Operational Excellence in the Age of AI: Why Human Capability, Not Technology, Will Decide Who Thrives
- Allan Ung
- Dec 14
- 5 min read
Updated: 5 days ago
by Allan Ung
Artificial Intelligence is no longer an emerging force—it is a structural shift in how work is conceived, executed, and valued. Across industries, AI is automating tasks once considered high-skill: coding, data analysis, forecasting, reporting, and even elements of problem diagnosis and solution design. This reality is unsettling for many, particularly professionals whose identities have long been anchored in technical expertise.
Yet history offers a consistent lesson. Technology does not eliminate value creation; it redefines where value sits.
In the era of AI, Operational Excellence (OE) must evolve from a discipline centred on tools, efficiency, and optimisation into one grounded in human judgement, adaptability, and change capability. The future of work will not be decided by who adopts AI fastest, but by who adapts their mindsets, behaviours, and capabilities most effectively.
From Task Excellence to Value Excellence
AI fundamentally alters the economics of work. Tasks that are repeatable, rules-based, and data-heavy are increasingly commoditised. What once differentiated high performers—technical proficiency, analytical speed, mastery of frameworks—now forms the baseline expectation.
A familiar example can be found in Lean Six Sigma practice.
In the past, Green and Black Belts spent a significant amount of time using tools such as Minitab to determine whether data was normally or non-normally distributed, whether p-values indicated statistical significance, or whether differences between groups were meaningful. These analyses required specialised training and were often seen as the hallmark of technical expertise.
Today, AI can perform these analyses instantly—often more accurately and with far less effort.
This does not diminish the role of the Green or Black Belt. It repositions it.
With analysis increasingly automated, the value of OE professionals no longer lies in running statistical tests, but in interpreting insights in context and turning them into sustained performance improvement.
What Comes After the Analysis
When AI handles the “number crunching,” OE professionals must redirect their effort to areas where human judgement is irreplaceable:
Framing the right business problem before analysis begins
Translating statistical output into decisions leaders can act on
Managing stakeholders with competing priorities and incentives
Facilitating difficult conversations where data challenges entrenched beliefs
Designing countermeasures that are operationally and culturally viable
Supporting teams through the behavioural changes required to implement solutions
In other words, AI shortens the path to insight, but lengthens the importance of change leadership.
Problem Solving Reimagined: Where AI Ends and Human Work Begins
With AI:
Data from multiple systems can be analysed in minutes
Patterns and correlations are surfaced rapidly
Likely root causes are prioritised statistically
Countermeasures and action plans can be generated automatically
What previously took weeks can now be done in hours.
Yet the most common failure point remains unchanged.
AI does not:
Resolve conflicting stakeholder agendas
Address fear of loss of control or relevance
Build trust between management and frontline teams
Adapt solutions to local constraints and informal workarounds
Sustain new behaviours once initial momentum fades
In practice, the hardest part of problem solving has never been the analysis. It is the orchestration of people, priorities, and behaviour during change.
As AI compresses the technical cycle, the human side of Operational Excellence becomes the dominant source of value.
Martec’s Law, Amplified by AI
Martec’s Law states that technology changes exponentially, while organisations change logarithmically. AI accelerates this imbalance.
Many organisations respond by investing heavily in AI tools and technical upskilling. Far fewer invest adequately in:
Sensemaking and problem framing
Stakeholder alignment and influence
Change leadership at every level
The result is a widening capability absorption gap: powerful analytics, limited adoption, and disappointing outcomes.
Operational Excellence must now explicitly address this gap.
What Leaders, OE Professionals, Change Agents — and Individuals — Must Focus On
Leaders: Designing the Conditions for Adaptation
In AI-enabled organisations, leadership is less about having better answers and more about creating environments where better decisions emerge.
Leaders must recognise that:
AI-driven change destabilises roles and identities
Resistance is often rooted in fear, not incompetence
Psychological safety is a prerequisite for learning and experimentation
The most effective leaders design conditions that allow people to adapt—clarity of purpose, permission to experiment, and visible investment in human capability.
OE Professionals and Consultants: From Analysts to Integrators
For OE practitioners, AI challenges traditional sources of professional authority. Statistical and technical mastery are no longer differentiators.
Future-ready OE professionals will:
Use AI to accelerate insight, not to abdicate judgement
Focus on framing meaningful problems, not just solving technical ones
Integrate data, context, and human dynamics into coherent recommendations
Facilitate alignment across functions, levels, and mindsets
Their impact will be measured less by the sophistication of their analyses and more by the quality of decisions and behaviours they enable.
Change Managers and Agents: From Rollouts to Human Transitions
AI-driven change is faster, broader, and more disruptive.
Effective change managers must shift focus from delivery milestones to human transitions:
Helping people make sense of why change matters
Acknowledging loss of expertise, status, or certainty
Supporting individuals as they redefine how they contribute
Change is no longer episodic. It is continuous—and requires deeper human capability.
Individuals: Sustaining Relevance in an AI-Enabled World
At the individual level, the implications are profound.
Technical skills, while necessary, have a shorter half-life than ever before. Continuous upskilling matters—but skills alone will only take people so far.
What sustains relevance is mindset:
Openness to change rather than attachment to past expertise
Willingness to work with AI rather than compete against it
Comfort with ambiguity and evolving roles
A positive, receptive, and adaptive mindset allows individuals to continuously reposition how they add value—even as tasks are automated.
Before and After: The Evolving Problem Solver
Before AI
Manually analyses data and validates assumptions
Spends weeks building statistical evidence
Delivers technically sound solutions
Struggles with adoption and sustainability
After Adapting to AI
Uses AI to accelerate analysis and insight generation
Invests time in stakeholder engagement and problem framing
Co-creates solutions with those who must implement them
Manages resistance, emotions, and behaviour change
Focuses on long-term adoption, not short-term closure
The role shifts from technical expert to change enabler.
The Real Blind Spot in AI Adoption
Most organisations treat AI readiness as a technology challenge.
In reality, it is a human systems challenge.
AI does not fail to deliver value. Organisations fail to absorb it—because mindsets, behaviours, and change capability have not kept pace.
Without deliberate investment in the human side of Operational Excellence, AI simply accelerates existing dysfunctions.
The Future of Operational Excellence with AI
In the AI era, Operational Excellence must return to its core purpose: enabling organisations to perform reliably through people, not despite them.
Sustainable advantage will belong to those who:
Combine AI-powered insight with human judgement
Balance analytical speed with empathy and context
Build organisations that can adapt continuously
AI will continue to reshape work.
Human capability will determine whether that reshaping creates lasting value.
Article by Allan Ung, Principal Consultant at Operational Excellence Consulting (Singapore) — a practitioner-led management consultancy specializing in Design Thinking and Lean management. OEC develops facilitation-ready, workshop-proven frameworks and training that help leaders and teams think clearly, solve problems systematically, and deliver sustainable customer value. Learn more at www.oeconsulting.com.sg
