For decades, we’ve heard the same refrain: “Change management is crucial for project success.” Yet leaders have nodded politely and ignored this advice, particularly in data and technology initiatives. The result? According to McKinsey, a staggering 70% of change programs fail to achieve their goals.[1] So why do we keep making the same mistake, and more importantly, why should we care now?
The Comfort of Ignorance
Let’s face it: Change management is hard, messy, and often uncomfortable. It’s far easier to focus on the tangible aspects of data projects — the shiny new tools, the complex algorithms, and the promise of game-changing insights. These are concrete, measurable, and frankly, more exciting than the nebulous world of human behavior and organizational psychology.
Moreover, many leaders harbor a secret belief: “Our people are different. They’ll adapt because they have to.” This comforting delusion has led to countless data initiatives crashing and burning, leaving behind a wasteland of unused dashboards and resentful employees.
The High Stakes of the Data Revolution
But here’s the kicker: We’re no longer in the era of incremental change. AI has brought a pace of exponential change with it. The data revolution isn’t just another IT project — it’s fundamentally reshaping how businesses operate, compete, and survive. The stakes have never been higher.
Consider this: By 2025, the global datasphere will grow to 175 zettabytes. That’s 175 trillion gigabytes of data that organizations must manage, analyze, and leverage. The companies that can effectively harness this data will thrive; those that can’t become obsolete.
The Human Element in the Age of AI
As artificial intelligence and machine learning become ubiquitous, the irony is that the human element becomes more critical, not less. AI can crunch numbers and spot patterns, but can’t determine context. It can’t navigate the complex web of human emotions, politics, and resistance accompanying significant organizational change.
A case in point: A major bank invested $500 million in an advanced AI-driven risk assessment system. The technology was flawless, but adoption was abysmal. Why? The system threatened the status of seasoned risk analysts who had built careers on their intuition and experience. The bank’s massive investment yielded minimal returns without addressing these human factors.
The New Imperative: Adapt or Die
In today’s hyper-competitive landscape, the luxury of ignoring change management no longer exists. The pace of technological advancement means that organizations must constantly change and adapt to new data-driven paradigms. With all of its potential, AI brings a true “change or perish” message.
Companies that master this continuous adaptation will have a significant competitive advantage. Amazon, for instance, attributes much of its success to its ability to implement data-driven changes rapidly across its vast organization.
The Way Forward: Embracing the Uncomfortable
So, how do we break the long-standing cycle of benign neglect? It starts with acknowledging that change management isn’t a soft, optional add-on to data projects. It’s a critical, non-negotiable component that deserves as much attention and resources as the technical aspects.
This means:
- Integrating change management experts into data project teams from day one.
- Allocating significant budget and time for change management activities.
- Making change readiness a key metric for project success, alongside technical deliverables.
- Training leaders at all levels in change management principles and techniques.
The Choice: Discomfort Now or Obsolescence Later
The message is clear: We can no longer afford to ignore the human side of data transformation. The discomfort of addressing change head-on pales in comparison to the pain of failed projects and lost competitive edge.
As we stand on the brink of the zettabyte era, the question isn’t whether we can afford to invest in change management, but whether we can afford not to. The coming years of business success will belong to those who master data technology and the art and science of organizational change.
References
- Bucy, M., Finlayson, A., Kelly, G., & Moye, C. (2016). The ‘how’ of transformation. McKinsey & Company.
- Reinsel, D., Gantz, J., & Rydning, J. (2018). The Digitization of the World From Edge to Core. IDC White Paper.
- Davenport, T. H., & Redman, T. C. (2020). Digital Transformation Comes Down to Talent in 4 Key Areas. Harvard Business Review.
- Haskell, C. (2024). Driving Data Projects: A comprehensive guide. BCS.
