Understanding AI Augmentation: Enhancing Intelligence in the Workplace

AI augmentation (also called intelligence augmentation) involves the use of AI technology and machine learning to support, complement, and enhance human cognitive functions. This approach is centered on augmenting human intelligence, focusing on enhancing human decision-making and cognitive capabilities rather than replacing human intelligence with artificial intelligence.

Under this approach, AI doesn’t replace or operate independently of human intelligence (as is the case with automation), but acts as a support mechanism to help humans achieve their full potential.

The division of labor in AI augmentation varies based on the approach and systems used. However, in most cases, AI provides computational power, memory, and pattern recognition at scale, while humans contribute intuition, the ability to generalize information, a wider appreciation of context, and ethical judgment.

The key difference between intelligence augmentation and traditional AI lies in autonomy and collaboration—AI augmentation emphasizes working alongside humans, supporting rather than fully automating tasks. It is a discipline that encompasses a wide range of capabilities compatible with various artificial intelligence models and systems, including Agentic AI, Generative AI, Natural Language Processing (NLP), and machine learning.

Thus, it’s important not to think of AI augmentation as a “silver bullet” solution to core business problems. Success is far from automatic. AI augmentation focuses on supporting and enhancing human decision-making, rather than automating every process. It all depends on designing your tools and workflows to support your business objectives, and upskilling human employees so they can extract the most value from these augmentations.

How AI Augments Human Intelligence in the Workplace

Think of AI augmentation as a skilled teammate in a relay race; artificial intelligence takes the baton for repetitive, data-heavy tasks, allowing humans to sprint ahead with creativity and critical thinking. In the workspace, AI helps in automating routine tasks, delivers insights, and suggests the next best action, so employees can focus on solving more complex problems.

For example, AI virtual assistants manage routine inquiries, and in customer service, AI analyzes vast data to help agents connect personally with customers. In customer service, AI analyzes large volumes of data to identify trends and additionally provides context based on customer data points to make the agent more equipped to handle the customer query more efficiently.

By amplifying human strengths, such as intuition, empathy, and ethical judgment, AI helps workers become more productive, innovative, and engaged, ultimately driving better business outcomes through a collaborative process of making decisions that keeps humans at the center.

The human-centric approach to AI augmentation is changing how organisations and individuals use artificial intelligence to supercharge human intelligence and capabilities. Instead of replacing humans, this paradigm puts humans at the heart of the decision-making process, so AI is used as a tool to support, amplify, and extend human ability.

At its core, the human-centric approach involves human oversight and intervention throughout the decision-making process. AI systems powered by machine learning and natural language processing are designed to help humans in many ways, whether by providing real-time recommendations, automating routine tasks, or surfacing insights from big data.

Ultimately, the human-centric approach means technology serves human life and society. By keeping humans in control and using AI as a tool for intelligence amplification, organisations can reap the benefits of advanced technology while maintaining trust, accountability, and a focus on human well-being. This approach supports better decision-making and problem-solving and paves the way for a future in which the thoughtful integration of AI continually supercharges human ability.

A recent study from the MIT Center of Collective Intelligence (CCI) shocked a number of people, including Thomas W. Malone, the program’s own director. He says, “Some of the most important and interesting use cases for AI involve a combination of humans and computers.

Many people would have assumed the combination would be quite a bit better, but it was statistically significantly worse.” Computers are crucial in processing vast amounts of information and supporting human decision-making, especially through technologies like machine learning, computer vision, and data analysis.

So, is AI augmentation actually a net-negative? That’s what this study seems to be saying on the surface. Yet if you dig deeper into the results, the reality is a lot more complicated. In essence, these MIT researchers discovered that AI-human collaboration is most effective not when they collaborate on every single task, but when there’s a clear division of labor between the two.

For example, if you look at a typical HR department’s workload, those tasks divide into three categories:

1) Tasks humans perform better alone,

2) Tasks AIs perform better alone, and

3) Tasks that humans and AIs perform better collaboratively:

What the MIT study revealed was that the most effective AI augmentation strategy doesn’t involve collaboration between humans and machines in all three areas. Rather, it involves a clear division of labor between 1 and 2, and collaboration only in 3.

So, what does this division of labor look like? To be sure, it varies based on the specific humans and AIs involved. Humans with limited training in the use of AI tools will struggle to augment their own processes and workflows effectively. Likewise, AI without domain-specific knowledge may struggle to accelerate certain activities for certain specialized teams like HR or IT.

