Over the past several years, one of the most significant shifts I have witnessed with respect to artificial intelligence is not simply the advancement of technology itself, but the acceleration of how quickly organizations can evaluate information, generate insights, and model strategic scenarios.
Tasks which once required extensive manual effort — researching across multiple sources, validating information, comparing viewpoints, building models, and evaluating possible strategies — can now be performed at a speed that would have been difficult to imagine only a few years ago.
AI has fundamentally accelerated analysis.
Instead of spending the majority of time gathering and organizing information, professionals are increasingly shifting toward evaluating the quality of AI-generated outputs, validating recommendations against real-world operational realities, and refining strategies based upon experience, organizational dynamics, and business context.
This is particularly true in areas involving structured mathematical or financial modeling. AI performs exceptionally well when working with quantifiable variables, constraints, probabilities, and scenario analysis. Organizations can now run “what-if” evaluations far more quickly than before, allowing leaders to assess potential outcomes, risks, and opportunities at unprecedented speed.
However, despite these advances, AI still struggles in the areas that are often the most difficult — and most important — within business transformation and operational leadership.
AI can model labor efficiency, optimize inventory, calculate production output, and evaluate financial trade-offs. What it cannot fully understand is the human side of organizational decision-making.
It cannot accurately quantify trust.
It cannot fully evaluate leadership behavior, organizational resistance, emotional response, relationship dynamics, or the cultural consequences of decisions. It struggles to recognize how fear, uncertainty, politics, ego, morale, or human motivation influence outcomes within organizations.
More importantly, AI remains limited in its ability to anticipate “Black Swan” events — those unpredictable disruptions, opportunities, or moments of human decision-making that fundamentally reshape outcomes.
Many of the most important business decisions are not purely mathematical.
They are shaped by judgment, experience, intuition, timing, leadership maturity, organizational culture, and relationships developed over years of human interaction.
Because of this, I believe AI will continue to dramatically improve the computational and binary elements of business, but it will not replace the importance of human judgment or human-centered leadership.
In fact, the organizations that gain the greatest advantage from AI will likely not be those that rely on technology alone, but those that successfully integrate technology, operational understanding, human behavior, and organizational alignment into a connected system.
This shift will significantly impact both the consulting industry and industrial operations over the coming decade.
The Future of Consulting
Within consulting, I believe organizations will increasingly stop relying on consulting firms solely to generate analyses, models, or “what-if” scenarios. AI will continue improving its ability to produce these internally, and at far greater speed than traditional consulting approaches.
As this occurs, the value proposition of consulting firms will need to evolve.
The future consultant cannot simply be someone who creates presentations, documents process flows, or generates analytical outputs. AI will increasingly automate portions of this work.
Instead, consulting firms will be sought out for their ability to validate AI-generated recommendations, identify what the models may be missing, and help organizations operationalize and execute transformation successfully.
This will require consulting firms to fundamentally rethink their delivery models, talent strategies, and organizational structures.
The future of consulting will require more systems thinkers — individuals capable of understanding not only technology and process, but also organizational behavior, operational interdependencies, leadership dynamics, and the human realities AI cannot fully interpret.
These individuals must be able to evaluate how decisions impact employees, customers, suppliers, operations, leadership teams, and enterprise culture simultaneously. They must understand where theoretical optimization collides with organizational reality.
Consulting firms will also need to place far greater emphasis on true subject matter expertise and transformation leadership.
In my experience, successful organizational transformation is not achieved simply through technology implementation or process redesign. It requires leaders who have personally experienced operational disruption, organizational resistance, failed assumptions, and the realities of large-scale change.
This level of judgment typically comes through tenure, pattern recognition, and lived operational experience.
AI can accelerate analysis.
It cannot replace wisdom.
It cannot replace the ability to recognize subtle organizational warning signs before they become major failures. It cannot replicate the instincts developed through years of navigating uncertainty, conflict, disruption, and transformation across complex business environments.
