The recent release of TIME’s 2025 TIME100 AI list underscores how much attention is focused on foundation models, generative agents and consumer‑facing AI tools. Yet a closer look suggests that many powerful AI applications are still flying under the radar.
That’s where the Senior Executive AI Think Tank comes in—a curated group of experts in machine learning, generative AI and enterprise AI applications who combine technical depth with executive perspective.
In this article, they use real-world insight to examine which industries and use cases are underrepresented in lists like TIME’s and explore the biggest AI frontiers that deserve attention now.
“The absence of such ‘behind-the-scenes’ industrial AI applications … can make it seem as though these sectors are lagging.”
Aviation and Industrial AI: The Hidden Frontier
While consumer-facing AI dominates headlines, powerful innovations in aviation and industrial sectors often go unnoticed. AI is already transforming ground operations at airports, optimizing turnaround times, staff deployment and logistics in real time. In manufacturing and heavy industry, AI is streamlining supply chains, reducing operational costs and enhancing decision-making in dynamic, high-risk environments.
Justin Newell, CEO of INFORM, highlights that these “behind-the-scenes” use cases are frequently excluded from mainstream rankings like TIME100 AI. “The true next frontier for AI is not just in creating more advanced chatbots or consumer apps,” Newell says, “but in applying its power to solve some of the world’s most difficult, tangible problems.” Executives seeking ROI from AI should look beyond headline-grabbing applications and consider where AI can create measurable impact in overlooked operational systems.
“AI in agriculture remains underrepresented, despite its vast potential to tackle food security and sustainability challenges.”
AI in Agriculture: A Critical Yet Undervalued Opportunity
Despite its transformative potential, agriculture remains one of the most underrepresented sectors in mainstream AI innovation discussions. As food security and sustainability concerns intensify globally, AI can help optimize nearly every aspect of farming—from improving crop yields and monitoring soil health to deploying autonomous machinery and streamlining agricultural supply chains.
Suri Nuthalapati, Data and AI Leader for the Americas at Cloudera, stresses that this sector is ripe for innovation. “AI in agriculture remains underrepresented, despite its vast potential to tackle food security and sustainability challenges,” he says. “The biggest opportunities lie in autonomous farm equipment, supply chain optimization and climate-resilient crop innovation.”
Data Science: From Models to Meaningful Solutions
AI systems built in isolation often miss the mark—what truly matters is how well they align with real-world customer needs. Soner Baburoglu, President of SonerB, emphasizes that the companies succeeding with AI are those integrating data science with deep client understanding. “The real value lies in listening to customers, optimizing and customizing according to their actual needs,” he explains.
Baburoglu advocates for continuous dialogue with customers, ensuring that AI tools provide practical, not just theoretical, value. “It’s not about ‘nice-to-have’ features,” he adds, “but producing outcomes that directly address real needs.”
EdTech, Mental Health and More: AI’s Growth Markets
Several high-impact industries remain underrepresented in mainstream AI conversations, argues Roman Vinogradov, VP of Product at Improvado—sectors that offer transformative potential. “Education technology presents a vast landscape where personalized learning experiences powered by AI could transform student engagement and outcomes,” he says.
He also points to underutilized AI potential in healthcare, notably in the mental health field, where scalable AI support tools could bridge gaps in access and personalization. “Exploring these sectors could yield substantial returns,” Vinogradov says, especially as societal needs—and regulatory demands—evolve.
“History will remember the AI that made us endure, not the apps that made us faster.”
AI as Civilization’s Operating System
Enterprise AI leaders are increasingly shifting from narrow automation use cases to more systemic, worldwide challenges. For Aditya Vikram Kashyap, Vice President of Firmwide Innovation at Morgan Stanley, the most impactful AI will address global issues: climate change, energy resilience, food systems and education access. “History will remember the AI that made us endure,” he says, “not the apps that made us faster.”
Kashyap’s view challenges companies to invest in AI as long-term infrastructure rather than short-term productivity tools. This requires aligning R&D with societal challenges and integrating AI into foundational systems—energy grids, transportation networks and public health—not just as an enhancement, but as a new operating layer for modern civilization.
Vertical AI: Real-World Impact, Less Hype
The AI media landscape tends to focus on foundational models and startup unicorns—but much of the real change is happening within traditional industries. Jim Liddle, Chief Innovation Officer of Data Intelligence and AI at Nasuni, emphasizes the value of domain-specific AI. “Vertical specialists often have more measurable real‑world impact,” he says, noting applications like reducing food waste or preventing industrial accidents.
These specialists, like Chief Data Officers and AI heads, are frequently embedded within enterprise companies, solving problems that don’t generate headlines but do drive economic resilience and operational efficiency. “The biggest untapped opportunity,” Liddle adds, “might be highlighting vertical AI specialists who are solving industry-specific problems rather than building general‑purpose tools.”
Industrial AI and Government Investment
As AI becomes embedded in the economy, traditional industries—like manufacturing and agriculture—will require dedicated public and private investment. Nikhil Jathar, CTO of AvanSaber Technologies, calls attention to the lack of recognition for foundational sectors in rankings like the TIME100 AI. “There’s a significant gap in highlighting AI for predictive maintenance in manufacturing, agricultural yield optimization or supply chain resilience,” he says.
Jathar recently advocated before Congress alongside IEEE colleagues for more R&D funding in foundational AI applications. “Modernizing these foundational industries,” he adds, “is essential for boosting our economic productivity and ensuring national resilience.”
AI That Augments, Not Replaces
Despite the hype around AI replacement, the most impactful systems may be those that support and augment human professionals. Mohan Krishna Mannava, Data and AI Leader at Texas Health, emphasizes this overlooked opportunity. “AI that assists clinicians, educators or creatives to amplify their expertise is where the most meaningful impact lies,” he explains.
Mannava also highlights the importance of AI ethics, safety and security—areas that often go unnoticed in flashy lists. He believes these “unsung heroes” are doing the essential work that will determine whether AI earns long-term public trust.
Key Takeaways for Executives
- Industrial domains deserve the spotlight: Many high‑impact AI use cases in aviation, logistics and manufacturing are overlooked—yet they already deliver ROI.
- Agriculture remains rich soil: Precision farming, climate resilience and personalized learning are sectors ripe for transformation.
- Center around customer value: AI projects succeed when they are closely aligned with customer outcomes, not just technical elegance.
- Innovate within EdTech and mental health: AI can personalize learning and expand access to mental health support, making these underrepresented sectors critical areas for meaningful, human-centered impact.
- Solve for worldwide challenges. From climate and energy to food and education, AI should be treated not just as a tool, but as critical infrastructure for humanity’s long-term resilience.
- Elevate vertical AI specialists: Domain experts who build AI within industries often outperform generalist efforts in real-world settings.
- Invest in “unsexy” infrastructure: AI for predictive maintenance, supply chain resilience and augmentation can underpin enterprise strength.
- Build trust and domain safety: Governance, augmentation-first design and ethical frameworks are essential for sustainable AI adoption.
Looking Beyond the Hype
The TIME100 AI list provides a compelling snapshot of prominent AI leadership—but it necessarily skews toward visibility, novelty and consumer appeal. Behind the scenes, transformative work is happening in the machine rooms of aviation, agriculture, energy and more.
For senior leaders and executives, the path forward is to balance headline-worthy AI innovation (chatbots, generative tools, agentic systems) with durable investment in augmentation, trust, vertical specialization and “boring but essential” infrastructure. Those who seize the gaps now—not just the glamour—can anchor AI strategies that pay dividends now and form foundational positioning for the next decade.