Generally speaking, however, the division of labor falls along the following lines:

Humans excel at intuition, creativity, and contextual understanding. It’s easy for humans to generalize from small amounts of data, understand the subtleties of language and emotion, and handle new experiences by leveraging our common sense and past experience to develop creative solutions. Humans are also interested in fairness and rightness, making us far more effective at making ethical decisions.

AI is unparalleled in its capacity for speed, scale, and precision. It can detect patterns in large datasets that would easily overwhelm a human’s working memory. It can also consistently execute determined routines without error. In some cases, for example, in Agentic AI, it can even learn from its environment and adjust its AI workflows to achieve the stated outcome more efficiently.

Tasks where both skill sets are required are the perfect place to apply AI augmentation. Below, we’ll look at some examples specific to employee experience.

Augmented intelligence doesn’t describe a singular, uniform discipline, but a range of frameworks, processes, and workflows. Some AI systems can operate autonomously, performing tasks without human intervention, while others require close human collaboration and oversight.
These approaches can be adapted to varying human skill levels, sophistication of the AIs at your disposal (e.g., agentic autonomy or domain-specific knowledge), and your leadership’s strategic mandates regarding AI implementation. As augmented intelligence advances, new roles are created while old ones evolve or become obsolete, reflecting the ongoing changes in the workforce driven by technological progress.

1. AI as an Assistant

Under this model, humans maintain primary control and decision-making authority over the system in question. On the other hand, it acts as an AI assistant to the human operator, providing recommendations, analysis, and handling routine or complex tasks. While AI offers valuable insights and support, it is ultimately the human who uses this information to make decisions. This model works best in environments where human insight and intuition are key to optimal performance.

For example, in an IT role, the assistant can quickly provide employees with relevant information, automate routine workflows like ticket updates or asset lookups, and offer real-time recommendations based on data analysis. This reduces manual effort and allows employees to focus on higher-value activities, enhancing their overall experience and engagement at work.

2. Equal Human-AI Collaboration

This model represents a near-equal division of labor between humans and AI. For example, HR teams use AI technology to automate candidate screening and interview scheduling, while humans conduct the interviews themselves, especially the final rounds.

3. Human-in-the-Loop

This AI augmentation approach features more AI autonomy than an equal collaboration model, but still requires human insight at key points in the workflow, such as edge cases or high-stakes situations. For example, IT teams can use human-in-the-loop augmentation to deploy AI in real-time system performance monitoring, handle routine operations like password resets, and maintain policy compliance.

However, the AI will require human approval when it comes to making adjustments that can affect the entire system’s functionality, like a major software update.

4. AI as Principal, Human as Overseer

In contrast with human-in-the-loop, this AI augmentation approach has no set points at which human input is necessary to make certain decisions. Rather, humans act as safety nets vs. active collaborators.

For example, in IT Service Management (ITSM), autonomous AI systems will automate ticket categorization, prioritization, and routing to accelerate issue resolution. Next-gen ITSM systems that deploy Agentic AI can function without human insight, with humans only stepping in when there’s a nuanced, complex scenario that the AI can’t handle.

5. Minimal Collaboration

This final approach to AI augmentation is the closest one can get to full autonomy while technically remaining a form of intelligence augmentation. Under this model, AI handles tasks completely autonomously, with humans only setting initial parameters, reviewing outcomes, and making periodic adjustments.

For instance, AI service desks can operate 100% autonomously, and often provide greater speed and scale without the need for increased costs. However, if human operators see that MTTR is starting to slip, they can engage the agentic AI to figure out the issue, make adjustments, and then allow it to resume autonomy.

One of the advantages of Agentic AI is its ability to adapt to any of these approaches or a combination of them. Continuing with the service desk example, fully autonomous agents would engage users and fulfill requests.

However, if the agent suspects a user complaint is indicative of a broader, system-wide issue, it can pass the request onto a human operator to investigate and address the root cause.

Although AI augmentation can accelerate human productivity, reduce waste, and boost organizational efficiency, success isn’t automatic. AI augmentation can impact jobs by eliminating certain roles while also creating new opportunities, making workforce adaptation essential. The processes, tools, and human skill sets used to implement intelligence augmentation will determine whether an organization can actually realize its business value.

A key challenge is that human augmentation is a necessary step in adapting to new roles and collaborating effectively with AI systems.

