Automation vs. Augmentation: Setting Decision Rights in AI-Enabled Work

One of the most pivotal leadership decisions in artificial intelligence (AI)-enabled work is deciding what should be automated, what should be augmented, and what should remain under direct human judgment. As Slack and Flickr Co-founder Stewart Butterfield has stated, “There’s a lot of automation that can happen that isn’t a replacement of humans but of mind-numbing behavior.” When people can stop performing as many monotonous tasks, they enjoy freedom to focus on more challenging work — which is where augmentation comes in.

AI-Enabled Work Starts with a Leadership Decision

Former IBM Chief Executive Officer (CEO) Ginni Rometty speaks often about AI and its role in the future world of work. She said, “Some people call this artificial intelligence, but the reality is this technology will enhance us. So, instead of artificial intelligence, I think we’ll augment our intelligence.” This implies that AI leaders of today and the future will need a solid understanding of when full automation is the most effective approach versus where augmentation of human work and ability is the optimal choice.

Not Every Task Should Be Automated

AI can automate data-heavy, repetitive tasks, such as basic customer support, data entry, and regularly scheduled communications like weekly or daily emails. However, when human judgment comes into play — with more advanced customer service responses, decisions about bill payment or authorizations for vendor engagement, or any other activity where risk is involved — human oversight is an essential management step.

AI in Business Is Really About Workflow Design

Artificial intelligence alters how things are done in the workplace along with decision-making processes. The people who own outcomes may encounter different reporting rules and structures after AI is introduced. 

At Boston University (BU), the online Master’s in AI in Business degree emphasizes AI’s ability to change workflows and create various decision-making processes. The program also underscores its effect on handoffs, roles, and responsibilities.

Automation and Augmentation Are Not the Same Thing

Automation and augmentation represent two distinct approaches to incorporating AI into real-world workflows. Specifically:

• Automation refers to complete machine task execution, end-to-end, in a process. For example, a fully autonomous Level 5 vehicle would be an example of end-to-end automation.full self-driving cars are completely automated. 
• Augmentation refers to the use of AI to enhance human capabilities, keeping humans in the loop to drive decision-making.

Automation Shifts Execution to the System

Automation in AI is intended to replace repetitive, rules-based, and predictable tasks that formerly have been performed by humans. It cedes human control to the AI system itself, allowing machine labor to execute tasks without the need for human intervention.

Augmentation Supports Human Decision-Making

Google/Alphabet CEO Sundar Pichai has asserted, “The future of AI is not about replacing humans; it’s about augmenting human capabilities.” AI augmentation uses technology to assist people to work faster, provide more consistent work, and achieve insights that weren’t possible before AI enablement. Boston University’s online Master’s in AI in Business degree approaches how to leverage human-AI augmentation to address business challenges.

Decision Rights Matter When AI Enters the Workflow

Decision rights surrounding who gets to “call the shots” are a critical question when implementing AI-enabled workflows.

What Decision Rights Actually Mean

Deloitte’s 2026 Global Human Capital Trends survey found that 60% of executives now regularly use AI to support their decisions. However, the survey also found that without clear chains of responsibility and rules, AI may pose additional challenges for businesses. Clear decision rights should identify who can approve, act, and override decisions made during AI-involved processes.

Why AI Makes Decision Rights More Important

AI models utilize human data, and “black box” algorithms can add confusion to artificial intelligence in business. Ownership of decision-making and decision rights provides the means to clarify who is making calls in organizations and how reliability can be built into AI workflows.

Some Work Should Be Automated, Some Should Be Augmented, and Some Should Stay Human-Led

AI augmentation vs. automation presents a chance for organizations to think their work processes through, start to finish. The AI enablement process offers valuable opportunities for review of processes that may have persisted unchanged for years or not have been well-planned.

Routine, Repeatable Work May Be Better for Automation

Have you heard of the 30% rule for AI? Work experts estimate that about 70% of daily tasks in many jobs can be automated. This type of work is data-heavy and repetitive, and if automated, it could leave the remaining 30% for creative work, critical thinking, and human judgment. Work activities with stable rules, clear inputs, and measurable outputs are often stronger candidates for automation.

Judgment-Heavy Work Often Benefits More From Augmentation

No one truly knows exactly how many opportunities are missed in workplaces due to a focus on repetitive work (while more in-depth, human-involved opportunities are overlooked). Augmented AI represents collaboration between humans and AI. For example, in medical diagnostics, physicians can identify potential disease causes more quickly with the help of AI augmentation but make the actual diagnosis and recommend treatment themselves.