For this reason, consulting firms should be evaluating how they identify, retain, and elevate experienced systems thinkers whose value extends beyond technical capability into strategic interpretation, organizational leadership, and human-centered transformation guidance.
The Evolution of Industrial Operations and Supply Chain
From an industrial perspective, AI will continue advancing across many operational, planning, and transactional functions throughout the enterprise.
Traditional back-office activities will increasingly become automated. Many planning-related supply chain functions — including demand forecasting, inventory planning, production scheduling, warehouse optimization, and transportation planning — will continue evolving through AI-driven capabilities.
As manufacturing operations become more digitally connected through automation, MES platforms, sensors, IoT technologies, and real-time operational data, AI models will continue becoming more intelligent and responsive.
Organizations will increasingly gain the ability to:
- predict maintenance requirements before failures occur
- optimize production sequencing and changeovers dynamically
- respond to real-time shifts in consumer demand
- identify operational disruptions earlier
- evaluate multiple response scenarios simultaneously
- improve operational efficiency across interconnected networks
However, despite these advancements, people will remain essential to industrial operations for the foreseeable future.
Physical maintenance, quality inspections, operational oversight, troubleshooting, supplier collaboration, and many executional manufacturing activities will still require human involvement.
More importantly, the role of planners and supply chain professionals will fundamentally evolve.
Demand planners, for example, will increasingly transition from primarily generating forecasts to becoming scenario evaluators, business interpreters, and strategic communicators.
Their role will shift toward evaluating AI-generated recommendations, understanding assumptions, identifying risks, interpreting market signals, and clearly communicating the implications of changing conditions to Sales, Marketing, Finance, Operations, and executive leadership teams.
Supply planners will undergo a similar transformation.
Rather than manually building plans, they will increasingly focus on understanding system-wide impacts when disruptions occur — including material shortages, transportation delays, geopolitical instability, labor disruptions, weather events, supplier failures, or economic volatility.
The planner of the future will not simply create plans.
They will validate AI-generated recommendations, rerun scenarios as conditions change, interpret risks, understand trade-offs, and communicate the operational and financial implications of decisions across the enterprise.
This evolution will require stronger systems thinking, business acumen, communication capability, and organizational awareness than many traditional planning roles have historically required.
The Real Limitation: Data and Connectivity
However, I believe the true industrial inflection point for AI will only occur once organizations achieve far greater end-to-end digital connectivity across their supply chains and operational environments.
For years, organizations have discussed the future of smart manufacturing, automation, and connected supply chains. Yet the reality is that many industrial operations today still remain only partially digitized and only partially connected.
In many cases, this is understandable.
Upgrading machinery, implementing sensors, integrating MES platforms, modernizing ERP environments, and capturing execution-level operational data requires significant investment. Many organizations — particularly within highly cost-focused industries — struggle to justify these investments based solely on short-term financial metrics.
As a result, much operational data today is still manually entered or operator-reported.
And ultimately, AI is dependent upon data quality.
AI models improve through large volumes of connected, reliable, and accurate information. If the underlying data is incomplete, delayed, inconsistent, or inaccurate, then the resulting recommendations will carry the same limitations.
Technology alone does not create intelligence.
Connected systems, operational visibility, organizational discipline, and high-quality data are what enable AI to produce meaningful and trustworthy outputs.
The Human Element Remains the Differentiator
As AI continues advancing, I believe many organizations will initially overestimate what technology alone can accomplish while underestimating the continued importance of leadership, organizational behavior, and human adaptability.
AI will absolutely transform business operations.
It will improve speed, analysis, optimization, automation, and decision-support capabilities across industries.
But the future competitive advantage will not belong solely to the organizations with the most advanced AI platforms.
It will belong to the organizations that successfully align:
- technology
- data
- operations
- leadership
- organizational behavior
- and human decision-making
into a fully connected system.
Because ultimately, business transformation has never been solely about technology.
It has always been about people navigating change inside increasingly complex systems.
And that is something AI alone still cannot fully solve
