Optimizing Human-Machine Combinations: Part One

Mehmet Tuzel
Slalom Data & AI
Published in
5 min readMay 16, 2023
Photo by Tara Winstead from Pexels

AI is rapidly transforming how we live and work, and its implementation is becoming increasingly prevalent across industries. From healthcare and finance to manufacturing and transportation, AI enables us to automate routine tasks, augment human decision-making, and create new opportunities for innovation and growth.

However, to fully realize the potential of AI, we need to understand the nature of the jobs and tasks involved and how they can be optimized through a holistic framework that considers both human and machine capabilities. This requires a deep understanding of the specific contexts in which AI is deployed and the skills and expertise needed to design, develop, and deploy AI systems.

In this article series, we’ll explore a framework for optimizing human-machine combinations. We’ll also discuss the workforce strategies, upskilling imperatives, team composition, and indicative timeline required for successful AI implementation.

AI, like automation, doesn’t eliminate people. It simply changes the nature of our jobs. It makes new jobs possible. — Steve Jobs

People’s jobs will not disappear because of the rise of AI and automation. Instead, the impact of these technologies will transform the nature of employment and create new opportunities for people.

This will enable people to collaborate with AI and automation to achieve better outcomes. They allow us to work more efficiently and effectively, creating new opportunities for innovation and growth.

The change is happening. To adapt and respond, we must understand the work better than ever. The future operating models shaped by AI will be characterized by:

  1. Efficiency: AI will enable organizations to automate specific tasks and reduce the time and cost of completing them. This will ultimately result in greater productivity.
  2. Agility: AI can help businesses quickly adapt to changing market conditions and customer preferences, allowing them to remain competitive and relevant.
  3. Personalization: AI can enable businesses to personalize their offerings to individual customers based on their preferences and behavior. This will result in more satisfied customers and increased loyalty.
  4. Data-driven decisions: AI can enable organizations to analyze large amounts of data in real time and make more informed decisions based on the insights gained.
  5. Human-machine combination: There will be increased collaboration between humans and machines.

What does an operating model look like in future organizations augmented by AI?

Figure 1: The three pillars of AI operating models

AI-augmented jobs have collateral effects on other jobs and impact the organizations’ internal operating mechanisms. Organizations must consider the implications of introducing AI into their workflows and plan for holistic changes. Any AI implementation efforts should address these key questions:

  1. How can we identify or predict how job and work-level changes affect the larger organization?
  2. How should we organize work by reconfiguring human-machine interactions?
  3. How do we govern optimized human-machine interactions?

Understanding the nature of the jobs and tasks

Jobs are the containers of the tasks that determine the overall outcomes.
A task has three key dimensions: human, machine (AI), and business impact/value.

Figure 2: The nature of the task

The machine dimension has been a pivotal aspect of human progress since the advent of the first Industrial Revolution. Throughout history, we’ve been leveraging machines and tools to augment our productivity, increase our speed and quality, and reduce errors, but the extent of their intelligence was limited. However, the landscape is about to undergo a paradigm shift, and the role of machines will evolve into something unprecedented over the next few decades. The forms and the nature of the jobs and tasks will change dramatically.

Optimizing human-machine combinations will be at the work level; in other words, at the task level. To optimize human-machine combinations, we should start at the task level.

AI is not an autonomous sentient being that wanders among us like T-1000 or R2-D2, capable of independently completing tasks end to end.

Instead, AI can take over specific tasks from jobs, which can lead to the substitution or augmentation of human capabilities and capacities. However, this transformation has far-reaching consequences, as it alters the form and nature of jobs and introduces new complexities to the interactions between other jobs in the ecosystem.

Let’s look at these sample tasks below.

Figure 3: Logistics Engineer, sample tasks

We will quickly identify their characteristics if we carefully examine the sample tasks. Let’s focus on the “verbs.”

Figure 4: Characteristics of the sample tasks

Once we have identified the nature of the jobs and tasks, we can evaluate potential human-machine optimization scenarios.

Figure 5: A potential human-machine optimization point of view

Let’s return to where we started: AI will take over some tasks from the jobs and shape their forms, natures, and interactions with the other jobs and parties.

Figure 6: Optimized human-machine combination

Evaluating optimization scenarios requires a consistent framework, and we need to address these key questions:

Figure 7: Critical questions for human-machine optimization

There’s a holistic approach to address all these questions. Find out more in part two.

Slalom is a global consulting firm that helps people and organizations dream bigger, move faster, and build better tomorrows for all. Learn more and reach out today.



Mehmet Tuzel
Slalom Data & AI

Management Consultant at Slalom focusing on artificial intelligence, strategy & operations, innovation and future of work. https://www.linkedin.com/in/mtuzel/