AI Augmentation Benefits

• Productivity gains, saving thousands of user and employee hours without diminishing performance
• Cost reduction due to reduced manual workload and operational savings
• Enhanced user experience, as consistent AI implementation across omnichannel interactions improves satisfaction, while human intervention at critical moments builds trust
• Consistency and accuracy in the interpretation of queries, reducing wasted efforts and accelerating resolution
• Operational scalability, as teams can maintain the same level of human headcount while increasing capacity
• Proactive issue resolution, as AI can identify problems before they escalate and alert your human team
• Continuous adaptation to market and user demands, as AI-human collaboration can provide an answer to consumer demands for speed, personalization, and authenticity
• A powerful solution to the “AI vs. human dichotomy”, it’s both-and, not either-or

AI Augmentation Challenges

• AI augmentation is an evolving discipline, meaning best practices for human-AI collaboration aren’t set in stone and need to be tested by individual organizations
• Different AI models handle different tasks differently, which requires strategic planning as to when and where specific AIs are deployed
• Continued need for human oversight and intervention, especially when dealing with edge cases and high-stakes scenarios
• Potential for bias and error, as well as the need for ongoing maintenance and updates
• Need for specialized skills and training, especially in highly technical domains
• Potential for human skill atrophy and automation complacency, both of which impact human ability to effectively collaborate with AIs

Augmented intelligence is effective at solving a number of problems faced by human operators, both in routine and strategic tasks. However, implementing and integrating these systems into your organization requires careful planning and execution. Here are some tips for successfully blending the two types of intelligence.

1. Select the right platform

Not every AI platform enables seamless collaboration with human employees. For example, many tools scatter virtual assistants, chatbots, automation, analytics, and self-service portals across a range of tools. An AI system that unifies these functions into a single platform will be more effective at fostering collaboration and actually enhancing human intelligence.

2. Align models and methods with outcomes

Different AI models provide different strengths and weaknesses. For example, Natural Language Processing (NLP) models are excellent at interpreting queries and responding in natural language, while Natural Language Understanding (NLU) models perform deeper analysis into user intent, sentiment, and textual meaning. Transformer models excel at high-context tasks that require fast execution, while recurrent neural networks (RNNs) are suited for small-scale, less resource-intensive tasks.

Additionally, with leveraging machine learning methods, domain-specific LLMs can provide more accurate, focused results in highly technical fields, like IT, HR, and more. One advantage of Agentic AI is the ability to orchestrate different models and agents (e.g., universal agents, domain agents, and task agents) based on your desired outcomes.

3. Upskill your human operators

Like any technology, AI is a tool. Not only do human operators need to learn how to interpret AI advice, but they also need to know how to operate and engage with these AIs to achieve the intended result. What’s more, human operators need to understand enough of the functionality and limitations of AIs to know when to intervene and when to step back.

4. Ensure a clear division of labor

As the MIT study above showed, one reason AI implementation fails is that organizations apply AI to tasks best handled by humans, and vice versa. One way to improve the odds of success is to engage in a clear division of labor and clearly defined areas of collaboration for humans vs. AIs.

For example, in ITSM, AIs can autonomously handle service tickets and routine tasks, humans can engage in more strategic work, and they can collaborate on nuanced system errors that require AI analysis and human decision-making.

5. Continuously evaluate your augmented intelligence systems

Effective AI augmentation isn’t a constant state, but an ever-evolving discipline that requires you to adapt to changing circumstances, user demands, technical capabilities, and more. It’s important to learn from new data and feedback, ensuring AI augmentation solutions remain current and effective.

6. Proactively manage the risks of intelligence augmentation

Intelligence augmentation isn’t without its risks. While regular human intervention can help to mitigate traditional AI risks like bias, lack of transparency (“black box” decisions), hallucinations, cybersecurity exploitation, etc, it presents new risks of its own.

For example, automation complacency can set in, especially in scenarios where operators are overloaded with tasks and decisions. Proactive risk mitigation is critical to avoid a counterproductive scenario from arising.

Given the emphasis within the field of Agentic AI on agent autonomy, it’s worth asking the question: Are Agentic AI and AI augmentation mutually exclusive? In other words, does Agentic AI remove the need for human engagement?

The answer is a solid no. Despite Agentic AI’s ability to self-evaluate, orchestrate various tools and platforms, and develop its strategies for achieving stated outcomes, humans still play a vital role in its capabilities. Human beings provide oversight, guardrails, feedback, supervision, exception handling, governance, and more.

Ultimately, the goal of Agentic AI is to enhance human decision-making and handle rote tasks so humans can focus on more meaningful work. The future of augmented intelligence will involve the continued deployment of AI systems that support, rather than replace, human insight and intuition. Organizations that harness the power of augmented intelligence will drive innovation, boost productivity, and enhance human capabilities in ways we’ve never seen before. For a real-world window into how Aisera is leading this charge, schedule an AI demo of our Agentic platform today.