Kevin Cushnie leads Product Engineering, Innovation and Transformation for MC Systems.
Global investment in digital transformation is on track to approach $4 trillion by 2027, according to IDC. Yet research from BCG shows that up to 70% of these initiatives fail, and Gartner found that less than half meet or exceed business outcome targets.
The persistence of these failure rates, despite decades of experience and monumental investment, points to a fundamental problem: applying linear, project-based thinking to a challenge that is inherently complex and interconnected.
This disconnect has become especially critical as organizations race to integrate AI into their operations. These technologies go beyond simply task automation; they reshape information flows, decision-making authority and organizational knowledge itself.
Without a systems approach, AI implementations can become another expensive failure in the transformation graveyard.
Why Project-Based Thinking Leads To Failure
The traditional approach treats digital transformation as a checklist: gather requirements, select technology, deploy and train users. This model typically confines the effort to the IT department, treating it as a purely technical task with a defined endpoint. The result is siloed technology investments that collide with organizational reality.
There are several obstacles with this approach: organizational culture, change management failures and corporate inertia, to name a few.
Despite this, organizations continue to underinvest in change management, with only 30% having a focused strategy as part of their digital transformation program, according to TEKsystems research. Investment is being poured into what we can see and measure (technology) while ignoring the invisible forces that determine success (people, culture and processes).
The value leaks out through these unattended human and cultural components, producing low user adoption and missed business outcomes.
Seeing Your Organization As A Living Ecosystem
Systems thinking offers a radically different lens. Rather than viewing your organization as a collection of departments executing tasks, it recognizes that you’re managing an intricate ecosystem where components— people, processes, culture and technology—are interdependent and constantly influencing each other.
Three principles matter most:
1. Relationships between components are as important as the components themselves. A brilliant AI tool means nothing if employees don’t trust it or understand when to override its recommendations.
2. Causality is circular, not linear. The outcome of one process becomes the input for another, creating continuous cycles. Employee resistance to a new system, for instance, might prompt leadership to improve communication, which then reduces resistance.
3. New behaviors emerge from interactions that you cannot predict by studying individual parts in isolation.
Applied to digital transformation, this means recognizing three interconnected subsystems: the human system (culture, fears, skills, trust), the process system (workflows, workarounds, operational realities) and the technology system (tools, platforms, integrations).
Change any one element, and ripples propagate throughout the entire structure.
For AI integration specifically, systems thinking forces leaders to map where AI intersects with human decision-making authority. Where should the algorithm operate autonomously and where must humans retain override capability? How do we design feedback loops so human expertise continuously improves AI performance, while AI-generated insights enhance human capability?
These are systemic questions that determine whether AI augments your workforce or alienates it. Here are three ways to take this from theory to practice:
1. Map your ecosystem before you deploy.
Ecosystem mapping goes beyond traditional organizational charts to reveal how work actually gets done. It visualizes entities, relationships, value flows and, crucially, the workarounds people have created to manage inefficiencies.
These informal processes signal system stress and reveal where new technology will face friction. For AI implementations, mapping shows you which business processes genuinely benefit from automation versus where human judgment remains essential.
Leaders who map first identify strategic intervention points before committing capital, rather than discovering obstacles after deployment.
2. Reframe resistance as system intelligence.
When employees push back against change, the instinct is to overcome their objections. Systems thinking applies a different approach, seeing resistance as vital system feedback.
People naturally protect stability, predictability and control—it’s how human systems maintain equilibrium.
When Clorox embarked on its five-year, $580 million AI-powered transformation (subscription required), leadership made a strategic choice to upskill staff rather than replace them, as reported by The Wall Street Journal. This decision addressed the root cause of resistance, fear of job vulnerability, and turned a potential balancing force into a reinforcing one. The result was a culture of curiosity and problem-solving that positioned Clorox as an industry model for AI transformation.
Resistance often reveals legitimate concerns about feasibility, unintended consequences or flawed implementation plans. By treating pushback as a diagnostic tool rather than an obstacle, you improve the initiative itself while also building trust.
3. Build continuous feedback loops.
In complex systems, long delays between cause and effect hide critical problems. Annual surveys provide outdated data for initiatives that need real-time adjustment. Instead, embed continuous feedback mechanisms—pulse surveys, sentiment analysis and agile retrospectives.
For AI systems specifically, this means monitoring not just technical performance but human trust, override rates and decision quality. When you can identify patterns immediately and adjust before small issues cascade, you enhance organizational agility while demonstrating that employee input actively shapes decisions.
From Project Manager To Systems Architect
The contrast between Kodak and Domino’s illustrates what’s at stake.
Kodak invented the first digital camera, but couldn’t pivot away from its film-based business model. The company’s structure, incentives and culture acted as a powerful balancing loop that actively resisted change.
Conversely, Domino’s holistically re-engineered its entire customer journey and operational model, redefining itself as a technology company that happens to sell pizza. That systemic alignment drove genuine transformation.
Leaders must evolve from managing isolated projects to architecting learning organizations—building adaptive capacity for continuous evolution rather than executing one-time initiatives.
Systems thinking isn’t just a theory but a strategic necessity in an era where AI, automation and digital platforms fundamentally reshape how organizations create value. The challenge for technology leaders is to shift from managing parts to orchestrating the whole.
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