High-Stakes Decisions May Need Clear Human Control

Few people suggest that artificial intelligence should be allowed to make decisions that have legal, regulatory, or critical financial implications. At BU, the online MS in AI in Business program curriculum reinforces how human critical thinking abilities, judgment, and accountability affect AI governance and control.

The Real Question Is Where AI Changes the Decision

AI is equipped to analyze massive datasets in seconds. It can also read large amounts of written information and reports nearly instantaneously. How and when people use these abilities impacts human decision points and understanding.

AI Can Change Inputs, Timing, and Handoffs

AI can handle large, heavy cognitive workloads, for example, reviewing thousands of medical images or writing many lines of code in seconds. Think of it as an extra set of eyes or extra time to study and analyze a situation. AI can make or execute decisions within designed systems, but organizations need to determine where human judgment and accountability remain necessary.AI can’t make decisions on its own, but it can reveal information that was previously unseen or unknown. Artificial intelligence in business environments can provide insights that affect which decisions are made, how they’re made, and who acts on them.

Leaders Need to Map Where Accountability Moves

Redesigning pre-AI workflows to post-AI workflows requires an understanding of how work gets done and how it should be performed. When decisions are made, when handoffs occur, and which roles AI and humans have in the process all influence performance and results. Boston University’s curriculum covers how to redesign workflows as well as identify change points, handoff rules, and roles and responsibilities.

Good AI Leadership Means Deciding Where Human Judgment Belongs

According to Stuart Russell, founder of the Center for Human-Compatible Artificial Intelligence, “The biggest challenge with AI isn’t making it smarter, but making it more humane.” Successfully meeting this challenge is key to successful AI implementation.

Human-in-the-Loop Only Works When It Is Clearly Designed

Artificial intelligence in business works best when the role of humans involved is clearly defined. When considering AI augmentation vs. automation, human judgment and critical thinking are central to defining when and how people step into the process.

Ownership Has to Stay Clear as AI Scales

Issues such as data drift and changing work priorities mean that AI leaders need structures that explicitly enforce accountability. Boston University’s program provides practice and experience in keeping human judgment in AI systems, plus making ownership of outcomes explicit as AI scales.

AI Enablement Requires More Than Technology Adoption

Beyond just powerful technology, AI-enabled projects call for human management, critical thinking, and administrative skills.

AI Enablement Is Also About Roles, Incentives, and Oversight

The people of a business need to be in sync with AI technology. Skills in successful AI implementation involve aligning people and workflows as well as enacting effective controls and decision-making processes.

Better AI Decisions Support Better Business Outcomes

AI enablement should improve work performance and lead to better decisions alongside a higher level of trust in the workplace. This is the meaningful value of AI. Understanding how to deliver these outcomes is a primary benefit of a Master’s in AI in Business degree.

How BU’s Curriculum Turns This Into a Leadership Capability

The online Master’s in AI in Business at Boston University features the curriculum and educational experience necessary to develop leadership capability in the field.

Redesigning Processes and Decision Routines

When it comes to redesigning business processes, Module 2 in the program provides practice in improving business processes and decision systems by integrating AI into existing workflows, roles, and decision-making routines. It also offers experience in evaluating performance, risks, and tradeoffs.

Governance, Decision Rights, and Human-AI Boundaries

Module 4 addresses how organizations clarify decision rights and accountability, define escalation paths, and establish human-AI interaction boundaries as AI systems scale. This is the type of education needed to ensure that AI systems are reliable and in line with organizational values.

Decision Rights as a Core Leadership Skill

Understanding how and when to incorporate decision rights into AI-enabled workflows is a core leadership skill. BU’s artificial intelligence program helps students hone the core skills to redesign workflows, clarify ownership, and build robust governance.

Why This Matters for Future AI Leaders

AI-enabled workflows are likely to enhance work and provide advancement in many fields, particularly in areas where AI augmentation vs. automation is a key decision.

Leaders Need to Know More Than How AI Works

Future AI leaders need human and management skills alongside AI proficiency — enabling them to decide where AI belongs in work and how to keep clear roles and responsibilities for decision-making.

Good Judgment About Automation and Augmentation Can Create Real Business Value

AI initiatives succeed or fail based on human interaction, not just technology. Being able to implement AI-enabled workflows without losing accountability, trust, or control is crucial to building real value.

Start Leading AI-Enabled Work with BU’s Online Master’s in AI in Business

Learn more and request further information about BU’s online MS in AI in Business to discover the ways it helps students redesign workflows, clarify decision rights, and responsibly lead AI-enabled work for organizational success. When you’re ready, review application requirements and apply today.