulnerability management platforms. Sumedh drives the company’s vision of using AI to make enterprise security more efficient, scalable, and proactive, enabling organizations to detect threats, manage assets, and respond faster to emerging risks.

12.Liz Neely, Director of CEO Comm at Qualcomm

Liz Neely is the Director of CEO Communications at Qualcomm, where she shapes executive strategy, narrative, and external engagement around the company’s AI and advanced technology initiatives. In her role, Liz amplifies Qualcomm’s leadership in AI-enabled mobile computing, edge intelligence, semiconductor innovation, and 5G connectivity, ensuring clear alignment between technical breakthroughs and business impact.

13.Baris Gultekin, VP of AI at Snowflake

Baris Gultekin is the Vice President of AI at Snowflake, where he leads the company’s artificial intelligence strategy and product direction. He is responsible for the Cortex AI portfolio, machine learning capabilities, and the overall AI product roadmap. Baris focuses on building practical, enterprise-ready AI solutions that integrate seamlessly within the Snowflake platform. His work plays a key role in advancing Snowflake’s vision for the AI Data Cloud, enabling customers to develop, deploy, and govern AI applications securely and at scale.

14.Nick Kurayev, CEO of ScienceSoft

Nick Kurayev is the Chief Executive Officer of ScienceSoft, a global IT consulting and software development company, where he drives AI and digital transformation initiatives for enterprise clients. Under his leadership, Nick has focused on integrating AI, machine learning, and advanced analytics into ScienceSoft’s solutions, enabling businesses to leverage data for smarter decision-making, process automation, and scalable innovation. His vision positions ScienceSoft as a trusted partner for enterprises seeking competitive advantage through intelligent technology adoption across diverse industries.

15.Vibhuti R Sinha, Chief Product Officer at Saviynt

Vibhuti R Sinha is the Chief Product Officer at Saviynt, where he leads the development of AI-driven identity governance and cloud security solutions. At Saviynt, Vibhuti focuses on leveraging machine learning and intelligent automation to enhance access management, risk detection, and compliance across enterprise environments. He drives product strategy that integrates AI capabilities into scalable, secure platforms, helping organizations strengthen cybersecurity posture while enabling digital transformation.

16.Mark Chen, Chief Research Officer, OpenAI

Mark Chen is the Chief Research Officer at OpenAI, where he leads the organization’s research strategy and long-term scientific direction. He oversees foundational AI research focused on advancing large-scale models, multimodal intelligence, and safe deployment of artificial general intelligence. With a strong background in machine learning and AI systems, Mark plays a central role in guiding OpenAI’s research priorities, translating cutting-edge breakthroughs into real-world capabilities that power widely used AI technologies.

17.Scott Harrell, President and CEO, Infoblox

Scott Harrell is the President and CEO of Infoblox, where he leads the company’s strategy at the intersection of networking, security, and AI-driven threat intelligence. With a strong focus on DNS-based security, zero-trust architectures, and cloud-first networks, Scott has positioned Infoblox as a critical player in helping enterprises strengthen cyber resilience. His leadership advances predictive analytics and automated response capabilities, safeguarding digital infrastructure against evolving cyber threats.

18.Bryan Harris, EVP and CTO at SAS

Bryan Harris is the Executive Vice President and Chief Technology Officer at SAS, where he drives the company’s technology vision across advanced analytics, artificial intelligence, and data-driven innovation. With deep expertise in AI platforms, machine learning, and enterprise analytics, Bryan plays a key role in advancing SAS’s mission to help organizations turn data into trusted, actionable intelligence. His leadership accelerates the development of open, scalable AI solutions that empower businesses to innovate responsibly and achieve measurable outcomes.

19.Ryan Edge, Director of Strategy, OneTrust

Ryan Edge is the Director of Strategy at OneTrust, where he helps shape the company’s direction in data privacy, AI governance, risk management, and compliance. His strategic leadership supports organizations in balancing innovation with regulatory and ethical requirements in an increasingly data-driven and AI-powered landscape. With his forward-thinking approach, he drives OneTrust’s leadership in emerging technologies like responsible AI frameworks and global privacy standards.

20.Kari Ann Briski, VP, Generative AI Software for Enterprise of NVIDIA

Kari Ann Briski is the Vice President of Generative AI Software for Enterprise at NVIDIA, where she leads the development and strategy of enterprise-grade generative AI platforms and solutions. She plays a pivotal role in advancing NVIDIA’s ecosystem for large language models, AI frameworks, and accelerated computing, enabling organizations to deploy scalable, secure, and high-performance generative AI applications.

What Today’s AI Leaders Are Saying About the Future of Intelligence?

# Vibhuti R Sinha, Chief Product Officer at Saviynt

“For me, 2025 was the year AI stopped being a demo and started becoming real work inside enterprises. The biggest shift was from AI as an assistant to AI as an actor, agentic systems that don’t just recommend, but take actions across production environments.
What many teams learned the hard way is that AI is only as good as the identity and access foundations underneath it. Messy identity data, over-privileged access, and disconnected systems don’t just slow AI down, they make it unsafe.
The practical lesson from 2025: before scaling AI, organizations had to pause and ask uncomfortable questions about trust, control, and accountability. The companies that succeeded weren’t the ones moving fastest, but the ones willing to fix the fundamentals first.

My most meaningful contribution this year was helping teams move from AI excitement to AI readiness.”
We focused on bringing discipline to AI adoption by extending identity governance and posture management to what was previously invisible, non-human identities and AI agents. That meant treating AI agents like real users: registering them, defining what they’re allowed to do, continuously monitoring their access, and auditing their actions.

Practically, this helped customers:
Give security, IT, and product teams a shared language instead of competing priorities.
What I’m most proud of isn’t just what we shipped, it’s how many teams told us, “This finally makes AI feel manageable.” That was the real win.

The biggest lesson for me in 2025 was realizing that leading through AI change requires empathy as much as vision. AI creates excitement, but it also creates fear. Engineers worry about quality, security teams worry about risk, and leaders worry about being wrong in public. I learned that progress happens faster when you acknowledge those fears instead of dismissing them.

Personally, this year reinforced a simple belief: clarity builds trust. When teams understand why decisions are being made and how AI will help rather than replace them, they move with you, not behind you. It was about proving that AI can be responsible, human-centered, and worthy of trust, and that’s a much harder, but far more meaningful, journey.”

# Dong Yu, CEO of Tencent

“In 2025, significant advancements in artificial intelligence models’ reasoning capabilities, coupled with the transition from passive chatbots to autonomous multimodal agents, marked a pivotal stage in AI development. Artificial intelligence has progressed beyond its role as an information retrieval tool to serve as an active collaborator on complex tasks. This evolution presents substantial opportunities within scientific research, particularly as AI contributes to uncovering foundational principles in biology and physics through collaboration with scientists.
Concurrently, the benefits of merely scaling the model and data size are diminishing, suggesting that future enhancements in AI will increasingly depend on advancing our understanding of the principles underlying intelligence, as well as innovations in model architecture and learning algorithms.”

Final Thoughts

As artificial intelligence becomes embedded in core business and operational strategies, its success will increasingly depend on how thoughtfully it is governed, scaled, and aligned with organizational goals. The next phase of AI adoption will be defined by practical execution, responsible innovation, and long-term value creation rather than experimentation alone.

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