Beyond the Technology-First Hype: The Real Path to IT Transformation

Expert analysis of IT transformation realities: why most initiatives fail, how to manage risk vs. opportunity, and practical frameworks for data-driven change management.

The technology transformation rhetoric is everywhere. Every conference keynote promises that organizations must become "technology-first enterprises" or face extinction. But what does that mean, and why are so many transformation initiatives failing to deliver real business value?

At Info-Tech Live 2025, while executives painted visions of exponential technology curves and agentic AI, Advisory Fellow Valence Howden offered a more nuanced perspective during our interview. His insights reveal the gap between transformation hype and transformation reality, a gap that data-driven leaders must understand how to navigate successfully.

The Technology-First Paradox

"For some reason, we can't get our heads out of the silo," Howden explained when I challenged the "technology-first" premise. "At some point, we need to be unified and work together. I think getting technology into a leadership space alongside the other leaders in the organization is consistent and normal. I think we're still fighting to get there."

This struggle isn't about technical capability; it's about organizational dynamics. While every company has become a technology company by necessity, many IT leaders remain trapped in operational thinking rather than strategic leadership.

The paradox becomes clear when examining the data: organizations recognize the need for transformation, but they're approaching it from the wrong angle. Rather than asking "How do we become technology-first?" the better question is "How do we become strategically integrated?"

The Timeline Reality Check

When I asked Howden about realistic transformation timelines, his answer was sobering: "I think it's years, because they're not even at the point where they can change it. They haven't even established it. We're still 20 years behind, going, 'How do I run a service desk?' We know how to run a service desk. That's a solved problem. The fact that you can't run it in organizations, you don't know how to do it because your leadership doesn't care about it."

This insight reveals a fundamental measurement problem in IT transformation. Organizations are investing in advanced AI capabilities while struggling with basic operational excellence. The data tells the story: companies pursuing transformation without solid foundational metrics are building on unstable ground.

Risk Assessment: The Missing Analytics Component

One of the most striking aspects of our conversation was Howden's emphasis on risk-informed decision-making. "I think we oversell how ready AI is right now," he cautioned. "I think there are advantages to be had, but we oversell what's capable. We assess the opportunity and say it's big, but that's okay; it's manageable. You don't know what the risk is, it's not manageable if you don't honestly assess the risk dynamics."

This observation highlights a critical gap in how organizations approach transformation analytics. Most focus exclusively on opportunity metrics, potential cost savings, efficiency gains, and competitive advantages while systematically under-analyzing risk factors.

The Risk-Opportunity Analytics Framework

Effective transformation requires balanced analysis across multiple dimensions:

• Opportunity Metrics: ROI projections, efficiency gains, competitive positioning

• Risk Metrics: Implementation failure rates, security vulnerabilities, operational disruption potential

• Capability Metrics: Current state assessments, skills gap analysis, infrastructure readiness

• Environmental Metrics: Market conditions, regulatory changes, competitive pressures

"We invest in risk for security, but we don't invest in other risks, and other risk is 90% of the risk in the world," Howden noted. This skewed risk assessment leads to transformation initiatives that appear promising on paper but ultimately fail in practice.

The Kodak Warning: What the Data Actually Shows

When discussing companies displaced by failing to transform, Howden offered a nuanced perspective: "It's interesting now, because it's harder to spot. We used to have old examples like Kodak, but even if I automate or implement other technological advances, I don't know that any single technology is a company killer. It's usually a combination of things."

This insight reveals a critical shift in how we should analyze transformation risk. Unlike the dramatic, single-technology disruptions of the past, today's competitive displacement happens gradually through accumulated disadvantages in decision-making speed and data utilization.

"The winners write success, but we have survivor's bias," Howden observed. "There are a lot of people who moved too fast, and we never hear their story."

Practical Measurement Frameworks

When I asked how Info-Tech measures transformation success, Howden's response was illuminating: "It depends on whether you've defined what you expected from the transformation in the first place. Many people fail to define their purpose. You need benchmarks or an idea of what you're trying to get to."

The Three-Layer Measurement Approach:

1. Strategic Alignment Metrics: How well does the transformation support organizational goals?

2. Progress Indicators: Are we hitting the way markers that indicate we're moving in the right direction?

3. Outcome Validation: Are we achieving the intended behavioral and performance changes?

"You can make all the right decisions and still fail," Howden reminded me. "The right decision doesn't guarantee the right result." This acknowledgment of uncertainty necessitates transformational analytics that focus on decision quality, rather than merely predicting outcomes.

The Legacy Integration Challenge

For data and analytics leaders, one of the most practical challenges is striking a balance between transformation and existing systems. Howden's advice: "Explain the implications of your strategy. Does it affect the business plan? Can you meet the laws you're required to meet?"

This isn't just about technical integration; it's about data lineage, governance continuity, and preserving analytical capabilities during periods of transition. Organizations need frameworks for maintaining analytical capabilities while modernizing underlying infrastructure.

Leadership Evolution: From Metrics to Meaning

Perhaps the most critical insight from our conversation was about leadership transformation itself. "They have to start thinking about the organizational direction first. Technology is a means to that end. In a lot of cases, they don't know the end."

For analytics leaders, this means shifting from reporting what happened to helping organizations understand what should happen next. It requires creating environments where "it's safe to bring up issues and act on information rather than suppress it."

The Environmental Change Framework:

• Psychological Safety: Creating conditions where teams can surface problems without fear

• Information Flow: Ensuring data reaches decision-makers without distortion

• Response Capability: Building systems that can act on insights quickly

• Measurement Alignment: Ensuring incentives support collaborative, data-driven behavior

The Path Forward: Honest Analytics

Howden's most practical advice centered on honesty about uncertainty: "Be honest about the risks that we're seeing and how much information you can provide. I cannot give you a probability—that's no longer possible at this level."

This honesty extends to analytics practices. Rather than creating false precision in uncertain environments, successful transformation requires probabilistic thinking, scenario planning, and adaptive measurement frameworks.

The organizations that succeed in transformation won't be those with the most advanced AI or the most significant budgets. They'll be those with the most honest, comprehensive, and actionable analytics—organizations that can clearly see opportunity and risk, measure progress meaningfully, and adapt quickly when conditions change.

As Howden concluded: "The level of danger, quantum combined with AI, introduces exponential risk. It's not only risky—I don't even know what the risk is. That's market advantage, but you have to have some containment."

For analytics leaders, the message is clear: transformation success requires better risk analytics, honest acknowledgment of uncertainty, and measurement frameworks designed for continuous adaptation rather than fixed outcomes.