Beyond the AI Hype: How Agentic AI Will Transform Data Analytics and Enterprise Decision-Making

A deep dive into Info-Tech Research Group's vision for the future of data-driven organizations

Standing before 4,000 IT leaders at Info-Tech Live 2025 in Las Vegas, CEO Tom Zehren delivered a stark message: the world is experiencing uncertainty levels 481% higher than baseline, driven by six major factors, including policy changes, commercial tariffs, financial market volatility, deglobalization, soaring deficits, and unprecedented spending scrutiny.

Yet this chaos creates the perfect storm for technology transformation. For data and analytics professionals, his vision of "agentic AI" represents not just another buzzword, but a fundamental shift in how organizations will harness data for competitive advantage.

The Uncertainty Paradox: Why Now is the Perfect Time for Analytics Innovation

Zehren opened with compelling research from the International Monetary Fund showing global uncertainty has spiked dramatically in the last six months. "We measured this by analyzing reports from central banks and governments of 143 countries, counting how many times 'uncertainty' appears," he explained. "It's not just you feeling overwhelmed—it's everyone."

But rather than retreating, Zehren argues this is precisely when data-driven organizations should accelerate. "Don't try to control what you can't control," he told the audience. "Focus on the certainty of the technology curve."

This philosophy directly impacts analytics teams who often find themselves caught between executive demands for AI transformation and the reality of poor data quality, siloed systems, and governance concerns. Zehren's comprehensive six-step methodology for navigating uncertainty puts data at the center of organizational resilience:

1. Neutralize uncertainty

2. Pick your first bets

3. Fund innovation by cutting costs

4. Pursue IT excellence

5. Build an adaptive workforce

6. Slash AI transformation timelines.

When I asked Zehren where he sees the biggest implementation failure of this methodology today, he didn't hesitate -- "not doing anything." It appears that the Agile adage of "fail fast and iterate" remains relevant.

From Generative AI to Agentic AI: The Next Analytics Revolution

While most organizations are still experimenting with generative AI for report creation and data analysis, Zehren believes we're on the cusp of something far more transformative: agentic AI. This next IT super trend builds on the convergence of cloud infrastructure, big data capabilities, neural networks, transformer architecture, alignment techniques like reinforcement learning from human feedback (RLHF), and emerging agent orchestration frameworks.

"The biggest differentiator is the level of autonomy," Zehren explained in our exclusive interview. "Agentic AI gets a strategic goal and figures out what sub-goals it needs, what information it requires, and who it needs to talk to. It thinks."

His formula is compelling: augmentation + high autonomization + automation = agentic AI. This represents the manifestation of what Info-Tech calls the "era of autonomization," where organizational capabilities become truly autonomous rather than automated.

For analytics professionals, this represents a significant shift in perspective. Instead of building dashboards and reports that humans interpret, future analytics systems will autonomously identify data needs, clean datasets, discover insights, and recommend actions, all while learning and adapting in real-time.

Real-World Transformation: Beyond Theoretical Possibilities

Unlike typical conference presentations filled with hypothetical scenarios, Zehren shared concrete examples of organizations already achieving dramatic results. He detailed three transformative use cases that illustrate agentic AI's potential:

• Software Development Revolution: An AI underwriter for restaurant insurance that achieves 80%+ process automation, a project completed in four months for $400,000 versus the traditional two-year, $3-million approach.
  

• HR Transformation: Moving beyond simple question-answering to complete action execution, including one-click onboarding processes and virtual HR support analysts with real-time omnichannel communication capabilities, all without requiring complex system integrations.
  

• Executive Productivity: Digital twins for executives that function as virtual chiefs of staff, managing communications across multiple channels while preserving human decision-making authority. "Imagine a world where everyone has a chief of staff and an AI virtual assistant," Zehren explained. "Decision-making remains yours; communication does not."

"This isn't the future, this is today," Zehren emphasized, highlighting Felix Schmidt's remarkable achievement: 14,000 lines of code generated in one day, with only 200 lines written by humans. "This is happening because agentic AI will ultimately be faster, cheaper, and at some point, better than what we're doing today."

