Do you believe algorithms should guide most operational decisions, or is there still an irreplaceable role for human intuition and experience?

As an MBA student with a background in IT, I've gained a deep appreciation for the power of data and algorithms to drive operational efficiency and performance. At the same time, my studies have reinforced the vital role that human intuition, cultivated through real-world experience, plays in effective decision-making and leadership. The question of how to balance these two elements is one that every operations leader should carefully consider.

The Case for Algorithmic Operations The current capabilities of machine learning and optimization algorithms are nothing short of astonishing. By ingesting and analyzing vast troves of data, these systems can surface insights and recommendations that would be virtually impossible for any individual to discern. Algorithm-driven decision-making has become indispensable across areas like:

? Supply Chain Optimization: Algorithms can optimize network flows, inventory management, routing and much more by crunching staggering numbers of variables and constraints. Companies like Amazon, Walmart and UPS are leaders in this space.

? Predictive Maintenance: Rather than reacting to asset breakdowns, ML models can predict failures before they occur based on sensor data and maintenance history. This drives massive cost savings.

? Workforce Management: From forecasting labor needs to optimizing schedules and staffing decisions, algorithms enable a more scientific approach than legacy methods.

At the same time, constructing and deploying these algorithms is highly complex from a data engineering and model training perspective. As an IT enthuasiat, I understand the intricate work required to build the data pipelines, clean and process the training data, tune the models, manage drift over time, and more. It's not just flipping a switch to "be data-driven."

The Indispensable Human Element While the quantitative potential of algorithms is unmatched, it's critical not to underestimate the value that experienced human decision-makers and leaders bring to the table, especially when it comes to areas like:

??Personnel Management: Reading situations, motivating teams, navigating conflicts - these distinctly human skills are tough to fully codify in rules-based models.

??Strategic Planning: Long-term strategy is too dynamic and subjective to be fully automated. Human strategists can think more diversely about risks, opportunities, and future scenarios.

??Competitive Dynamics: No model can fully grasp market landscapes evolving in response to human behaviors, emotions, and other highly complex variables.

Moreover, operational decisions often require contextualizing and sense-making around analytical outputs. A seasoned leader's deep intuition is vital for interpreting recommendations and judiciously applying them.

As a thought experiment, even if the world's most advanced supply chain algorithm recommended a particular distribution center location, I would still want an executive with decades of real-world experience to concur before pulling the trigger.

Towards a Unified Operational Model Fundamentally, the most effective operational model needs to blend the best of algorithms and human intelligence. Rather than one side fully automating the other out, we should envision a collaborative decision-making framework where:

1 . Algorithms identify insights, surface recommendations, and illuminate key decision factors based on data

2. Human leaders then apply judgment, intuition and experience to make the ultimate call on how to operationalize those insights

3. Over time, the algorithm outputs further train and sharpen human intuition in a virtuous feedback loop

Perhaps the biggest challenge is cultural - getting human decision-makers to have faith in algorithms and be open to re-evaluating long-held assumptions and gut feelings. Likewise, analytics professionals need to appreciate the nuanced human factors that can't always be quantified.

As AI and machine learning capabilities exponentially evolve, the balance will likely shift towards increased automation over time. But human ingenuity, creativity and intuition developed through years of hard-won experience seem irreplaceable for the foreseeable future. Finding the right ways to combine these two potent forces will determine the leaders in operational excellence.