For data teams struggling with resource constraints and growing analytical demands, these examples illustrate how AI-augmented development can accelerate everything from data pipeline creation to the deployment of advanced analytics models.

The Missing Piece: Orchestration and Integration

Despite the promise, Zehren acknowledges that agentic AI faces one critical challenge: orchestration between multiple AI agents. Currently, sophisticated organizations are building custom solutions internally, but the industry lacks standardized frameworks for agent collaboration.

"The 10x developers are building these modular, swappable architectures today," he revealed. "They create specialized bots for different tasks—research, analysis, visualization—then build orchestration layers that let them work together."

This technical challenge represents both an immediate opportunity and a strategic imperative for analytics leaders. Organizations that invest now in building flexible, agent-ready data architectures will be positioned to capitalize on emerging orchestration standards. Zehren predicts this will evolve into marketplace models, similar to app stores, where organizations can deploy specialized analytics agents for particular use cases, like "the research bot that does best market research with direct-to-consumer brands in North America."

The Analytics Leadership Imperative

Perhaps most importantly for data professionals, Zehren's vision requires analytics leaders to reimagine their role within organizations fundamentally. The traditional model of analytics as a support function—generating reports and dashboards on request—becomes obsolete in an agentic AI world.

"You have to lead the organization, not just IT," Zehren argued. "Don't just say 'no' to AI initiatives because of governance concerns. Stop saying no and start democratizing AI. Build sandbox environments. Provide tools for people to experiment."

This shift demands what Zehren calls "AQ"—Adaptability Quotient—alongside traditional IQ and EQ. For analytics teams often caught between executive AI enthusiasm and practical implementation challenges, success requires balancing innovation with governance while creating safe spaces for experimentation that protect production data and customer information.

The methodology includes specific guidance for analytics leaders, such as "Just do it, deliver AI now, overfund innovation," and critically,"kill failing projects quickly while funding initiatives based on measurable outcomes."

From Pilots to Industrial Scale

One of the most striking aspects of Zehren's message was his emphasis on moving beyond AI pilots to industrial-scale implementation. "We're not talking about experiments anymore," he stressed. "AI isn't just running code—it's designing how decisions are made, how services are delivered."

For analytics organizations, this transformation requires addressing foundational issues while simultaneously pursuing innovation. Zehren's framework includes specific guidance: get the baseline right, focus on what matters most, enable everyone across the organization, and "aim high selectively."

The funding strategy is equally direct: find real dollars quickly by cutting operational expenses, then redirect those resources into innovation. "There's always fat in the system," Zehren noted. "If someone says they can't cut anything in an IT organization, I guarantee we can find 30%. It's painful, but you take those resources and reinvest them into innovation."

The Strategic Imperative for Analytics Leaders

Zehren's ultimate message for analytics professionals centers on the importance of organizational survival and achieving a competitive advantage. Organizations that successfully transform into "technology-first enterprises" will displace those that don't. The path forward requires executing across multiple dimensions simultaneously: building adaptive IT workforces, slashing AI transformation timelines, and executing with the agility to pivot when needed.

"AI strategy equals IT strategy equals enterprise strategy," Zehren declared. "If in doubt, buy over build and select the right vendors. Match talent to impact, not organizational charts."

The transformation demands both strategic thinking and tactical execution: aim for impact by saying yes to promising pilots, measure what's real and fund what works, but also "let things go"—kill failing projects quickly to redirect resources toward successful initiatives.

"Either you become the landslide, or you get swept away by it," Zehren concluded. For data and analytics professionals, the time for incremental change has passed. The question isn't whether agentic AI will transform how organizations use data—it's whether your analytics organization will lead that transformation or become a casualty of it.

As uncertainty continues to reshape business landscapes, Zehren's message is clear: "This is just going to happen because it's better than what we are doing today." The organizations that harness agentic AI for data-driven decision-making will define the competitive landscape of the next decade. The transformation starts now, and it begins with leaders who embrace adaptability as their core competency.